{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "urC2JsY4GD1K"
   },
   "source": [
    "## Identification of all the members of the group.\n",
    "\n",
    "\n",
    "### GRUPO 5 - Atividade 2 - 09-10-2020\n",
    "\n",
    "\n",
    "*   Aguimar Ribeiro Júnior - 11516281\n",
    "*   André Luiz Akabane Solak - 9793572\n",
    "*   Bruna Bazaluk - 9797002\n",
    "*   Evandro Nakayama Mota - 10737230\n",
    "*   Gabriel Bustamante Ferrada Silva - 7629000\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w6-E3rb0GD1M"
   },
   "source": [
    "## Name of the dataset\n",
    "Dataset: Wine Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-cMmMEkYGD1N"
   },
   "source": [
    "## Abstract\n",
    "Abstract of the dataset:\n",
    "The present dataset intends to explore the data of different wines and its features like country, description, price, etc. Originally the author's goal was to make a recommendation system of wines, but here it's only intended to make an statiscal analysis and data exploring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBz2KSxcGD1O"
   },
   "source": [
    "## Questions to the dataset\n",
    "\n",
    "1. Qual país tem mais avaliações de seus vinhos?\n",
    "2. O vinho mais caro é o vinho com a melhor avaliação?\n",
    "3. Qual o vinho de melhor custo benefício?\n",
    "4. Quais uvas produzem os vinhos mais caros?\n",
    "5. Quais sommeliers possuem maior número de avaliações?\n",
    "6. Quais vinhos têm avaliações, positivas e negativas, parecidas entre\n",
    "os sommeliers?\n",
    "7. Quais países têm mais províncias produtoras de vinhos?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oo24Im8vGD1P"
   },
   "source": [
    "## EDA\n",
    "Present your EDA strategy: Nós fizemos uma análise sucinta dos dados no Setup e focamos em responder as perguntas propostas na atividade anterior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hd2hCFxoGD1Q"
   },
   "source": [
    "## Some examples of analysis/visualizations:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wjCu8MK6GD1R"
   },
   "source": [
    "1) Present the mean, variance, min, median and max values for each attribute. If the dataset has too many attibutes, choose the more significant ones. Present your code and some remarks to help the understanding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QAFfIYSVGD1S"
   },
   "source": [
    "2) How well distributed are the dataset in relation the each attribute. How can you visually check that? Hint: plot a histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EoWtGg-_GD1T"
   },
   "source": [
    "3) Is there any attribute that can be used to better interpret the dataset? Show this in a graphic where you used this attribute to group (groupby) the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4mH8ylr3GD1U"
   },
   "source": [
    "4) Is there any outlier's pattern? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9yl9AKgaGD1V"
   },
   "source": [
    "5) Make your own remarks about the dataset. Try to use one or more graphics to justify your remarks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ehtg9HuoGD1V"
   },
   "source": [
    "6) Is there any symmetry that can be stressed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5j7572hbGD1W"
   },
   "source": [
    "7) How important was EDA to help you understand the dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWiU_Rjvmp6I"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "sTJOZMlsKOgA"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 660
    },
    "id": "dBoG0eGeKdzN",
    "outputId": "dee65d1d-3d14-41e9-aadf-600c4d3b4100"
   },
   "outputs": [
    {
     "data": {
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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>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas include tropical fruit, broom, brimston...</td>\n",
       "      <td>Vulkà Bianco</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sicily &amp; Sardinia</td>\n",
       "      <td>Etna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Nicosia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>This is ripe and fruity, a wine that is smooth...</td>\n",
       "      <td>Avidagos</td>\n",
       "      <td>87</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Douro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Quinta dos Avidagos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>US</td>\n",
       "      <td>Tart and snappy, the flavors of lime flesh and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Rainstorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>US</td>\n",
       "      <td>Pineapple rind, lemon pith and orange blossom ...</td>\n",
       "      <td>Reserve Late Harvest</td>\n",
       "      <td>87</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Lake Michigan Shore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alexander Peartree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>St. Julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>Much like the regular bottling from 2012, this...</td>\n",
       "      <td>Vintner's Reserve Wild Child Block</td>\n",
       "      <td>87</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Willamette Valley</td>\n",
       "      <td>Paul Gregutt</td>\n",
       "      <td>@paulgwine</td>\n",
       "      <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Sweet Cheeks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   country                                        description  \\\n",
       "0           0     Italy  Aromas include tropical fruit, broom, brimston...   \n",
       "1           1  Portugal  This is ripe and fruity, a wine that is smooth...   \n",
       "2           2        US  Tart and snappy, the flavors of lime flesh and...   \n",
       "3           3        US  Pineapple rind, lemon pith and orange blossom ...   \n",
       "4           4        US  Much like the regular bottling from 2012, this...   \n",
       "\n",
       "                          designation  points  price           province  \\\n",
       "0                        Vulkà Bianco      87    NaN  Sicily & Sardinia   \n",
       "1                            Avidagos      87   15.0              Douro   \n",
       "2                                 NaN      87   14.0             Oregon   \n",
       "3                Reserve Late Harvest      87   13.0           Michigan   \n",
       "4  Vintner's Reserve Wild Child Block      87   65.0             Oregon   \n",
       "\n",
       "              region_1           region_2         taster_name  \\\n",
       "0                 Etna                NaN       Kerin O’Keefe   \n",
       "1                  NaN                NaN          Roger Voss   \n",
       "2    Willamette Valley  Willamette Valley        Paul Gregutt   \n",
       "3  Lake Michigan Shore                NaN  Alexander Peartree   \n",
       "4    Willamette Valley  Willamette Valley        Paul Gregutt   \n",
       "\n",
       "  taster_twitter_handle                                              title  \\\n",
       "0          @kerinokeefe                  Nicosia 2013 Vulkà Bianco  (Etna)   \n",
       "1            @vossroger      Quinta dos Avidagos 2011 Avidagos Red (Douro)   \n",
       "2           @paulgwine       Rainstorm 2013 Pinot Gris (Willamette Valley)   \n",
       "3                   NaN  St. Julian 2013 Reserve Late Harvest Riesling ...   \n",
       "4           @paulgwine   Sweet Cheeks 2012 Vintner's Reserve Wild Child...   \n",
       "\n",
       "          variety               winery  \n",
       "0     White Blend              Nicosia  \n",
       "1  Portuguese Red  Quinta dos Avidagos  \n",
       "2      Pinot Gris            Rainstorm  \n",
       "3        Riesling           St. Julian  \n",
       "4      Pinot Noir         Sweet Cheeks  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_reviews = pd.read_csv('https://raw.githubusercontent.com/aguimarjr/topicos/master/winemag-data-130k-v2.csv')\n",
    "wine_reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "PiaWHn7fqbSy",
    "outputId": "b7ca36d7-b8f5-4821-8ee8-b84c7ecd9c47"
   },
   "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>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>129971.000000</td>\n",
       "      <td>120975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>88.447138</td>\n",
       "      <td>35.363389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.039730</td>\n",
       "      <td>41.022218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              points          price\n",
       "count  129971.000000  120975.000000\n",
       "mean       88.447138      35.363389\n",
       "std         3.039730      41.022218\n",
       "min        80.000000       4.000000\n",
       "25%        86.000000      17.000000\n",
       "50%        88.000000      25.000000\n",
       "75%        91.000000      42.000000\n",
       "max       100.000000    3300.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_reviews[['points', 'price']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-6K6K5ku9Yt6"
   },
   "source": [
    "Note que a distribuição das notas de avaliação possui um desvio padrão de 3.03, enquanto que o preço é 41.02 que é maior que a média."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "6OuxF6yQr6lj",
    "outputId": "d8922f44-0f1a-41cb-b7d7-b39254dec49e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Pontuação dos vinhos nas avaliações')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pvwW4pZGxmZlZ/dZ2TOSnlQlJNzQ5FjMzG2TWlkSKZ0Dt0MxAzMxs8FlbEoka02ZmZms9sP4uSS+R9khG5GnyfETEpk2NzszM1ml9JpGIGNaqQMzMbPDpz/1EzMzM3sRJxMzMSqv3YkMbosZPa9g1pvYWNZDvyOLphzYwEmsH74mYmVlpTiJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZleYkYmZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmalOYmYmVlpbUsikhZLmidpjqTOXLaFpNmSFuXnkblcks6X1CVprqQ9C8uZkusvkjSlXe/HzGwoaveeyAcjYmJEdOT5acCdETEBuDPPAxwMTMiPqcAFkJIOcCbwHmBv4MxK4jEzs+ZrdxLpbTJwaZ6+FDi8UH5ZJPcDm0saDRwEzI6IFyLiRWA2MKnVQZuZDVXtTCIB3C7pYUlTc9k2EbEUID9vncvHAL8ttO3OZbXKzcysBdp5Z8P3RcQSSVsDsyUt7KOuqpRFH+VvbpyS1FSAcePGlYnVzMyqaNueSEQsyc/LgRtJxzSW5W4q8vPyXL0b2K7QfCywpI/y3uuaEREdEdExatSoRr8VM7Mhqy1JRNLGkt5emQYOBOYDs4DKGVZTgJvy9CzguHyW1j7AH3J3123AgZJG5gPqB+YyMzNrgXZ1Z20D3CipEsOVEfFzSQ8B10o6EXgWOCrXvwU4BOgCXgVOAIiIFyR9E3go1/tGRLzQurdhZja0tSWJRMTTwLuqlP8OOKBKeQCn1FjWTGBmo2M0M7O1W9dO8TUzs0HEScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKa+dQ8GY2xI2fdnPptounH9rASKws74mYmVlpTiJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZlebrRIaAgZyLb2bWF++JmJlZaU4iZmZWmpOImZmV1vIkImk7Sb+Q9ISkBZL+MZefJek5SXPy45BCmzMkdUl6UtJBhfJJuaxL0rRWvxczs6GuHQfWVwFfjIhHJL0deFjS7PzaeRHxnWJlSbsCRwO7AdsCd0jaKb/8A+AjQDfwkKRZEfF4S96FmZm1PolExFJgaZ5+WdITwJg+mkwGro6IlcAzkrqAvfNrXRHxNICkq3NdJxEzsxZp6zERSeOBPYAHctGpkuZKmilpZC4bA/y20Kw7l9Uqr7aeqZI6JXWuWLGige/AzGxoa1sSkbQJcANwWkS8BFwA7AhMJO2pnFupWqV59FG+ZmHEjIjoiIiOUaNGDTh2MzNL2nKxoaT1SAnkioj4CUBELCu8fhHwszzbDWxXaD4WWJKna5WbmVkLtOPsLAEXA09ExHcL5aML1Y4A5ufpWcDRkjaQtD0wAXgQeAiYIGl7SeuTDr7PasV7MDOzpB17Iu8DPgHMkzQnl30ZOEbSRFKX1GLgMwARsUDStaQD5quAUyJiNYCkU4HbgGHAzIhY0Mo3YmY21LXj7Kz/ofrxjFv6aHM2cHaV8lv6amdmZs3lK9bNzKw0JxEzMyvNQ8EPAh7K3czWVd4TMTOz0pxEzMysNHdnmdmgNNBu3sXTD21QJEOb90TMzKw0JxEzMyvNScTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNF+x3iIeRNHM3oq8J2JmZqU5iZiZWWnuzjKzIWkgXcwevLGHk0g/+LiGmdmbuTvLzMxKG/R7IpImAd8DhgH/GRHT2xySmb3FuSusx6DeE5E0DPgBcDCwK3CMpF3bG5WZ2dAx2PdE9ga6IuJpAElXA5OBx9salZlZDe06ttqsPaDBnkTGAL8tzHcD7+ldSdJUYGqefUXSkyXXtxXwfMm2zeS4+sdx9Y/j6p91Mi6dM+C43lGtcLAnEVUpizUKImYAMwa8MqkzIjoGupxGc1z947j6x3H1z1CLa1AfEyHteWxXmB8LLGlTLGZmQ85gTyIPARMkbS9pfeBoYFabYzIzGzIGdXdWRKySdCpwG+kU35kRsaCJqxxwl1iTOK7+cVz947j6Z0jFpYg1DiGYmZnVZbB3Z5mZWRs5iZiZWWlOIpmkf5K0QNJ8SVdJ2jAfsH9A0iJJ1+SD99XaniGpS9KTkg5qQVxX5HXNlzRT0no12q6WNCc/GnrCQY24LpH0TGGdE2u0nZK36SJJU1oQ172FmJZI+mmNts3cXv+YY1og6bRctoWk2Xk7zJY0skbbZm6vanF9W9JCSXMl3Shp8xptF0ual7dXZwviOkvSc4XP6JAabSflv48uSdNaENc1hZgWS5pTo23Dtlf+u18uaX6hrOr3Scn5eXvMlbRnjWXulePryvWrXUKxpogY8g/SRYvPACPy/LXA8fn56Fx2IXBylba7Ao8BGwDbA08Bw5oc1yGka2QEXFUtrlz/lRZvr0uAI9fSdgvg6fw8Mk+PbGZcvercABzX4u21OzAf2Ih0MssdwATgW8C0XGcacE6Lt1etuA4Ehuc651SLK7+2GNiqhdvrLOBLa2k7LP8N7gCsn/82d21mXL3qnAt8rdnbC9gP2BOYXyir+n3K/y9uzf8v9gEeqLHMB4H35nq3AgfXE4v3RHoMB0ZIGk76kiwFPgRcn1+/FDi8SrvJwNURsTIingG6SMOxNCuuJRFxS2SkD35sA9dXOq462x0EzI6IFyLiRWA2MKkVcUl6O+kzrbon0kR/BdwfEa9GxCrgbuAI0nfn0lyn1vermduralwRcXueB7if1n+/am2verwxFFJEvA5UhkJqelz5l/vHSD/smioi7gFe6FVc6/s0Gbgs/8u4H9hc0uhiwzy/aUTcl/+vXEb17+ManESAiHgO+A7wLCl5/AF4GPh94Y+pm/RLt7dqQ69Uq9eQuCLi9srruRvrE8DPayxiQ0mdku6XVNcXogFxnZ13mc+TtEGV5m3bXqQ/+Dsj4qUai2jK9iL9et1P0paSNiL9MtwO2CYilubYlwJbV2nbtO3VR1xFnyT9Kq0mgNslPaw0tFCj9BXXqfn7NbNG9187t9e+wLKIWFSjfbO2V0Wt71M922RMLu+rTlVOIkD+Mk4mdUdtC2xMGhm4t2rnQ9c19Eqj4pL08UKVHwL3RMS9NRYxLtIwB/8A/JukHZsc1xnALsC7Sd0vp1drXqWsVdvrGPr+ldiU7RURT5C6hWaTEv5jwKo+G/Vo2vZaW1ySvpLnr6ixiPdFxJ6kv5VTJO3X5LguAHYEJpJ+JJxbpXnbthdr/341ZXvVoZ5tUnq7OYkkHwaeiYgVEfEn4CfAX5N2+yoXZNYaUqWZQ6/UigtJZwKjgC/UahwRS/Lz08BdwB7NjCsiluZd5pXAj6jerdeu7bVljqfmEKpN3F5ExMURsWdE7EfqhlgELKt0K+Tn5VWaNnVonxpxkQ/g/w1wbO7eqNa2sr2WAzfSwG7canFFxLKIWB0RfwYuqrG+dm2v4cBHgWv6aNu07ZXV+j7Vs026eXO3Zd3bzUkkeRbYR9JGuV/zANJw8r8Ajsx1pgA3VWk7Czha0gaSticdAHywiXE9IelTpL7yY/If1Bokjax0J0naCngfjRsiv1ZclS+wSP2p86u0vQ04MMc3knQQ97ZmxpVfOwr4WUT8b7WGTd5eSNo6P48j/bO5ivTdqZxtVev71cztVTUupRu9nQ4cFhGv1mi3cT7GhKSNc1zVPu9GxlXsxz+ixvqaOhRSjc8R0g+YhRHRXaNdU7dXVuv7NAs4Lp+ltQ+pm3dpsWGef1nSPvlv5ziqfx/XVM/R96HwAL4OLCR9sJeTzrbagZQQuoDrgA1y3cOAbxTafoV0RsiT1HlGwwDjWpXXNyc/vpbrdpDu7gjpF/g80i73PODEFsT133ld84EfA5v0jivPfzJv0y7ghGbHlcvvAib1qtvK7XUvKSk9BhyQy7YE7iT9mr0T2KIN26taXF2kPvTK9+vCXL4tcEue3iG3eQxYAHylBXFdnj+buaR/jKN7x5XnDwF+nf9Gmh5XLr8EOKlX3aZtL1LyWgr8ibQXcWIf3yeRbt73VN5+HYXlzOn19zA/1/t38ogma3t42BMzMyvN3VlmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiNWknlFt50u6Lg/1UGY5x0vatgnxfVDSfUojzf7dAJYzXoXRUBtN0q/qqLM4X58yJEh6JT9vK+n6tdWvsYwdJN0t6VZJX25shFYvn+JrNUl6JSI2ydNXAA9HxHdLLOcu0uirDR0uvFEkjSddiLh7G2NYTDp///l2xdBKxe+WDW7eE7F63Qv8JYCkL+S9k/nquafCeElPSLpI6V4Lt0saIelI0kVMV+S9mhHFX92SOnKSQdLekn4l6dH8vHMuHybp3Ly+ucqD10m6QGnAxAWSvl4JVNIBeRnzlAbqW2MgSKV7Jzwm6T7glEL5hpJ+lNs+KumDuXw3SQ/m9zBX0oReyztZ0rcK88dL+n6ervzq3l/SXZKuV7pfxxX56uCKz0l6JK97l9xmC0k/zeu8X9I7c/kH1HMPi0crV0MX1l/188ivfVrSQ/n931DZw5R0VN7Gj0m6p8o220TSnYUYJ+fycyR9tlDvLElfrFW/SpzzC9P35vqPSPrrQr3TC7F9va/POX+2dysNdHibekZS+Lykx/O2vLp3LFZSI6/m9OOt9SDfX4M0vPpNwMnAXqSrXjcGNiFdfbsHMJ50Jf3E3OZa4ON5+i7efJXsYvJ9FUgJ5q48vSk997H4MHBDnj45L29Ynt+i1/OwvI53AhuSrrbeKb92GXBalfc2F/hAnv42+b4MwBeBH+XpXUhDqWwIfJ80jhSk+1SM6LW8UaQhyCvztwLv77Ud9yeNED2W9APuvkKdxcDn8vRn6bmS/vvAmXn6Q+QrjIH/Ig3oR/4chveKp6/PY8tCvX8prHceMCZPb15lmw0nDRcOsBXpynblz//uQr3HgXG16vfaJuML234jYMM8PQHozNMHA/9Dz31itsifSTewS/FzBtYDfgWMyuV/D8zM00voGcFgjffnR7mH90SsLyOU7tLWSfpnejHwfuDGiPhjRLxCGuRw31z/mYio3NXtYdI/iP7YDLgu/zI9D9gtl38YmBERqwEionIfhY9JegR4NNfdFdg5x/HrXOdS0g183iBpM9I/kbtz0eWFl99fmY+IhcBvgJ1I//C/LOl04B0R8VpxmRGxAnhaaeyhLXMcv6zyHh+MiO5IY57N4c3b6Cf5ubjtivH8N7Bljv+XwHclfT6/l2qjAtf6PHbPv/jnAcfSs51/CVwi6dOkxNybgH+VNJd0Q6YxpOHHHwW2Vjq+8S7gxYh4tlb9KsutWA+4KMd1HenzhPT5X1LZ5vnzr3zOC3Odyue8M+nmUbPzd/er9AwsOJe0R/xx6h9F2dZi+Nqr2BD2WkS86Ra3vbpfeltZmF4NjKhRbxU9XakbFsq/CfwiIo5QOk5xV2W1vRegNNjll4B3R8SLki7Jy6rnlp6i9jDXVdtHxJWSHgAOBW6T9Kn8T73oGtJNiRaSEm21dfTeRsOrvFYsrzpEd0RMl3QzaZyo+yV9uPAPtda6Kp/HJcDhEfGYpONJe0hExEmS3pPf4xxJEyPid4VlHEva49orIv6kdByn8vldTxqs9C9IN4JaW/1q/glYBryL9P2oDJZZbRvU+gwFLIiI91Z57VBSojkM+GdJu9VIvtYP3hOx/roHOFxppNyNSaOp1rqfScXLQLHPfjGpWwygeFbVZsBzefr4QvntwFRJwyAdJyB1ff0R+IOkbei5/8tCYLykv8zznyDdge4NEfH73O79uejYXu/v2LyenUjdMk9K2gF4OiLOJw3+984q7/MnpNGLj6GPIcH7qRjP/sDzEfGSpB0jYl5EnEPaU9ylH8t8O7BU6aZmb7z3vMwHIuJrwPOseYOqzYDlOSF8EHhH4bWrSSPmHknP3UD7ql/NZsDSvJf2CXr2hm4HphSO6WxB7c/5SWCUpPfmuuspHc96G7BdRPwC+L/A5qRuQBsgJxHrl4h4hPRL9kHgAVLf/aNraXYJcGE+CDyCNNLu9yTdS/qFXPEt4P9J+iVv7k75T1L/96COErwAAAEcSURBVCJJXcDfRcRjpG6sBcBMctdRpKHeTyB1i80D/gxcWCWmE4AfKB1YL3ZN/RAYltteQ7pH+0pS3/r83EWyC6kPvve2eZF0POAdEdGo2wGcBXTkLqHp9Az1fVrlQHOOv9bdB6v5Z9JnN5v0z7ji2/kg9XxS8nqsV7srciydpOTzRtuIWEBKTs9FzzDjNevX8ENSsrif1IX4x7zsn5PuA/NY3pv5XK3POdItcY8EzsnbZg5phOZhwI9z3UeB8/KPCRsgn+Jrg4akDYHvRMSp7Y7FWi93pc6IiE+3Oxbr4T0RGxRy19JDpPuW2BAjaRPSHkTvLjZrM++JmJlZad4TMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PS/j/VWrKZ2BOXPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "points_hist = wine_reviews['points'].plot.hist(bins=20)\n",
    "points_hist.set_xlabel('Pontuação dos vinhos nas avaliações')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ajw5HaxOpppG"
   },
   "source": [
    "# 1) Qual país tem mais avaliações de seus vinhos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "L_0vWYr_pyZY",
    "outputId": "99178e81-a141-4cdb-f235-aa9445d5988b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_reviews = wine_reviews['country'].value_counts()\n",
    "\n",
    "country_reviews = country_reviews[country_reviews>=2000]\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "plt.bar(x=country_reviews.index, height=country_reviews.values, color='blue')\n",
    "plt.xticks(rotation=-45, ha='left')\n",
    "\n",
    "for country in country_reviews.index:\n",
    "    plt.text(x=country, y=country_reviews[country]+5, \n",
    "             s=str(country_reviews[country]), ha='center', rotation=45)\n",
    "\n",
    "plt.title('Quantidade de vinhos avaliados por país')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WPhF6eUGtWcW"
   },
   "source": [
    "A pergunta original era \"Qual o país que mais produz vinho?\", porém não se pode concluir sobre o número de vinhos produzidos no país pelo número de reviews no dataset, pois podem existir vinhos que não tem nenhuma avaliação no dataset utilizado. Portanto, a questão foi adaptada para verificar quantos reviews de vinhos produzidos por país existem no dataset. Vê-se no gráfico acima que mais de 40% (41,92%) dos reviews são de vinhos norte-americanos.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TRnHdsgNkbcV"
   },
   "source": [
    "# 2) O vinho mais caro é o vinho com a melhor avaliação?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "0hAJCcCDcJSu"
   },
   "outputs": [],
   "source": [
    "# columns of price and points\n",
    "price = wine_reviews['price']\n",
    "points = wine_reviews['points']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "y1O8BYxgcMUC"
   },
   "outputs": [],
   "source": [
    "most_expensive_wine = wine_reviews[wine_reviews['price'] == price.max()]\n",
    "best_reviewed_wines = wine_reviews[wine_reviews['points'] == points.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "YNOhGUNTcTWK",
    "outputId": "4729a2db-3934-49a4-80fc-8f8be7352125"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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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>points</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>100.0</td>\n",
       "      <td>485.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.142451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>100.0</td>\n",
       "      <td>230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>100.0</td>\n",
       "      <td>359.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>100.0</td>\n",
       "      <td>583.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points        price\n",
       "count    19.0    19.000000\n",
       "mean    100.0   485.947368\n",
       "std       0.0   410.142451\n",
       "min     100.0    80.000000\n",
       "25%     100.0   230.000000\n",
       "50%     100.0   359.000000\n",
       "75%     100.0   583.500000\n",
       "max     100.0  1500.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_reviewed_wines.describe()[['points', 'price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "id": "Y7QUDVAKcZDT",
    "outputId": "034cc9ce-c985-4caf-c298-7d8718e7239e"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>80290</td>\n",
       "      <td>80290</td>\n",
       "      <td>France</td>\n",
       "      <td>This ripe wine shows plenty of blackberry frui...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>Médoc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Château les Ormes Sorbet 2013  Médoc</td>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "      <td>Château les Ormes Sorbet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0 country                                        description  \\\n",
       "80290       80290  France  This ripe wine shows plenty of blackberry frui...   \n",
       "\n",
       "      designation  points   price  province region_1 region_2 taster_name  \\\n",
       "80290         NaN      88  3300.0  Bordeaux    Médoc      NaN  Roger Voss   \n",
       "\n",
       "      taster_twitter_handle                                 title  \\\n",
       "80290            @vossroger  Château les Ormes Sorbet 2013  Médoc   \n",
       "\n",
       "                        variety                    winery  \n",
       "80290  Bordeaux-style Red Blend  Château les Ormes Sorbet  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_expensive_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jo_ItkVMd4WH"
   },
   "source": [
    "Os dados acima demonstram que o vinho mais caro custa US\\\\$3300.00 e possui avaliação de 88 pontos. Logo, ele não está na lista de vinhos com pontuação máxima (100 pontos). É interessante notar também que a média de preço dos vinhos com pontuação máxima é de US\\\\$485.94, o que nos permite concluir que os vinhos mais bem pontuados são, em média, aproximadamente 6,7 vezes mais baratos que o vinho mais caro do dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xbyMyXhXRuzW"
   },
   "source": [
    "# 3) Qual o vinho de melhor custo benefício?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bPyTrwVi_wFj"
   },
   "source": [
    "Alguns vinhos têm mais de uma review, então a tabela a seguir considera as médias de preço e pontos de cada vinho.\n",
    "\n",
    "(O custo benefício é calculado pela diferença entre os pontos e o preço)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 524
    },
    "id": "lseucA7OR8VA",
    "outputId": "88af965b-85cb-4882-b2d2-e780e9efa054"
   },
   "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>points</th>\n",
       "      <th>price</th>\n",
       "      <th>custo benefício</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Cramele Recas 2011 UnWineD Pinot Grigio (Viile Timisului)</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dancing Coyote 2015 White (Clarksburg)</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Felix Solis 2013 Flirty Bird Syrah (Vino de la Tierra de Castilla)</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bandit NV Merlot (California)</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bandit NV Chardonnay (California)</td>\n",
       "      <td>84.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Domaine du Comte Liger-Belair 2005  La Romanée</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.048000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Blair 2013 Roger Rose Vineyard Chardonnay (Arroyo Seco)</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>0.045206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Château Pétrus 2014  Pomerol</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.038400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Domaine du Comte Liger-Belair 2010  La Romanée</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.038400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Château les Ormes Sorbet 2013  Médoc</td>\n",
       "      <td>88.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.026667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110638 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    points   price  \\\n",
       "title                                                                \n",
       "Cramele Recas 2011 UnWineD Pinot Grigio (Viile ...    86.0     4.0   \n",
       "Dancing Coyote 2015 White (Clarksburg)                85.0     4.0   \n",
       "Felix Solis 2013 Flirty Bird Syrah (Vino de la ...    85.0     4.0   \n",
       "Bandit NV Merlot (California)                         85.0     4.0   \n",
       "Bandit NV Chardonnay (California)                     84.0     4.0   \n",
       "...                                                    ...     ...   \n",
       "Domaine du Comte Liger-Belair 2005  La Romanée        96.0  2000.0   \n",
       "Blair 2013 Roger Rose Vineyard Chardonnay (Arro...    91.0  2013.0   \n",
       "Château Pétrus 2014  Pomerol                          96.0  2500.0   \n",
       "Domaine du Comte Liger-Belair 2010  La Romanée        96.0  2500.0   \n",
       "Château les Ormes Sorbet 2013  Médoc                  88.0  3300.0   \n",
       "\n",
       "                                                    custo benefício  \n",
       "title                                                                \n",
       "Cramele Recas 2011 UnWineD Pinot Grigio (Viile ...        21.500000  \n",
       "Dancing Coyote 2015 White (Clarksburg)                    21.250000  \n",
       "Felix Solis 2013 Flirty Bird Syrah (Vino de la ...        21.250000  \n",
       "Bandit NV Merlot (California)                             21.250000  \n",
       "Bandit NV Chardonnay (California)                         21.000000  \n",
       "...                                                             ...  \n",
       "Domaine du Comte Liger-Belair 2005  La Romanée             0.048000  \n",
       "Blair 2013 Roger Rose Vineyard Chardonnay (Arro...         0.045206  \n",
       "Château Pétrus 2014  Pomerol                               0.038400  \n",
       "Domaine du Comte Liger-Belair 2010  La Romanée             0.038400  \n",
       "Château les Ormes Sorbet 2013  Médoc                       0.026667  \n",
       "\n",
       "[110638 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Não estamos considerando vinhos com valores de pontos ou preço nulos\n",
    "grouped = wine_reviews.groupby('title')\n",
    "pp=grouped[['points','price','title']].agg(np.mean).dropna() \n",
    "pp['custo benefício'] = pp['points']/pp['price']\n",
    "pp.sort_values(by='custo benefício', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6Cz94Rt9-8xC"
   },
   "source": [
    "O vinho com melhor custo-benefício portanto é **Cramele Recas 2011 UnWineD Pinot Grigio (Viile Timisului)**.\n",
    "\n",
    "Para analisar visualmente a diferença entre os custo-benefício"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "bn7rCAZe-9YM",
    "outputId": "cc6dd269-78fb-4415-f5aa-27ce0585947a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7febe382bed0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.hist('custo benefício')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "effyg-6PluIq"
   },
   "source": [
    "# 4) Quais uvas produzem os vinhos mais caros?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "atHtOKsKl5DL",
    "outputId": "21791929-9ca4-4a6a-911a-9defc75cd91c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>80290</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15840</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98380</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>Pinot Noir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120391</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Chardonnay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113564</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Pinot Noir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         price                   variety\n",
       "80290   3300.0  Bordeaux-style Red Blend\n",
       "15840   2500.0  Bordeaux-style Red Blend\n",
       "98380   2500.0                Pinot Noir\n",
       "120391  2013.0                Chardonnay\n",
       "113564  2000.0                Pinot Noir"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr_price = wine_reviews.dropna(subset=['price']) # Apaga os preços que são nulos\n",
    "wr_price.sort_values(by=['price'], ascending=False)[['price','variety']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aBDo_VGbl8xh"
   },
   "source": [
    "\n",
    "Em preço absoluto, a uva que produz o vinho mais caro é a **Bordeaux-style Red Blend** com um custo de **3300.00**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "DaQxaERKl6km",
    "outputId": "3e269322-e956-4698-c02c-156009db3815"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Ramisco</td>\n",
       "      <td>1</td>\n",
       "      <td>495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Terrantez</td>\n",
       "      <td>1</td>\n",
       "      <td>236.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Francisa</td>\n",
       "      <td>1</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rosenmuskateller</td>\n",
       "      <td>1</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Malbec-Cabernet</td>\n",
       "      <td>3</td>\n",
       "      <td>113.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price            \n",
       "                 count        mean\n",
       "variety                           \n",
       "Ramisco              1  495.000000\n",
       "Terrantez            1  236.000000\n",
       "Francisa             1  160.000000\n",
       "Rosenmuskateller     1  150.000000\n",
       "Malbec-Cabernet      3  113.333333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr_groupby_variety = wr_price.groupby(['variety'])[['price']].agg(['count','mean'])\n",
    "wr_groupby_variety.sort_values(by=[('price','mean')], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S3pKFvgKl_Rn"
   },
   "source": [
    "Entretanto, se considerarmos a média de cada grupo de uvas, temos que a uva **Ramisco** ganha, com uma média de **495.00**, porém possui apenas 1 avaliação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "IfNJSO-M4wwz",
    "outputId": "22abb5c5-5edb-4955-f6a9-422ece5d7c08"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Média do preço de um vinho por uva')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "variety_mean_hist = wr_groupby_variety[('price','mean')].plot.hist(bins=50)\n",
    "variety_mean_hist.set_xlabel('Média do preço de um vinho por uva')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KKwIKr5PkzUL"
   },
   "source": [
    "# 5) Quais sommeliers possuem maior número de avaliações?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "j68ok5SnXZ55",
    "outputId": "5429ee43-2a7b-4e21-897b-3637a96b3d5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Roger Voss            25514\n",
       "Michael Schachner     15134\n",
       "Kerin O’Keefe         10776\n",
       "Virginie Boone         9537\n",
       "Paul Gregutt           9532\n",
       "Matt Kettmann          6332\n",
       "Joe Czerwinski         5147\n",
       "Sean P. Sullivan       4966\n",
       "Anna Lee C. Iijima     4415\n",
       "Jim Gordon             4177\n",
       "Anne Krebiehl MW       3685\n",
       "Lauren Buzzeo          1835\n",
       "Susan Kostrzewa        1085\n",
       "Mike DeSimone           514\n",
       "Jeff Jenssen            491\n",
       "Alexander Peartree      415\n",
       "Carrie Dykes            139\n",
       "Fiona Adams              27\n",
       "Christina Pickard         6\n",
       "Name: taster_name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_reviews['taster_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 536
    },
    "id": "pTmuOZtMXaMn",
    "outputId": "cb417058-a7c4-4d16-b320-4ddd9b4b9953"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12.5, 7.5))\n",
    "plt.bar(x=wine_reviews['taster_name'].value_counts().index, height=wine_reviews['taster_name'].value_counts().values, color='blue')\n",
    "plt.xticks(rotation=-45, ha='left')\n",
    "\n",
    "for taster in wine_reviews['taster_name'].value_counts().index:\n",
    "    plt.text(x=taster, y=wine_reviews['taster_name'].value_counts()[taster]+250, \n",
    "             s=str(wine_reviews['taster_name'].value_counts()[taster]), ha='center')\n",
    "\n",
    "plt.title('Quantidade de avaliações por taster')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ontO3mXslBFk"
   },
   "source": [
    "# 6) Quais vinhos têm avaliações, positivas e negativas, parecidas entre os sommeliers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "eEYQEthjWoVY",
    "outputId": "9b213f72-38e1-44dd-9b4e-77f8ba0a7569"
   },
   "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>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Nicosia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Quinta dos Avidagos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Rainstorm 2013 Pinot Gris (Willamette Valley)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Rainstorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>St. Julian 2013 Reserve Late Harvest Riesling ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>St. Julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Sweet Cheeks 2012 Vintner's Reserve Wild Child...</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Sweet Cheeks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129966</td>\n",
       "      <td>Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>Dr. H. Thanisch (Erben Müller-Burggraef)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129967</td>\n",
       "      <td>Citation 2004 Pinot Noir (Oregon)</td>\n",
       "      <td>Pinot Noir</td>\n",
       "      <td>Citation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129968</td>\n",
       "      <td>Domaine Gresser 2013 Kritt Gewurztraminer (Als...</td>\n",
       "      <td>Gewürztraminer</td>\n",
       "      <td>Domaine Gresser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129969</td>\n",
       "      <td>Domaine Marcel Deiss 2012 Pinot Gris (Alsace)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Domaine Marcel Deiss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129970</td>\n",
       "      <td>Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car...</td>\n",
       "      <td>Gewürztraminer</td>\n",
       "      <td>Domaine Schoffit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119090 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    title         variety  \\\n",
       "0                       Nicosia 2013 Vulkà Bianco  (Etna)     White Blend   \n",
       "1           Quinta dos Avidagos 2011 Avidagos Red (Douro)  Portuguese Red   \n",
       "2           Rainstorm 2013 Pinot Gris (Willamette Valley)      Pinot Gris   \n",
       "3       St. Julian 2013 Reserve Late Harvest Riesling ...        Riesling   \n",
       "4       Sweet Cheeks 2012 Vintner's Reserve Wild Child...      Pinot Noir   \n",
       "...                                                   ...             ...   \n",
       "129966  Dr. H. Thanisch (Erben Müller-Burggraef) 2013 ...        Riesling   \n",
       "129967                  Citation 2004 Pinot Noir (Oregon)      Pinot Noir   \n",
       "129968  Domaine Gresser 2013 Kritt Gewurztraminer (Als...  Gewürztraminer   \n",
       "129969      Domaine Marcel Deiss 2012 Pinot Gris (Alsace)      Pinot Gris   \n",
       "129970  Domaine Schoffit 2012 Lieu-dit Harth Cuvée Car...  Gewürztraminer   \n",
       "\n",
       "                                          winery  \n",
       "0                                        Nicosia  \n",
       "1                            Quinta dos Avidagos  \n",
       "2                                      Rainstorm  \n",
       "3                                     St. Julian  \n",
       "4                                   Sweet Cheeks  \n",
       "...                                          ...  \n",
       "129966  Dr. H. Thanisch (Erben Müller-Burggraef)  \n",
       "129967                                  Citation  \n",
       "129968                           Domaine Gresser  \n",
       "129969                      Domaine Marcel Deiss  \n",
       "129970                          Domaine Schoffit  \n",
       "\n",
       "[119090 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_reviews[['title', 'variety', 'winery']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FUUduiMXbbsL"
   },
   "source": [
    "\n",
    "Notamos que cada diferente vinho possui, em geral, apenas uma única avaliação, o que dificulta avaliar a uniformidade de critério entre os tasters. Por isso, optamos por verificar quais vinículas são melhor avaliadas.\n",
    "\n",
    "A avaliação a seguir indica que a winery 'Williams Selyem' possui a melhor avaliação dentre os vinhos estudados.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "m5iMcuHCWoa9"
   },
   "outputs": [],
   "source": [
    "winery_reviews = wine_reviews['winery'].value_counts()\n",
    "winery_reviews = winery_reviews[winery_reviews>=150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "hR22lwYGcBVr",
    "outputId": "79b94c1c-dbd9-4654-cbab-6a7d7b728b1c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.bar(x=winery_reviews.index, height=winery_reviews.values, color='blue')\n",
    "plt.xticks(rotation=-45, ha='left')\n",
    "\n",
    "for winery in winery_reviews.index:\n",
    "    plt.text(x=winery, y=winery_reviews[winery]+5, \n",
    "             s=str(winery_reviews[winery]), ha='center', rotation=45)\n",
    "\n",
    "plt.title('Quantidade de avaliações por winery')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "id": "Cg6W0z8_dGCK",
    "outputId": "48974c5c-343c-4766-d660-0efbc946393d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wines & Winemakers       222\n",
       "Testarossa               218\n",
       "DFJ Vinhos               215\n",
       "Williams Selyem          211\n",
       "Louis Latour             199\n",
       "Georges Duboeuf          196\n",
       "Chateau Ste. Michelle    194\n",
       "Concha y Toro            164\n",
       "Columbia Crest           159\n",
       "Name: winery, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winery_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 499
    },
    "id": "y1oMv_cBeHQO",
    "outputId": "150aefd5-3a0d-4791-8a7b-3d5da8517c48"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "winery_points = wine_reviews.loc[wine_reviews['winery'].isin(winery_reviews.index)]\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "for winery in winery_points['winery'].unique():\n",
    "    p = plt.hist(winery_points.loc[winery_points['winery']==winery, 'points'], alpha=0.75, label=winery)\n",
    "    plt.axvline(x=winery_points.loc[winery_points['winery']==winery, 'points'].mean(), ls='--', color=p[2][-1].get_facecolor())\n",
    "    plt.text(x=winery_points.loc[winery_points['winery']==winery, 'points'].mean(), y=65, \n",
    "            s=str(winery), ha='center', rotation=90)\n",
    "\n",
    "plt.title('Distribuição de points por winery')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LG0AQ8B0u7xt"
   },
   "source": [
    "# 7) Quais países têm mais províncias produtoras de vinhos?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "LBNMokJ9vA1I",
    "outputId": "9b7465f1-7097-45c9-b8d4-1d1556ec4c47"
   },
   "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>country</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Greece</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Chile</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>US</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  province\n",
       "18        Greece        54\n",
       "8          Chile        43\n",
       "36  South Africa        42\n",
       "31      Portugal        40\n",
       "40            US        27"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wr_groupby_country_province = wine_reviews.groupby(['country','province']).count().reset_index()\n",
    "wr_groupby_country = wr_groupby_country_province.groupby('country')['province'].count().reset_index()\n",
    "wr_groupby_country.sort_values(by=['province'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L7RP0FL72sB1"
   },
   "source": [
    "Segundo esse dataset, a Grécia possui **54** províncias produtoras de vinhos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "OWIK2u1c6wtY",
    "outputId": "f80c7159-740d-4e5f-ff54-c9eb257d3d46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Número de províncias em cada país')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wr_province_hist = wr_groupby_country.plot.hist(bins=30)\n",
    "wr_province_hist.set_xlabel('Número de províncias em cada país')"
   ]
  }
 ],
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  "colab": {
   "collapsed_sections": [],
   "name": "DataScienceEx2.ipynb",
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   "labels_anchors": false,
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  "toc": {
   "colors": {
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   "moveMenuLeft": true,
   "nav_menu": {
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}