Anthony_Question5_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations\n",
    "\n",
    "1: Riesling is excellent versatile wine because of it's popularity and price\n",
    "\n",
    "2: Pinot Noir is the most popular and expensive wine\n",
    "\n",
    "3: It appears that based off of the most 8 common wines popularity does seem to favor price  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</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",
       "      <th>0</th>\n",
       "      <td>26038</td>\n",
       "      <td>26038</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Raw, gamy aromas of herbal black fruits and we...</td>\n",
       "      <td>Finca Resalso</td>\n",
       "      <td>84</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Northern Spain</td>\n",
       "      <td>Ribera del Duero</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael Schachner</td>\n",
       "      <td>@wineschach</td>\n",
       "      <td>Emilio Moro 2015 Finca Resalso  (Ribera del Du...</td>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>Emilio Moro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115439</td>\n",
       "      <td>115439</td>\n",
       "      <td>US</td>\n",
       "      <td>This wine is mild in acidity and simple in str...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>24.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Virginie Boone</td>\n",
       "      <td>@vboone</td>\n",
       "      <td>Rutherford Ranch 2015 Chardonnay (Napa Valley)</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Rutherford Ranch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111361</td>\n",
       "      <td>111361</td>\n",
       "      <td>Austria</td>\n",
       "      <td>This is described as Brut dry, but the perfume...</td>\n",
       "      <td>Sparkling Mosi Brut</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Österreichischer Sekt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Hermann Moser NV Sparkling Mosi Brut Gelber Mu...</td>\n",
       "      <td>Gelber Muskateller</td>\n",
       "      <td>Hermann Moser</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>109611</td>\n",
       "      <td>109611</td>\n",
       "      <td>France</td>\n",
       "      <td>This big, ripe wine is opulent and smooth. Wit...</td>\n",
       "      <td>Tradition</td>\n",
       "      <td>87</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Domaine Schoffit 2012 Tradition Pinot Gris (Al...</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Domaine Schoffit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33921</td>\n",
       "      <td>33921</td>\n",
       "      <td>US</td>\n",
       "      <td>The wine has tempting aromas like marzipan and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>12.0</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California Other</td>\n",
       "      <td>Jim Gordon</td>\n",
       "      <td>@gordone_cellars</td>\n",
       "      <td>Leese-Fitch 2014 Chardonnay (California)</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Leese-Fitch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  country  \\\n",
       "0       26038         26038    Spain   \n",
       "1      115439        115439       US   \n",
       "2      111361        111361  Austria   \n",
       "3      109611        109611   France   \n",
       "4       33921         33921       US   \n",
       "\n",
       "                                         description          designation  \\\n",
       "0  Raw, gamy aromas of herbal black fruits and we...        Finca Resalso   \n",
       "1  This wine is mild in acidity and simple in str...                  NaN   \n",
       "2  This is described as Brut dry, but the perfume...  Sparkling Mosi Brut   \n",
       "3  This big, ripe wine is opulent and smooth. Wit...            Tradition   \n",
       "4  The wine has tempting aromas like marzipan and...                  NaN   \n",
       "\n",
       "   points  price               province          region_1          region_2  \\\n",
       "0      84   15.0         Northern Spain  Ribera del Duero               NaN   \n",
       "1      87   24.0             California       Napa Valley              Napa   \n",
       "2      85    NaN  Österreichischer Sekt               NaN               NaN   \n",
       "3      87   27.0                 Alsace            Alsace               NaN   \n",
       "4      86   12.0             California        California  California Other   \n",
       "\n",
       "         taster_name taster_twitter_handle  \\\n",
       "0  Michael Schachner           @wineschach   \n",
       "1     Virginie Boone               @vboone   \n",
       "2         Roger Voss            @vossroger   \n",
       "3         Roger Voss            @vossroger   \n",
       "4         Jim Gordon      @gordone_cellars   \n",
       "\n",
       "                                               title             variety  \\\n",
       "0  Emilio Moro 2015 Finca Resalso  (Ribera del Du...         Tempranillo   \n",
       "1     Rutherford Ranch 2015 Chardonnay (Napa Valley)          Chardonnay   \n",
       "2  Hermann Moser NV Sparkling Mosi Brut Gelber Mu...  Gelber Muskateller   \n",
       "3  Domaine Schoffit 2012 Tradition Pinot Gris (Al...          Pinot Gris   \n",
       "4           Leese-Fitch 2014 Chardonnay (California)          Chardonnay   \n",
       "\n",
       "             winery  \n",
       "0       Emilio Moro  \n",
       "1  Rutherford Ranch  \n",
       "2     Hermann Moser  \n",
       "3  Domaine Schoffit  \n",
       "4       Leese-Fitch  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "wine_data = pd.read_csv(\"Resources/sample_data.csv\")\n",
    "wine_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>variety</th>\n",
       "      <th>price</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tempranillo</td>\n",
       "      <td>15.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>24.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gelber Muskateller</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>27.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>12.0</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sangiovese</td>\n",
       "      <td>19.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>White Blend</td>\n",
       "      <td>10.0</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Red Blend</td>\n",
       "      <td>33.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Grüner Veltliner</td>\n",
       "      <td>14.0</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Red Blend</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mourvèdre</td>\n",
       "      <td>52.0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chenin Blanc-Chardonnay</td>\n",
       "      <td>15.0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tinta Fina</td>\n",
       "      <td>33.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bordeaux-style Red Blend</td>\n",
       "      <td>20.0</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     variety  price  points\n",
       "0                Tempranillo   15.0      84\n",
       "1                 Chardonnay   24.0      87\n",
       "2         Gelber Muskateller    NaN      85\n",
       "3                 Pinot Gris   27.0      87\n",
       "4                 Chardonnay   12.0      86\n",
       "5                 Sangiovese   19.0      90\n",
       "6                White Blend   10.0      86\n",
       "7                  Red Blend   33.0      90\n",
       "8           Grüner Veltliner   14.0      88\n",
       "9                  Red Blend   25.0      84\n",
       "10                 Mourvèdre   52.0      94\n",
       "11   Chenin Blanc-Chardonnay   15.0      89\n",
       "12            Portuguese Red    NaN      85\n",
       "13                Tinta Fina   33.0      90\n",
       "14  Bordeaux-style Red Blend   20.0      89"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Question 5: How does the price of wine affect popularity? (price/points)\n",
    "# Make a dataframe with price and points\n",
    "wine_df = wine_data[[\"variety\", \"price\", \"points\"]]\n",
    "wine_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>price</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variety</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aglianico</th>\n",
       "      <td>24.800000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albariño</th>\n",
       "      <td>20.500000</td>\n",
       "      <td>89.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alvarinho</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arinto</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arneis</th>\n",
       "      <td>21.333333</td>\n",
       "      <td>88.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assyrtico</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbera</th>\n",
       "      <td>26.714286</td>\n",
       "      <td>87.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biancale</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blaufränkisch</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>89.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonarda</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bordeaux-style Red Blend</th>\n",
       "      <td>37.333333</td>\n",
       "      <td>88.774194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bordeaux-style White Blend</th>\n",
       "      <td>15.875000</td>\n",
       "      <td>89.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bual</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabernet</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabernet Blend</th>\n",
       "      <td>142.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 price     points\n",
       "variety                                          \n",
       "Aglianico                    24.800000  87.000000\n",
       "Albariño                     20.500000  89.750000\n",
       "Alvarinho                    20.000000  83.000000\n",
       "Arinto                       10.500000  85.000000\n",
       "Arneis                       21.333333  88.333333\n",
       "Assyrtico                    20.000000  84.000000\n",
       "Barbera                      26.714286  87.111111\n",
       "Biancale                     18.000000  85.000000\n",
       "Blaufränkisch                28.750000  89.500000\n",
       "Bonarda                      38.000000  89.000000\n",
       "Bordeaux-style Red Blend     37.333333  88.774194\n",
       "Bordeaux-style White Blend   15.875000  89.083333\n",
       "Bual                         34.000000  94.000000\n",
       "Cabernet                     40.000000  88.000000\n",
       "Cabernet Blend              142.000000  90.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make new dataframe with groupby varieties\n",
    "wine_group = wine_df.groupby([\"variety\"])\n",
    "wine_mean = wine_group.mean()\n",
    "\n",
    "wines = pd.DataFrame(wine_mean)\n",
    "wines.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>variety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chardonnay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Malbec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Merlot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pinot Grigio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pinot Noir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Riesling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sauvignon Blanc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              variety\n",
       "0  Cabernet Sauvignon\n",
       "1          Chardonnay\n",
       "2              Malbec\n",
       "3              Merlot\n",
       "4        Pinot Grigio\n",
       "5          Pinot Noir\n",
       "6            Riesling\n",
       "7     Sauvignon Blanc"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make new dataframe with common wine varities\n",
    "common_wines = {\"variety\": [\"Cabernet Sauvignon\", \"Chardonnay\", \"Malbec\", \"Merlot\", \"Pinot Grigio\", \"Pinot Noir\", \"Riesling\", \"Sauvignon Blanc\"]}\n",
    "common_wine_df = pd.DataFrame(common_wines)\n",
    "\n",
    "common_wine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_0128bf66_de30_11e8_b673_e82a44feff58\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >variety</th> \n",
       "        <th class=\"col_heading level0 col1\" >price</th> \n",
       "        <th class=\"col_heading level0 col2\" >points</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row0_col0\" class=\"data row0 col0\" >Cabernet Sauvignon</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row0_col1\" class=\"data row0 col1\" >$46.50</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row0_col2\" class=\"data row0 col2\" >88.51</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row1_col0\" class=\"data row1 col0\" >Chardonnay</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row1_col1\" class=\"data row1 col1\" >$37.77</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row1_col2\" class=\"data row1 col2\" >88.83</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row2_col0\" class=\"data row2 col0\" >Malbec</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row2_col1\" class=\"data row2 col1\" >$29.11</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row2_col2\" class=\"data row2 col2\" >88.10</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row3_col0\" class=\"data row3 col0\" >Merlot</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row3_col1\" class=\"data row3 col1\" >$44.79</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row3_col2\" class=\"data row3 col2\" >87.29</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row4_col0\" class=\"data row4 col0\" >Pinot Grigio</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row4_col1\" class=\"data row4 col1\" >$13.29</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row4_col2\" class=\"data row4 col2\" >86.29</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row5_col0\" class=\"data row5 col0\" >Pinot Noir</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row5_col1\" class=\"data row5 col1\" >$46.64</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row5_col2\" class=\"data row5 col2\" >89.53</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row6_col0\" class=\"data row6 col0\" >Riesling</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row6_col1\" class=\"data row6 col1\" >$31.80</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row6_col2\" class=\"data row6 col2\" >89.45</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_0128bf66_de30_11e8_b673_e82a44feff58level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row7_col0\" class=\"data row7 col0\" >Sauvignon Blanc</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row7_col1\" class=\"data row7 col1\" >$20.44</td> \n",
       "        <td id=\"T_0128bf66_de30_11e8_b673_e82a44feff58row7_col2\" class=\"data row7 col2\" >88.22</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18a6ac73be0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge both dataframes together\n",
    "merge_table = pd.merge(common_wine_df, wines, on=\"variety\")\n",
    "\n",
    "# Format dataframe \n",
    "merge_table.style.format({\"price\":\"${:,.2f}\",\"points\":\"{:,.2f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Put dataframe into a plot (scatter) \n",
    "colors = [\"crimson\", \"gold\", \"darkmagenta\", \"darkred\", \"khaki\", \"indianred\", \"yellow\", \"blanchedalmond\"]\n",
    "labels = [\"Cabernet Sauvignon\", \"Chardonnay\", \"Malbec\", \"Merlot\", \"Pinot Grigio\", \"Pinot Noir\", \"Riesling\", \"Sauvignon Blanc\"]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for color in [\"crimson\", \"gold\", \"darkmagenta\", \"darkred\", \"khaki\", \"indianred\", \"yellow\", \"blanchedalmond\"]:\n",
    "    \n",
    "    x, y = (merge_table [\"points\"], merge_table [\"price\"]) \n",
    "    scale = 300\n",
    "    ax.scatter(x, y, c=colors, s=scale, label=labels,\n",
    "               alpha=0.3, edgecolors= \"k\")\n",
    "\n",
    "# label legend handles to easily identify wines on scatter plot\n",
    "legend_elements = [Line2D([0], [0], marker=\"o\",  color=\"crimson\", label=\"Cabernet Sauvignon\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"gold\", label=\"Chardonnay\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"darkmagenta\", label=\"Malbec\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"darkred\", label=\"Merlot\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"khaki\", label=\"Pinot Grigio\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"indianred\", label=\"Pinot Noir\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"yellow\", label=\"Riesling\", markersize=15),\n",
    "                  Line2D([0], [0], marker=\"o\", color=\"blanchedalmond\", label=\"Sauvignon Blanc\", markersize=15)]\n",
    "\n",
    "# Make legend, x and y titles for plot\n",
    "ax.legend(title=\"Common Wines\",handles=legend_elements, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
    "plt.xlabel(\"Popularity of Wine\")\n",
    "plt.ylabel(\"Price of Wine\")\n",
    "plt.title(\"Price versus Popularity of 8 Common Wines\")\n",
    "ax.grid(True)\n",
    "# Save and show scatter plot\n",
    "plt.savefig(\"question5_images/Price_vs_Popularity.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a comparison bar chart of wine price and popularity\n",
    "\n",
    "# data to plot\n",
    "n_groups = 8\n",
    "wine_points = merge_table [\"points\"]\n",
    "wine_price = merge_table [\"price\"]\n",
    " \n",
    "# create plot\n",
    "fig, ax = plt.subplots()\n",
    "index = np.arange(n_groups)\n",
    "bar_width = 0.25\n",
    "bar_width2 = 0.35\n",
    "opacity = 0.5\n",
    " \n",
    "rects1 = plt.bar(index, wine_points, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='m',\n",
    "                 label='Points')\n",
    " \n",
    "rects2 = plt.bar(index + bar_width2, wine_price, bar_width,\n",
    "                 alpha=opacity,\n",
    "                 color='g',\n",
    "                 label='Price')\n",
    " \n",
    "# plt.xlabel('Wine')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Scores by wine')\n",
    "plt.xticks(index + bar_width, (\"Cabernet Sauvignon\", \"Chardonnay\", \"Malbec\", \"Merlot\", \"Pinot Grigio\", \"Pinot Noir\", \"Riesling\", \"Sauvignon Blanc\"),rotation=90)\n",
    "plt.legend(title=\"Wine Points and Price\", loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
    " \n",
    "plt.tight_layout()\n",
    "\n",
    "# Save figure\n",
    "plt.savefig(\"question5_images/Wine_score.png\")\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}