cleaning-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning: iFood data from Web Scraping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iFood is a Brazilian company founded in 2011, active in the field of food delivery over the internet, being a leader in the sector in Latin America, with a presence in Argentina, Mexico and Colombia.   \n",
    "This notebook is about *cleaning some data* from a web scraping proccess. There are 4 data sets collected from [iFood website](https://www.ifood.com.br/) by 4 people from IME Jr. There are, also, [my code from another web scraping proccess](https://github.com/KenzoBH/Web-Scraping-and-EDA/blob/main/web-scraping-ifood.py), that collects a clean data already.   \n",
    "\n",
    "> [Bruno Kenzo](https://kenzobh.github.io/)."
   ]
  },
  {
   "attachments": {
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86b7Gn/+yHb5R1tXZ9elLS5XEbpTaW1//jW+uu+zLxZJbA75aT09x29K+R++P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\nQ0Nq1IjkqFHJUSOTq41KrbF6at11kmuukVpndE9fWYvDDFo2m1taz/h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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![ifood-image.png](attachment:b9d1d529-09ba-4cf6-929e-6e48dc85ae51.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the 4 DataFrames, from Alexandre, Emanuel, Emerson and Marco."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('alexandre_felix.csv')\n",
    "df2 = pd.read_csv('emanuel_hark.csv')\n",
    "df3 = pd.read_csv('emerson_aragao.csv')\n",
    "df4 = pd.read_csv('marco_rodrigues.csv')\n",
    "\n",
    "dfs = [df1, df2, df3, df4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['web-scraper-order', 'web-scraper-start-url', 'Título', 'Nota', 'Tipo',\n",
      "       'Tempo', 'preco_entrega'],\n",
      "      dtype='object') (30, 7)\n",
      "\n",
      "Index(['web-scraper-order', 'web-scraper-start-url', 'Titulo', 'Notas', 'Tipo',\n",
      "       'Tempo', 'Preco'],\n",
      "      dtype='object') (30, 7)\n",
      "\n",
      "Index(['web-scraper-order', 'web-scraper-start-url', 'Titulo', 'Nota', 'Tipo',\n",
      "       'Tempo', 'preço_entrega'],\n",
      "      dtype='object') (30, 7)\n",
      "\n",
      "Index(['web-scraper-order', 'web-scraper-start-url', 'título', 'nota', 'tipo',\n",
      "       'tempo', 'preço_entrega'],\n",
      "      dtype='object') (30, 7)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for df in dfs:\n",
    "    print(df.columns, df.shape) # Verify the variables and the shape of each DataFrame\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe, it's possible to make just one pipeline to clean the data, and repeat to the others df's, once that the data came from the same method from collection, and the columns are in the same order, but with different names. Let's see if that's possible."
   ]
  },
  {
   "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>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preco_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615840324-14</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Brasileira • 2,8 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>40-50 min • Grátis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615840324-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1 • Pizza • 1,5 km</td>\n",
       "      <td>45-55 min</td>\n",
       "      <td>45-55 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615840324-2</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Doces &amp; Bolos • 2,3 km</td>\n",
       "      <td>80-90 min</td>\n",
       "      <td>80-90 min • R$ 7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615840324-19</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.9 • Lanches • 3,0 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615840324-9</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Japonesa • 3,2 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0     1615840324-14  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615840324-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2      1615840324-2  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1615840324-19  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1615840324-9  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                                   Título Nota                          Tipo  \\\n",
       "0           FILHO DA FRUTA  \"RESTAURANTE\"  4.7     4.7 • Brasileira • 2,8 km   \n",
       "1                 Ponto da Esfiha Itapevi  4.1          4.1 • Pizza • 1,5 km   \n",
       "2  Sodiê Doces - Itapevi Jardim da Rainha  4.6  4.6 • Doces & Bolos • 2,3 km   \n",
       "3                    🍔lanches Bem Bolado🍔  4.9        4.9 • Lanches • 3,0 km   \n",
       "4                              Japa House  4.5       4.5 • Japonesa • 3,2 km   \n",
       "\n",
       "       Tempo        preco_entrega  \n",
       "0  40-50 min   40-50 min • Grátis  \n",
       "1  45-55 min  45-55 min • R$ 5.00  \n",
       "2  80-90 min  80-90 min • R$ 7.00  \n",
       "3  60-70 min  60-70 min • R$ 7.99  \n",
       "4  60-70 min  60-70 min • R$ 5.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head() # See the first 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Titulo</th>\n",
       "      <th>Notas</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615674534-4</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Z Deli Dogs</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Lanches • 3,8 km</td>\n",
       "      <td>30-40 min</td>\n",
       "      <td>R$ 10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615674534-23</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Andiamo Ristorante - Cidade São Paulo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Italiana • 3,3 km</td>\n",
       "      <td>34-44 min</td>\n",
       "      <td>R$ 9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615674534-20</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Amor Aos Pedaços - West Plaza</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3 • Doces &amp; Bolos • 3,0 km</td>\n",
       "      <td>34-44 min</td>\n",
       "      <td>R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615674534-6</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Casa Europa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8 • Italiana • 4,8 km</td>\n",
       "      <td>35-45 min</td>\n",
       "      <td>R$ 12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615674534-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Lancheria Original Brasil Santana</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Açaí • 4,0 km</td>\n",
       "      <td>22-32 min</td>\n",
       "      <td>R$ 6.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0      1615674534-4  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615674534-23  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1615674534-20  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3      1615674534-6  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4     1615674534-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                                  Titulo  Notas                          Tipo  \\\n",
       "0                            Z Deli Dogs    4.6        4.6 • Lanches • 3,8 km   \n",
       "1  Andiamo Ristorante - Cidade São Paulo    4.7       4.7 • Italiana • 3,3 km   \n",
       "2          Amor Aos Pedaços - West Plaza    4.3  4.3 • Doces & Bolos • 3,0 km   \n",
       "3                            Casa Europa    4.8       4.8 • Italiana • 4,8 km   \n",
       "4      Lancheria Original Brasil Santana  Novo!         Novo! • Açaí • 4,0 km   \n",
       "\n",
       "       Tempo     Preco  \n",
       "0  30-40 min  R$ 10.49  \n",
       "1  34-44 min   R$ 9.99  \n",
       "2  34-44 min   R$ 7.99  \n",
       "3  35-45 min  R$ 12.99  \n",
       "4  22-32 min   R$ 6.99  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Titulo</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preço_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615673252-4</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Boutique Burger</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Lanches • 3,9 km</td>\n",
       "      <td>46-56 min</td>\n",
       "      <td>R$ 11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615673252-16</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>China in Box - Itaquera</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Chinesa • 4,6 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>R$ 8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615673252-30</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria Kailane</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Pizza • 3,6 km</td>\n",
       "      <td>55-65 min</td>\n",
       "      <td>R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615673252-22</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Padaria Carisma</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Padaria • 2,3 km</td>\n",
       "      <td>39-49 min</td>\n",
       "      <td>R$ 5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615673252-9</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Divino Fogão - Metrô Itaquera</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Brasileira • 6,0 km</td>\n",
       "      <td>51-61 min</td>\n",
       "      <td>R$ 13.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0      1615673252-4  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615673252-16  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1615673252-30  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1615673252-22  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1615673252-9  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                          Titulo  Nota                       Tipo      Tempo  \\\n",
       "0                Boutique Burger   4.6     4.6 • Lanches • 3,9 km  46-56 min   \n",
       "1        China in Box - Itaquera   4.7     4.7 • Chinesa • 4,6 km  40-50 min   \n",
       "2               Pizzaria Kailane   4.6       4.6 • Pizza • 3,6 km  55-65 min   \n",
       "3                Padaria Carisma   4.5     4.5 • Padaria • 2,3 km  39-49 min   \n",
       "4  Divino Fogão - Metrô Itaquera   4.6  4.6 • Brasileira • 6,0 km  51-61 min   \n",
       "\n",
       "  preço_entrega  \n",
       "0      R$ 11.99  \n",
       "1       R$ 8.50  \n",
       "2       R$ 5.00  \n",
       "3       R$ 5.99  \n",
       "4      R$ 13.49  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>título</th>\n",
       "      <th>nota</th>\n",
       "      <th>tipo</th>\n",
       "      <th>tempo</th>\n",
       "      <th>preço_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1616135872-16</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria e Refeiçoes Levi</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Pizza • 0,8 km</td>\n",
       "      <td>25-35 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1616135872-23</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria e Esfiharia Massa Mania</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Brasileira • 4,2 km</td>\n",
       "      <td>50-60 min</td>\n",
       "      <td>R$ 15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1616135872-27</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Riviera Delivery</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Brasileira • 3,0 km</td>\n",
       "      <td>30-40 min</td>\n",
       "      <td>R$ 4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1616135872-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria da Madrugada</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Pizza • 0,8 km</td>\n",
       "      <td>25-35 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1616135872-8</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Formiguento's Burguer</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2 • Variada • 2,8 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>R$ 3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0     1616135872-16  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1616135872-23  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1616135872-27  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1616135872-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1616135872-8  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                             título   nota                         tipo  \\\n",
       "0         Pizzaria e Refeiçoes Levi    4.5         4.5 • Pizza • 0,8 km   \n",
       "1  Pizzaria e Esfiharia Massa Mania  Novo!  Novo! • Brasileira • 4,2 km   \n",
       "2                  Riviera Delivery  Novo!  Novo! • Brasileira • 3,0 km   \n",
       "3             Pizzaria da Madrugada    4.7         4.7 • Pizza • 0,8 km   \n",
       "4             Formiguento's Burguer    4.2       4.2 • Variada • 2,8 km   \n",
       "\n",
       "       tempo preço_entrega  \n",
       "0  25-35 min           NaN  \n",
       "1  50-60 min      R$ 15.00  \n",
       "2  30-40 min       R$ 4.00  \n",
       "3  25-35 min           NaN  \n",
       "4  40-50 min       R$ 3.50  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As above, the df's look similar. Then, I opted for using 5 steps to clean the data:\n",
    "- **Step 01:** drop the first 2 columns from all 4 df's;\n",
    "- **Step 02:** get the ratings and the food types from the restaurants from the column that contains both informations;\n",
    "- **Step 03:** get the times, from the column that contains a range of times - I caught the mean value from the range;\n",
    "- **Step 04:** finally, the price, from the last columns from all df's, that contains the time too;\n",
    "- **Step 05:** then, I analysed the NaN's, and saved the final dataframe - the concatenation of the 4 df's - in a csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 01\n",
    "*Drop columns from all 4 df's*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step function definition\n",
    "def step01_drop_columns(df):\n",
    "    df = df.drop(['web-scraper-order', 'web-scraper-start-url'], axis = 1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preco_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Brasileira • 2,8 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>40-50 min • Grátis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1 • Pizza • 1,5 km</td>\n",
       "      <td>45-55 min</td>\n",
       "      <td>45-55 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Doces &amp; Bolos • 2,3 km</td>\n",
       "      <td>80-90 min</td>\n",
       "      <td>80-90 min • R$ 7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.9 • Lanches • 3,0 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Japonesa • 3,2 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Título Nota                          Tipo  \\\n",
       "0           FILHO DA FRUTA  \"RESTAURANTE\"  4.7     4.7 • Brasileira • 2,8 km   \n",
       "1                 Ponto da Esfiha Itapevi  4.1          4.1 • Pizza • 1,5 km   \n",
       "2  Sodiê Doces - Itapevi Jardim da Rainha  4.6  4.6 • Doces & Bolos • 2,3 km   \n",
       "3                    🍔lanches Bem Bolado🍔  4.9        4.9 • Lanches • 3,0 km   \n",
       "4                              Japa House  4.5       4.5 • Japonesa • 3,2 km   \n",
       "\n",
       "       Tempo        preco_entrega  \n",
       "0  40-50 min   40-50 min • Grátis  \n",
       "1  45-55 min  45-55 min • R$ 5.00  \n",
       "2  80-90 min  80-90 min • R$ 7.00  \n",
       "3  60-70 min  60-70 min • R$ 7.99  \n",
       "4  60-70 min  60-70 min • R$ 5.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Updates each dataframe\n",
    "for i in range(len(dfs)):\n",
    "    dfs[i] = step01_drop_columns(dfs[i])\n",
    "dfs[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool! The first 2 columns from all 4 dataframes are gone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 02\n",
    "*Get the ratings and the food types from the restaurants from the column that contains both informations*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Título           object\n",
       "Nota             object\n",
       "Tipo             object\n",
       "Tempo            object\n",
       "preco_entrega    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].dtypes # See the variable types of the first dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.9      6\n",
       "4.5      5\n",
       "4.6      3\n",
       "4.8      3\n",
       "4.1      3\n",
       "4.3      2\n",
       "4.7      2\n",
       "Novo!    1\n",
       "4.0      1\n",
       "3.8      1\n",
       "4.2      1\n",
       "Name: Nota, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# About \"Nota\" column\n",
    "print(df1['Nota'].isna().sum())   # There are 2 NaN's\n",
    "df1['Nota'].value_counts()        # There's a data named \"Novo!\": it has to be NaN or 0, I opted for 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        4.1 • Pizza • 1,5 km\n",
       "19     4.2 • Lanches • 2,4 km\n",
       "21     4.3 • Lanches • 1,2 km\n",
       "24     4.8 • Lanches • 2,5 km\n",
       "28    4.5 • Japonesa • 2,0 km\n",
       "Name: Tipo, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# About \"Tipo\" column\n",
    "df1['Tipo'].sample(5) # The data contains 3 informations: the rate, the >food type< and \n",
    "                      # the distance from the data colector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step function definition\n",
    "# First, from the column that contain both informations, we'll split the data into\n",
    "# a list like ['4.3', 'Lanches', '1,2 km']. Then, I'll get the first data to be the rate,\n",
    "# and the second one to be the food type\n",
    "def step02_get_ratings_and_food_types(df):\n",
    "    ratings = []\n",
    "    food_types = []\n",
    "    for data in df.iloc[:, 2]: # A loop for each data in the 3rd column \n",
    "        data_list = data.split(' • ') # Splits the data into a list\n",
    "        if len(data_list) == 3:\n",
    "            food_type = data_list[1]\n",
    "            if data_list[0] == 'Novo!':\n",
    "                rate = 0\n",
    "            else:\n",
    "                rate = float(data_list[0])\n",
    "        else:\n",
    "            rate = 0\n",
    "            food_type = data_list[0]\n",
    "        ratings.append(rate)\n",
    "        food_types.append(food_type)\n",
    "    df.iloc[:, 1] = ratings\n",
    "    df.iloc[:, 2] = food_types\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "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>Titulo</th>\n",
       "      <th>Notas</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Brasileirinho Delivery - Centro - Sp</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>50-60 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bob's - Bráulio Gomes</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>33-43 min</td>\n",
       "      <td>R$ 4.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sushi Barra Funda</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>45-55 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Al Capizza Pacaembu - Entrega própia</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>35-45 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Açaí Concept - Augusta</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Açaí</td>\n",
       "      <td>22-32 min</td>\n",
       "      <td>R$ 5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Titulo  Notas        Tipo      Tempo  \\\n",
       "8   Brasileirinho Delivery - Centro - Sp    4.4  Brasileira  50-60 min   \n",
       "22                 Bob's - Bráulio Gomes    4.7     Lanches  33-43 min   \n",
       "16                     Sushi Barra Funda    4.4    Japonesa  45-55 min   \n",
       "5   Al Capizza Pacaembu - Entrega própia    4.2       Pizza  35-45 min   \n",
       "20                Açaí Concept - Augusta    4.3        Açaí  22-32 min   \n",
       "\n",
       "      Preco  \n",
       "8       NaN  \n",
       "22  R$ 4.99  \n",
       "16      NaN  \n",
       "5       NaN  \n",
       "20  R$ 5.99  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Updates each dataframe\n",
    "for df in dfs:\n",
    "    df = step02_get_ratings_and_food_types(df)\n",
    "dfs[1].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 03\n",
    "*Get the times, from the column that contains a range of times - I caught the mean value from the range*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step function definition\n",
    "# For each time range in the \"Tempo\" column, we'll split the data into a list\n",
    "# like ['19', '29 min'], get the two numbers, and return the mean of them both\n",
    "def step03_get_times(df):\n",
    "    time_ranges = []\n",
    "    for time in df.iloc[:, 3]:\n",
    "        if pd.isna(time): # Looks if that's NaN\n",
    "            time_ranges.append(np.nan)\n",
    "        else:\n",
    "            times = time.split('-')\n",
    "            for x in times:\n",
    "                times[1] = times[1][:-2]\n",
    "            time_ranges.append([int(times[0]), int(times[1])])\n",
    "    mean_times = []\n",
    "    for time_range in time_ranges:\n",
    "        if type(time_range) == float: # Looks if that's NaN\n",
    "            mean_times.append(np.nan)\n",
    "        else:\n",
    "            mean_time = (time_range[0] + time_range[1])/2\n",
    "            mean_times.append(mean_time)\n",
    "    df.iloc[:, 3] = mean_times\n",
    "    return df"
   ]
  },
  {
   "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>título</th>\n",
       "      <th>nota</th>\n",
       "      <th>tipo</th>\n",
       "      <th>tempo</th>\n",
       "      <th>preço_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Rei do Medalhão</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>35.0</td>\n",
       "      <td>R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Adega Parceria drinks</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Bebidas</td>\n",
       "      <td>25.0</td>\n",
       "      <td>R$ 9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Yamamoto Sushi R$ 1,00</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>35.0</td>\n",
       "      <td>R$ 6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Pizzaria Delícia das Massas</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pizzaria da Madrugada</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         título  nota        tipo  tempo preço_entrega\n",
       "19              Rei do Medalhão   3.7  Brasileira   35.0       R$ 5.00\n",
       "23        Adega Parceria drinks   4.7     Bebidas   25.0       R$ 9.00\n",
       "17       Yamamoto Sushi R$ 1,00   4.8    Japonesa   35.0       R$ 6.00\n",
       "21  Pizzaria Delícia das Massas   4.6       Pizza   35.0           NaN\n",
       "3         Pizzaria da Madrugada   4.7       Pizza   30.0           NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Updates each dataframe\n",
    "for df in dfs:\n",
    "    df = step03_get_times(df)\n",
    "dfs[3].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 04\n",
    "*Finally, the price, from the last columns from all df's, that contains the time too*\n",
    "\n",
    "There's a subtlety here. The first dataframe has the time and the price in the same column, while the others have the price in a isolated column. Then, I made a function to clean the first df and another one to clean the remaining ones."
   ]
  },
  {
   "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></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preco_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bistrô Lázara Beer</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>65.0</td>\n",
       "      <td>60-70 min • R$ 4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fogas Búrguer</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50-60 min • R$ 4.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Título  Nota        Tipo  Tempo        preco_entrega\n",
       "18  Bistrô Lázara Beer   4.5  Brasileira   65.0  60-70 min • R$ 4.00\n",
       "25       Fogas Búrguer   0.0     Lanches   55.0  50-60 min • R$ 4.00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].sample(2) # See the last column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Titulo</th>\n",
       "      <th>Notas</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Joy Korean Food</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Coreana</td>\n",
       "      <td>34.0</td>\n",
       "      <td>R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Açaí Concept - Augusta</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Açaí</td>\n",
       "      <td>27.0</td>\n",
       "      <td>R$ 5.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Titulo  Notas     Tipo  Tempo    Preco\n",
       "28         Joy Korean Food    4.8  Coreana   34.0  R$ 7.99\n",
       "20  Açaí Concept - Augusta    4.3     Açaí   27.0  R$ 5.99"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[1].sample(2) # See the last column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step function definition\n",
    "# We'll split by ' • ' string on the first dataframe, and get the price\n",
    "# from a list like ['60-70 min', 'R$ 7.00']\n",
    "def step04_get_prices_df1(df):\n",
    "    prices = []\n",
    "    for x in df.iloc[:, 4]:\n",
    "        x_list = x.split(' • ')\n",
    "        if x_list[1] == 'Grátis':\n",
    "            price = 0\n",
    "        else:\n",
    "            price = float(x_list[1][3:])\n",
    "        prices.append(price)\n",
    "    df.iloc[:, 4] = prices\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Updates the first dataframe\n",
    "dfs[0] = step04_get_prices_df1(dfs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preco_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Trilha em Sabor</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Restaurante Recanto São Judas</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tapiocaria Vovo Lili</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Canto da Esfiha</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mc Pizzaria e Burgueria</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Título  Nota        Tipo  Tempo  preco_entrega\n",
       "22                Trilha em Sabor   4.9     Lanches   50.0            7.0\n",
       "17  Restaurante Recanto São Judas   4.6  Brasileira   55.0            6.0\n",
       "24           Tapiocaria Vovo Lili   4.8     Lanches   65.0            7.0\n",
       "9                 Canto da Esfiha   4.7     Lanches   65.0            5.0\n",
       "14        Mc Pizzaria e Burgueria   4.0       Pizza   55.0            0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks clean!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step function definition\n",
    "# Just cuts the 'R$ ' from the last column\n",
    "def step04_get_prices(df):\n",
    "    prices = []\n",
    "    for price in df.iloc[:, 4]:\n",
    "        if pd.isna(price): # Looks if that's NaN\n",
    "            prices.append(np.nan)\n",
    "        else:\n",
    "            prices.append(float(price[3:]))\n",
    "    df.iloc[:, 4] = prices\n",
    "    return df"
   ]
  },
  {
   "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 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>Titulo</th>\n",
       "      <th>Notas</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Casa Europa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Italiana</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Poke Haus</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>41.0</td>\n",
       "      <td>16.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Joy Korean Food</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Coreana</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mocotó Café -  Shopping D</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>45.0</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amor Aos Pedaços - West Plaza</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Titulo  Notas           Tipo  Tempo  Preco\n",
       "3                     Casa Europa    4.8       Italiana   40.0  12.99\n",
       "23                      Poke Haus    4.8       Japonesa   41.0  16.99\n",
       "28                Joy Korean Food    4.8        Coreana   34.0   7.99\n",
       "11      Mocotó Café -  Shopping D    4.9     Brasileira   45.0   7.99\n",
       "2   Amor Aos Pedaços - West Plaza    4.3  Doces & Bolos   39.0   7.99"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Updates the remaining dataframes\n",
    "dfs[1] = step04_get_prices(dfs[1])\n",
    "dfs[2] = step04_get_prices(dfs[2])\n",
    "dfs[3] = step04_get_prices(dfs[3])\n",
    "dfs[1].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 05\n",
    "*Then, I analysed the NaN's, and saved the final dataframe - the concatenation of the 4 df's - in a csv file*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Added the data collectior from each dataframe and renamed all collumns from each dataframe\n",
    "\n",
    "dfs[0]['Dono'] = 'Alexandre Felix'\n",
    "dfs[1]['Dono'] = 'Emanuel Hark'\n",
    "dfs[2]['Dono'] = 'Emerson Aragão'\n",
    "dfs[3]['Dono'] = 'Marco Rodrigues'\n",
    "\n",
    "names = ['Título', 'Nota', 'Tipo', 'Tempo', 'Preço', 'Dono']\n",
    "for df in dfs:\n",
    "    df.columns = names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Título     object\n",
      "Nota      float64\n",
      "Tipo       object\n",
      "Tempo     float64\n",
      "Preço     float64\n",
      "Dono       object\n",
      "dtype: object\n",
      "Título     object\n",
      "Nota      float64\n",
      "Tipo       object\n",
      "Tempo     float64\n",
      "Preço     float64\n",
      "Dono       object\n",
      "dtype: object\n",
      "Título     object\n",
      "Nota      float64\n",
      "Tipo       object\n",
      "Tempo     float64\n",
      "Preço     float64\n",
      "Dono       object\n",
      "dtype: object\n",
      "Título     object\n",
      "Nota      float64\n",
      "Tipo       object\n",
      "Tempo     float64\n",
      "Preço     float64\n",
      "Dono       object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Verify the data types\n",
    "for df in dfs:\n",
    "    print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good! Now, let's compare them - the first dataframes to the clean ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preco_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615840324-14</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Brasileira • 2,8 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>40-50 min • Grátis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615840324-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1 • Pizza • 1,5 km</td>\n",
       "      <td>45-55 min</td>\n",
       "      <td>45-55 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615840324-2</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Doces &amp; Bolos • 2,3 km</td>\n",
       "      <td>80-90 min</td>\n",
       "      <td>80-90 min • R$ 7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615840324-19</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.9 • Lanches • 3,0 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615840324-9</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Japonesa • 3,2 km</td>\n",
       "      <td>60-70 min</td>\n",
       "      <td>60-70 min • R$ 5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0     1615840324-14  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615840324-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2      1615840324-2  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1615840324-19  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1615840324-9  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                                   Título Nota                          Tipo  \\\n",
       "0           FILHO DA FRUTA  \"RESTAURANTE\"  4.7     4.7 • Brasileira • 2,8 km   \n",
       "1                 Ponto da Esfiha Itapevi  4.1          4.1 • Pizza • 1,5 km   \n",
       "2  Sodiê Doces - Itapevi Jardim da Rainha  4.6  4.6 • Doces & Bolos • 2,3 km   \n",
       "3                    🍔lanches Bem Bolado🍔  4.9        4.9 • Lanches • 3,0 km   \n",
       "4                              Japa House  4.5       4.5 • Japonesa • 3,2 km   \n",
       "\n",
       "       Tempo        preco_entrega  \n",
       "0  40-50 min   40-50 min • Grátis  \n",
       "1  45-55 min  45-55 min • R$ 5.00  \n",
       "2  80-90 min  80-90 min • R$ 7.00  \n",
       "3  60-70 min  60-70 min • R$ 7.99  \n",
       "4  60-70 min  60-70 min • R$ 5.00  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Título  Nota           Tipo  Tempo  Preço  \\\n",
       "0           FILHO DA FRUTA  \"RESTAURANTE\"   4.7     Brasileira   45.0   0.00   \n",
       "1                 Ponto da Esfiha Itapevi   4.1          Pizza   50.0   5.00   \n",
       "2  Sodiê Doces - Itapevi Jardim da Rainha   4.6  Doces & Bolos   85.0   7.00   \n",
       "3                    🍔lanches Bem Bolado🍔   4.9        Lanches   65.0   7.99   \n",
       "4                              Japa House   4.5       Japonesa   65.0   5.00   \n",
       "\n",
       "              Dono  \n",
       "0  Alexandre Felix  \n",
       "1  Alexandre Felix  \n",
       "2  Alexandre Felix  \n",
       "3  Alexandre Felix  \n",
       "4  Alexandre Felix  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Titulo</th>\n",
       "      <th>Notas</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preco</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615674534-4</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Z Deli Dogs</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Lanches • 3,8 km</td>\n",
       "      <td>30-40 min</td>\n",
       "      <td>R$ 10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615674534-23</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Andiamo Ristorante - Cidade São Paulo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Italiana • 3,3 km</td>\n",
       "      <td>34-44 min</td>\n",
       "      <td>R$ 9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615674534-20</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Amor Aos Pedaços - West Plaza</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.3 • Doces &amp; Bolos • 3,0 km</td>\n",
       "      <td>34-44 min</td>\n",
       "      <td>R$ 7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615674534-6</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Casa Europa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.8 • Italiana • 4,8 km</td>\n",
       "      <td>35-45 min</td>\n",
       "      <td>R$ 12.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615674534-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Lancheria Original Brasil Santana</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Açaí • 4,0 km</td>\n",
       "      <td>22-32 min</td>\n",
       "      <td>R$ 6.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0      1615674534-4  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615674534-23  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1615674534-20  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3      1615674534-6  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4     1615674534-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                                  Titulo  Notas                          Tipo  \\\n",
       "0                            Z Deli Dogs    4.6        4.6 • Lanches • 3,8 km   \n",
       "1  Andiamo Ristorante - Cidade São Paulo    4.7       4.7 • Italiana • 3,3 km   \n",
       "2          Amor Aos Pedaços - West Plaza    4.3  4.3 • Doces & Bolos • 3,0 km   \n",
       "3                            Casa Europa    4.8       4.8 • Italiana • 4,8 km   \n",
       "4      Lancheria Original Brasil Santana  Novo!         Novo! • Açaí • 4,0 km   \n",
       "\n",
       "       Tempo     Preco  \n",
       "0  30-40 min  R$ 10.49  \n",
       "1  34-44 min   R$ 9.99  \n",
       "2  34-44 min   R$ 7.99  \n",
       "3  35-45 min  R$ 12.99  \n",
       "4  22-32 min   R$ 6.99  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Z Deli Dogs</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10.49</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andiamo Ristorante - Cidade São Paulo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Italiana</td>\n",
       "      <td>39.0</td>\n",
       "      <td>9.99</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amor Aos Pedaços - West Plaza</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Casa Europa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Italiana</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.99</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lancheria Original Brasil Santana</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Açaí</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Título  Nota           Tipo  Tempo  Preço  \\\n",
       "0                            Z Deli Dogs   4.6        Lanches   35.0  10.49   \n",
       "1  Andiamo Ristorante - Cidade São Paulo   4.7       Italiana   39.0   9.99   \n",
       "2          Amor Aos Pedaços - West Plaza   4.3  Doces & Bolos   39.0   7.99   \n",
       "3                            Casa Europa   4.8       Italiana   40.0  12.99   \n",
       "4      Lancheria Original Brasil Santana   0.0           Açaí   27.0   6.99   \n",
       "\n",
       "           Dono  \n",
       "0  Emanuel Hark  \n",
       "1  Emanuel Hark  \n",
       "2  Emanuel Hark  \n",
       "3  Emanuel Hark  \n",
       "4  Emanuel Hark  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>Titulo</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>preço_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1615673252-4</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Boutique Burger</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Lanches • 3,9 km</td>\n",
       "      <td>46-56 min</td>\n",
       "      <td>R$ 11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1615673252-16</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>China in Box - Itaquera</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Chinesa • 4,6 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>R$ 8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1615673252-30</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria Kailane</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Pizza • 3,6 km</td>\n",
       "      <td>55-65 min</td>\n",
       "      <td>R$ 5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1615673252-22</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Padaria Carisma</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Padaria • 2,3 km</td>\n",
       "      <td>39-49 min</td>\n",
       "      <td>R$ 5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1615673252-9</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Divino Fogão - Metrô Itaquera</td>\n",
       "      <td>4.6</td>\n",
       "      <td>4.6 • Brasileira • 6,0 km</td>\n",
       "      <td>51-61 min</td>\n",
       "      <td>R$ 13.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0      1615673252-4  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1615673252-16  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1615673252-30  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1615673252-22  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1615673252-9  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                          Titulo  Nota                       Tipo      Tempo  \\\n",
       "0                Boutique Burger   4.6     4.6 • Lanches • 3,9 km  46-56 min   \n",
       "1        China in Box - Itaquera   4.7     4.7 • Chinesa • 4,6 km  40-50 min   \n",
       "2               Pizzaria Kailane   4.6       4.6 • Pizza • 3,6 km  55-65 min   \n",
       "3                Padaria Carisma   4.5     4.5 • Padaria • 2,3 km  39-49 min   \n",
       "4  Divino Fogão - Metrô Itaquera   4.6  4.6 • Brasileira • 6,0 km  51-61 min   \n",
       "\n",
       "  preço_entrega  \n",
       "0      R$ 11.99  \n",
       "1       R$ 8.50  \n",
       "2       R$ 5.00  \n",
       "3       R$ 5.99  \n",
       "4      R$ 13.49  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boutique Burger</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>51.0</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China in Box - Itaquera</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Chinesa</td>\n",
       "      <td>45.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pizzaria Kailane</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Padaria Carisma</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Padaria</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.99</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Divino Fogão - Metrô Itaquera</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>56.0</td>\n",
       "      <td>13.49</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Título  Nota        Tipo  Tempo  Preço  \\\n",
       "0                Boutique Burger   4.6     Lanches   51.0  11.99   \n",
       "1        China in Box - Itaquera   4.7     Chinesa   45.0   8.50   \n",
       "2               Pizzaria Kailane   4.6       Pizza   60.0   5.00   \n",
       "3                Padaria Carisma   4.5     Padaria   44.0   5.99   \n",
       "4  Divino Fogão - Metrô Itaquera   4.6  Brasileira   56.0  13.49   \n",
       "\n",
       "             Dono  \n",
       "0  Emerson Aragão  \n",
       "1  Emerson Aragão  \n",
       "2  Emerson Aragão  \n",
       "3  Emerson Aragão  \n",
       "4  Emerson Aragão  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>web-scraper-order</th>\n",
       "      <th>web-scraper-start-url</th>\n",
       "      <th>título</th>\n",
       "      <th>nota</th>\n",
       "      <th>tipo</th>\n",
       "      <th>tempo</th>\n",
       "      <th>preço_entrega</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1616135872-16</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria e Refeiçoes Levi</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5 • Pizza • 0,8 km</td>\n",
       "      <td>25-35 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1616135872-23</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria e Esfiharia Massa Mania</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Brasileira • 4,2 km</td>\n",
       "      <td>50-60 min</td>\n",
       "      <td>R$ 15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1616135872-27</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Riviera Delivery</td>\n",
       "      <td>Novo!</td>\n",
       "      <td>Novo! • Brasileira • 3,0 km</td>\n",
       "      <td>30-40 min</td>\n",
       "      <td>R$ 4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1616135872-12</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Pizzaria da Madrugada</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.7 • Pizza • 0,8 km</td>\n",
       "      <td>25-35 min</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1616135872-8</td>\n",
       "      <td>https://www.ifood.com.br/lista-restaurantes?f_...</td>\n",
       "      <td>Formiguento's Burguer</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2 • Variada • 2,8 km</td>\n",
       "      <td>40-50 min</td>\n",
       "      <td>R$ 3.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  web-scraper-order                              web-scraper-start-url  \\\n",
       "0     1616135872-16  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "1     1616135872-23  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "2     1616135872-27  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "3     1616135872-12  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "4      1616135872-8  https://www.ifood.com.br/lista-restaurantes?f_...   \n",
       "\n",
       "                             título   nota                         tipo  \\\n",
       "0         Pizzaria e Refeiçoes Levi    4.5         4.5 • Pizza • 0,8 km   \n",
       "1  Pizzaria e Esfiharia Massa Mania  Novo!  Novo! • Brasileira • 4,2 km   \n",
       "2                  Riviera Delivery  Novo!  Novo! • Brasileira • 3,0 km   \n",
       "3             Pizzaria da Madrugada    4.7         4.7 • Pizza • 0,8 km   \n",
       "4             Formiguento's Burguer    4.2       4.2 • Variada • 2,8 km   \n",
       "\n",
       "       tempo preço_entrega  \n",
       "0  25-35 min           NaN  \n",
       "1  50-60 min      R$ 15.00  \n",
       "2  30-40 min       R$ 4.00  \n",
       "3  25-35 min           NaN  \n",
       "4  40-50 min       R$ 3.50  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pizzaria e Refeiçoes Levi</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pizzaria e Esfiharia Massa Mania</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>55.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Riviera Delivery</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pizzaria da Madrugada</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Formiguento's Burguer</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Variada</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Título  Nota        Tipo  Tempo  Preço  \\\n",
       "0         Pizzaria e Refeiçoes Levi   4.5       Pizza   30.0    NaN   \n",
       "1  Pizzaria e Esfiharia Massa Mania   0.0  Brasileira   55.0   15.0   \n",
       "2                  Riviera Delivery   0.0  Brasileira   35.0    4.0   \n",
       "3             Pizzaria da Madrugada   4.7       Pizza   30.0    NaN   \n",
       "4             Formiguento's Burguer   4.2     Variada   45.0    3.5   \n",
       "\n",
       "              Dono  \n",
       "0  Marco Rodrigues  \n",
       "1  Marco Rodrigues  \n",
       "2  Marco Rodrigues  \n",
       "3  Marco Rodrigues  \n",
       "4  Marco Rodrigues  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks pretty good. Now, I'll concatenate them into one dataframe `df`, and look for NaN's, to save into a `.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Ebenézer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Marmita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Johnny Rockets - Dk Campo Limpo</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Hambúrguer</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Lima´s Lanches</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>95.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Jakes Food</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Divino Fogão - Shopping Campo Limpo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.49</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Título  Nota           Tipo  Tempo  \\\n",
       "0             FILHO DA FRUTA  \"RESTAURANTE\"   4.7     Brasileira   45.0   \n",
       "1                   Ponto da Esfiha Itapevi   4.1          Pizza   50.0   \n",
       "2    Sodiê Doces - Itapevi Jardim da Rainha   4.6  Doces & Bolos   85.0   \n",
       "3                      🍔lanches Bem Bolado🍔   4.9        Lanches   65.0   \n",
       "4                                Japa House   4.5       Japonesa   65.0   \n",
       "..                                      ...   ...            ...    ...   \n",
       "115                                Ebenézer   5.0        Marmita    NaN   \n",
       "116         Johnny Rockets - Dk Campo Limpo   4.3     Hambúrguer   65.0   \n",
       "117                          Lima´s Lanches   4.7        Lanches   95.0   \n",
       "118                              Jakes Food   5.0        Lanches   45.0   \n",
       "119     Divino Fogão - Shopping Campo Limpo   4.7     Brasileira    NaN   \n",
       "\n",
       "     Preço             Dono  \n",
       "0     0.00  Alexandre Felix  \n",
       "1     5.00  Alexandre Felix  \n",
       "2     7.00  Alexandre Felix  \n",
       "3     7.99  Alexandre Felix  \n",
       "4     5.00  Alexandre Felix  \n",
       "..     ...              ...  \n",
       "115   6.90  Marco Rodrigues  \n",
       "116   7.99  Marco Rodrigues  \n",
       "117  22.00  Marco Rodrigues  \n",
       "118    NaN  Marco Rodrigues  \n",
       "119  14.49  Marco Rodrigues  \n",
       "\n",
       "[120 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Título     0\n",
       "Nota       0\n",
       "Tipo       0\n",
       "Tempo      2\n",
       "Preço     20\n",
       "Dono       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of them comes from \"Preço\" column: that's why they're 0, in fact. Maybe, in the web scraping proccess, when the price was `R$ 0,00`, the website showed in a different way - in a different object on HTML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Nota</th>\n",
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       "      <th>Dono</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Al Capizza Pacaembu - Entrega própia</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Brasileirinho Delivery - Centro - Sp</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Eskina Pacaembu</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Sushi Barra Funda</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Pizzaria São Paulo City</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emanuel Hark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Don Corleone</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Rita Refeicoes</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Pizzaria D'villa</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Hamburgueria Texas</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>A Preferida</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emerson Aragão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Pizzaria e Refeiçoes Levi</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Pizzaria da Madrugada</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>La Casa do Burguer &amp; Açai</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hambúrguer</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Thalla Burguer &amp; Açaí (sob nova direção)</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Doceria Cassia Lopes</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Fideles Lanches e Doces</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Variada</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Jama Masjid</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Pizzaria Delícia das Massas</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>pizzaria ducheff</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Ebenézer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Marmita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Jakes Food</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Divino Fogão - Shopping Campo Limpo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.49</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Título  Nota           Tipo  Tempo  \\\n",
       "35       Al Capizza Pacaembu - Entrega própia   4.2          Pizza   40.0   \n",
       "38       Brasileirinho Delivery - Centro - Sp   4.4     Brasileira   55.0   \n",
       "43                            Eskina Pacaembu   4.1          Pizza   25.0   \n",
       "46                          Sushi Barra Funda   4.4       Japonesa   50.0   \n",
       "54                    Pizzaria São Paulo City   4.4          Pizza   25.0   \n",
       "69                               Don Corleone   4.8          Pizza   55.0   \n",
       "70                             Rita Refeicoes   4.6     Brasileira   50.0   \n",
       "76                           Pizzaria D'villa   4.2          Pizza   40.0   \n",
       "79                         Hamburgueria Texas   4.8        Lanches   50.0   \n",
       "83                                A Preferida   4.5          Pizza   40.0   \n",
       "90                  Pizzaria e Refeiçoes Levi   4.5          Pizza   30.0   \n",
       "93                      Pizzaria da Madrugada   4.7          Pizza   30.0   \n",
       "95                  La Casa do Burguer & Açai   3.8     Hambúrguer   25.0   \n",
       "99   Thalla Burguer & Açaí (sob nova direção)   3.7        Lanches   35.0   \n",
       "103                      Doceria Cassia Lopes   4.0  Doces & Bolos   60.0   \n",
       "105                   Fideles Lanches e Doces   0.0        Variada   55.0   \n",
       "108                               Jama Masjid   3.2        Indiana   35.0   \n",
       "111               Pizzaria Delícia das Massas   4.6          Pizza   35.0   \n",
       "112                          pizzaria ducheff   0.0          Pizza   40.0   \n",
       "115                                  Ebenézer   5.0        Marmita    NaN   \n",
       "118                                Jakes Food   5.0        Lanches   45.0   \n",
       "119       Divino Fogão - Shopping Campo Limpo   4.7     Brasileira    NaN   \n",
       "\n",
       "     Preço             Dono  \n",
       "35     NaN     Emanuel Hark  \n",
       "38     NaN     Emanuel Hark  \n",
       "43     NaN     Emanuel Hark  \n",
       "46     NaN     Emanuel Hark  \n",
       "54     NaN     Emanuel Hark  \n",
       "69     NaN   Emerson Aragão  \n",
       "70     NaN   Emerson Aragão  \n",
       "76     NaN   Emerson Aragão  \n",
       "79     NaN   Emerson Aragão  \n",
       "83     NaN   Emerson Aragão  \n",
       "90     NaN  Marco Rodrigues  \n",
       "93     NaN  Marco Rodrigues  \n",
       "95     NaN  Marco Rodrigues  \n",
       "99     NaN  Marco Rodrigues  \n",
       "103    NaN  Marco Rodrigues  \n",
       "105    NaN  Marco Rodrigues  \n",
       "108    NaN  Marco Rodrigues  \n",
       "111    NaN  Marco Rodrigues  \n",
       "112    NaN  Marco Rodrigues  \n",
       "115   6.90  Marco Rodrigues  \n",
       "118    NaN  Marco Rodrigues  \n",
       "119  14.49  Marco Rodrigues  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Preço'] = df['Preço'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Ebenézer</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Marmita</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.90</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Divino Fogão - Shopping Campo Limpo</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.49</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Título  Nota        Tipo  Tempo  Preço  \\\n",
       "115                             Ebenézer   5.0     Marmita    NaN   6.90   \n",
       "119  Divino Fogão - Shopping Campo Limpo   4.7  Brasileira    NaN  14.49   \n",
       "\n",
       "                Dono  \n",
       "115  Marco Rodrigues  \n",
       "119  Marco Rodrigues  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis = 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other ones I'll drop from the dataframe - I could fill them with the mean or the median value from the data collector dataframe, but it's only two values, then I opted to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the data is **clean** - not at all, because some restaurant names are capitalizes, other ones are all in uppercase, there are some emojis too, and so on; but, that's good for me.    \n",
    "Yay!\n",
    "Now, let's save it in a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('final-ifood-data.csv', index = False) # I saved without index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Título</th>\n",
       "      <th>Nota</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Preço</th>\n",
       "      <th>Dono</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FILHO DA FRUTA  \"RESTAURANTE\"</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Brasileira</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ponto da Esfiha Itapevi</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sodiê Doces - Itapevi Jardim da Rainha</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Doces &amp; Bolos</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>🍔lanches Bem Bolado🍔</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Alexandre Felix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Japa House</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Japonesa</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Alexandre Felix</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Adega Parceria drinks</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Bebidas</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Burguer Store</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>125.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Johnny Rockets - Dk Campo Limpo</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Hambúrguer</td>\n",
       "      <td>65.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Lima´s Lanches</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>95.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Jakes Food</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lanches</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Marco Rodrigues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Título  Nota           Tipo  Tempo  \\\n",
       "0             FILHO DA FRUTA  \"RESTAURANTE\"   4.7     Brasileira   45.0   \n",
       "1                   Ponto da Esfiha Itapevi   4.1          Pizza   50.0   \n",
       "2    Sodiê Doces - Itapevi Jardim da Rainha   4.6  Doces & Bolos   85.0   \n",
       "3                      🍔lanches Bem Bolado🍔   4.9        Lanches   65.0   \n",
       "4                                Japa House   4.5       Japonesa   65.0   \n",
       "..                                      ...   ...            ...    ...   \n",
       "113                   Adega Parceria drinks   4.7        Bebidas   25.0   \n",
       "114                           Burguer Store   0.0        Lanches  125.0   \n",
       "116         Johnny Rockets - Dk Campo Limpo   4.3     Hambúrguer   65.0   \n",
       "117                          Lima´s Lanches   4.7        Lanches   95.0   \n",
       "118                              Jakes Food   5.0        Lanches   45.0   \n",
       "\n",
       "     Preço             Dono  \n",
       "0     0.00  Alexandre Felix  \n",
       "1     5.00  Alexandre Felix  \n",
       "2     7.00  Alexandre Felix  \n",
       "3     7.99  Alexandre Felix  \n",
       "4     5.00  Alexandre Felix  \n",
       "..     ...              ...  \n",
       "113   9.00  Marco Rodrigues  \n",
       "114  13.00  Marco Rodrigues  \n",
       "116   7.99  Marco Rodrigues  \n",
       "117  22.00  Marco Rodrigues  \n",
       "118   0.00  Marco Rodrigues  \n",
       "\n",
       "[118 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the final file [here](https://github.com/KenzoBH/Web-Scraping-and-EDA/blob/main/final-ifood-data.csv).   \n",
    "The next step is a EDA with this data. You can see this project repository [here](https://github.com/KenzoBH/Web-Scraping-and-EDA).\n",
    "\n",
    "Thanks for your reading!\n",
    "\n",
    "> ## **Bruno Kenzo.**   \n",
    "> Follow me on my [LinkedIn](https://www.linkedin.com/in/bruno-kenzo/) :D    \n",
    "> or on my [Github](https://github.com/KenzoBH) XD    \n",
    "> and take a look at [my website](https://kenzobh.github.io/) :p"
   ]
  }
 ],
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