coffee_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "36367871",
   "metadata": {},
   "source": [
    "# Brewing Personalized Delights:\n",
    "## A Recommendation System for Starbucks Drinks\n",
    "\n",
    "## The following research questions will be examined and addressed:\n",
    "\n",
    "#### What is the distribution of ratings for each coffee drink? Are there any patterns or trends in terms of the frequency of different rating scores (e.g., mostly high ratings, mostly low ratings, or a bell-shaped distribution)?\n",
    "#### Which coffee reported the best ratings among NJIT students polled from an April 2023 survey?\n",
    "#### What was the ratio to user-rating vs. amount of times the drink was tried?\n",
    "#### How was the performance on the recommendation model measured?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8eb6456",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from pymongo import MongoClient\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from config import user, password\n",
    "plt.style.use('seaborn-notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f69d397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>drink_name</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Iced Coffee with Milk</td>\n",
       "      <td>Freshly brewed coffee with ice and milk.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Iced Caramel Cloud Macchiato</td>\n",
       "      <td>Espresso with milk and vanilla syrup, topped w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Iced Cinnamon Dolce Latte</td>\n",
       "      <td>Espresso with milk, cinnamon dolce syrup, and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Iced Green Tea Latte</td>\n",
       "      <td>Green tea with milk and ice, sweetened with cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Cinnamon Roll Frappuccino</td>\n",
       "      <td>A secret menu item made with a Vanilla Bean Fr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      drink_name  \\\n",
       "43         Iced Coffee with Milk   \n",
       "44  Iced Caramel Cloud Macchiato   \n",
       "45     Iced Cinnamon Dolce Latte   \n",
       "46          Iced Green Tea Latte   \n",
       "47     Cinnamon Roll Frappuccino   \n",
       "\n",
       "                                          description  \n",
       "43           Freshly brewed coffee with ice and milk.  \n",
       "44  Espresso with milk and vanilla syrup, topped w...  \n",
       "45  Espresso with milk, cinnamon dolce syrup, and ...  \n",
       "46  Green tea with milk and ice, sweetened with cl...  \n",
       "47  A secret menu item made with a Vanilla Bean Fr...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coffee_descriptions = pd.read_csv(\"coffee_descriptions.csv\")\n",
    "coffee_descriptions.drop_duplicates(subset=['drink_name'], inplace=True)\n",
    "coffee_descriptions = coffee_descriptions.reset_index(drop=True)\n",
    "coffee_descriptions.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca0c94cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              price\n",
       "item_id                            \n",
       "Iced Coffee with Milk          3.45\n",
       "Iced Caramel Cloud Macchiato   4.95\n",
       "Iced Cinnamon Dolce Latte      4.75\n",
       "Iced Green Tea Latte           4.45\n",
       "Cinnamon Roll Frappuccino      4.95"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starbucks average Grande (16 fl. ounces) drink prices\n",
    "coffee_prices = pd.read_csv(\"coffee_prices.csv\")\n",
    "coffee_prices.drop_duplicates(subset=['Drink'], inplace=True)\n",
    "coffee_prices = coffee_prices.reset_index(drop=True)\n",
    "coffee_prices = coffee_prices.rename(columns={'Drink': 'item_id', 'Price': 'price'}).set_index('item_id')\n",
    "coffee_prices.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b9bb91",
   "metadata": {},
   "source": [
    "## Preprocess the description data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6abf12ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.12405355 0.01646913 ... 0.02831155 0.01610832 0.00865829]\n",
      " [0.12405355 1.         0.19562209 ... 0.11356886 0.11449193 0.14525141]\n",
      " [0.01646913 0.19562209 1.         ... 0.12952507 0.13566615 0.10979217]\n",
      " ...\n",
      " [0.02831155 0.11356886 0.12952507 ... 1.         0.19552226 0.63690176]\n",
      " [0.01610832 0.11449193 0.13566615 ... 0.19552226 1.         0.09058831]\n",
      " [0.00865829 0.14525141 0.10979217 ... 0.63690176 0.09058831 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Preprocess the text\n",
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "X = vectorizer.fit_transform(coffee_descriptions['description'])\n",
    "\n",
    "# Calculate the cosine similarity matrix\n",
    "cosine_sim = cosine_similarity(X)\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "print(cosine_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7028e90d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>drink_name</th>\n",
       "      <th>Caffè Americano</th>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <th>Cinnamon Dolce Latte</th>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <th>White Chocolate Mocha</th>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <th>Green Tea Latte</th>\n",
       "      <th>...</th>\n",
       "      <th>Iced Golden Ginger Drink</th>\n",
       "      <th>Iced Caffè Latte</th>\n",
       "      <th>Iced Cascara Latte</th>\n",
       "      <th>Caramelized Honey Latte</th>\n",
       "      <th>Gingerbread Latte</th>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drink_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caffè Americano</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124054</td>\n",
       "      <td>0.016469</td>\n",
       "      <td>0.028258</td>\n",
       "      <td>0.020390</td>\n",
       "      <td>0.036943</td>\n",
       "      <td>0.035163</td>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.017679</td>\n",
       "      <td>0.010438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065708</td>\n",
       "      <td>0.046966</td>\n",
       "      <td>0.040820</td>\n",
       "      <td>0.027656</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035768</td>\n",
       "      <td>0.028312</td>\n",
       "      <td>0.016108</td>\n",
       "      <td>0.008658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <td>0.124054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195622</td>\n",
       "      <td>0.154059</td>\n",
       "      <td>0.111161</td>\n",
       "      <td>0.515945</td>\n",
       "      <td>0.191706</td>\n",
       "      <td>0.107564</td>\n",
       "      <td>0.493167</td>\n",
       "      <td>0.107931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015540</td>\n",
       "      <td>0.139299</td>\n",
       "      <td>0.099565</td>\n",
       "      <td>0.152245</td>\n",
       "      <td>0.150774</td>\n",
       "      <td>0.027514</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.113569</td>\n",
       "      <td>0.114492</td>\n",
       "      <td>0.145251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <td>0.016469</td>\n",
       "      <td>0.195622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168915</td>\n",
       "      <td>0.094769</td>\n",
       "      <td>0.236867</td>\n",
       "      <td>0.384570</td>\n",
       "      <td>0.574584</td>\n",
       "      <td>0.166114</td>\n",
       "      <td>0.089620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.092229</td>\n",
       "      <td>0.128370</td>\n",
       "      <td>0.057295</td>\n",
       "      <td>0.165313</td>\n",
       "      <td>0.236077</td>\n",
       "      <td>0.331814</td>\n",
       "      <td>0.129525</td>\n",
       "      <td>0.135666</td>\n",
       "      <td>0.109792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinnamon Dolce Latte</th>\n",
       "      <td>0.028258</td>\n",
       "      <td>0.154059</td>\n",
       "      <td>0.168915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159690</td>\n",
       "      <td>0.186541</td>\n",
       "      <td>0.319872</td>\n",
       "      <td>0.085970</td>\n",
       "      <td>0.116379</td>\n",
       "      <td>0.099964</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>0.129016</td>\n",
       "      <td>0.151616</td>\n",
       "      <td>0.080149</td>\n",
       "      <td>0.174622</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>0.115467</td>\n",
       "      <td>0.964122</td>\n",
       "      <td>0.151758</td>\n",
       "      <td>0.635698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <td>0.020390</td>\n",
       "      <td>0.111161</td>\n",
       "      <td>0.094769</td>\n",
       "      <td>0.159690</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.134598</td>\n",
       "      <td>0.198713</td>\n",
       "      <td>0.031855</td>\n",
       "      <td>0.083973</td>\n",
       "      <td>0.072129</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.093091</td>\n",
       "      <td>0.066538</td>\n",
       "      <td>0.057831</td>\n",
       "      <td>0.100760</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>0.050673</td>\n",
       "      <td>0.132739</td>\n",
       "      <td>0.076513</td>\n",
       "      <td>0.072376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White Chocolate Mocha</th>\n",
       "      <td>0.036943</td>\n",
       "      <td>0.515945</td>\n",
       "      <td>0.236867</td>\n",
       "      <td>0.186541</td>\n",
       "      <td>0.134598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.232125</td>\n",
       "      <td>0.130242</td>\n",
       "      <td>0.561124</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018816</td>\n",
       "      <td>0.168668</td>\n",
       "      <td>0.120558</td>\n",
       "      <td>0.184344</td>\n",
       "      <td>0.182563</td>\n",
       "      <td>0.033316</td>\n",
       "      <td>0.313025</td>\n",
       "      <td>0.137514</td>\n",
       "      <td>0.138631</td>\n",
       "      <td>0.175876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <td>0.035163</td>\n",
       "      <td>0.191706</td>\n",
       "      <td>0.384570</td>\n",
       "      <td>0.319872</td>\n",
       "      <td>0.198713</td>\n",
       "      <td>0.232125</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301071</td>\n",
       "      <td>0.144818</td>\n",
       "      <td>0.124391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017910</td>\n",
       "      <td>0.160543</td>\n",
       "      <td>0.188666</td>\n",
       "      <td>0.099734</td>\n",
       "      <td>0.217294</td>\n",
       "      <td>0.031711</td>\n",
       "      <td>0.353628</td>\n",
       "      <td>0.273476</td>\n",
       "      <td>0.188842</td>\n",
       "      <td>0.373961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.107564</td>\n",
       "      <td>0.574584</td>\n",
       "      <td>0.085970</td>\n",
       "      <td>0.031855</td>\n",
       "      <td>0.130242</td>\n",
       "      <td>0.301071</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159303</td>\n",
       "      <td>0.015169</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053377</td>\n",
       "      <td>0.194964</td>\n",
       "      <td>0.142884</td>\n",
       "      <td>0.063773</td>\n",
       "      <td>0.084137</td>\n",
       "      <td>0.600206</td>\n",
       "      <td>0.369330</td>\n",
       "      <td>0.125905</td>\n",
       "      <td>0.127686</td>\n",
       "      <td>0.082377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <td>0.017679</td>\n",
       "      <td>0.493167</td>\n",
       "      <td>0.166114</td>\n",
       "      <td>0.116379</td>\n",
       "      <td>0.083973</td>\n",
       "      <td>0.561124</td>\n",
       "      <td>0.144818</td>\n",
       "      <td>0.159303</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096467</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064037</td>\n",
       "      <td>0.219760</td>\n",
       "      <td>0.077920</td>\n",
       "      <td>0.153161</td>\n",
       "      <td>0.113897</td>\n",
       "      <td>0.254832</td>\n",
       "      <td>0.143584</td>\n",
       "      <td>0.164315</td>\n",
       "      <td>0.209790</td>\n",
       "      <td>0.272139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Green Tea Latte</th>\n",
       "      <td>0.010438</td>\n",
       "      <td>0.107931</td>\n",
       "      <td>0.089620</td>\n",
       "      <td>0.099964</td>\n",
       "      <td>0.072129</td>\n",
       "      <td>0.130687</td>\n",
       "      <td>0.124391</td>\n",
       "      <td>0.015169</td>\n",
       "      <td>0.096467</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105125</td>\n",
       "      <td>0.064367</td>\n",
       "      <td>0.046007</td>\n",
       "      <td>0.039987</td>\n",
       "      <td>0.097832</td>\n",
       "      <td>0.021123</td>\n",
       "      <td>0.035037</td>\n",
       "      <td>0.068844</td>\n",
       "      <td>0.292939</td>\n",
       "      <td>0.038332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chai Tea Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053894</td>\n",
       "      <td>0.052477</td>\n",
       "      <td>0.197085</td>\n",
       "      <td>0.118508</td>\n",
       "      <td>0.065257</td>\n",
       "      <td>0.062114</td>\n",
       "      <td>0.017126</td>\n",
       "      <td>0.020546</td>\n",
       "      <td>0.314021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210936</td>\n",
       "      <td>0.034031</td>\n",
       "      <td>0.024324</td>\n",
       "      <td>0.021141</td>\n",
       "      <td>0.048852</td>\n",
       "      <td>0.023848</td>\n",
       "      <td>0.018524</td>\n",
       "      <td>0.162110</td>\n",
       "      <td>0.121998</td>\n",
       "      <td>0.101187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Chip Frappuccino</th>\n",
       "      <td>0.013656</td>\n",
       "      <td>0.458977</td>\n",
       "      <td>0.128314</td>\n",
       "      <td>0.089896</td>\n",
       "      <td>0.064864</td>\n",
       "      <td>0.622409</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>0.123053</td>\n",
       "      <td>0.873906</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049465</td>\n",
       "      <td>0.169752</td>\n",
       "      <td>0.060189</td>\n",
       "      <td>0.118308</td>\n",
       "      <td>0.087979</td>\n",
       "      <td>0.196843</td>\n",
       "      <td>0.110911</td>\n",
       "      <td>0.126924</td>\n",
       "      <td>0.162051</td>\n",
       "      <td>0.210212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat White</th>\n",
       "      <td>0.110437</td>\n",
       "      <td>0.098566</td>\n",
       "      <td>0.095975</td>\n",
       "      <td>0.091290</td>\n",
       "      <td>0.065870</td>\n",
       "      <td>0.119348</td>\n",
       "      <td>0.113598</td>\n",
       "      <td>0.065527</td>\n",
       "      <td>0.020553</td>\n",
       "      <td>0.041390</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013474</td>\n",
       "      <td>0.130210</td>\n",
       "      <td>0.093069</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>0.089344</td>\n",
       "      <td>0.023856</td>\n",
       "      <td>0.070879</td>\n",
       "      <td>0.056103</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Espresso Con Panna</th>\n",
       "      <td>0.120020</td>\n",
       "      <td>0.100771</td>\n",
       "      <td>0.078622</td>\n",
       "      <td>0.093332</td>\n",
       "      <td>0.067344</td>\n",
       "      <td>0.122018</td>\n",
       "      <td>0.116140</td>\n",
       "      <td>0.027001</td>\n",
       "      <td>0.189309</td>\n",
       "      <td>0.058237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096784</td>\n",
       "      <td>0.069177</td>\n",
       "      <td>0.060125</td>\n",
       "      <td>0.189013</td>\n",
       "      <td>0.105248</td>\n",
       "      <td>0.052683</td>\n",
       "      <td>0.093509</td>\n",
       "      <td>0.281002</td>\n",
       "      <td>0.048307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitro Cold Brew</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084319</td>\n",
       "      <td>0.072499</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084152</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced White Chocolate Mocha</th>\n",
       "      <td>0.031798</td>\n",
       "      <td>0.398284</td>\n",
       "      <td>0.159278</td>\n",
       "      <td>0.118138</td>\n",
       "      <td>0.085242</td>\n",
       "      <td>0.805266</td>\n",
       "      <td>0.147007</td>\n",
       "      <td>0.270375</td>\n",
       "      <td>0.536567</td>\n",
       "      <td>0.077322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051328</td>\n",
       "      <td>0.233943</td>\n",
       "      <td>0.103767</td>\n",
       "      <td>0.158670</td>\n",
       "      <td>0.115619</td>\n",
       "      <td>0.090881</td>\n",
       "      <td>0.269429</td>\n",
       "      <td>0.156608</td>\n",
       "      <td>0.168155</td>\n",
       "      <td>0.151381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salted Caramel Mocha</th>\n",
       "      <td>0.025732</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.317590</td>\n",
       "      <td>0.234080</td>\n",
       "      <td>0.145417</td>\n",
       "      <td>0.276461</td>\n",
       "      <td>0.291281</td>\n",
       "      <td>0.260575</td>\n",
       "      <td>0.330734</td>\n",
       "      <td>0.091029</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013106</td>\n",
       "      <td>0.117484</td>\n",
       "      <td>0.138065</td>\n",
       "      <td>0.128403</td>\n",
       "      <td>0.159014</td>\n",
       "      <td>0.023206</td>\n",
       "      <td>0.302323</td>\n",
       "      <td>0.200128</td>\n",
       "      <td>0.138193</td>\n",
       "      <td>0.194112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cold Brew Coffee</th>\n",
       "      <td>0.075621</td>\n",
       "      <td>0.177936</td>\n",
       "      <td>0.056738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.152102</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097802</td>\n",
       "      <td>0.199001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142154</td>\n",
       "      <td>0.068853</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136194</td>\n",
       "      <td>0.068310</td>\n",
       "      <td>0.029666</td>\n",
       "      <td>0.037877</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Roast</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americano Misto</th>\n",
       "      <td>0.294596</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>0.080448</td>\n",
       "      <td>0.076522</td>\n",
       "      <td>0.055214</td>\n",
       "      <td>0.100040</td>\n",
       "      <td>0.095221</td>\n",
       "      <td>0.044788</td>\n",
       "      <td>0.110336</td>\n",
       "      <td>0.044854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014602</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.063614</td>\n",
       "      <td>0.055289</td>\n",
       "      <td>0.169748</td>\n",
       "      <td>0.128070</td>\n",
       "      <td>0.048446</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>0.020295</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Americano</th>\n",
       "      <td>0.456343</td>\n",
       "      <td>0.041223</td>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.038180</td>\n",
       "      <td>0.027549</td>\n",
       "      <td>0.049914</td>\n",
       "      <td>0.047510</td>\n",
       "      <td>0.159541</td>\n",
       "      <td>0.069521</td>\n",
       "      <td>0.014103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029917</td>\n",
       "      <td>0.164365</td>\n",
       "      <td>0.063456</td>\n",
       "      <td>0.055152</td>\n",
       "      <td>0.037366</td>\n",
       "      <td>0.052970</td>\n",
       "      <td>0.048326</td>\n",
       "      <td>0.070820</td>\n",
       "      <td>0.063345</td>\n",
       "      <td>0.011698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Frappuccino</th>\n",
       "      <td>0.016320</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>0.370527</td>\n",
       "      <td>0.153752</td>\n",
       "      <td>0.077518</td>\n",
       "      <td>0.220775</td>\n",
       "      <td>0.191323</td>\n",
       "      <td>0.388794</td>\n",
       "      <td>0.568261</td>\n",
       "      <td>0.089052</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059115</td>\n",
       "      <td>0.202868</td>\n",
       "      <td>0.148913</td>\n",
       "      <td>0.062518</td>\n",
       "      <td>0.150473</td>\n",
       "      <td>0.235244</td>\n",
       "      <td>0.394026</td>\n",
       "      <td>0.198091</td>\n",
       "      <td>0.252913</td>\n",
       "      <td>0.168653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Chai Tea Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>0.015811</td>\n",
       "      <td>0.166272</td>\n",
       "      <td>0.095621</td>\n",
       "      <td>0.019661</td>\n",
       "      <td>0.018714</td>\n",
       "      <td>0.152861</td>\n",
       "      <td>0.066914</td>\n",
       "      <td>0.081238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138663</td>\n",
       "      <td>0.110831</td>\n",
       "      <td>0.024995</td>\n",
       "      <td>0.021725</td>\n",
       "      <td>0.014719</td>\n",
       "      <td>0.077669</td>\n",
       "      <td>0.019036</td>\n",
       "      <td>0.199272</td>\n",
       "      <td>0.167099</td>\n",
       "      <td>0.103981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hazelnut Latte</th>\n",
       "      <td>0.029352</td>\n",
       "      <td>0.213192</td>\n",
       "      <td>0.227225</td>\n",
       "      <td>0.185335</td>\n",
       "      <td>0.106941</td>\n",
       "      <td>0.258141</td>\n",
       "      <td>0.230624</td>\n",
       "      <td>0.146922</td>\n",
       "      <td>0.190017</td>\n",
       "      <td>0.103834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014950</td>\n",
       "      <td>0.134011</td>\n",
       "      <td>0.157487</td>\n",
       "      <td>0.083252</td>\n",
       "      <td>0.181383</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>0.182267</td>\n",
       "      <td>0.146452</td>\n",
       "      <td>0.157633</td>\n",
       "      <td>0.073560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Bean Frappuccino</th>\n",
       "      <td>0.012366</td>\n",
       "      <td>0.087895</td>\n",
       "      <td>0.204865</td>\n",
       "      <td>0.158622</td>\n",
       "      <td>0.114453</td>\n",
       "      <td>0.106427</td>\n",
       "      <td>0.821715</td>\n",
       "      <td>0.210126</td>\n",
       "      <td>0.279994</td>\n",
       "      <td>0.067478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044794</td>\n",
       "      <td>0.153721</td>\n",
       "      <td>0.054505</td>\n",
       "      <td>0.047373</td>\n",
       "      <td>0.079671</td>\n",
       "      <td>0.079311</td>\n",
       "      <td>0.207191</td>\n",
       "      <td>0.192299</td>\n",
       "      <td>0.146747</td>\n",
       "      <td>0.411721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Sweet Cream Cold Brew</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033143</td>\n",
       "      <td>0.248722</td>\n",
       "      <td>0.030696</td>\n",
       "      <td>0.022149</td>\n",
       "      <td>0.040131</td>\n",
       "      <td>0.248755</td>\n",
       "      <td>0.369079</td>\n",
       "      <td>0.153286</td>\n",
       "      <td>0.025444</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030042</td>\n",
       "      <td>0.127903</td>\n",
       "      <td>0.107088</td>\n",
       "      <td>0.030754</td>\n",
       "      <td>0.039266</td>\n",
       "      <td>0.079276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Brulée Latte</th>\n",
       "      <td>0.022010</td>\n",
       "      <td>0.155428</td>\n",
       "      <td>0.203566</td>\n",
       "      <td>0.111140</td>\n",
       "      <td>0.080193</td>\n",
       "      <td>0.188199</td>\n",
       "      <td>0.138299</td>\n",
       "      <td>0.147100</td>\n",
       "      <td>0.136717</td>\n",
       "      <td>0.077863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011211</td>\n",
       "      <td>0.100492</td>\n",
       "      <td>0.071828</td>\n",
       "      <td>0.109831</td>\n",
       "      <td>0.367851</td>\n",
       "      <td>0.019849</td>\n",
       "      <td>0.176620</td>\n",
       "      <td>0.081930</td>\n",
       "      <td>0.082596</td>\n",
       "      <td>0.058583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toasted White Chocolate Mocha</th>\n",
       "      <td>0.020825</td>\n",
       "      <td>0.253114</td>\n",
       "      <td>0.096791</td>\n",
       "      <td>0.105152</td>\n",
       "      <td>0.075872</td>\n",
       "      <td>0.601141</td>\n",
       "      <td>0.130848</td>\n",
       "      <td>0.032534</td>\n",
       "      <td>0.267256</td>\n",
       "      <td>0.073668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010607</td>\n",
       "      <td>0.095078</td>\n",
       "      <td>0.067958</td>\n",
       "      <td>0.408455</td>\n",
       "      <td>0.102910</td>\n",
       "      <td>0.018780</td>\n",
       "      <td>0.212704</td>\n",
       "      <td>0.077516</td>\n",
       "      <td>0.078146</td>\n",
       "      <td>0.142855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chestnut Praline Latte</th>\n",
       "      <td>0.021391</td>\n",
       "      <td>0.116622</td>\n",
       "      <td>0.127868</td>\n",
       "      <td>0.135069</td>\n",
       "      <td>0.077937</td>\n",
       "      <td>0.141211</td>\n",
       "      <td>0.168075</td>\n",
       "      <td>0.065079</td>\n",
       "      <td>0.088099</td>\n",
       "      <td>0.075672</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010895</td>\n",
       "      <td>0.097665</td>\n",
       "      <td>0.114773</td>\n",
       "      <td>0.060672</td>\n",
       "      <td>0.383982</td>\n",
       "      <td>0.019291</td>\n",
       "      <td>0.087408</td>\n",
       "      <td>0.106732</td>\n",
       "      <td>0.114881</td>\n",
       "      <td>0.053609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Mocha</th>\n",
       "      <td>0.054655</td>\n",
       "      <td>0.487388</td>\n",
       "      <td>0.177373</td>\n",
       "      <td>0.203059</td>\n",
       "      <td>0.146517</td>\n",
       "      <td>0.371997</td>\n",
       "      <td>0.252680</td>\n",
       "      <td>0.162169</td>\n",
       "      <td>0.665864</td>\n",
       "      <td>0.132903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088225</td>\n",
       "      <td>0.402109</td>\n",
       "      <td>0.178358</td>\n",
       "      <td>0.272726</td>\n",
       "      <td>0.198730</td>\n",
       "      <td>0.156210</td>\n",
       "      <td>0.135832</td>\n",
       "      <td>0.269183</td>\n",
       "      <td>0.289030</td>\n",
       "      <td>0.260199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Apple Spice</th>\n",
       "      <td>0.013225</td>\n",
       "      <td>0.220539</td>\n",
       "      <td>0.324615</td>\n",
       "      <td>0.312914</td>\n",
       "      <td>0.079534</td>\n",
       "      <td>0.267037</td>\n",
       "      <td>0.183869</td>\n",
       "      <td>0.254151</td>\n",
       "      <td>0.231174</td>\n",
       "      <td>0.091368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043362</td>\n",
       "      <td>0.093376</td>\n",
       "      <td>0.090850</td>\n",
       "      <td>0.144611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.298512</td>\n",
       "      <td>0.273839</td>\n",
       "      <td>0.138367</td>\n",
       "      <td>0.218345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato with Coconut Milk</th>\n",
       "      <td>0.034479</td>\n",
       "      <td>0.191051</td>\n",
       "      <td>0.399176</td>\n",
       "      <td>0.111307</td>\n",
       "      <td>0.048848</td>\n",
       "      <td>0.231332</td>\n",
       "      <td>0.340890</td>\n",
       "      <td>0.492744</td>\n",
       "      <td>0.268689</td>\n",
       "      <td>0.033775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216281</td>\n",
       "      <td>0.253670</td>\n",
       "      <td>0.184995</td>\n",
       "      <td>0.172049</td>\n",
       "      <td>0.108934</td>\n",
       "      <td>0.098545</td>\n",
       "      <td>0.508018</td>\n",
       "      <td>0.152989</td>\n",
       "      <td>0.160853</td>\n",
       "      <td>0.136135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced White Chocolate Mocha with Raspberry Syrup</th>\n",
       "      <td>0.039529</td>\n",
       "      <td>0.322860</td>\n",
       "      <td>0.180847</td>\n",
       "      <td>0.196858</td>\n",
       "      <td>0.105968</td>\n",
       "      <td>0.670593</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>0.120261</td>\n",
       "      <td>0.376420</td>\n",
       "      <td>0.096122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.180477</td>\n",
       "      <td>0.212092</td>\n",
       "      <td>0.197249</td>\n",
       "      <td>0.192661</td>\n",
       "      <td>0.035648</td>\n",
       "      <td>0.314280</td>\n",
       "      <td>0.197231</td>\n",
       "      <td>0.212290</td>\n",
       "      <td>0.222564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Vanilla Latte</th>\n",
       "      <td>0.052374</td>\n",
       "      <td>0.186473</td>\n",
       "      <td>0.346475</td>\n",
       "      <td>0.238949</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>0.225789</td>\n",
       "      <td>0.604759</td>\n",
       "      <td>0.303883</td>\n",
       "      <td>0.086893</td>\n",
       "      <td>0.109223</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026676</td>\n",
       "      <td>0.650863</td>\n",
       "      <td>0.575306</td>\n",
       "      <td>0.404336</td>\n",
       "      <td>0.233854</td>\n",
       "      <td>0.047231</td>\n",
       "      <td>0.594487</td>\n",
       "      <td>0.169396</td>\n",
       "      <td>0.163907</td>\n",
       "      <td>0.153103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Double Chocolaty Chip Frappuccino</th>\n",
       "      <td>0.012905</td>\n",
       "      <td>0.285815</td>\n",
       "      <td>0.070179</td>\n",
       "      <td>0.084950</td>\n",
       "      <td>0.061296</td>\n",
       "      <td>0.524651</td>\n",
       "      <td>0.105709</td>\n",
       "      <td>0.059433</td>\n",
       "      <td>0.544553</td>\n",
       "      <td>0.070415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046744</td>\n",
       "      <td>0.160412</td>\n",
       "      <td>0.056877</td>\n",
       "      <td>0.111799</td>\n",
       "      <td>0.083139</td>\n",
       "      <td>0.082764</td>\n",
       "      <td>0.043316</td>\n",
       "      <td>0.119941</td>\n",
       "      <td>0.153135</td>\n",
       "      <td>0.137860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Pineapple Green Tea Infusion</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133193</td>\n",
       "      <td>0.041872</td>\n",
       "      <td>0.140928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126299</td>\n",
       "      <td>0.069354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029882</td>\n",
       "      <td>0.393024</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Peach Green Tea Infusion</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133193</td>\n",
       "      <td>0.041872</td>\n",
       "      <td>0.140928</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126299</td>\n",
       "      <td>0.069354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029882</td>\n",
       "      <td>0.393024</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cinnamon Almondmilk Macchiato</th>\n",
       "      <td>0.031149</td>\n",
       "      <td>0.172598</td>\n",
       "      <td>0.283386</td>\n",
       "      <td>0.510031</td>\n",
       "      <td>0.044130</td>\n",
       "      <td>0.208989</td>\n",
       "      <td>0.125129</td>\n",
       "      <td>0.359185</td>\n",
       "      <td>0.242737</td>\n",
       "      <td>0.030513</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050281</td>\n",
       "      <td>0.229169</td>\n",
       "      <td>0.167127</td>\n",
       "      <td>0.155431</td>\n",
       "      <td>0.098412</td>\n",
       "      <td>0.089027</td>\n",
       "      <td>0.365962</td>\n",
       "      <td>0.548463</td>\n",
       "      <td>0.145317</td>\n",
       "      <td>0.354013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Golden Ginger Drink</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015540</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.018816</td>\n",
       "      <td>0.017910</td>\n",
       "      <td>0.053377</td>\n",
       "      <td>0.064037</td>\n",
       "      <td>0.105125</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106067</td>\n",
       "      <td>0.023921</td>\n",
       "      <td>0.210954</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.074330</td>\n",
       "      <td>0.018217</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.058349</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Latte</th>\n",
       "      <td>0.065708</td>\n",
       "      <td>0.139299</td>\n",
       "      <td>0.092229</td>\n",
       "      <td>0.129016</td>\n",
       "      <td>0.093091</td>\n",
       "      <td>0.168668</td>\n",
       "      <td>0.160543</td>\n",
       "      <td>0.194964</td>\n",
       "      <td>0.219760</td>\n",
       "      <td>0.064367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106067</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583654</td>\n",
       "      <td>0.507279</td>\n",
       "      <td>0.126265</td>\n",
       "      <td>0.187800</td>\n",
       "      <td>0.444493</td>\n",
       "      <td>0.208293</td>\n",
       "      <td>0.200237</td>\n",
       "      <td>0.028389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cascara Latte</th>\n",
       "      <td>0.046966</td>\n",
       "      <td>0.099565</td>\n",
       "      <td>0.128370</td>\n",
       "      <td>0.151616</td>\n",
       "      <td>0.066538</td>\n",
       "      <td>0.120558</td>\n",
       "      <td>0.188666</td>\n",
       "      <td>0.142884</td>\n",
       "      <td>0.077920</td>\n",
       "      <td>0.046007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023921</td>\n",
       "      <td>0.583654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362583</td>\n",
       "      <td>0.148384</td>\n",
       "      <td>0.042354</td>\n",
       "      <td>0.392893</td>\n",
       "      <td>0.151903</td>\n",
       "      <td>0.146982</td>\n",
       "      <td>0.061133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramelized Honey Latte</th>\n",
       "      <td>0.040820</td>\n",
       "      <td>0.152245</td>\n",
       "      <td>0.057295</td>\n",
       "      <td>0.080149</td>\n",
       "      <td>0.057831</td>\n",
       "      <td>0.184344</td>\n",
       "      <td>0.099734</td>\n",
       "      <td>0.063773</td>\n",
       "      <td>0.153161</td>\n",
       "      <td>0.039987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210954</td>\n",
       "      <td>0.507279</td>\n",
       "      <td>0.362583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078440</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.276133</td>\n",
       "      <td>0.080300</td>\n",
       "      <td>0.061708</td>\n",
       "      <td>0.059479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gingerbread Latte</th>\n",
       "      <td>0.027656</td>\n",
       "      <td>0.150774</td>\n",
       "      <td>0.165313</td>\n",
       "      <td>0.174622</td>\n",
       "      <td>0.100760</td>\n",
       "      <td>0.182563</td>\n",
       "      <td>0.217294</td>\n",
       "      <td>0.084137</td>\n",
       "      <td>0.113897</td>\n",
       "      <td>0.097832</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.126265</td>\n",
       "      <td>0.148384</td>\n",
       "      <td>0.078440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024940</td>\n",
       "      <td>0.113005</td>\n",
       "      <td>0.137987</td>\n",
       "      <td>0.148522</td>\n",
       "      <td>0.069308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027514</td>\n",
       "      <td>0.236077</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>0.033316</td>\n",
       "      <td>0.031711</td>\n",
       "      <td>0.600206</td>\n",
       "      <td>0.254832</td>\n",
       "      <td>0.021123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074330</td>\n",
       "      <td>0.187800</td>\n",
       "      <td>0.042354</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.024940</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032256</td>\n",
       "      <td>0.080917</td>\n",
       "      <td>0.103311</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <td>0.035768</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.331814</td>\n",
       "      <td>0.115467</td>\n",
       "      <td>0.050673</td>\n",
       "      <td>0.313025</td>\n",
       "      <td>0.353628</td>\n",
       "      <td>0.369330</td>\n",
       "      <td>0.143584</td>\n",
       "      <td>0.035037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018217</td>\n",
       "      <td>0.444493</td>\n",
       "      <td>0.392893</td>\n",
       "      <td>0.276133</td>\n",
       "      <td>0.113005</td>\n",
       "      <td>0.032256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.115685</td>\n",
       "      <td>0.111937</td>\n",
       "      <td>0.179640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <td>0.028312</td>\n",
       "      <td>0.113569</td>\n",
       "      <td>0.129525</td>\n",
       "      <td>0.964122</td>\n",
       "      <td>0.132739</td>\n",
       "      <td>0.137514</td>\n",
       "      <td>0.273476</td>\n",
       "      <td>0.125905</td>\n",
       "      <td>0.164315</td>\n",
       "      <td>0.068844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.208293</td>\n",
       "      <td>0.151903</td>\n",
       "      <td>0.080300</td>\n",
       "      <td>0.137987</td>\n",
       "      <td>0.080917</td>\n",
       "      <td>0.115685</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195522</td>\n",
       "      <td>0.636902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <td>0.016108</td>\n",
       "      <td>0.114492</td>\n",
       "      <td>0.135666</td>\n",
       "      <td>0.151758</td>\n",
       "      <td>0.076513</td>\n",
       "      <td>0.138631</td>\n",
       "      <td>0.188842</td>\n",
       "      <td>0.127686</td>\n",
       "      <td>0.209790</td>\n",
       "      <td>0.292939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058349</td>\n",
       "      <td>0.200237</td>\n",
       "      <td>0.146982</td>\n",
       "      <td>0.061708</td>\n",
       "      <td>0.148522</td>\n",
       "      <td>0.103311</td>\n",
       "      <td>0.111937</td>\n",
       "      <td>0.195522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.145251</td>\n",
       "      <td>0.109792</td>\n",
       "      <td>0.635698</td>\n",
       "      <td>0.072376</td>\n",
       "      <td>0.175876</td>\n",
       "      <td>0.373961</td>\n",
       "      <td>0.082377</td>\n",
       "      <td>0.272139</td>\n",
       "      <td>0.038332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028389</td>\n",
       "      <td>0.061133</td>\n",
       "      <td>0.059479</td>\n",
       "      <td>0.069308</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.179640</td>\n",
       "      <td>0.636902</td>\n",
       "      <td>0.090588</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "drink_name                                       Caffè Americano  Caffè Mocha  \\\n",
       "drink_name                                                                      \n",
       "Caffè Americano                                         1.000000     0.124054   \n",
       "Caffè Mocha                                             0.124054     1.000000   \n",
       "Caramel Macchiato                                       0.016469     0.195622   \n",
       "Cinnamon Dolce Latte                                    0.028258     0.154059   \n",
       "Pumpkin Spice Latte                                     0.020390     0.111161   \n",
       "White Chocolate Mocha                                   0.036943     0.515945   \n",
       "Vanilla Latte                                           0.035163     0.191706   \n",
       "Iced Caramel Macchiato                                  0.018331     0.107564   \n",
       "Mocha Frappuccino                                       0.017679     0.493167   \n",
       "Green Tea Latte                                         0.010438     0.107931   \n",
       "Chai Tea Latte                                          0.000000     0.053894   \n",
       "Java Chip Frappuccino                                   0.013656     0.458977   \n",
       "Flat White                                              0.110437     0.098566   \n",
       "Espresso Con Panna                                      0.120020     0.100771   \n",
       "Nitro Cold Brew                                         0.000000     0.000000   \n",
       "Iced White Chocolate Mocha                              0.031798     0.398284   \n",
       "Salted Caramel Mocha                                    0.025732     0.313145   \n",
       "Cold Brew Coffee                                        0.075621     0.177936   \n",
       "Blonde Roast                                            0.000000     0.000000   \n",
       "Americano Misto                                         0.294596     0.082621   \n",
       "Iced Caffè Americano                                    0.456343     0.041223   \n",
       "Caramel Frappuccino                                     0.016320     0.182333   \n",
       "Iced Chai Tea Latte                                     0.000000     0.016238   \n",
       "Hazelnut Latte                                          0.029352     0.213192   \n",
       "Vanilla Bean Frappuccino                                0.012366     0.087895   \n",
       "Vanilla Sweet Cream Cold Brew                           0.000000     0.033143   \n",
       "Caramel Brulée Latte                                    0.022010     0.155428   \n",
       "Toasted White Chocolate Mocha                           0.020825     0.253114   \n",
       "Chestnut Praline Latte                                  0.021391     0.116622   \n",
       "Iced Caffè Mocha                                        0.054655     0.487388   \n",
       "Caramel Apple Spice                                     0.013225     0.220539   \n",
       "Iced Caramel Macchiato with Coconut Milk                0.034479     0.191051   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup         0.039529     0.322860   \n",
       "Blonde Vanilla Latte                                    0.052374     0.186473   \n",
       "Double Chocolaty Chip Frappuccino                       0.012905     0.285815   \n",
       "Iced Pineapple Green Tea Infusion                       0.000000     0.000000   \n",
       "Iced Peach Green Tea Infusion                           0.000000     0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                      0.031149     0.172598   \n",
       "Iced Golden Ginger Drink                                0.000000     0.015540   \n",
       "Iced Caffè Latte                                        0.065708     0.139299   \n",
       "Iced Cascara Latte                                      0.046966     0.099565   \n",
       "Caramelized Honey Latte                                 0.040820     0.152245   \n",
       "Gingerbread Latte                                       0.027656     0.150774   \n",
       "Iced Coffee with Milk                                   0.000000     0.027514   \n",
       "Iced Caramel Cloud Macchiato                            0.035768     0.140615   \n",
       "Iced Cinnamon Dolce Latte                               0.028312     0.113569   \n",
       "Iced Green Tea Latte                                    0.016108     0.114492   \n",
       "Cinnamon Roll Frappuccino                               0.008658     0.145251   \n",
       "\n",
       "drink_name                                       Caramel Macchiato  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.016469   \n",
       "Caffè Mocha                                               0.195622   \n",
       "Caramel Macchiato                                         1.000000   \n",
       "Cinnamon Dolce Latte                                      0.168915   \n",
       "Pumpkin Spice Latte                                       0.094769   \n",
       "White Chocolate Mocha                                     0.236867   \n",
       "Vanilla Latte                                             0.384570   \n",
       "Iced Caramel Macchiato                                    0.574584   \n",
       "Mocha Frappuccino                                         0.166114   \n",
       "Green Tea Latte                                           0.089620   \n",
       "Chai Tea Latte                                            0.052477   \n",
       "Java Chip Frappuccino                                     0.128314   \n",
       "Flat White                                                0.095975   \n",
       "Espresso Con Panna                                        0.078622   \n",
       "Nitro Cold Brew                                           0.000000   \n",
       "Iced White Chocolate Mocha                                0.159278   \n",
       "Salted Caramel Mocha                                      0.317590   \n",
       "Cold Brew Coffee                                          0.056738   \n",
       "Blonde Roast                                              0.000000   \n",
       "Americano Misto                                           0.080448   \n",
       "Iced Caffè Americano                                      0.022252   \n",
       "Caramel Frappuccino                                       0.370527   \n",
       "Iced Chai Tea Latte                                       0.015811   \n",
       "Hazelnut Latte                                            0.227225   \n",
       "Vanilla Bean Frappuccino                                  0.204865   \n",
       "Vanilla Sweet Cream Cold Brew                             0.248722   \n",
       "Caramel Brulée Latte                                      0.203566   \n",
       "Toasted White Chocolate Mocha                             0.096791   \n",
       "Chestnut Praline Latte                                    0.127868   \n",
       "Iced Caffè Mocha                                          0.177373   \n",
       "Caramel Apple Spice                                       0.324615   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.399176   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.180847   \n",
       "Blonde Vanilla Latte                                      0.346475   \n",
       "Double Chocolaty Chip Frappuccino                         0.070179   \n",
       "Iced Pineapple Green Tea Infusion                         0.088307   \n",
       "Iced Peach Green Tea Infusion                             0.088307   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.283386   \n",
       "Iced Golden Ginger Drink                                  0.015131   \n",
       "Iced Caffè Latte                                          0.092229   \n",
       "Iced Cascara Latte                                        0.128370   \n",
       "Caramelized Honey Latte                                   0.057295   \n",
       "Gingerbread Latte                                         0.165313   \n",
       "Iced Coffee with Milk                                     0.236077   \n",
       "Iced Caramel Cloud Macchiato                              0.331814   \n",
       "Iced Cinnamon Dolce Latte                                 0.129525   \n",
       "Iced Green Tea Latte                                      0.135666   \n",
       "Cinnamon Roll Frappuccino                                 0.109792   \n",
       "\n",
       "drink_name                                       Cinnamon Dolce Latte  \\\n",
       "drink_name                                                              \n",
       "Caffè Americano                                              0.028258   \n",
       "Caffè Mocha                                                  0.154059   \n",
       "Caramel Macchiato                                            0.168915   \n",
       "Cinnamon Dolce Latte                                         1.000000   \n",
       "Pumpkin Spice Latte                                          0.159690   \n",
       "White Chocolate Mocha                                        0.186541   \n",
       "Vanilla Latte                                                0.319872   \n",
       "Iced Caramel Macchiato                                       0.085970   \n",
       "Mocha Frappuccino                                            0.116379   \n",
       "Green Tea Latte                                              0.099964   \n",
       "Chai Tea Latte                                               0.197085   \n",
       "Java Chip Frappuccino                                        0.089896   \n",
       "Flat White                                                   0.091290   \n",
       "Espresso Con Panna                                           0.093332   \n",
       "Nitro Cold Brew                                              0.000000   \n",
       "Iced White Chocolate Mocha                                   0.118138   \n",
       "Salted Caramel Mocha                                         0.234080   \n",
       "Cold Brew Coffee                                             0.000000   \n",
       "Blonde Roast                                                 0.000000   \n",
       "Americano Misto                                              0.076522   \n",
       "Iced Caffè Americano                                         0.038180   \n",
       "Caramel Frappuccino                                          0.153752   \n",
       "Iced Chai Tea Latte                                          0.166272   \n",
       "Hazelnut Latte                                               0.185335   \n",
       "Vanilla Bean Frappuccino                                     0.158622   \n",
       "Vanilla Sweet Cream Cold Brew                                0.030696   \n",
       "Caramel Brulée Latte                                         0.111140   \n",
       "Toasted White Chocolate Mocha                                0.105152   \n",
       "Chestnut Praline Latte                                       0.135069   \n",
       "Iced Caffè Mocha                                             0.203059   \n",
       "Caramel Apple Spice                                          0.312914   \n",
       "Iced Caramel Macchiato with Coconut Milk                     0.111307   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup              0.196858   \n",
       "Blonde Vanilla Latte                                         0.238949   \n",
       "Double Chocolaty Chip Frappuccino                            0.084950   \n",
       "Iced Pineapple Green Tea Infusion                            0.000000   \n",
       "Iced Peach Green Tea Infusion                                0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                           0.510031   \n",
       "Iced Golden Ginger Drink                                     0.014393   \n",
       "Iced Caffè Latte                                             0.129016   \n",
       "Iced Cascara Latte                                           0.151616   \n",
       "Caramelized Honey Latte                                      0.080149   \n",
       "Gingerbread Latte                                            0.174622   \n",
       "Iced Coffee with Milk                                        0.025483   \n",
       "Iced Caramel Cloud Macchiato                                 0.115467   \n",
       "Iced Cinnamon Dolce Latte                                    0.964122   \n",
       "Iced Green Tea Latte                                         0.151758   \n",
       "Cinnamon Roll Frappuccino                                    0.635698   \n",
       "\n",
       "drink_name                                       Pumpkin Spice Latte  \\\n",
       "drink_name                                                             \n",
       "Caffè Americano                                             0.020390   \n",
       "Caffè Mocha                                                 0.111161   \n",
       "Caramel Macchiato                                           0.094769   \n",
       "Cinnamon Dolce Latte                                        0.159690   \n",
       "Pumpkin Spice Latte                                         1.000000   \n",
       "White Chocolate Mocha                                       0.134598   \n",
       "Vanilla Latte                                               0.198713   \n",
       "Iced Caramel Macchiato                                      0.031855   \n",
       "Mocha Frappuccino                                           0.083973   \n",
       "Green Tea Latte                                             0.072129   \n",
       "Chai Tea Latte                                              0.118508   \n",
       "Java Chip Frappuccino                                       0.064864   \n",
       "Flat White                                                  0.065870   \n",
       "Espresso Con Panna                                          0.067344   \n",
       "Nitro Cold Brew                                             0.000000   \n",
       "Iced White Chocolate Mocha                                  0.085242   \n",
       "Salted Caramel Mocha                                        0.145417   \n",
       "Cold Brew Coffee                                            0.000000   \n",
       "Blonde Roast                                                0.000000   \n",
       "Americano Misto                                             0.055214   \n",
       "Iced Caffè Americano                                        0.027549   \n",
       "Caramel Frappuccino                                         0.077518   \n",
       "Iced Chai Tea Latte                                         0.095621   \n",
       "Hazelnut Latte                                              0.106941   \n",
       "Vanilla Bean Frappuccino                                    0.114453   \n",
       "Vanilla Sweet Cream Cold Brew                               0.022149   \n",
       "Caramel Brulée Latte                                        0.080193   \n",
       "Toasted White Chocolate Mocha                               0.075872   \n",
       "Chestnut Praline Latte                                      0.077937   \n",
       "Iced Caffè Mocha                                            0.146517   \n",
       "Caramel Apple Spice                                         0.079534   \n",
       "Iced Caramel Macchiato with Coconut Milk                    0.048848   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup             0.105968   \n",
       "Blonde Vanilla Latte                                        0.124617   \n",
       "Double Chocolaty Chip Frappuccino                           0.061296   \n",
       "Iced Pineapple Green Tea Infusion                           0.000000   \n",
       "Iced Peach Green Tea Infusion                               0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                          0.044130   \n",
       "Iced Golden Ginger Drink                                    0.010385   \n",
       "Iced Caffè Latte                                            0.093091   \n",
       "Iced Cascara Latte                                          0.066538   \n",
       "Caramelized Honey Latte                                     0.057831   \n",
       "Gingerbread Latte                                           0.100760   \n",
       "Iced Coffee with Milk                                       0.018387   \n",
       "Iced Caramel Cloud Macchiato                                0.050673   \n",
       "Iced Cinnamon Dolce Latte                                   0.132739   \n",
       "Iced Green Tea Latte                                        0.076513   \n",
       "Cinnamon Roll Frappuccino                                   0.072376   \n",
       "\n",
       "drink_name                                       White Chocolate Mocha  \\\n",
       "drink_name                                                               \n",
       "Caffè Americano                                               0.036943   \n",
       "Caffè Mocha                                                   0.515945   \n",
       "Caramel Macchiato                                             0.236867   \n",
       "Cinnamon Dolce Latte                                          0.186541   \n",
       "Pumpkin Spice Latte                                           0.134598   \n",
       "White Chocolate Mocha                                         1.000000   \n",
       "Vanilla Latte                                                 0.232125   \n",
       "Iced Caramel Macchiato                                        0.130242   \n",
       "Mocha Frappuccino                                             0.561124   \n",
       "Green Tea Latte                                               0.130687   \n",
       "Chai Tea Latte                                                0.065257   \n",
       "Java Chip Frappuccino                                         0.622409   \n",
       "Flat White                                                    0.119348   \n",
       "Espresso Con Panna                                            0.122018   \n",
       "Nitro Cold Brew                                               0.000000   \n",
       "Iced White Chocolate Mocha                                    0.805266   \n",
       "Salted Caramel Mocha                                          0.276461   \n",
       "Cold Brew Coffee                                              0.152102   \n",
       "Blonde Roast                                                  0.000000   \n",
       "Americano Misto                                               0.100040   \n",
       "Iced Caffè Americano                                          0.049914   \n",
       "Caramel Frappuccino                                           0.220775   \n",
       "Iced Chai Tea Latte                                           0.019661   \n",
       "Hazelnut Latte                                                0.258141   \n",
       "Vanilla Bean Frappuccino                                      0.106427   \n",
       "Vanilla Sweet Cream Cold Brew                                 0.040131   \n",
       "Caramel Brulée Latte                                          0.188199   \n",
       "Toasted White Chocolate Mocha                                 0.601141   \n",
       "Chestnut Praline Latte                                        0.141211   \n",
       "Iced Caffè Mocha                                              0.371997   \n",
       "Caramel Apple Spice                                           0.267037   \n",
       "Iced Caramel Macchiato with Coconut Milk                      0.231332   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup               0.670593   \n",
       "Blonde Vanilla Latte                                          0.225789   \n",
       "Double Chocolaty Chip Frappuccino                             0.524651   \n",
       "Iced Pineapple Green Tea Infusion                             0.000000   \n",
       "Iced Peach Green Tea Infusion                                 0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                            0.208989   \n",
       "Iced Golden Ginger Drink                                      0.018816   \n",
       "Iced Caffè Latte                                              0.168668   \n",
       "Iced Cascara Latte                                            0.120558   \n",
       "Caramelized Honey Latte                                       0.184344   \n",
       "Gingerbread Latte                                             0.182563   \n",
       "Iced Coffee with Milk                                         0.033316   \n",
       "Iced Caramel Cloud Macchiato                                  0.313025   \n",
       "Iced Cinnamon Dolce Latte                                     0.137514   \n",
       "Iced Green Tea Latte                                          0.138631   \n",
       "Cinnamon Roll Frappuccino                                     0.175876   \n",
       "\n",
       "drink_name                                       Vanilla Latte  \\\n",
       "drink_name                                                       \n",
       "Caffè Americano                                       0.035163   \n",
       "Caffè Mocha                                           0.191706   \n",
       "Caramel Macchiato                                     0.384570   \n",
       "Cinnamon Dolce Latte                                  0.319872   \n",
       "Pumpkin Spice Latte                                   0.198713   \n",
       "White Chocolate Mocha                                 0.232125   \n",
       "Vanilla Latte                                         1.000000   \n",
       "Iced Caramel Macchiato                                0.301071   \n",
       "Mocha Frappuccino                                     0.144818   \n",
       "Green Tea Latte                                       0.124391   \n",
       "Chai Tea Latte                                        0.062114   \n",
       "Java Chip Frappuccino                                 0.111864   \n",
       "Flat White                                            0.113598   \n",
       "Espresso Con Panna                                    0.116140   \n",
       "Nitro Cold Brew                                       0.000000   \n",
       "Iced White Chocolate Mocha                            0.147007   \n",
       "Salted Caramel Mocha                                  0.291281   \n",
       "Cold Brew Coffee                                      0.000000   \n",
       "Blonde Roast                                          0.000000   \n",
       "Americano Misto                                       0.095221   \n",
       "Iced Caffè Americano                                  0.047510   \n",
       "Caramel Frappuccino                                   0.191323   \n",
       "Iced Chai Tea Latte                                   0.018714   \n",
       "Hazelnut Latte                                        0.230624   \n",
       "Vanilla Bean Frappuccino                              0.821715   \n",
       "Vanilla Sweet Cream Cold Brew                         0.248755   \n",
       "Caramel Brulée Latte                                  0.138299   \n",
       "Toasted White Chocolate Mocha                         0.130848   \n",
       "Chestnut Praline Latte                                0.168075   \n",
       "Iced Caffè Mocha                                      0.252680   \n",
       "Caramel Apple Spice                                   0.183869   \n",
       "Iced Caramel Macchiato with Coconut Milk              0.340890   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup       0.244964   \n",
       "Blonde Vanilla Latte                                  0.604759   \n",
       "Double Chocolaty Chip Frappuccino                     0.105709   \n",
       "Iced Pineapple Green Tea Infusion                     0.000000   \n",
       "Iced Peach Green Tea Infusion                         0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                    0.125129   \n",
       "Iced Golden Ginger Drink                              0.017910   \n",
       "Iced Caffè Latte                                      0.160543   \n",
       "Iced Cascara Latte                                    0.188666   \n",
       "Caramelized Honey Latte                               0.099734   \n",
       "Gingerbread Latte                                     0.217294   \n",
       "Iced Coffee with Milk                                 0.031711   \n",
       "Iced Caramel Cloud Macchiato                          0.353628   \n",
       "Iced Cinnamon Dolce Latte                             0.273476   \n",
       "Iced Green Tea Latte                                  0.188842   \n",
       "Cinnamon Roll Frappuccino                             0.373961   \n",
       "\n",
       "drink_name                                       Iced Caramel Macchiato  \\\n",
       "drink_name                                                                \n",
       "Caffè Americano                                                0.018331   \n",
       "Caffè Mocha                                                    0.107564   \n",
       "Caramel Macchiato                                              0.574584   \n",
       "Cinnamon Dolce Latte                                           0.085970   \n",
       "Pumpkin Spice Latte                                            0.031855   \n",
       "White Chocolate Mocha                                          0.130242   \n",
       "Vanilla Latte                                                  0.301071   \n",
       "Iced Caramel Macchiato                                         1.000000   \n",
       "Mocha Frappuccino                                              0.159303   \n",
       "Green Tea Latte                                                0.015169   \n",
       "Chai Tea Latte                                                 0.017126   \n",
       "Java Chip Frappuccino                                          0.123053   \n",
       "Flat White                                                     0.065527   \n",
       "Espresso Con Panna                                             0.027001   \n",
       "Nitro Cold Brew                                                0.084319   \n",
       "Iced White Chocolate Mocha                                     0.270375   \n",
       "Salted Caramel Mocha                                           0.260575   \n",
       "Cold Brew Coffee                                               0.097802   \n",
       "Blonde Roast                                                   0.000000   \n",
       "Americano Misto                                                0.044788   \n",
       "Iced Caffè Americano                                           0.159541   \n",
       "Caramel Frappuccino                                            0.388794   \n",
       "Iced Chai Tea Latte                                            0.152861   \n",
       "Hazelnut Latte                                                 0.146922   \n",
       "Vanilla Bean Frappuccino                                       0.210126   \n",
       "Vanilla Sweet Cream Cold Brew                                  0.369079   \n",
       "Caramel Brulée Latte                                           0.147100   \n",
       "Toasted White Chocolate Mocha                                  0.032534   \n",
       "Chestnut Praline Latte                                         0.065079   \n",
       "Iced Caffè Mocha                                               0.162169   \n",
       "Caramel Apple Spice                                            0.254151   \n",
       "Iced Caramel Macchiato with Coconut Milk                       0.492744   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                0.120261   \n",
       "Blonde Vanilla Latte                                           0.303883   \n",
       "Double Chocolaty Chip Frappuccino                              0.059433   \n",
       "Iced Pineapple Green Tea Infusion                              0.133193   \n",
       "Iced Peach Green Tea Infusion                                  0.133193   \n",
       "Iced Cinnamon Almondmilk Macchiato                             0.359185   \n",
       "Iced Golden Ginger Drink                                       0.053377   \n",
       "Iced Caffè Latte                                               0.194964   \n",
       "Iced Cascara Latte                                             0.142884   \n",
       "Caramelized Honey Latte                                        0.063773   \n",
       "Gingerbread Latte                                              0.084137   \n",
       "Iced Coffee with Milk                                          0.600206   \n",
       "Iced Caramel Cloud Macchiato                                   0.369330   \n",
       "Iced Cinnamon Dolce Latte                                      0.125905   \n",
       "Iced Green Tea Latte                                           0.127686   \n",
       "Cinnamon Roll Frappuccino                                      0.082377   \n",
       "\n",
       "drink_name                                       Mocha Frappuccino  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.017679   \n",
       "Caffè Mocha                                               0.493167   \n",
       "Caramel Macchiato                                         0.166114   \n",
       "Cinnamon Dolce Latte                                      0.116379   \n",
       "Pumpkin Spice Latte                                       0.083973   \n",
       "White Chocolate Mocha                                     0.561124   \n",
       "Vanilla Latte                                             0.144818   \n",
       "Iced Caramel Macchiato                                    0.159303   \n",
       "Mocha Frappuccino                                         1.000000   \n",
       "Green Tea Latte                                           0.096467   \n",
       "Chai Tea Latte                                            0.020546   \n",
       "Java Chip Frappuccino                                     0.873906   \n",
       "Flat White                                                0.020553   \n",
       "Espresso Con Panna                                        0.189309   \n",
       "Nitro Cold Brew                                           0.072499   \n",
       "Iced White Chocolate Mocha                                0.536567   \n",
       "Salted Caramel Mocha                                      0.330734   \n",
       "Cold Brew Coffee                                          0.199001   \n",
       "Blonde Roast                                              0.071722   \n",
       "Americano Misto                                           0.110336   \n",
       "Iced Caffè Americano                                      0.069521   \n",
       "Caramel Frappuccino                                       0.568261   \n",
       "Iced Chai Tea Latte                                       0.066914   \n",
       "Hazelnut Latte                                            0.190017   \n",
       "Vanilla Bean Frappuccino                                  0.279994   \n",
       "Vanilla Sweet Cream Cold Brew                             0.153286   \n",
       "Caramel Brulée Latte                                      0.136717   \n",
       "Toasted White Chocolate Mocha                             0.267256   \n",
       "Chestnut Praline Latte                                    0.088099   \n",
       "Iced Caffè Mocha                                          0.665864   \n",
       "Caramel Apple Spice                                       0.231174   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.268689   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.376420   \n",
       "Blonde Vanilla Latte                                      0.086893   \n",
       "Double Chocolaty Chip Frappuccino                         0.544553   \n",
       "Iced Pineapple Green Tea Infusion                         0.041872   \n",
       "Iced Peach Green Tea Infusion                             0.041872   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.242737   \n",
       "Iced Golden Ginger Drink                                  0.064037   \n",
       "Iced Caffè Latte                                          0.219760   \n",
       "Iced Cascara Latte                                        0.077920   \n",
       "Caramelized Honey Latte                                   0.153161   \n",
       "Gingerbread Latte                                         0.113897   \n",
       "Iced Coffee with Milk                                     0.254832   \n",
       "Iced Caramel Cloud Macchiato                              0.143584   \n",
       "Iced Cinnamon Dolce Latte                                 0.164315   \n",
       "Iced Green Tea Latte                                      0.209790   \n",
       "Cinnamon Roll Frappuccino                                 0.272139   \n",
       "\n",
       "drink_name                                       Green Tea Latte  ...  \\\n",
       "drink_name                                                        ...   \n",
       "Caffè Americano                                         0.010438  ...   \n",
       "Caffè Mocha                                             0.107931  ...   \n",
       "Caramel Macchiato                                       0.089620  ...   \n",
       "Cinnamon Dolce Latte                                    0.099964  ...   \n",
       "Pumpkin Spice Latte                                     0.072129  ...   \n",
       "White Chocolate Mocha                                   0.130687  ...   \n",
       "Vanilla Latte                                           0.124391  ...   \n",
       "Iced Caramel Macchiato                                  0.015169  ...   \n",
       "Mocha Frappuccino                                       0.096467  ...   \n",
       "Green Tea Latte                                         1.000000  ...   \n",
       "Chai Tea Latte                                          0.314021  ...   \n",
       "Java Chip Frappuccino                                   0.074515  ...   \n",
       "Flat White                                              0.041390  ...   \n",
       "Espresso Con Panna                                      0.058237  ...   \n",
       "Nitro Cold Brew                                         0.000000  ...   \n",
       "Iced White Chocolate Mocha                              0.077322  ...   \n",
       "Salted Caramel Mocha                                    0.091029  ...   \n",
       "Cold Brew Coffee                                        0.000000  ...   \n",
       "Blonde Roast                                            0.000000  ...   \n",
       "Americano Misto                                         0.044854  ...   \n",
       "Iced Caffè Americano                                    0.014103  ...   \n",
       "Caramel Frappuccino                                     0.089052  ...   \n",
       "Iced Chai Tea Latte                                     0.081238  ...   \n",
       "Hazelnut Latte                                          0.103834  ...   \n",
       "Vanilla Bean Frappuccino                                0.067478  ...   \n",
       "Vanilla Sweet Cream Cold Brew                           0.025444  ...   \n",
       "Caramel Brulée Latte                                    0.077863  ...   \n",
       "Toasted White Chocolate Mocha                           0.073668  ...   \n",
       "Chestnut Praline Latte                                  0.075672  ...   \n",
       "Iced Caffè Mocha                                        0.132903  ...   \n",
       "Caramel Apple Spice                                     0.091368  ...   \n",
       "Iced Caramel Macchiato with Coconut Milk                0.033775  ...   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup         0.096122  ...   \n",
       "Blonde Vanilla Latte                                    0.109223  ...   \n",
       "Double Chocolaty Chip Frappuccino                       0.070415  ...   \n",
       "Iced Pineapple Green Tea Infusion                       0.140928  ...   \n",
       "Iced Peach Green Tea Infusion                           0.140928  ...   \n",
       "Iced Cinnamon Almondmilk Macchiato                      0.030513  ...   \n",
       "Iced Golden Ginger Drink                                0.105125  ...   \n",
       "Iced Caffè Latte                                        0.064367  ...   \n",
       "Iced Cascara Latte                                      0.046007  ...   \n",
       "Caramelized Honey Latte                                 0.039987  ...   \n",
       "Gingerbread Latte                                       0.097832  ...   \n",
       "Iced Coffee with Milk                                   0.021123  ...   \n",
       "Iced Caramel Cloud Macchiato                            0.035037  ...   \n",
       "Iced Cinnamon Dolce Latte                               0.068844  ...   \n",
       "Iced Green Tea Latte                                    0.292939  ...   \n",
       "Cinnamon Roll Frappuccino                               0.038332  ...   \n",
       "\n",
       "drink_name                                       Iced Golden Ginger Drink  \\\n",
       "drink_name                                                                  \n",
       "Caffè Americano                                                  0.000000   \n",
       "Caffè Mocha                                                      0.015540   \n",
       "Caramel Macchiato                                                0.015131   \n",
       "Cinnamon Dolce Latte                                             0.014393   \n",
       "Pumpkin Spice Latte                                              0.010385   \n",
       "White Chocolate Mocha                                            0.018816   \n",
       "Vanilla Latte                                                    0.017910   \n",
       "Iced Caramel Macchiato                                           0.053377   \n",
       "Mocha Frappuccino                                                0.064037   \n",
       "Green Tea Latte                                                  0.105125   \n",
       "Chai Tea Latte                                                   0.210936   \n",
       "Java Chip Frappuccino                                            0.049465   \n",
       "Flat White                                                       0.013474   \n",
       "Espresso Con Panna                                               0.000000   \n",
       "Nitro Cold Brew                                                  0.082330   \n",
       "Iced White Chocolate Mocha                                       0.051328   \n",
       "Salted Caramel Mocha                                             0.013106   \n",
       "Cold Brew Coffee                                                 0.142154   \n",
       "Blonde Roast                                                     0.000000   \n",
       "Americano Misto                                                  0.014602   \n",
       "Iced Caffè Americano                                             0.029917   \n",
       "Caramel Frappuccino                                              0.059115   \n",
       "Iced Chai Tea Latte                                              0.138663   \n",
       "Hazelnut Latte                                                   0.014950   \n",
       "Vanilla Bean Frappuccino                                         0.044794   \n",
       "Vanilla Sweet Cream Cold Brew                                    0.000000   \n",
       "Caramel Brulée Latte                                             0.011211   \n",
       "Toasted White Chocolate Mocha                                    0.010607   \n",
       "Chestnut Praline Latte                                           0.010895   \n",
       "Iced Caffè Mocha                                                 0.088225   \n",
       "Caramel Apple Spice                                              0.000000   \n",
       "Iced Caramel Macchiato with Coconut Milk                         0.216281   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                  0.020133   \n",
       "Blonde Vanilla Latte                                             0.026676   \n",
       "Double Chocolaty Chip Frappuccino                                0.046744   \n",
       "Iced Pineapple Green Tea Infusion                                0.126299   \n",
       "Iced Peach Green Tea Infusion                                    0.126299   \n",
       "Iced Cinnamon Almondmilk Macchiato                               0.050281   \n",
       "Iced Golden Ginger Drink                                         1.000000   \n",
       "Iced Caffè Latte                                                 0.106067   \n",
       "Iced Cascara Latte                                               0.023921   \n",
       "Caramelized Honey Latte                                          0.210954   \n",
       "Gingerbread Latte                                                0.014086   \n",
       "Iced Coffee with Milk                                            0.074330   \n",
       "Iced Caramel Cloud Macchiato                                     0.018217   \n",
       "Iced Cinnamon Dolce Latte                                        0.045701   \n",
       "Iced Green Tea Latte                                             0.058349   \n",
       "Cinnamon Roll Frappuccino                                        0.000000   \n",
       "\n",
       "drink_name                                       Iced Caffè Latte  \\\n",
       "drink_name                                                          \n",
       "Caffè Americano                                          0.065708   \n",
       "Caffè Mocha                                              0.139299   \n",
       "Caramel Macchiato                                        0.092229   \n",
       "Cinnamon Dolce Latte                                     0.129016   \n",
       "Pumpkin Spice Latte                                      0.093091   \n",
       "White Chocolate Mocha                                    0.168668   \n",
       "Vanilla Latte                                            0.160543   \n",
       "Iced Caramel Macchiato                                   0.194964   \n",
       "Mocha Frappuccino                                        0.219760   \n",
       "Green Tea Latte                                          0.064367   \n",
       "Chai Tea Latte                                           0.034031   \n",
       "Java Chip Frappuccino                                    0.169752   \n",
       "Flat White                                               0.130210   \n",
       "Espresso Con Panna                                       0.096784   \n",
       "Nitro Cold Brew                                          0.000000   \n",
       "Iced White Chocolate Mocha                               0.233943   \n",
       "Salted Caramel Mocha                                     0.117484   \n",
       "Cold Brew Coffee                                         0.068853   \n",
       "Blonde Roast                                             0.000000   \n",
       "Americano Misto                                          0.089000   \n",
       "Iced Caffè Americano                                     0.164365   \n",
       "Caramel Frappuccino                                      0.202868   \n",
       "Iced Chai Tea Latte                                      0.110831   \n",
       "Hazelnut Latte                                           0.134011   \n",
       "Vanilla Bean Frappuccino                                 0.153721   \n",
       "Vanilla Sweet Cream Cold Brew                            0.000000   \n",
       "Caramel Brulée Latte                                     0.100492   \n",
       "Toasted White Chocolate Mocha                            0.095078   \n",
       "Chestnut Praline Latte                                   0.097665   \n",
       "Iced Caffè Mocha                                         0.402109   \n",
       "Caramel Apple Spice                                      0.043362   \n",
       "Iced Caramel Macchiato with Coconut Milk                 0.253670   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup          0.180477   \n",
       "Blonde Vanilla Latte                                     0.650863   \n",
       "Double Chocolaty Chip Frappuccino                        0.160412   \n",
       "Iced Pineapple Green Tea Infusion                        0.069354   \n",
       "Iced Peach Green Tea Infusion                            0.069354   \n",
       "Iced Cinnamon Almondmilk Macchiato                       0.229169   \n",
       "Iced Golden Ginger Drink                                 0.106067   \n",
       "Iced Caffè Latte                                         1.000000   \n",
       "Iced Cascara Latte                                       0.583654   \n",
       "Caramelized Honey Latte                                  0.507279   \n",
       "Gingerbread Latte                                        0.126265   \n",
       "Iced Coffee with Milk                                    0.187800   \n",
       "Iced Caramel Cloud Macchiato                             0.444493   \n",
       "Iced Cinnamon Dolce Latte                                0.208293   \n",
       "Iced Green Tea Latte                                     0.200237   \n",
       "Cinnamon Roll Frappuccino                                0.028389   \n",
       "\n",
       "drink_name                                       Iced Cascara Latte  \\\n",
       "drink_name                                                            \n",
       "Caffè Americano                                            0.046966   \n",
       "Caffè Mocha                                                0.099565   \n",
       "Caramel Macchiato                                          0.128370   \n",
       "Cinnamon Dolce Latte                                       0.151616   \n",
       "Pumpkin Spice Latte                                        0.066538   \n",
       "White Chocolate Mocha                                      0.120558   \n",
       "Vanilla Latte                                              0.188666   \n",
       "Iced Caramel Macchiato                                     0.142884   \n",
       "Mocha Frappuccino                                          0.077920   \n",
       "Green Tea Latte                                            0.046007   \n",
       "Chai Tea Latte                                             0.024324   \n",
       "Java Chip Frappuccino                                      0.060189   \n",
       "Flat White                                                 0.093069   \n",
       "Espresso Con Panna                                         0.069177   \n",
       "Nitro Cold Brew                                            0.000000   \n",
       "Iced White Chocolate Mocha                                 0.103767   \n",
       "Salted Caramel Mocha                                       0.138065   \n",
       "Cold Brew Coffee                                           0.000000   \n",
       "Blonde Roast                                               0.000000   \n",
       "Americano Misto                                            0.063614   \n",
       "Iced Caffè Americano                                       0.063456   \n",
       "Caramel Frappuccino                                        0.148913   \n",
       "Iced Chai Tea Latte                                        0.024995   \n",
       "Hazelnut Latte                                             0.157487   \n",
       "Vanilla Bean Frappuccino                                   0.054505   \n",
       "Vanilla Sweet Cream Cold Brew                              0.000000   \n",
       "Caramel Brulée Latte                                       0.071828   \n",
       "Toasted White Chocolate Mocha                              0.067958   \n",
       "Chestnut Praline Latte                                     0.114773   \n",
       "Iced Caffè Mocha                                           0.178358   \n",
       "Caramel Apple Spice                                        0.093376   \n",
       "Iced Caramel Macchiato with Coconut Milk                   0.184995   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup            0.212092   \n",
       "Blonde Vanilla Latte                                       0.575306   \n",
       "Double Chocolaty Chip Frappuccino                          0.056877   \n",
       "Iced Pineapple Green Tea Infusion                          0.000000   \n",
       "Iced Peach Green Tea Infusion                              0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                         0.167127   \n",
       "Iced Golden Ginger Drink                                   0.023921   \n",
       "Iced Caffè Latte                                           0.583654   \n",
       "Iced Cascara Latte                                         1.000000   \n",
       "Caramelized Honey Latte                                    0.362583   \n",
       "Gingerbread Latte                                          0.148384   \n",
       "Iced Coffee with Milk                                      0.042354   \n",
       "Iced Caramel Cloud Macchiato                               0.392893   \n",
       "Iced Cinnamon Dolce Latte                                  0.151903   \n",
       "Iced Green Tea Latte                                       0.146982   \n",
       "Cinnamon Roll Frappuccino                                  0.061133   \n",
       "\n",
       "drink_name                                       Caramelized Honey Latte  \\\n",
       "drink_name                                                                 \n",
       "Caffè Americano                                                 0.040820   \n",
       "Caffè Mocha                                                     0.152245   \n",
       "Caramel Macchiato                                               0.057295   \n",
       "Cinnamon Dolce Latte                                            0.080149   \n",
       "Pumpkin Spice Latte                                             0.057831   \n",
       "White Chocolate Mocha                                           0.184344   \n",
       "Vanilla Latte                                                   0.099734   \n",
       "Iced Caramel Macchiato                                          0.063773   \n",
       "Mocha Frappuccino                                               0.153161   \n",
       "Green Tea Latte                                                 0.039987   \n",
       "Chai Tea Latte                                                  0.021141   \n",
       "Java Chip Frappuccino                                           0.118308   \n",
       "Flat White                                                      0.080890   \n",
       "Espresso Con Panna                                              0.060125   \n",
       "Nitro Cold Brew                                                 0.000000   \n",
       "Iced White Chocolate Mocha                                      0.158670   \n",
       "Salted Caramel Mocha                                            0.128403   \n",
       "Cold Brew Coffee                                                0.000000   \n",
       "Blonde Roast                                                    0.000000   \n",
       "Americano Misto                                                 0.055289   \n",
       "Iced Caffè Americano                                            0.055152   \n",
       "Caramel Frappuccino                                             0.062518   \n",
       "Iced Chai Tea Latte                                             0.021725   \n",
       "Hazelnut Latte                                                  0.083252   \n",
       "Vanilla Bean Frappuccino                                        0.047373   \n",
       "Vanilla Sweet Cream Cold Brew                                   0.000000   \n",
       "Caramel Brulée Latte                                            0.109831   \n",
       "Toasted White Chocolate Mocha                                   0.408455   \n",
       "Chestnut Praline Latte                                          0.060672   \n",
       "Iced Caffè Mocha                                                0.272726   \n",
       "Caramel Apple Spice                                             0.090850   \n",
       "Iced Caramel Macchiato with Coconut Milk                        0.172049   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                 0.197249   \n",
       "Blonde Vanilla Latte                                            0.404336   \n",
       "Double Chocolaty Chip Frappuccino                               0.111799   \n",
       "Iced Pineapple Green Tea Infusion                               0.000000   \n",
       "Iced Peach Green Tea Infusion                                   0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                              0.155431   \n",
       "Iced Golden Ginger Drink                                        0.210954   \n",
       "Iced Caffè Latte                                                0.507279   \n",
       "Iced Cascara Latte                                              0.362583   \n",
       "Caramelized Honey Latte                                         1.000000   \n",
       "Gingerbread Latte                                               0.078440   \n",
       "Iced Coffee with Milk                                           0.036812   \n",
       "Iced Caramel Cloud Macchiato                                    0.276133   \n",
       "Iced Cinnamon Dolce Latte                                       0.080300   \n",
       "Iced Green Tea Latte                                            0.061708   \n",
       "Cinnamon Roll Frappuccino                                       0.059479   \n",
       "\n",
       "drink_name                                       Gingerbread Latte  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.027656   \n",
       "Caffè Mocha                                               0.150774   \n",
       "Caramel Macchiato                                         0.165313   \n",
       "Cinnamon Dolce Latte                                      0.174622   \n",
       "Pumpkin Spice Latte                                       0.100760   \n",
       "White Chocolate Mocha                                     0.182563   \n",
       "Vanilla Latte                                             0.217294   \n",
       "Iced Caramel Macchiato                                    0.084137   \n",
       "Mocha Frappuccino                                         0.113897   \n",
       "Green Tea Latte                                           0.097832   \n",
       "Chai Tea Latte                                            0.048852   \n",
       "Java Chip Frappuccino                                     0.087979   \n",
       "Flat White                                                0.089344   \n",
       "Espresso Con Panna                                        0.189013   \n",
       "Nitro Cold Brew                                           0.000000   \n",
       "Iced White Chocolate Mocha                                0.115619   \n",
       "Salted Caramel Mocha                                      0.159014   \n",
       "Cold Brew Coffee                                          0.000000   \n",
       "Blonde Roast                                              0.000000   \n",
       "Americano Misto                                           0.169748   \n",
       "Iced Caffè Americano                                      0.037366   \n",
       "Caramel Frappuccino                                       0.150473   \n",
       "Iced Chai Tea Latte                                       0.014719   \n",
       "Hazelnut Latte                                            0.181383   \n",
       "Vanilla Bean Frappuccino                                  0.079671   \n",
       "Vanilla Sweet Cream Cold Brew                             0.030042   \n",
       "Caramel Brulée Latte                                      0.367851   \n",
       "Toasted White Chocolate Mocha                             0.102910   \n",
       "Chestnut Praline Latte                                    0.383982   \n",
       "Iced Caffè Mocha                                          0.198730   \n",
       "Caramel Apple Spice                                       0.144611   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.108934   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.192661   \n",
       "Blonde Vanilla Latte                                      0.233854   \n",
       "Double Chocolaty Chip Frappuccino                         0.083139   \n",
       "Iced Pineapple Green Tea Infusion                         0.000000   \n",
       "Iced Peach Green Tea Infusion                             0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.098412   \n",
       "Iced Golden Ginger Drink                                  0.014086   \n",
       "Iced Caffè Latte                                          0.126265   \n",
       "Iced Cascara Latte                                        0.148384   \n",
       "Caramelized Honey Latte                                   0.078440   \n",
       "Gingerbread Latte                                         1.000000   \n",
       "Iced Coffee with Milk                                     0.024940   \n",
       "Iced Caramel Cloud Macchiato                              0.113005   \n",
       "Iced Cinnamon Dolce Latte                                 0.137987   \n",
       "Iced Green Tea Latte                                      0.148522   \n",
       "Cinnamon Roll Frappuccino                                 0.069308   \n",
       "\n",
       "drink_name                                       Iced Coffee with Milk  \\\n",
       "drink_name                                                               \n",
       "Caffè Americano                                               0.000000   \n",
       "Caffè Mocha                                                   0.027514   \n",
       "Caramel Macchiato                                             0.236077   \n",
       "Cinnamon Dolce Latte                                          0.025483   \n",
       "Pumpkin Spice Latte                                           0.018387   \n",
       "White Chocolate Mocha                                         0.033316   \n",
       "Vanilla Latte                                                 0.031711   \n",
       "Iced Caramel Macchiato                                        0.600206   \n",
       "Mocha Frappuccino                                             0.254832   \n",
       "Green Tea Latte                                               0.021123   \n",
       "Chai Tea Latte                                                0.023848   \n",
       "Java Chip Frappuccino                                         0.196843   \n",
       "Flat White                                                    0.023856   \n",
       "Espresso Con Panna                                            0.105248   \n",
       "Nitro Cold Brew                                               0.084152   \n",
       "Iced White Chocolate Mocha                                    0.090881   \n",
       "Salted Caramel Mocha                                          0.023206   \n",
       "Cold Brew Coffee                                              0.136194   \n",
       "Blonde Roast                                                  0.083249   \n",
       "Americano Misto                                               0.128070   \n",
       "Iced Caffè Americano                                          0.052970   \n",
       "Caramel Frappuccino                                           0.235244   \n",
       "Iced Chai Tea Latte                                           0.077669   \n",
       "Hazelnut Latte                                                0.026470   \n",
       "Vanilla Bean Frappuccino                                      0.079311   \n",
       "Vanilla Sweet Cream Cold Brew                                 0.127903   \n",
       "Caramel Brulée Latte                                          0.019849   \n",
       "Toasted White Chocolate Mocha                                 0.018780   \n",
       "Chestnut Praline Latte                                        0.019291   \n",
       "Iced Caffè Mocha                                              0.156210   \n",
       "Caramel Apple Spice                                           0.000000   \n",
       "Iced Caramel Macchiato with Coconut Milk                      0.098545   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup               0.035648   \n",
       "Blonde Vanilla Latte                                          0.047231   \n",
       "Double Chocolaty Chip Frappuccino                             0.082764   \n",
       "Iced Pineapple Green Tea Infusion                             0.048602   \n",
       "Iced Peach Green Tea Infusion                                 0.048602   \n",
       "Iced Cinnamon Almondmilk Macchiato                            0.089027   \n",
       "Iced Golden Ginger Drink                                      0.074330   \n",
       "Iced Caffè Latte                                              0.187800   \n",
       "Iced Cascara Latte                                            0.042354   \n",
       "Caramelized Honey Latte                                       0.036812   \n",
       "Gingerbread Latte                                             0.024940   \n",
       "Iced Coffee with Milk                                         1.000000   \n",
       "Iced Caramel Cloud Macchiato                                  0.032256   \n",
       "Iced Cinnamon Dolce Latte                                     0.080917   \n",
       "Iced Green Tea Latte                                          0.103311   \n",
       "Cinnamon Roll Frappuccino                                     0.000000   \n",
       "\n",
       "drink_name                                       Iced Caramel Cloud Macchiato  \\\n",
       "drink_name                                                                      \n",
       "Caffè Americano                                                      0.035768   \n",
       "Caffè Mocha                                                          0.140615   \n",
       "Caramel Macchiato                                                    0.331814   \n",
       "Cinnamon Dolce Latte                                                 0.115467   \n",
       "Pumpkin Spice Latte                                                  0.050673   \n",
       "White Chocolate Mocha                                                0.313025   \n",
       "Vanilla Latte                                                        0.353628   \n",
       "Iced Caramel Macchiato                                               0.369330   \n",
       "Mocha Frappuccino                                                    0.143584   \n",
       "Green Tea Latte                                                      0.035037   \n",
       "Chai Tea Latte                                                       0.018524   \n",
       "Java Chip Frappuccino                                                0.110911   \n",
       "Flat White                                                           0.070879   \n",
       "Espresso Con Panna                                                   0.052683   \n",
       "Nitro Cold Brew                                                      0.000000   \n",
       "Iced White Chocolate Mocha                                           0.269429   \n",
       "Salted Caramel Mocha                                                 0.302323   \n",
       "Cold Brew Coffee                                                     0.068310   \n",
       "Blonde Roast                                                         0.000000   \n",
       "Americano Misto                                                      0.048446   \n",
       "Iced Caffè Americano                                                 0.048326   \n",
       "Caramel Frappuccino                                                  0.394026   \n",
       "Iced Chai Tea Latte                                                  0.019036   \n",
       "Hazelnut Latte                                                       0.182267   \n",
       "Vanilla Bean Frappuccino                                             0.207191   \n",
       "Vanilla Sweet Cream Cold Brew                                        0.107088   \n",
       "Caramel Brulée Latte                                                 0.176620   \n",
       "Toasted White Chocolate Mocha                                        0.212704   \n",
       "Chestnut Praline Latte                                               0.087408   \n",
       "Iced Caffè Mocha                                                     0.135832   \n",
       "Caramel Apple Spice                                                  0.298512   \n",
       "Iced Caramel Macchiato with Coconut Milk                             0.508018   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                      0.314280   \n",
       "Blonde Vanilla Latte                                                 0.594487   \n",
       "Double Chocolaty Chip Frappuccino                                    0.043316   \n",
       "Iced Pineapple Green Tea Infusion                                    0.000000   \n",
       "Iced Peach Green Tea Infusion                                        0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                                   0.365962   \n",
       "Iced Golden Ginger Drink                                             0.018217   \n",
       "Iced Caffè Latte                                                     0.444493   \n",
       "Iced Cascara Latte                                                   0.392893   \n",
       "Caramelized Honey Latte                                              0.276133   \n",
       "Gingerbread Latte                                                    0.113005   \n",
       "Iced Coffee with Milk                                                0.032256   \n",
       "Iced Caramel Cloud Macchiato                                         1.000000   \n",
       "Iced Cinnamon Dolce Latte                                            0.115685   \n",
       "Iced Green Tea Latte                                                 0.111937   \n",
       "Cinnamon Roll Frappuccino                                            0.179640   \n",
       "\n",
       "drink_name                                       Iced Cinnamon Dolce Latte  \\\n",
       "drink_name                                                                   \n",
       "Caffè Americano                                                   0.028312   \n",
       "Caffè Mocha                                                       0.113569   \n",
       "Caramel Macchiato                                                 0.129525   \n",
       "Cinnamon Dolce Latte                                              0.964122   \n",
       "Pumpkin Spice Latte                                               0.132739   \n",
       "White Chocolate Mocha                                             0.137514   \n",
       "Vanilla Latte                                                     0.273476   \n",
       "Iced Caramel Macchiato                                            0.125905   \n",
       "Mocha Frappuccino                                                 0.164315   \n",
       "Green Tea Latte                                                   0.068844   \n",
       "Chai Tea Latte                                                    0.162110   \n",
       "Java Chip Frappuccino                                             0.126924   \n",
       "Flat White                                                        0.056103   \n",
       "Espresso Con Panna                                                0.093509   \n",
       "Nitro Cold Brew                                                   0.000000   \n",
       "Iced White Chocolate Mocha                                        0.156608   \n",
       "Salted Caramel Mocha                                              0.200128   \n",
       "Cold Brew Coffee                                                  0.029666   \n",
       "Blonde Roast                                                      0.000000   \n",
       "Americano Misto                                                   0.038347   \n",
       "Iced Caffè Americano                                              0.070820   \n",
       "Caramel Frappuccino                                               0.198091   \n",
       "Iced Chai Tea Latte                                               0.199272   \n",
       "Hazelnut Latte                                                    0.146452   \n",
       "Vanilla Bean Frappuccino                                          0.192299   \n",
       "Vanilla Sweet Cream Cold Brew                                     0.030754   \n",
       "Caramel Brulée Latte                                              0.081930   \n",
       "Toasted White Chocolate Mocha                                     0.077516   \n",
       "Chestnut Praline Latte                                            0.106732   \n",
       "Iced Caffè Mocha                                                  0.269183   \n",
       "Caramel Apple Spice                                               0.273839   \n",
       "Iced Caramel Macchiato with Coconut Milk                          0.152989   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                   0.197231   \n",
       "Blonde Vanilla Latte                                              0.169396   \n",
       "Double Chocolaty Chip Frappuccino                                 0.119941   \n",
       "Iced Pineapple Green Tea Infusion                                 0.029882   \n",
       "Iced Peach Green Tea Infusion                                     0.029882   \n",
       "Iced Cinnamon Almondmilk Macchiato                                0.548463   \n",
       "Iced Golden Ginger Drink                                          0.045701   \n",
       "Iced Caffè Latte                                                  0.208293   \n",
       "Iced Cascara Latte                                                0.151903   \n",
       "Caramelized Honey Latte                                           0.080300   \n",
       "Gingerbread Latte                                                 0.137987   \n",
       "Iced Coffee with Milk                                             0.080917   \n",
       "Iced Caramel Cloud Macchiato                                      0.115685   \n",
       "Iced Cinnamon Dolce Latte                                         1.000000   \n",
       "Iced Green Tea Latte                                              0.195522   \n",
       "Cinnamon Roll Frappuccino                                         0.636902   \n",
       "\n",
       "drink_name                                       Iced Green Tea Latte  \\\n",
       "drink_name                                                              \n",
       "Caffè Americano                                              0.016108   \n",
       "Caffè Mocha                                                  0.114492   \n",
       "Caramel Macchiato                                            0.135666   \n",
       "Cinnamon Dolce Latte                                         0.151758   \n",
       "Pumpkin Spice Latte                                          0.076513   \n",
       "White Chocolate Mocha                                        0.138631   \n",
       "Vanilla Latte                                                0.188842   \n",
       "Iced Caramel Macchiato                                       0.127686   \n",
       "Mocha Frappuccino                                            0.209790   \n",
       "Green Tea Latte                                              0.292939   \n",
       "Chai Tea Latte                                               0.121998   \n",
       "Java Chip Frappuccino                                        0.162051   \n",
       "Flat White                                                   0.018727   \n",
       "Espresso Con Panna                                           0.281002   \n",
       "Nitro Cold Brew                                              0.000000   \n",
       "Iced White Chocolate Mocha                                   0.168155   \n",
       "Salted Caramel Mocha                                         0.138193   \n",
       "Cold Brew Coffee                                             0.037877   \n",
       "Blonde Roast                                                 0.000000   \n",
       "Americano Misto                                              0.020295   \n",
       "Iced Caffè Americano                                         0.063345   \n",
       "Caramel Frappuccino                                          0.252913   \n",
       "Iced Chai Tea Latte                                          0.167099   \n",
       "Hazelnut Latte                                               0.157633   \n",
       "Vanilla Bean Frappuccino                                     0.146747   \n",
       "Vanilla Sweet Cream Cold Brew                                0.039266   \n",
       "Caramel Brulée Latte                                         0.082596   \n",
       "Toasted White Chocolate Mocha                                0.078146   \n",
       "Chestnut Praline Latte                                       0.114881   \n",
       "Iced Caffè Mocha                                             0.289030   \n",
       "Caramel Apple Spice                                          0.138367   \n",
       "Iced Caramel Macchiato with Coconut Milk                     0.160853   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup              0.212290   \n",
       "Blonde Vanilla Latte                                         0.163907   \n",
       "Double Chocolaty Chip Frappuccino                            0.153135   \n",
       "Iced Pineapple Green Tea Infusion                            0.393024   \n",
       "Iced Peach Green Tea Infusion                                0.393024   \n",
       "Iced Cinnamon Almondmilk Macchiato                           0.145317   \n",
       "Iced Golden Ginger Drink                                     0.058349   \n",
       "Iced Caffè Latte                                             0.200237   \n",
       "Iced Cascara Latte                                           0.146982   \n",
       "Caramelized Honey Latte                                      0.061708   \n",
       "Gingerbread Latte                                            0.148522   \n",
       "Iced Coffee with Milk                                        0.103311   \n",
       "Iced Caramel Cloud Macchiato                                 0.111937   \n",
       "Iced Cinnamon Dolce Latte                                    0.195522   \n",
       "Iced Green Tea Latte                                         1.000000   \n",
       "Cinnamon Roll Frappuccino                                    0.090588   \n",
       "\n",
       "drink_name                                       Cinnamon Roll Frappuccino  \n",
       "drink_name                                                                  \n",
       "Caffè Americano                                                   0.008658  \n",
       "Caffè Mocha                                                       0.145251  \n",
       "Caramel Macchiato                                                 0.109792  \n",
       "Cinnamon Dolce Latte                                              0.635698  \n",
       "Pumpkin Spice Latte                                               0.072376  \n",
       "White Chocolate Mocha                                             0.175876  \n",
       "Vanilla Latte                                                     0.373961  \n",
       "Iced Caramel Macchiato                                            0.082377  \n",
       "Mocha Frappuccino                                                 0.272139  \n",
       "Green Tea Latte                                                   0.038332  \n",
       "Chai Tea Latte                                                    0.101187  \n",
       "Java Chip Frappuccino                                             0.210212  \n",
       "Flat White                                                        0.000000  \n",
       "Espresso Con Panna                                                0.048307  \n",
       "Nitro Cold Brew                                                   0.000000  \n",
       "Iced White Chocolate Mocha                                        0.151381  \n",
       "Salted Caramel Mocha                                              0.194112  \n",
       "Cold Brew Coffee                                                  0.000000  \n",
       "Blonde Roast                                                      0.000000  \n",
       "Americano Misto                                                   0.000000  \n",
       "Iced Caffè Americano                                              0.011698  \n",
       "Caramel Frappuccino                                               0.168653  \n",
       "Iced Chai Tea Latte                                               0.103981  \n",
       "Hazelnut Latte                                                    0.073560  \n",
       "Vanilla Bean Frappuccino                                          0.411721  \n",
       "Vanilla Sweet Cream Cold Brew                                     0.079276  \n",
       "Caramel Brulée Latte                                              0.058583  \n",
       "Toasted White Chocolate Mocha                                     0.142855  \n",
       "Chestnut Praline Latte                                            0.053609  \n",
       "Iced Caffè Mocha                                                  0.260199  \n",
       "Caramel Apple Spice                                               0.218345  \n",
       "Iced Caramel Macchiato with Coconut Milk                          0.136135  \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                   0.222564  \n",
       "Blonde Vanilla Latte                                              0.153103  \n",
       "Double Chocolaty Chip Frappuccino                                 0.137860  \n",
       "Iced Pineapple Green Tea Infusion                                 0.000000  \n",
       "Iced Peach Green Tea Infusion                                     0.000000  \n",
       "Iced Cinnamon Almondmilk Macchiato                                0.354013  \n",
       "Iced Golden Ginger Drink                                          0.000000  \n",
       "Iced Caffè Latte                                                  0.028389  \n",
       "Iced Cascara Latte                                                0.061133  \n",
       "Caramelized Honey Latte                                           0.059479  \n",
       "Gingerbread Latte                                                 0.069308  \n",
       "Iced Coffee with Milk                                             0.000000  \n",
       "Iced Caramel Cloud Macchiato                                      0.179640  \n",
       "Iced Cinnamon Dolce Latte                                         0.636902  \n",
       "Iced Green Tea Latte                                              0.090588  \n",
       "Cinnamon Roll Frappuccino                                         1.000000  \n",
       "\n",
       "[48 rows x 48 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe with the cosine similarity matrix\n",
    "cosine_sim_df = pd.DataFrame(cosine_sim, columns=coffee_descriptions['drink_name'], index=coffee_descriptions['drink_name'])\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "cosine_sim_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f90ba7",
   "metadata": {},
   "source": [
    "### Now that I created a cosine similarity matrix based off the coffee descriptions, I can leverage it for various data-driven tasks, such as product recommendation, item clustering, content analysis, and identifying patterns or trends in the coffee descriptions. The cosine similarity matrix provides a quantitative measure of similarity between coffee descriptions, which can be used to uncover hidden relationships, insights, or opportunities for further analysis and decision-making. With the cosine similarity matrix in hand, I can now explore and exploit the similarities and dissimilarities between coffee descriptions to gain valuable insights and make informed decisions in my project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1deffee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drink_name\n",
       "Caffè Americano           0.018331\n",
       "Caffè Mocha               0.107564\n",
       "Caramel Macchiato         0.574584\n",
       "Cinnamon Dolce Latte      0.085970\n",
       "Pumpkin Spice Latte       0.031855\n",
       "White Chocolate Mocha     0.130242\n",
       "Vanilla Latte             0.301071\n",
       "Iced Caramel Macchiato    1.000000\n",
       "Mocha Frappuccino         0.159303\n",
       "Green Tea Latte           0.015169\n",
       "Name: Iced Caramel Macchiato, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df['Iced Caramel Macchiato'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0615d087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drink_name\n",
       "Vanilla Latte                               1.000000\n",
       "Vanilla Bean Frappuccino                    0.821715\n",
       "Blonde Vanilla Latte                        0.604759\n",
       "Caramel Macchiato                           0.384570\n",
       "Cinnamon Roll Frappuccino                   0.373961\n",
       "Iced Caramel Cloud Macchiato                0.353628\n",
       "Iced Caramel Macchiato with Coconut Milk    0.340890\n",
       "Cinnamon Dolce Latte                        0.319872\n",
       "Iced Caramel Macchiato                      0.301071\n",
       "Salted Caramel Mocha                        0.291281\n",
       "Name: Vanilla Latte, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df['Vanilla Latte'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c36e6b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iced Coffee with Milk\n",
      "Caramel Macchiato\n",
      "Iced Caramel Macchiato with Coconut Milk\n",
      "Caramel Frappuccino\n",
      "Iced Caramel Cloud Macchiato\n"
     ]
    }
   ],
   "source": [
    "query = 'Iced Caramel Macchiato'\n",
    "for i in (cosine_sim_df[query].sort_values(ascending=False)[1:6]).index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdb76278",
   "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>drink_name</th>\n",
       "      <th>Caffè Americano</th>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <th>Cinnamon Dolce Latte</th>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <th>White Chocolate Mocha</th>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <th>Green Tea Latte</th>\n",
       "      <th>...</th>\n",
       "      <th>Iced Golden Ginger Drink</th>\n",
       "      <th>Iced Caffè Latte</th>\n",
       "      <th>Iced Cascara Latte</th>\n",
       "      <th>Caramelized Honey Latte</th>\n",
       "      <th>Gingerbread Latte</th>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drink_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caffè Americano</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.456343</td>\n",
       "      <td>0.294596</td>\n",
       "      <td>0.124054</td>\n",
       "      <td>0.120020</td>\n",
       "      <td>0.110437</td>\n",
       "      <td>0.075621</td>\n",
       "      <td>0.065708</td>\n",
       "      <td>0.054655</td>\n",
       "      <td>0.052374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <td>0.456343</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258985</td>\n",
       "      <td>0.041223</td>\n",
       "      <td>0.162160</td>\n",
       "      <td>0.149213</td>\n",
       "      <td>0.028373</td>\n",
       "      <td>0.164365</td>\n",
       "      <td>0.136717</td>\n",
       "      <td>0.070763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.029917</td>\n",
       "      <td>0.028579</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069044</td>\n",
       "      <td>0.052970</td>\n",
       "      <td>0.104941</td>\n",
       "      <td>0.223331</td>\n",
       "      <td>0.028579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <td>0.294596</td>\n",
       "      <td>0.258985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>0.433151</td>\n",
       "      <td>0.310363</td>\n",
       "      <td>0.054751</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.074029</td>\n",
       "      <td>0.141826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141611</td>\n",
       "      <td>0.050640</td>\n",
       "      <td>0.155905</td>\n",
       "      <td>0.128070</td>\n",
       "      <td>0.079630</td>\n",
       "      <td>0.015257</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinnamon Dolce Latte</th>\n",
       "      <td>0.124054</td>\n",
       "      <td>0.041223</td>\n",
       "      <td>0.082621</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100771</td>\n",
       "      <td>0.098566</td>\n",
       "      <td>0.177936</td>\n",
       "      <td>0.139299</td>\n",
       "      <td>0.487388</td>\n",
       "      <td>0.186473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145251</td>\n",
       "      <td>0.015540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027514</td>\n",
       "      <td>0.033143</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <td>0.120020</td>\n",
       "      <td>0.162160</td>\n",
       "      <td>0.433151</td>\n",
       "      <td>0.100771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267406</td>\n",
       "      <td>0.056375</td>\n",
       "      <td>0.096784</td>\n",
       "      <td>0.180519</td>\n",
       "      <td>0.077143</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145810</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053945</td>\n",
       "      <td>0.105248</td>\n",
       "      <td>0.114056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White Chocolate Mocha</th>\n",
       "      <td>0.110437</td>\n",
       "      <td>0.149213</td>\n",
       "      <td>0.310363</td>\n",
       "      <td>0.098566</td>\n",
       "      <td>0.267406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130210</td>\n",
       "      <td>0.108307</td>\n",
       "      <td>0.169198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.177342</td>\n",
       "      <td>0.046729</td>\n",
       "      <td>0.095519</td>\n",
       "      <td>0.023856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014079</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <td>0.075621</td>\n",
       "      <td>0.028373</td>\n",
       "      <td>0.054751</td>\n",
       "      <td>0.177936</td>\n",
       "      <td>0.056375</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068853</td>\n",
       "      <td>0.057271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142154</td>\n",
       "      <td>0.026033</td>\n",
       "      <td>0.132695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.134133</td>\n",
       "      <td>0.136194</td>\n",
       "      <td>0.068509</td>\n",
       "      <td>0.028476</td>\n",
       "      <td>0.026033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <td>0.065708</td>\n",
       "      <td>0.164365</td>\n",
       "      <td>0.089000</td>\n",
       "      <td>0.139299</td>\n",
       "      <td>0.096784</td>\n",
       "      <td>0.130210</td>\n",
       "      <td>0.068853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402109</td>\n",
       "      <td>0.650863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028389</td>\n",
       "      <td>0.106067</td>\n",
       "      <td>0.069354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.187800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110831</td>\n",
       "      <td>0.069354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <td>0.054655</td>\n",
       "      <td>0.136717</td>\n",
       "      <td>0.074029</td>\n",
       "      <td>0.487388</td>\n",
       "      <td>0.180519</td>\n",
       "      <td>0.108307</td>\n",
       "      <td>0.057271</td>\n",
       "      <td>0.402109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.198897</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260199</td>\n",
       "      <td>0.088225</td>\n",
       "      <td>0.057688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156210</td>\n",
       "      <td>0.059371</td>\n",
       "      <td>0.092188</td>\n",
       "      <td>0.057688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Green Tea Latte</th>\n",
       "      <td>0.052374</td>\n",
       "      <td>0.070763</td>\n",
       "      <td>0.141826</td>\n",
       "      <td>0.186473</td>\n",
       "      <td>0.077143</td>\n",
       "      <td>0.169198</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.650863</td>\n",
       "      <td>0.198897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153103</td>\n",
       "      <td>0.026676</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047231</td>\n",
       "      <td>0.156807</td>\n",
       "      <td>0.027874</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chai Tea Latte</th>\n",
       "      <td>0.046966</td>\n",
       "      <td>0.063456</td>\n",
       "      <td>0.063614</td>\n",
       "      <td>0.099565</td>\n",
       "      <td>0.069177</td>\n",
       "      <td>0.093069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.583654</td>\n",
       "      <td>0.178358</td>\n",
       "      <td>0.575306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061133</td>\n",
       "      <td>0.023921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024324</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042354</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024995</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Chip Frappuccino</th>\n",
       "      <td>0.040820</td>\n",
       "      <td>0.055152</td>\n",
       "      <td>0.055289</td>\n",
       "      <td>0.152245</td>\n",
       "      <td>0.060125</td>\n",
       "      <td>0.080890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.507279</td>\n",
       "      <td>0.272726</td>\n",
       "      <td>0.404336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059479</td>\n",
       "      <td>0.210954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021725</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flat White</th>\n",
       "      <td>0.039529</td>\n",
       "      <td>0.053409</td>\n",
       "      <td>0.053542</td>\n",
       "      <td>0.322860</td>\n",
       "      <td>0.130560</td>\n",
       "      <td>0.078333</td>\n",
       "      <td>0.081375</td>\n",
       "      <td>0.180477</td>\n",
       "      <td>0.398040</td>\n",
       "      <td>0.236515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222564</td>\n",
       "      <td>0.020133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020473</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035648</td>\n",
       "      <td>0.042940</td>\n",
       "      <td>0.021038</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Espresso Con Panna</th>\n",
       "      <td>0.036943</td>\n",
       "      <td>0.049914</td>\n",
       "      <td>0.100040</td>\n",
       "      <td>0.515945</td>\n",
       "      <td>0.122018</td>\n",
       "      <td>0.119348</td>\n",
       "      <td>0.152102</td>\n",
       "      <td>0.168668</td>\n",
       "      <td>0.371997</td>\n",
       "      <td>0.225789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175876</td>\n",
       "      <td>0.018816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.065257</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033316</td>\n",
       "      <td>0.040131</td>\n",
       "      <td>0.019661</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitro Cold Brew</th>\n",
       "      <td>0.035768</td>\n",
       "      <td>0.048326</td>\n",
       "      <td>0.048446</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.052683</td>\n",
       "      <td>0.070879</td>\n",
       "      <td>0.068310</td>\n",
       "      <td>0.444493</td>\n",
       "      <td>0.135832</td>\n",
       "      <td>0.594487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179640</td>\n",
       "      <td>0.018217</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018524</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032256</td>\n",
       "      <td>0.107088</td>\n",
       "      <td>0.019036</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced White Chocolate Mocha</th>\n",
       "      <td>0.035163</td>\n",
       "      <td>0.047510</td>\n",
       "      <td>0.095221</td>\n",
       "      <td>0.191706</td>\n",
       "      <td>0.116140</td>\n",
       "      <td>0.113598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.160543</td>\n",
       "      <td>0.252680</td>\n",
       "      <td>0.604759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.373961</td>\n",
       "      <td>0.017910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062114</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031711</td>\n",
       "      <td>0.248755</td>\n",
       "      <td>0.018714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salted Caramel Mocha</th>\n",
       "      <td>0.034479</td>\n",
       "      <td>0.086248</td>\n",
       "      <td>0.046701</td>\n",
       "      <td>0.191051</td>\n",
       "      <td>0.050786</td>\n",
       "      <td>0.068325</td>\n",
       "      <td>0.167828</td>\n",
       "      <td>0.253670</td>\n",
       "      <td>0.310423</td>\n",
       "      <td>0.357017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136135</td>\n",
       "      <td>0.216281</td>\n",
       "      <td>0.036392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098545</td>\n",
       "      <td>0.103231</td>\n",
       "      <td>0.058157</td>\n",
       "      <td>0.036392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cold Brew Coffee</th>\n",
       "      <td>0.031798</td>\n",
       "      <td>0.304284</td>\n",
       "      <td>0.043069</td>\n",
       "      <td>0.398284</td>\n",
       "      <td>0.105024</td>\n",
       "      <td>0.063012</td>\n",
       "      <td>0.164238</td>\n",
       "      <td>0.233943</td>\n",
       "      <td>0.394022</td>\n",
       "      <td>0.115716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151381</td>\n",
       "      <td>0.051328</td>\n",
       "      <td>0.033562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016468</td>\n",
       "      <td>0.081083</td>\n",
       "      <td>0.090881</td>\n",
       "      <td>0.157780</td>\n",
       "      <td>0.279194</td>\n",
       "      <td>0.033562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Roast</th>\n",
       "      <td>0.031149</td>\n",
       "      <td>0.077917</td>\n",
       "      <td>0.042190</td>\n",
       "      <td>0.172598</td>\n",
       "      <td>0.045880</td>\n",
       "      <td>0.061726</td>\n",
       "      <td>0.151618</td>\n",
       "      <td>0.229169</td>\n",
       "      <td>0.280440</td>\n",
       "      <td>0.186372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.354013</td>\n",
       "      <td>0.050281</td>\n",
       "      <td>0.032877</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097244</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.089027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135892</td>\n",
       "      <td>0.032877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americano Misto</th>\n",
       "      <td>0.029352</td>\n",
       "      <td>0.039658</td>\n",
       "      <td>0.079484</td>\n",
       "      <td>0.213192</td>\n",
       "      <td>0.096946</td>\n",
       "      <td>0.094825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.134011</td>\n",
       "      <td>0.210921</td>\n",
       "      <td>0.248200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073560</td>\n",
       "      <td>0.014950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051849</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>0.031885</td>\n",
       "      <td>0.015621</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Americano</th>\n",
       "      <td>0.028312</td>\n",
       "      <td>0.070820</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>0.113569</td>\n",
       "      <td>0.093509</td>\n",
       "      <td>0.056103</td>\n",
       "      <td>0.029666</td>\n",
       "      <td>0.208293</td>\n",
       "      <td>0.269183</td>\n",
       "      <td>0.169396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636902</td>\n",
       "      <td>0.045701</td>\n",
       "      <td>0.029882</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.162110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080917</td>\n",
       "      <td>0.030754</td>\n",
       "      <td>0.199272</td>\n",
       "      <td>0.029882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Frappuccino</th>\n",
       "      <td>0.028258</td>\n",
       "      <td>0.038180</td>\n",
       "      <td>0.076522</td>\n",
       "      <td>0.154059</td>\n",
       "      <td>0.093332</td>\n",
       "      <td>0.091290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.129016</td>\n",
       "      <td>0.203059</td>\n",
       "      <td>0.238949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635698</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.197085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025483</td>\n",
       "      <td>0.030696</td>\n",
       "      <td>0.166272</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Chai Tea Latte</th>\n",
       "      <td>0.027656</td>\n",
       "      <td>0.037366</td>\n",
       "      <td>0.169748</td>\n",
       "      <td>0.150774</td>\n",
       "      <td>0.189013</td>\n",
       "      <td>0.089344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126265</td>\n",
       "      <td>0.198730</td>\n",
       "      <td>0.233854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069308</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024940</td>\n",
       "      <td>0.030042</td>\n",
       "      <td>0.014719</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hazelnut Latte</th>\n",
       "      <td>0.025732</td>\n",
       "      <td>0.034767</td>\n",
       "      <td>0.069682</td>\n",
       "      <td>0.313145</td>\n",
       "      <td>0.084990</td>\n",
       "      <td>0.083131</td>\n",
       "      <td>0.098289</td>\n",
       "      <td>0.117484</td>\n",
       "      <td>0.411061</td>\n",
       "      <td>0.217591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194112</td>\n",
       "      <td>0.013106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045454</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023206</td>\n",
       "      <td>0.027953</td>\n",
       "      <td>0.013695</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Bean Frappuccino</th>\n",
       "      <td>0.022010</td>\n",
       "      <td>0.029739</td>\n",
       "      <td>0.135099</td>\n",
       "      <td>0.155428</td>\n",
       "      <td>0.150432</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.084073</td>\n",
       "      <td>0.100492</td>\n",
       "      <td>0.221634</td>\n",
       "      <td>0.134524</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058583</td>\n",
       "      <td>0.011211</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019849</td>\n",
       "      <td>0.023910</td>\n",
       "      <td>0.011714</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Sweet Cream Cold Brew</th>\n",
       "      <td>0.021391</td>\n",
       "      <td>0.028902</td>\n",
       "      <td>0.131299</td>\n",
       "      <td>0.116622</td>\n",
       "      <td>0.146200</td>\n",
       "      <td>0.069107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097665</td>\n",
       "      <td>0.153716</td>\n",
       "      <td>0.180884</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053609</td>\n",
       "      <td>0.010895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019291</td>\n",
       "      <td>0.023237</td>\n",
       "      <td>0.011385</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Brulée Latte</th>\n",
       "      <td>0.020825</td>\n",
       "      <td>0.028136</td>\n",
       "      <td>0.056392</td>\n",
       "      <td>0.253114</td>\n",
       "      <td>0.068781</td>\n",
       "      <td>0.067276</td>\n",
       "      <td>0.085739</td>\n",
       "      <td>0.095078</td>\n",
       "      <td>0.209693</td>\n",
       "      <td>0.127276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142855</td>\n",
       "      <td>0.010607</td>\n",
       "      <td>0.079152</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.036785</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018780</td>\n",
       "      <td>0.022622</td>\n",
       "      <td>0.011083</td>\n",
       "      <td>0.079152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toasted White Chocolate Mocha</th>\n",
       "      <td>0.020390</td>\n",
       "      <td>0.027549</td>\n",
       "      <td>0.055214</td>\n",
       "      <td>0.111161</td>\n",
       "      <td>0.067344</td>\n",
       "      <td>0.065870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.093091</td>\n",
       "      <td>0.146517</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072376</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118508</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>0.022149</td>\n",
       "      <td>0.095621</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chestnut Praline Latte</th>\n",
       "      <td>0.018331</td>\n",
       "      <td>0.159541</td>\n",
       "      <td>0.044788</td>\n",
       "      <td>0.107564</td>\n",
       "      <td>0.027001</td>\n",
       "      <td>0.065527</td>\n",
       "      <td>0.097802</td>\n",
       "      <td>0.194964</td>\n",
       "      <td>0.162169</td>\n",
       "      <td>0.303883</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082377</td>\n",
       "      <td>0.053377</td>\n",
       "      <td>0.133193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017126</td>\n",
       "      <td>0.084319</td>\n",
       "      <td>0.600206</td>\n",
       "      <td>0.369079</td>\n",
       "      <td>0.152861</td>\n",
       "      <td>0.133193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Mocha</th>\n",
       "      <td>0.017679</td>\n",
       "      <td>0.069521</td>\n",
       "      <td>0.110336</td>\n",
       "      <td>0.493167</td>\n",
       "      <td>0.189309</td>\n",
       "      <td>0.020553</td>\n",
       "      <td>0.199001</td>\n",
       "      <td>0.219760</td>\n",
       "      <td>0.665864</td>\n",
       "      <td>0.086893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.272139</td>\n",
       "      <td>0.064037</td>\n",
       "      <td>0.041872</td>\n",
       "      <td>0.071722</td>\n",
       "      <td>0.020546</td>\n",
       "      <td>0.072499</td>\n",
       "      <td>0.254832</td>\n",
       "      <td>0.153286</td>\n",
       "      <td>0.066914</td>\n",
       "      <td>0.041872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Apple Spice</th>\n",
       "      <td>0.016469</td>\n",
       "      <td>0.022252</td>\n",
       "      <td>0.080448</td>\n",
       "      <td>0.195622</td>\n",
       "      <td>0.078622</td>\n",
       "      <td>0.095975</td>\n",
       "      <td>0.056738</td>\n",
       "      <td>0.092229</td>\n",
       "      <td>0.177373</td>\n",
       "      <td>0.346475</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109792</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.088307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052477</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.236077</td>\n",
       "      <td>0.248722</td>\n",
       "      <td>0.015811</td>\n",
       "      <td>0.088307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato with Coconut Milk</th>\n",
       "      <td>0.016320</td>\n",
       "      <td>0.064177</td>\n",
       "      <td>0.101855</td>\n",
       "      <td>0.182333</td>\n",
       "      <td>0.174758</td>\n",
       "      <td>0.018973</td>\n",
       "      <td>0.248198</td>\n",
       "      <td>0.202868</td>\n",
       "      <td>0.292827</td>\n",
       "      <td>0.166061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.168653</td>\n",
       "      <td>0.059115</td>\n",
       "      <td>0.038654</td>\n",
       "      <td>0.066209</td>\n",
       "      <td>0.018967</td>\n",
       "      <td>0.066926</td>\n",
       "      <td>0.235244</td>\n",
       "      <td>0.141503</td>\n",
       "      <td>0.061771</td>\n",
       "      <td>0.038654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced White Chocolate Mocha with Raspberry Syrup</th>\n",
       "      <td>0.016108</td>\n",
       "      <td>0.063345</td>\n",
       "      <td>0.020295</td>\n",
       "      <td>0.114492</td>\n",
       "      <td>0.281002</td>\n",
       "      <td>0.018727</td>\n",
       "      <td>0.037877</td>\n",
       "      <td>0.200237</td>\n",
       "      <td>0.289030</td>\n",
       "      <td>0.163907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090588</td>\n",
       "      <td>0.058349</td>\n",
       "      <td>0.393024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.121998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103311</td>\n",
       "      <td>0.039266</td>\n",
       "      <td>0.167099</td>\n",
       "      <td>0.393024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Vanilla Latte</th>\n",
       "      <td>0.013656</td>\n",
       "      <td>0.053701</td>\n",
       "      <td>0.085228</td>\n",
       "      <td>0.458977</td>\n",
       "      <td>0.146231</td>\n",
       "      <td>0.015876</td>\n",
       "      <td>0.216800</td>\n",
       "      <td>0.169752</td>\n",
       "      <td>0.514342</td>\n",
       "      <td>0.067120</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210212</td>\n",
       "      <td>0.049465</td>\n",
       "      <td>0.032344</td>\n",
       "      <td>0.055401</td>\n",
       "      <td>0.015871</td>\n",
       "      <td>0.056001</td>\n",
       "      <td>0.196843</td>\n",
       "      <td>0.118405</td>\n",
       "      <td>0.051687</td>\n",
       "      <td>0.032344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Double Chocolaty Chip Frappuccino</th>\n",
       "      <td>0.013225</td>\n",
       "      <td>0.017868</td>\n",
       "      <td>0.040167</td>\n",
       "      <td>0.220539</td>\n",
       "      <td>0.073785</td>\n",
       "      <td>0.037064</td>\n",
       "      <td>0.113354</td>\n",
       "      <td>0.043362</td>\n",
       "      <td>0.222194</td>\n",
       "      <td>0.177509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032237</td>\n",
       "      <td>0.079412</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Pineapple Green Tea Infusion</th>\n",
       "      <td>0.012905</td>\n",
       "      <td>0.050746</td>\n",
       "      <td>0.143263</td>\n",
       "      <td>0.285815</td>\n",
       "      <td>0.071998</td>\n",
       "      <td>0.015003</td>\n",
       "      <td>0.149568</td>\n",
       "      <td>0.160412</td>\n",
       "      <td>0.315046</td>\n",
       "      <td>0.063427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137860</td>\n",
       "      <td>0.046744</td>\n",
       "      <td>0.030564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014997</td>\n",
       "      <td>0.104559</td>\n",
       "      <td>0.082764</td>\n",
       "      <td>0.031456</td>\n",
       "      <td>0.048843</td>\n",
       "      <td>0.030564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Peach Green Tea Infusion</th>\n",
       "      <td>0.012366</td>\n",
       "      <td>0.048630</td>\n",
       "      <td>0.015580</td>\n",
       "      <td>0.087895</td>\n",
       "      <td>0.068995</td>\n",
       "      <td>0.014377</td>\n",
       "      <td>0.029078</td>\n",
       "      <td>0.153721</td>\n",
       "      <td>0.221887</td>\n",
       "      <td>0.303386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.411721</td>\n",
       "      <td>0.044794</td>\n",
       "      <td>0.029289</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079311</td>\n",
       "      <td>0.196310</td>\n",
       "      <td>0.046806</td>\n",
       "      <td>0.029289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cinnamon Almondmilk Macchiato</th>\n",
       "      <td>0.010438</td>\n",
       "      <td>0.014103</td>\n",
       "      <td>0.044854</td>\n",
       "      <td>0.107931</td>\n",
       "      <td>0.058237</td>\n",
       "      <td>0.041390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064367</td>\n",
       "      <td>0.132903</td>\n",
       "      <td>0.109223</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038332</td>\n",
       "      <td>0.105125</td>\n",
       "      <td>0.140928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.314021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021123</td>\n",
       "      <td>0.025444</td>\n",
       "      <td>0.081238</td>\n",
       "      <td>0.140928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Golden Ginger Drink</th>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.011698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145251</td>\n",
       "      <td>0.048307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028389</td>\n",
       "      <td>0.260199</td>\n",
       "      <td>0.153103</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101187</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079276</td>\n",
       "      <td>0.103981</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caffè Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029917</td>\n",
       "      <td>0.014602</td>\n",
       "      <td>0.015540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013474</td>\n",
       "      <td>0.142154</td>\n",
       "      <td>0.106067</td>\n",
       "      <td>0.088225</td>\n",
       "      <td>0.026676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.126299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.210936</td>\n",
       "      <td>0.082330</td>\n",
       "      <td>0.074330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.138663</td>\n",
       "      <td>0.126299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cascara Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028579</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026033</td>\n",
       "      <td>0.069354</td>\n",
       "      <td>0.057688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070984</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048602</td>\n",
       "      <td>0.106629</td>\n",
       "      <td>0.101627</td>\n",
       "      <td>0.864535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramelized Honey Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145810</td>\n",
       "      <td>0.177342</td>\n",
       "      <td>0.132695</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.211280</td>\n",
       "      <td>0.083249</td>\n",
       "      <td>0.064853</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gingerbread Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050640</td>\n",
       "      <td>0.053894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034031</td>\n",
       "      <td>0.028306</td>\n",
       "      <td>0.092515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101187</td>\n",
       "      <td>0.210936</td>\n",
       "      <td>0.070984</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083717</td>\n",
       "      <td>0.023848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755010</td>\n",
       "      <td>0.070984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069044</td>\n",
       "      <td>0.155905</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.053945</td>\n",
       "      <td>0.095519</td>\n",
       "      <td>0.134133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.211280</td>\n",
       "      <td>0.083717</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084152</td>\n",
       "      <td>0.460340</td>\n",
       "      <td>0.155323</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052970</td>\n",
       "      <td>0.128070</td>\n",
       "      <td>0.027514</td>\n",
       "      <td>0.105248</td>\n",
       "      <td>0.023856</td>\n",
       "      <td>0.136194</td>\n",
       "      <td>0.187800</td>\n",
       "      <td>0.156210</td>\n",
       "      <td>0.047231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.074330</td>\n",
       "      <td>0.048602</td>\n",
       "      <td>0.083249</td>\n",
       "      <td>0.023848</td>\n",
       "      <td>0.084152</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127903</td>\n",
       "      <td>0.077669</td>\n",
       "      <td>0.048602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104941</td>\n",
       "      <td>0.079630</td>\n",
       "      <td>0.033143</td>\n",
       "      <td>0.114056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068509</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059371</td>\n",
       "      <td>0.156807</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079276</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106629</td>\n",
       "      <td>0.064853</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460340</td>\n",
       "      <td>0.127903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105322</td>\n",
       "      <td>0.106629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.223331</td>\n",
       "      <td>0.015257</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014079</td>\n",
       "      <td>0.028476</td>\n",
       "      <td>0.110831</td>\n",
       "      <td>0.092188</td>\n",
       "      <td>0.027874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103981</td>\n",
       "      <td>0.138663</td>\n",
       "      <td>0.101627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755010</td>\n",
       "      <td>0.155323</td>\n",
       "      <td>0.077669</td>\n",
       "      <td>0.105322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028579</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026033</td>\n",
       "      <td>0.069354</td>\n",
       "      <td>0.057688</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.126299</td>\n",
       "      <td>0.864535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.070984</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048602</td>\n",
       "      <td>0.106629</td>\n",
       "      <td>0.101627</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "drink_name                                       Caffè Americano  Caffè Mocha  \\\n",
       "drink_name                                                                      \n",
       "Caffè Americano                                         1.000000     0.456343   \n",
       "Caffè Mocha                                             0.456343     1.000000   \n",
       "Caramel Macchiato                                       0.294596     0.258985   \n",
       "Cinnamon Dolce Latte                                    0.124054     0.041223   \n",
       "Pumpkin Spice Latte                                     0.120020     0.162160   \n",
       "White Chocolate Mocha                                   0.110437     0.149213   \n",
       "Vanilla Latte                                           0.075621     0.028373   \n",
       "Iced Caramel Macchiato                                  0.065708     0.164365   \n",
       "Mocha Frappuccino                                       0.054655     0.136717   \n",
       "Green Tea Latte                                         0.052374     0.070763   \n",
       "Chai Tea Latte                                          0.046966     0.063456   \n",
       "Java Chip Frappuccino                                   0.040820     0.055152   \n",
       "Flat White                                              0.039529     0.053409   \n",
       "Espresso Con Panna                                      0.036943     0.049914   \n",
       "Nitro Cold Brew                                         0.035768     0.048326   \n",
       "Iced White Chocolate Mocha                              0.035163     0.047510   \n",
       "Salted Caramel Mocha                                    0.034479     0.086248   \n",
       "Cold Brew Coffee                                        0.031798     0.304284   \n",
       "Blonde Roast                                            0.031149     0.077917   \n",
       "Americano Misto                                         0.029352     0.039658   \n",
       "Iced Caffè Americano                                    0.028312     0.070820   \n",
       "Caramel Frappuccino                                     0.028258     0.038180   \n",
       "Iced Chai Tea Latte                                     0.027656     0.037366   \n",
       "Hazelnut Latte                                          0.025732     0.034767   \n",
       "Vanilla Bean Frappuccino                                0.022010     0.029739   \n",
       "Vanilla Sweet Cream Cold Brew                           0.021391     0.028902   \n",
       "Caramel Brulée Latte                                    0.020825     0.028136   \n",
       "Toasted White Chocolate Mocha                           0.020390     0.027549   \n",
       "Chestnut Praline Latte                                  0.018331     0.159541   \n",
       "Iced Caffè Mocha                                        0.017679     0.069521   \n",
       "Caramel Apple Spice                                     0.016469     0.022252   \n",
       "Iced Caramel Macchiato with Coconut Milk                0.016320     0.064177   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup         0.016108     0.063345   \n",
       "Blonde Vanilla Latte                                    0.013656     0.053701   \n",
       "Double Chocolaty Chip Frappuccino                       0.013225     0.017868   \n",
       "Iced Pineapple Green Tea Infusion                       0.012905     0.050746   \n",
       "Iced Peach Green Tea Infusion                           0.012366     0.048630   \n",
       "Iced Cinnamon Almondmilk Macchiato                      0.010438     0.014103   \n",
       "Iced Golden Ginger Drink                                0.008658     0.011698   \n",
       "Iced Caffè Latte                                        0.000000     0.029917   \n",
       "Iced Cascara Latte                                      0.000000     0.028579   \n",
       "Caramelized Honey Latte                                 0.000000     0.000000   \n",
       "Gingerbread Latte                                       0.000000     0.000000   \n",
       "Iced Coffee with Milk                                   0.000000     0.069044   \n",
       "Iced Caramel Cloud Macchiato                            0.000000     0.052970   \n",
       "Iced Cinnamon Dolce Latte                               0.000000     0.104941   \n",
       "Iced Green Tea Latte                                    0.000000     0.223331   \n",
       "Cinnamon Roll Frappuccino                               0.000000     0.028579   \n",
       "\n",
       "drink_name                                       Caramel Macchiato  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.294596   \n",
       "Caffè Mocha                                               0.258985   \n",
       "Caramel Macchiato                                         1.000000   \n",
       "Cinnamon Dolce Latte                                      0.082621   \n",
       "Pumpkin Spice Latte                                       0.433151   \n",
       "White Chocolate Mocha                                     0.310363   \n",
       "Vanilla Latte                                             0.054751   \n",
       "Iced Caramel Macchiato                                    0.089000   \n",
       "Mocha Frappuccino                                         0.074029   \n",
       "Green Tea Latte                                           0.141826   \n",
       "Chai Tea Latte                                            0.063614   \n",
       "Java Chip Frappuccino                                     0.055289   \n",
       "Flat White                                                0.053542   \n",
       "Espresso Con Panna                                        0.100040   \n",
       "Nitro Cold Brew                                           0.048446   \n",
       "Iced White Chocolate Mocha                                0.095221   \n",
       "Salted Caramel Mocha                                      0.046701   \n",
       "Cold Brew Coffee                                          0.043069   \n",
       "Blonde Roast                                              0.042190   \n",
       "Americano Misto                                           0.079484   \n",
       "Iced Caffè Americano                                      0.038347   \n",
       "Caramel Frappuccino                                       0.076522   \n",
       "Iced Chai Tea Latte                                       0.169748   \n",
       "Hazelnut Latte                                            0.069682   \n",
       "Vanilla Bean Frappuccino                                  0.135099   \n",
       "Vanilla Sweet Cream Cold Brew                             0.131299   \n",
       "Caramel Brulée Latte                                      0.056392   \n",
       "Toasted White Chocolate Mocha                             0.055214   \n",
       "Chestnut Praline Latte                                    0.044788   \n",
       "Iced Caffè Mocha                                          0.110336   \n",
       "Caramel Apple Spice                                       0.080448   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.101855   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.020295   \n",
       "Blonde Vanilla Latte                                      0.085228   \n",
       "Double Chocolaty Chip Frappuccino                         0.040167   \n",
       "Iced Pineapple Green Tea Infusion                         0.143263   \n",
       "Iced Peach Green Tea Infusion                             0.015580   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.044854   \n",
       "Iced Golden Ginger Drink                                  0.000000   \n",
       "Iced Caffè Latte                                          0.014602   \n",
       "Iced Cascara Latte                                        0.000000   \n",
       "Caramelized Honey Latte                                   0.141611   \n",
       "Gingerbread Latte                                         0.050640   \n",
       "Iced Coffee with Milk                                     0.155905   \n",
       "Iced Caramel Cloud Macchiato                              0.128070   \n",
       "Iced Cinnamon Dolce Latte                                 0.079630   \n",
       "Iced Green Tea Latte                                      0.015257   \n",
       "Cinnamon Roll Frappuccino                                 0.000000   \n",
       "\n",
       "drink_name                                       Cinnamon Dolce Latte  \\\n",
       "drink_name                                                              \n",
       "Caffè Americano                                              0.124054   \n",
       "Caffè Mocha                                                  0.041223   \n",
       "Caramel Macchiato                                            0.082621   \n",
       "Cinnamon Dolce Latte                                         1.000000   \n",
       "Pumpkin Spice Latte                                          0.100771   \n",
       "White Chocolate Mocha                                        0.098566   \n",
       "Vanilla Latte                                                0.177936   \n",
       "Iced Caramel Macchiato                                       0.139299   \n",
       "Mocha Frappuccino                                            0.487388   \n",
       "Green Tea Latte                                              0.186473   \n",
       "Chai Tea Latte                                               0.099565   \n",
       "Java Chip Frappuccino                                        0.152245   \n",
       "Flat White                                                   0.322860   \n",
       "Espresso Con Panna                                           0.515945   \n",
       "Nitro Cold Brew                                              0.140615   \n",
       "Iced White Chocolate Mocha                                   0.191706   \n",
       "Salted Caramel Mocha                                         0.191051   \n",
       "Cold Brew Coffee                                             0.398284   \n",
       "Blonde Roast                                                 0.172598   \n",
       "Americano Misto                                              0.213192   \n",
       "Iced Caffè Americano                                         0.113569   \n",
       "Caramel Frappuccino                                          0.154059   \n",
       "Iced Chai Tea Latte                                          0.150774   \n",
       "Hazelnut Latte                                               0.313145   \n",
       "Vanilla Bean Frappuccino                                     0.155428   \n",
       "Vanilla Sweet Cream Cold Brew                                0.116622   \n",
       "Caramel Brulée Latte                                         0.253114   \n",
       "Toasted White Chocolate Mocha                                0.111161   \n",
       "Chestnut Praline Latte                                       0.107564   \n",
       "Iced Caffè Mocha                                             0.493167   \n",
       "Caramel Apple Spice                                          0.195622   \n",
       "Iced Caramel Macchiato with Coconut Milk                     0.182333   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup              0.114492   \n",
       "Blonde Vanilla Latte                                         0.458977   \n",
       "Double Chocolaty Chip Frappuccino                            0.220539   \n",
       "Iced Pineapple Green Tea Infusion                            0.285815   \n",
       "Iced Peach Green Tea Infusion                                0.087895   \n",
       "Iced Cinnamon Almondmilk Macchiato                           0.107931   \n",
       "Iced Golden Ginger Drink                                     0.145251   \n",
       "Iced Caffè Latte                                             0.015540   \n",
       "Iced Cascara Latte                                           0.000000   \n",
       "Caramelized Honey Latte                                      0.000000   \n",
       "Gingerbread Latte                                            0.053894   \n",
       "Iced Coffee with Milk                                        0.000000   \n",
       "Iced Caramel Cloud Macchiato                                 0.027514   \n",
       "Iced Cinnamon Dolce Latte                                    0.033143   \n",
       "Iced Green Tea Latte                                         0.016238   \n",
       "Cinnamon Roll Frappuccino                                    0.000000   \n",
       "\n",
       "drink_name                                       Pumpkin Spice Latte  \\\n",
       "drink_name                                                             \n",
       "Caffè Americano                                             0.120020   \n",
       "Caffè Mocha                                                 0.162160   \n",
       "Caramel Macchiato                                           0.433151   \n",
       "Cinnamon Dolce Latte                                        0.100771   \n",
       "Pumpkin Spice Latte                                         1.000000   \n",
       "White Chocolate Mocha                                       0.267406   \n",
       "Vanilla Latte                                               0.056375   \n",
       "Iced Caramel Macchiato                                      0.096784   \n",
       "Mocha Frappuccino                                           0.180519   \n",
       "Green Tea Latte                                             0.077143   \n",
       "Chai Tea Latte                                              0.069177   \n",
       "Java Chip Frappuccino                                       0.060125   \n",
       "Flat White                                                  0.130560   \n",
       "Espresso Con Panna                                          0.122018   \n",
       "Nitro Cold Brew                                             0.052683   \n",
       "Iced White Chocolate Mocha                                  0.116140   \n",
       "Salted Caramel Mocha                                        0.050786   \n",
       "Cold Brew Coffee                                            0.105024   \n",
       "Blonde Roast                                                0.045880   \n",
       "Americano Misto                                             0.096946   \n",
       "Iced Caffè Americano                                        0.093509   \n",
       "Caramel Frappuccino                                         0.093332   \n",
       "Iced Chai Tea Latte                                         0.189013   \n",
       "Hazelnut Latte                                              0.084990   \n",
       "Vanilla Bean Frappuccino                                    0.150432   \n",
       "Vanilla Sweet Cream Cold Brew                               0.146200   \n",
       "Caramel Brulée Latte                                        0.068781   \n",
       "Toasted White Chocolate Mocha                               0.067344   \n",
       "Chestnut Praline Latte                                      0.027001   \n",
       "Iced Caffè Mocha                                            0.189309   \n",
       "Caramel Apple Spice                                         0.078622   \n",
       "Iced Caramel Macchiato with Coconut Milk                    0.174758   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup             0.281002   \n",
       "Blonde Vanilla Latte                                        0.146231   \n",
       "Double Chocolaty Chip Frappuccino                           0.073785   \n",
       "Iced Pineapple Green Tea Infusion                           0.071998   \n",
       "Iced Peach Green Tea Infusion                               0.068995   \n",
       "Iced Cinnamon Almondmilk Macchiato                          0.058237   \n",
       "Iced Golden Ginger Drink                                    0.048307   \n",
       "Iced Caffè Latte                                            0.000000   \n",
       "Iced Cascara Latte                                          0.000000   \n",
       "Caramelized Honey Latte                                     0.145810   \n",
       "Gingerbread Latte                                           0.000000   \n",
       "Iced Coffee with Milk                                       0.053945   \n",
       "Iced Caramel Cloud Macchiato                                0.105248   \n",
       "Iced Cinnamon Dolce Latte                                   0.114056   \n",
       "Iced Green Tea Latte                                        0.000000   \n",
       "Cinnamon Roll Frappuccino                                   0.000000   \n",
       "\n",
       "drink_name                                       White Chocolate Mocha  \\\n",
       "drink_name                                                               \n",
       "Caffè Americano                                               0.110437   \n",
       "Caffè Mocha                                                   0.149213   \n",
       "Caramel Macchiato                                             0.310363   \n",
       "Cinnamon Dolce Latte                                          0.098566   \n",
       "Pumpkin Spice Latte                                           0.267406   \n",
       "White Chocolate Mocha                                         1.000000   \n",
       "Vanilla Latte                                                 0.000000   \n",
       "Iced Caramel Macchiato                                        0.130210   \n",
       "Mocha Frappuccino                                             0.108307   \n",
       "Green Tea Latte                                               0.169198   \n",
       "Chai Tea Latte                                                0.093069   \n",
       "Java Chip Frappuccino                                         0.080890   \n",
       "Flat White                                                    0.078333   \n",
       "Espresso Con Panna                                            0.119348   \n",
       "Nitro Cold Brew                                               0.070879   \n",
       "Iced White Chocolate Mocha                                    0.113598   \n",
       "Salted Caramel Mocha                                          0.068325   \n",
       "Cold Brew Coffee                                              0.063012   \n",
       "Blonde Roast                                                  0.061726   \n",
       "Americano Misto                                               0.094825   \n",
       "Iced Caffè Americano                                          0.056103   \n",
       "Caramel Frappuccino                                           0.091290   \n",
       "Iced Chai Tea Latte                                           0.089344   \n",
       "Hazelnut Latte                                                0.083131   \n",
       "Vanilla Bean Frappuccino                                      0.071107   \n",
       "Vanilla Sweet Cream Cold Brew                                 0.069107   \n",
       "Caramel Brulée Latte                                          0.067276   \n",
       "Toasted White Chocolate Mocha                                 0.065870   \n",
       "Chestnut Praline Latte                                        0.065527   \n",
       "Iced Caffè Mocha                                              0.020553   \n",
       "Caramel Apple Spice                                           0.095975   \n",
       "Iced Caramel Macchiato with Coconut Milk                      0.018973   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup               0.018727   \n",
       "Blonde Vanilla Latte                                          0.015876   \n",
       "Double Chocolaty Chip Frappuccino                             0.037064   \n",
       "Iced Pineapple Green Tea Infusion                             0.015003   \n",
       "Iced Peach Green Tea Infusion                                 0.014377   \n",
       "Iced Cinnamon Almondmilk Macchiato                            0.041390   \n",
       "Iced Golden Ginger Drink                                      0.000000   \n",
       "Iced Caffè Latte                                              0.013474   \n",
       "Iced Cascara Latte                                            0.000000   \n",
       "Caramelized Honey Latte                                       0.177342   \n",
       "Gingerbread Latte                                             0.046729   \n",
       "Iced Coffee with Milk                                         0.095519   \n",
       "Iced Caramel Cloud Macchiato                                  0.023856   \n",
       "Iced Cinnamon Dolce Latte                                     0.000000   \n",
       "Iced Green Tea Latte                                          0.014079   \n",
       "Cinnamon Roll Frappuccino                                     0.000000   \n",
       "\n",
       "drink_name                                       Vanilla Latte  \\\n",
       "drink_name                                                       \n",
       "Caffè Americano                                       0.075621   \n",
       "Caffè Mocha                                           0.028373   \n",
       "Caramel Macchiato                                     0.054751   \n",
       "Cinnamon Dolce Latte                                  0.177936   \n",
       "Pumpkin Spice Latte                                   0.056375   \n",
       "White Chocolate Mocha                                 0.000000   \n",
       "Vanilla Latte                                         1.000000   \n",
       "Iced Caramel Macchiato                                0.068853   \n",
       "Mocha Frappuccino                                     0.057271   \n",
       "Green Tea Latte                                       0.000000   \n",
       "Chai Tea Latte                                        0.000000   \n",
       "Java Chip Frappuccino                                 0.000000   \n",
       "Flat White                                            0.081375   \n",
       "Espresso Con Panna                                    0.152102   \n",
       "Nitro Cold Brew                                       0.068310   \n",
       "Iced White Chocolate Mocha                            0.000000   \n",
       "Salted Caramel Mocha                                  0.167828   \n",
       "Cold Brew Coffee                                      0.164238   \n",
       "Blonde Roast                                          0.151618   \n",
       "Americano Misto                                       0.000000   \n",
       "Iced Caffè Americano                                  0.029666   \n",
       "Caramel Frappuccino                                   0.000000   \n",
       "Iced Chai Tea Latte                                   0.000000   \n",
       "Hazelnut Latte                                        0.098289   \n",
       "Vanilla Bean Frappuccino                              0.084073   \n",
       "Vanilla Sweet Cream Cold Brew                         0.000000   \n",
       "Caramel Brulée Latte                                  0.085739   \n",
       "Toasted White Chocolate Mocha                         0.000000   \n",
       "Chestnut Praline Latte                                0.097802   \n",
       "Iced Caffè Mocha                                      0.199001   \n",
       "Caramel Apple Spice                                   0.056738   \n",
       "Iced Caramel Macchiato with Coconut Milk              0.248198   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup       0.037877   \n",
       "Blonde Vanilla Latte                                  0.216800   \n",
       "Double Chocolaty Chip Frappuccino                     0.113354   \n",
       "Iced Pineapple Green Tea Infusion                     0.149568   \n",
       "Iced Peach Green Tea Infusion                         0.029078   \n",
       "Iced Cinnamon Almondmilk Macchiato                    0.000000   \n",
       "Iced Golden Ginger Drink                              0.000000   \n",
       "Iced Caffè Latte                                      0.142154   \n",
       "Iced Cascara Latte                                    0.026033   \n",
       "Caramelized Honey Latte                               0.132695   \n",
       "Gingerbread Latte                                     0.000000   \n",
       "Iced Coffee with Milk                                 0.134133   \n",
       "Iced Caramel Cloud Macchiato                          0.136194   \n",
       "Iced Cinnamon Dolce Latte                             0.068509   \n",
       "Iced Green Tea Latte                                  0.028476   \n",
       "Cinnamon Roll Frappuccino                             0.026033   \n",
       "\n",
       "drink_name                                       Iced Caramel Macchiato  \\\n",
       "drink_name                                                                \n",
       "Caffè Americano                                                0.065708   \n",
       "Caffè Mocha                                                    0.164365   \n",
       "Caramel Macchiato                                              0.089000   \n",
       "Cinnamon Dolce Latte                                           0.139299   \n",
       "Pumpkin Spice Latte                                            0.096784   \n",
       "White Chocolate Mocha                                          0.130210   \n",
       "Vanilla Latte                                                  0.068853   \n",
       "Iced Caramel Macchiato                                         1.000000   \n",
       "Mocha Frappuccino                                              0.402109   \n",
       "Green Tea Latte                                                0.650863   \n",
       "Chai Tea Latte                                                 0.583654   \n",
       "Java Chip Frappuccino                                          0.507279   \n",
       "Flat White                                                     0.180477   \n",
       "Espresso Con Panna                                             0.168668   \n",
       "Nitro Cold Brew                                                0.444493   \n",
       "Iced White Chocolate Mocha                                     0.160543   \n",
       "Salted Caramel Mocha                                           0.253670   \n",
       "Cold Brew Coffee                                               0.233943   \n",
       "Blonde Roast                                                   0.229169   \n",
       "Americano Misto                                                0.134011   \n",
       "Iced Caffè Americano                                           0.208293   \n",
       "Caramel Frappuccino                                            0.129016   \n",
       "Iced Chai Tea Latte                                            0.126265   \n",
       "Hazelnut Latte                                                 0.117484   \n",
       "Vanilla Bean Frappuccino                                       0.100492   \n",
       "Vanilla Sweet Cream Cold Brew                                  0.097665   \n",
       "Caramel Brulée Latte                                           0.095078   \n",
       "Toasted White Chocolate Mocha                                  0.093091   \n",
       "Chestnut Praline Latte                                         0.194964   \n",
       "Iced Caffè Mocha                                               0.219760   \n",
       "Caramel Apple Spice                                            0.092229   \n",
       "Iced Caramel Macchiato with Coconut Milk                       0.202868   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                0.200237   \n",
       "Blonde Vanilla Latte                                           0.169752   \n",
       "Double Chocolaty Chip Frappuccino                              0.043362   \n",
       "Iced Pineapple Green Tea Infusion                              0.160412   \n",
       "Iced Peach Green Tea Infusion                                  0.153721   \n",
       "Iced Cinnamon Almondmilk Macchiato                             0.064367   \n",
       "Iced Golden Ginger Drink                                       0.028389   \n",
       "Iced Caffè Latte                                               0.106067   \n",
       "Iced Cascara Latte                                             0.069354   \n",
       "Caramelized Honey Latte                                        0.000000   \n",
       "Gingerbread Latte                                              0.034031   \n",
       "Iced Coffee with Milk                                          0.000000   \n",
       "Iced Caramel Cloud Macchiato                                   0.187800   \n",
       "Iced Cinnamon Dolce Latte                                      0.000000   \n",
       "Iced Green Tea Latte                                           0.110831   \n",
       "Cinnamon Roll Frappuccino                                      0.069354   \n",
       "\n",
       "drink_name                                       Mocha Frappuccino  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.054655   \n",
       "Caffè Mocha                                               0.136717   \n",
       "Caramel Macchiato                                         0.074029   \n",
       "Cinnamon Dolce Latte                                      0.487388   \n",
       "Pumpkin Spice Latte                                       0.180519   \n",
       "White Chocolate Mocha                                     0.108307   \n",
       "Vanilla Latte                                             0.057271   \n",
       "Iced Caramel Macchiato                                    0.402109   \n",
       "Mocha Frappuccino                                         1.000000   \n",
       "Green Tea Latte                                           0.198897   \n",
       "Chai Tea Latte                                            0.178358   \n",
       "Java Chip Frappuccino                                     0.272726   \n",
       "Flat White                                                0.398040   \n",
       "Espresso Con Panna                                        0.371997   \n",
       "Nitro Cold Brew                                           0.135832   \n",
       "Iced White Chocolate Mocha                                0.252680   \n",
       "Salted Caramel Mocha                                      0.310423   \n",
       "Cold Brew Coffee                                          0.394022   \n",
       "Blonde Roast                                              0.280440   \n",
       "Americano Misto                                           0.210921   \n",
       "Iced Caffè Americano                                      0.269183   \n",
       "Caramel Frappuccino                                       0.203059   \n",
       "Iced Chai Tea Latte                                       0.198730   \n",
       "Hazelnut Latte                                            0.411061   \n",
       "Vanilla Bean Frappuccino                                  0.221634   \n",
       "Vanilla Sweet Cream Cold Brew                             0.153716   \n",
       "Caramel Brulée Latte                                      0.209693   \n",
       "Toasted White Chocolate Mocha                             0.146517   \n",
       "Chestnut Praline Latte                                    0.162169   \n",
       "Iced Caffè Mocha                                          0.665864   \n",
       "Caramel Apple Spice                                       0.177373   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.292827   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.289030   \n",
       "Blonde Vanilla Latte                                      0.514342   \n",
       "Double Chocolaty Chip Frappuccino                         0.222194   \n",
       "Iced Pineapple Green Tea Infusion                         0.315046   \n",
       "Iced Peach Green Tea Infusion                             0.221887   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.132903   \n",
       "Iced Golden Ginger Drink                                  0.260199   \n",
       "Iced Caffè Latte                                          0.088225   \n",
       "Iced Cascara Latte                                        0.057688   \n",
       "Caramelized Honey Latte                                   0.000000   \n",
       "Gingerbread Latte                                         0.028306   \n",
       "Iced Coffee with Milk                                     0.000000   \n",
       "Iced Caramel Cloud Macchiato                              0.156210   \n",
       "Iced Cinnamon Dolce Latte                                 0.059371   \n",
       "Iced Green Tea Latte                                      0.092188   \n",
       "Cinnamon Roll Frappuccino                                 0.057688   \n",
       "\n",
       "drink_name                                       Green Tea Latte  ...  \\\n",
       "drink_name                                                        ...   \n",
       "Caffè Americano                                         0.052374  ...   \n",
       "Caffè Mocha                                             0.070763  ...   \n",
       "Caramel Macchiato                                       0.141826  ...   \n",
       "Cinnamon Dolce Latte                                    0.186473  ...   \n",
       "Pumpkin Spice Latte                                     0.077143  ...   \n",
       "White Chocolate Mocha                                   0.169198  ...   \n",
       "Vanilla Latte                                           0.000000  ...   \n",
       "Iced Caramel Macchiato                                  0.650863  ...   \n",
       "Mocha Frappuccino                                       0.198897  ...   \n",
       "Green Tea Latte                                         1.000000  ...   \n",
       "Chai Tea Latte                                          0.575306  ...   \n",
       "Java Chip Frappuccino                                   0.404336  ...   \n",
       "Flat White                                              0.236515  ...   \n",
       "Espresso Con Panna                                      0.225789  ...   \n",
       "Nitro Cold Brew                                         0.594487  ...   \n",
       "Iced White Chocolate Mocha                              0.604759  ...   \n",
       "Salted Caramel Mocha                                    0.357017  ...   \n",
       "Cold Brew Coffee                                        0.115716  ...   \n",
       "Blonde Roast                                            0.186372  ...   \n",
       "Americano Misto                                         0.248200  ...   \n",
       "Iced Caffè Americano                                    0.169396  ...   \n",
       "Caramel Frappuccino                                     0.238949  ...   \n",
       "Iced Chai Tea Latte                                     0.233854  ...   \n",
       "Hazelnut Latte                                          0.217591  ...   \n",
       "Vanilla Bean Frappuccino                                0.134524  ...   \n",
       "Vanilla Sweet Cream Cold Brew                           0.180884  ...   \n",
       "Caramel Brulée Latte                                    0.127276  ...   \n",
       "Toasted White Chocolate Mocha                           0.124617  ...   \n",
       "Chestnut Praline Latte                                  0.303883  ...   \n",
       "Iced Caffè Mocha                                        0.086893  ...   \n",
       "Caramel Apple Spice                                     0.346475  ...   \n",
       "Iced Caramel Macchiato with Coconut Milk                0.166061  ...   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup         0.163907  ...   \n",
       "Blonde Vanilla Latte                                    0.067120  ...   \n",
       "Double Chocolaty Chip Frappuccino                       0.177509  ...   \n",
       "Iced Pineapple Green Tea Infusion                       0.063427  ...   \n",
       "Iced Peach Green Tea Infusion                           0.303386  ...   \n",
       "Iced Cinnamon Almondmilk Macchiato                      0.109223  ...   \n",
       "Iced Golden Ginger Drink                                0.153103  ...   \n",
       "Iced Caffè Latte                                        0.026676  ...   \n",
       "Iced Cascara Latte                                      0.000000  ...   \n",
       "Caramelized Honey Latte                                 0.000000  ...   \n",
       "Gingerbread Latte                                       0.092515  ...   \n",
       "Iced Coffee with Milk                                   0.000000  ...   \n",
       "Iced Caramel Cloud Macchiato                            0.047231  ...   \n",
       "Iced Cinnamon Dolce Latte                               0.156807  ...   \n",
       "Iced Green Tea Latte                                    0.027874  ...   \n",
       "Cinnamon Roll Frappuccino                               0.000000  ...   \n",
       "\n",
       "drink_name                                       Iced Golden Ginger Drink  \\\n",
       "drink_name                                                                  \n",
       "Caffè Americano                                                  0.008658   \n",
       "Caffè Mocha                                                      0.011698   \n",
       "Caramel Macchiato                                                0.000000   \n",
       "Cinnamon Dolce Latte                                             0.145251   \n",
       "Pumpkin Spice Latte                                              0.048307   \n",
       "White Chocolate Mocha                                            0.000000   \n",
       "Vanilla Latte                                                    0.000000   \n",
       "Iced Caramel Macchiato                                           0.028389   \n",
       "Mocha Frappuccino                                                0.260199   \n",
       "Green Tea Latte                                                  0.153103   \n",
       "Chai Tea Latte                                                   0.061133   \n",
       "Java Chip Frappuccino                                            0.059479   \n",
       "Flat White                                                       0.222564   \n",
       "Espresso Con Panna                                               0.175876   \n",
       "Nitro Cold Brew                                                  0.179640   \n",
       "Iced White Chocolate Mocha                                       0.373961   \n",
       "Salted Caramel Mocha                                             0.136135   \n",
       "Cold Brew Coffee                                                 0.151381   \n",
       "Blonde Roast                                                     0.354013   \n",
       "Americano Misto                                                  0.073560   \n",
       "Iced Caffè Americano                                             0.636902   \n",
       "Caramel Frappuccino                                              0.635698   \n",
       "Iced Chai Tea Latte                                              0.069308   \n",
       "Hazelnut Latte                                                   0.194112   \n",
       "Vanilla Bean Frappuccino                                         0.058583   \n",
       "Vanilla Sweet Cream Cold Brew                                    0.053609   \n",
       "Caramel Brulée Latte                                             0.142855   \n",
       "Toasted White Chocolate Mocha                                    0.072376   \n",
       "Chestnut Praline Latte                                           0.082377   \n",
       "Iced Caffè Mocha                                                 0.272139   \n",
       "Caramel Apple Spice                                              0.109792   \n",
       "Iced Caramel Macchiato with Coconut Milk                         0.168653   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                  0.090588   \n",
       "Blonde Vanilla Latte                                             0.210212   \n",
       "Double Chocolaty Chip Frappuccino                                0.218345   \n",
       "Iced Pineapple Green Tea Infusion                                0.137860   \n",
       "Iced Peach Green Tea Infusion                                    0.411721   \n",
       "Iced Cinnamon Almondmilk Macchiato                               0.038332   \n",
       "Iced Golden Ginger Drink                                         1.000000   \n",
       "Iced Caffè Latte                                                 0.000000   \n",
       "Iced Cascara Latte                                               0.000000   \n",
       "Caramelized Honey Latte                                          0.000000   \n",
       "Gingerbread Latte                                                0.101187   \n",
       "Iced Coffee with Milk                                            0.000000   \n",
       "Iced Caramel Cloud Macchiato                                     0.000000   \n",
       "Iced Cinnamon Dolce Latte                                        0.079276   \n",
       "Iced Green Tea Latte                                             0.103981   \n",
       "Cinnamon Roll Frappuccino                                        0.000000   \n",
       "\n",
       "drink_name                                       Iced Caffè Latte  \\\n",
       "drink_name                                                          \n",
       "Caffè Americano                                          0.000000   \n",
       "Caffè Mocha                                              0.029917   \n",
       "Caramel Macchiato                                        0.014602   \n",
       "Cinnamon Dolce Latte                                     0.015540   \n",
       "Pumpkin Spice Latte                                      0.000000   \n",
       "White Chocolate Mocha                                    0.013474   \n",
       "Vanilla Latte                                            0.142154   \n",
       "Iced Caramel Macchiato                                   0.106067   \n",
       "Mocha Frappuccino                                        0.088225   \n",
       "Green Tea Latte                                          0.026676   \n",
       "Chai Tea Latte                                           0.023921   \n",
       "Java Chip Frappuccino                                    0.210954   \n",
       "Flat White                                               0.020133   \n",
       "Espresso Con Panna                                       0.018816   \n",
       "Nitro Cold Brew                                          0.018217   \n",
       "Iced White Chocolate Mocha                               0.017910   \n",
       "Salted Caramel Mocha                                     0.216281   \n",
       "Cold Brew Coffee                                         0.051328   \n",
       "Blonde Roast                                             0.050281   \n",
       "Americano Misto                                          0.014950   \n",
       "Iced Caffè Americano                                     0.045701   \n",
       "Caramel Frappuccino                                      0.014393   \n",
       "Iced Chai Tea Latte                                      0.014086   \n",
       "Hazelnut Latte                                           0.013106   \n",
       "Vanilla Bean Frappuccino                                 0.011211   \n",
       "Vanilla Sweet Cream Cold Brew                            0.010895   \n",
       "Caramel Brulée Latte                                     0.010607   \n",
       "Toasted White Chocolate Mocha                            0.010385   \n",
       "Chestnut Praline Latte                                   0.053377   \n",
       "Iced Caffè Mocha                                         0.064037   \n",
       "Caramel Apple Spice                                      0.015131   \n",
       "Iced Caramel Macchiato with Coconut Milk                 0.059115   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup          0.058349   \n",
       "Blonde Vanilla Latte                                     0.049465   \n",
       "Double Chocolaty Chip Frappuccino                        0.000000   \n",
       "Iced Pineapple Green Tea Infusion                        0.046744   \n",
       "Iced Peach Green Tea Infusion                            0.044794   \n",
       "Iced Cinnamon Almondmilk Macchiato                       0.105125   \n",
       "Iced Golden Ginger Drink                                 0.000000   \n",
       "Iced Caffè Latte                                         1.000000   \n",
       "Iced Cascara Latte                                       0.126299   \n",
       "Caramelized Honey Latte                                  0.000000   \n",
       "Gingerbread Latte                                        0.210936   \n",
       "Iced Coffee with Milk                                    0.082330   \n",
       "Iced Caramel Cloud Macchiato                             0.074330   \n",
       "Iced Cinnamon Dolce Latte                                0.000000   \n",
       "Iced Green Tea Latte                                     0.138663   \n",
       "Cinnamon Roll Frappuccino                                0.126299   \n",
       "\n",
       "drink_name                                       Iced Cascara Latte  \\\n",
       "drink_name                                                            \n",
       "Caffè Americano                                            0.000000   \n",
       "Caffè Mocha                                                0.028579   \n",
       "Caramel Macchiato                                          0.000000   \n",
       "Cinnamon Dolce Latte                                       0.000000   \n",
       "Pumpkin Spice Latte                                        0.000000   \n",
       "White Chocolate Mocha                                      0.000000   \n",
       "Vanilla Latte                                              0.026033   \n",
       "Iced Caramel Macchiato                                     0.069354   \n",
       "Mocha Frappuccino                                          0.057688   \n",
       "Green Tea Latte                                            0.000000   \n",
       "Chai Tea Latte                                             0.000000   \n",
       "Java Chip Frappuccino                                      0.000000   \n",
       "Flat White                                                 0.000000   \n",
       "Espresso Con Panna                                         0.000000   \n",
       "Nitro Cold Brew                                            0.000000   \n",
       "Iced White Chocolate Mocha                                 0.000000   \n",
       "Salted Caramel Mocha                                       0.036392   \n",
       "Cold Brew Coffee                                           0.033562   \n",
       "Blonde Roast                                               0.032877   \n",
       "Americano Misto                                            0.000000   \n",
       "Iced Caffè Americano                                       0.029882   \n",
       "Caramel Frappuccino                                        0.000000   \n",
       "Iced Chai Tea Latte                                        0.000000   \n",
       "Hazelnut Latte                                             0.000000   \n",
       "Vanilla Bean Frappuccino                                   0.000000   \n",
       "Vanilla Sweet Cream Cold Brew                              0.000000   \n",
       "Caramel Brulée Latte                                       0.079152   \n",
       "Toasted White Chocolate Mocha                              0.000000   \n",
       "Chestnut Praline Latte                                     0.133193   \n",
       "Iced Caffè Mocha                                           0.041872   \n",
       "Caramel Apple Spice                                        0.088307   \n",
       "Iced Caramel Macchiato with Coconut Milk                   0.038654   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup            0.393024   \n",
       "Blonde Vanilla Latte                                       0.032344   \n",
       "Double Chocolaty Chip Frappuccino                          0.000000   \n",
       "Iced Pineapple Green Tea Infusion                          0.030564   \n",
       "Iced Peach Green Tea Infusion                              0.029289   \n",
       "Iced Cinnamon Almondmilk Macchiato                         0.140928   \n",
       "Iced Golden Ginger Drink                                   0.000000   \n",
       "Iced Caffè Latte                                           0.126299   \n",
       "Iced Cascara Latte                                         1.000000   \n",
       "Caramelized Honey Latte                                    0.000000   \n",
       "Gingerbread Latte                                          0.070984   \n",
       "Iced Coffee with Milk                                      0.000000   \n",
       "Iced Caramel Cloud Macchiato                               0.048602   \n",
       "Iced Cinnamon Dolce Latte                                  0.106629   \n",
       "Iced Green Tea Latte                                       0.101627   \n",
       "Cinnamon Roll Frappuccino                                  0.864535   \n",
       "\n",
       "drink_name                                       Caramelized Honey Latte  \\\n",
       "drink_name                                                                 \n",
       "Caffè Americano                                                 0.000000   \n",
       "Caffè Mocha                                                     0.000000   \n",
       "Caramel Macchiato                                               0.141611   \n",
       "Cinnamon Dolce Latte                                            0.000000   \n",
       "Pumpkin Spice Latte                                             0.145810   \n",
       "White Chocolate Mocha                                           0.177342   \n",
       "Vanilla Latte                                                   0.132695   \n",
       "Iced Caramel Macchiato                                          0.000000   \n",
       "Mocha Frappuccino                                               0.000000   \n",
       "Green Tea Latte                                                 0.000000   \n",
       "Chai Tea Latte                                                  0.000000   \n",
       "Java Chip Frappuccino                                           0.000000   \n",
       "Flat White                                                      0.000000   \n",
       "Espresso Con Panna                                              0.000000   \n",
       "Nitro Cold Brew                                                 0.000000   \n",
       "Iced White Chocolate Mocha                                      0.000000   \n",
       "Salted Caramel Mocha                                            0.000000   \n",
       "Cold Brew Coffee                                                0.000000   \n",
       "Blonde Roast                                                    0.000000   \n",
       "Americano Misto                                                 0.000000   \n",
       "Iced Caffè Americano                                            0.000000   \n",
       "Caramel Frappuccino                                             0.000000   \n",
       "Iced Chai Tea Latte                                             0.000000   \n",
       "Hazelnut Latte                                                  0.000000   \n",
       "Vanilla Bean Frappuccino                                        0.000000   \n",
       "Vanilla Sweet Cream Cold Brew                                   0.000000   \n",
       "Caramel Brulée Latte                                            0.000000   \n",
       "Toasted White Chocolate Mocha                                   0.000000   \n",
       "Chestnut Praline Latte                                          0.000000   \n",
       "Iced Caffè Mocha                                                0.071722   \n",
       "Caramel Apple Spice                                             0.000000   \n",
       "Iced Caramel Macchiato with Coconut Milk                        0.066209   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                 0.000000   \n",
       "Blonde Vanilla Latte                                            0.055401   \n",
       "Double Chocolaty Chip Frappuccino                               0.000000   \n",
       "Iced Pineapple Green Tea Infusion                               0.000000   \n",
       "Iced Peach Green Tea Infusion                                   0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                              0.000000   \n",
       "Iced Golden Ginger Drink                                        0.000000   \n",
       "Iced Caffè Latte                                                0.000000   \n",
       "Iced Cascara Latte                                              0.000000   \n",
       "Caramelized Honey Latte                                         1.000000   \n",
       "Gingerbread Latte                                               0.000000   \n",
       "Iced Coffee with Milk                                           0.211280   \n",
       "Iced Caramel Cloud Macchiato                                    0.083249   \n",
       "Iced Cinnamon Dolce Latte                                       0.064853   \n",
       "Iced Green Tea Latte                                            0.000000   \n",
       "Cinnamon Roll Frappuccino                                       0.000000   \n",
       "\n",
       "drink_name                                       Gingerbread Latte  \\\n",
       "drink_name                                                           \n",
       "Caffè Americano                                           0.000000   \n",
       "Caffè Mocha                                               0.000000   \n",
       "Caramel Macchiato                                         0.050640   \n",
       "Cinnamon Dolce Latte                                      0.053894   \n",
       "Pumpkin Spice Latte                                       0.000000   \n",
       "White Chocolate Mocha                                     0.046729   \n",
       "Vanilla Latte                                             0.000000   \n",
       "Iced Caramel Macchiato                                    0.034031   \n",
       "Mocha Frappuccino                                         0.028306   \n",
       "Green Tea Latte                                           0.092515   \n",
       "Chai Tea Latte                                            0.024324   \n",
       "Java Chip Frappuccino                                     0.021141   \n",
       "Flat White                                                0.020473   \n",
       "Espresso Con Panna                                        0.065257   \n",
       "Nitro Cold Brew                                           0.018524   \n",
       "Iced White Chocolate Mocha                                0.062114   \n",
       "Salted Caramel Mocha                                      0.017857   \n",
       "Cold Brew Coffee                                          0.016468   \n",
       "Blonde Roast                                              0.097244   \n",
       "Americano Misto                                           0.051849   \n",
       "Iced Caffè Americano                                      0.162110   \n",
       "Caramel Frappuccino                                       0.197085   \n",
       "Iced Chai Tea Latte                                       0.048852   \n",
       "Hazelnut Latte                                            0.045454   \n",
       "Vanilla Bean Frappuccino                                  0.038880   \n",
       "Vanilla Sweet Cream Cold Brew                             0.037786   \n",
       "Caramel Brulée Latte                                      0.036785   \n",
       "Toasted White Chocolate Mocha                             0.118508   \n",
       "Chestnut Praline Latte                                    0.017126   \n",
       "Iced Caffè Mocha                                          0.020546   \n",
       "Caramel Apple Spice                                       0.052477   \n",
       "Iced Caramel Macchiato with Coconut Milk                  0.018967   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup           0.121998   \n",
       "Blonde Vanilla Latte                                      0.015871   \n",
       "Double Chocolaty Chip Frappuccino                         0.114330   \n",
       "Iced Pineapple Green Tea Infusion                         0.014997   \n",
       "Iced Peach Green Tea Infusion                             0.014372   \n",
       "Iced Cinnamon Almondmilk Macchiato                        0.314021   \n",
       "Iced Golden Ginger Drink                                  0.101187   \n",
       "Iced Caffè Latte                                          0.210936   \n",
       "Iced Cascara Latte                                        0.070984   \n",
       "Caramelized Honey Latte                                   0.000000   \n",
       "Gingerbread Latte                                         1.000000   \n",
       "Iced Coffee with Milk                                     0.083717   \n",
       "Iced Caramel Cloud Macchiato                              0.023848   \n",
       "Iced Cinnamon Dolce Latte                                 0.000000   \n",
       "Iced Green Tea Latte                                      0.755010   \n",
       "Cinnamon Roll Frappuccino                                 0.070984   \n",
       "\n",
       "drink_name                                       Iced Coffee with Milk  \\\n",
       "drink_name                                                               \n",
       "Caffè Americano                                               0.000000   \n",
       "Caffè Mocha                                                   0.069044   \n",
       "Caramel Macchiato                                             0.155905   \n",
       "Cinnamon Dolce Latte                                          0.000000   \n",
       "Pumpkin Spice Latte                                           0.053945   \n",
       "White Chocolate Mocha                                         0.095519   \n",
       "Vanilla Latte                                                 0.134133   \n",
       "Iced Caramel Macchiato                                        0.000000   \n",
       "Mocha Frappuccino                                             0.000000   \n",
       "Green Tea Latte                                               0.000000   \n",
       "Chai Tea Latte                                                0.000000   \n",
       "Java Chip Frappuccino                                         0.000000   \n",
       "Flat White                                                    0.000000   \n",
       "Espresso Con Panna                                            0.000000   \n",
       "Nitro Cold Brew                                               0.000000   \n",
       "Iced White Chocolate Mocha                                    0.000000   \n",
       "Salted Caramel Mocha                                          0.000000   \n",
       "Cold Brew Coffee                                              0.081083   \n",
       "Blonde Roast                                                  0.000000   \n",
       "Americano Misto                                               0.000000   \n",
       "Iced Caffè Americano                                          0.000000   \n",
       "Caramel Frappuccino                                           0.000000   \n",
       "Iced Chai Tea Latte                                           0.000000   \n",
       "Hazelnut Latte                                                0.000000   \n",
       "Vanilla Bean Frappuccino                                      0.000000   \n",
       "Vanilla Sweet Cream Cold Brew                                 0.000000   \n",
       "Caramel Brulée Latte                                          0.000000   \n",
       "Toasted White Chocolate Mocha                                 0.000000   \n",
       "Chestnut Praline Latte                                        0.084319   \n",
       "Iced Caffè Mocha                                              0.072499   \n",
       "Caramel Apple Spice                                           0.000000   \n",
       "Iced Caramel Macchiato with Coconut Milk                      0.066926   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup               0.000000   \n",
       "Blonde Vanilla Latte                                          0.056001   \n",
       "Double Chocolaty Chip Frappuccino                             0.000000   \n",
       "Iced Pineapple Green Tea Infusion                             0.104559   \n",
       "Iced Peach Green Tea Infusion                                 0.000000   \n",
       "Iced Cinnamon Almondmilk Macchiato                            0.000000   \n",
       "Iced Golden Ginger Drink                                      0.000000   \n",
       "Iced Caffè Latte                                              0.082330   \n",
       "Iced Cascara Latte                                            0.000000   \n",
       "Caramelized Honey Latte                                       0.211280   \n",
       "Gingerbread Latte                                             0.083717   \n",
       "Iced Coffee with Milk                                         1.000000   \n",
       "Iced Caramel Cloud Macchiato                                  0.084152   \n",
       "Iced Cinnamon Dolce Latte                                     0.460340   \n",
       "Iced Green Tea Latte                                          0.155323   \n",
       "Cinnamon Roll Frappuccino                                     0.000000   \n",
       "\n",
       "drink_name                                       Iced Caramel Cloud Macchiato  \\\n",
       "drink_name                                                                      \n",
       "Caffè Americano                                                      0.000000   \n",
       "Caffè Mocha                                                          0.052970   \n",
       "Caramel Macchiato                                                    0.128070   \n",
       "Cinnamon Dolce Latte                                                 0.027514   \n",
       "Pumpkin Spice Latte                                                  0.105248   \n",
       "White Chocolate Mocha                                                0.023856   \n",
       "Vanilla Latte                                                        0.136194   \n",
       "Iced Caramel Macchiato                                               0.187800   \n",
       "Mocha Frappuccino                                                    0.156210   \n",
       "Green Tea Latte                                                      0.047231   \n",
       "Chai Tea Latte                                                       0.042354   \n",
       "Java Chip Frappuccino                                                0.036812   \n",
       "Flat White                                                           0.035648   \n",
       "Espresso Con Panna                                                   0.033316   \n",
       "Nitro Cold Brew                                                      0.032256   \n",
       "Iced White Chocolate Mocha                                           0.031711   \n",
       "Salted Caramel Mocha                                                 0.098545   \n",
       "Cold Brew Coffee                                                     0.090881   \n",
       "Blonde Roast                                                         0.089027   \n",
       "Americano Misto                                                      0.026470   \n",
       "Iced Caffè Americano                                                 0.080917   \n",
       "Caramel Frappuccino                                                  0.025483   \n",
       "Iced Chai Tea Latte                                                  0.024940   \n",
       "Hazelnut Latte                                                       0.023206   \n",
       "Vanilla Bean Frappuccino                                             0.019849   \n",
       "Vanilla Sweet Cream Cold Brew                                        0.019291   \n",
       "Caramel Brulée Latte                                                 0.018780   \n",
       "Toasted White Chocolate Mocha                                        0.018387   \n",
       "Chestnut Praline Latte                                               0.600206   \n",
       "Iced Caffè Mocha                                                     0.254832   \n",
       "Caramel Apple Spice                                                  0.236077   \n",
       "Iced Caramel Macchiato with Coconut Milk                             0.235244   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                      0.103311   \n",
       "Blonde Vanilla Latte                                                 0.196843   \n",
       "Double Chocolaty Chip Frappuccino                                    0.000000   \n",
       "Iced Pineapple Green Tea Infusion                                    0.082764   \n",
       "Iced Peach Green Tea Infusion                                        0.079311   \n",
       "Iced Cinnamon Almondmilk Macchiato                                   0.021123   \n",
       "Iced Golden Ginger Drink                                             0.000000   \n",
       "Iced Caffè Latte                                                     0.074330   \n",
       "Iced Cascara Latte                                                   0.048602   \n",
       "Caramelized Honey Latte                                              0.083249   \n",
       "Gingerbread Latte                                                    0.023848   \n",
       "Iced Coffee with Milk                                                0.084152   \n",
       "Iced Caramel Cloud Macchiato                                         1.000000   \n",
       "Iced Cinnamon Dolce Latte                                            0.127903   \n",
       "Iced Green Tea Latte                                                 0.077669   \n",
       "Cinnamon Roll Frappuccino                                            0.048602   \n",
       "\n",
       "drink_name                                       Iced Cinnamon Dolce Latte  \\\n",
       "drink_name                                                                   \n",
       "Caffè Americano                                                   0.000000   \n",
       "Caffè Mocha                                                       0.104941   \n",
       "Caramel Macchiato                                                 0.079630   \n",
       "Cinnamon Dolce Latte                                              0.033143   \n",
       "Pumpkin Spice Latte                                               0.114056   \n",
       "White Chocolate Mocha                                             0.000000   \n",
       "Vanilla Latte                                                     0.068509   \n",
       "Iced Caramel Macchiato                                            0.000000   \n",
       "Mocha Frappuccino                                                 0.059371   \n",
       "Green Tea Latte                                                   0.156807   \n",
       "Chai Tea Latte                                                    0.000000   \n",
       "Java Chip Frappuccino                                             0.000000   \n",
       "Flat White                                                        0.042940   \n",
       "Espresso Con Panna                                                0.040131   \n",
       "Nitro Cold Brew                                                   0.107088   \n",
       "Iced White Chocolate Mocha                                        0.248755   \n",
       "Salted Caramel Mocha                                              0.103231   \n",
       "Cold Brew Coffee                                                  0.157780   \n",
       "Blonde Roast                                                      0.000000   \n",
       "Americano Misto                                                   0.031885   \n",
       "Iced Caffè Americano                                              0.030754   \n",
       "Caramel Frappuccino                                               0.030696   \n",
       "Iced Chai Tea Latte                                               0.030042   \n",
       "Hazelnut Latte                                                    0.027953   \n",
       "Vanilla Bean Frappuccino                                          0.023910   \n",
       "Vanilla Sweet Cream Cold Brew                                     0.023237   \n",
       "Caramel Brulée Latte                                              0.022622   \n",
       "Toasted White Chocolate Mocha                                     0.022149   \n",
       "Chestnut Praline Latte                                            0.369079   \n",
       "Iced Caffè Mocha                                                  0.153286   \n",
       "Caramel Apple Spice                                               0.248722   \n",
       "Iced Caramel Macchiato with Coconut Milk                          0.141503   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                   0.039266   \n",
       "Blonde Vanilla Latte                                              0.118405   \n",
       "Double Chocolaty Chip Frappuccino                                 0.032237   \n",
       "Iced Pineapple Green Tea Infusion                                 0.031456   \n",
       "Iced Peach Green Tea Infusion                                     0.196310   \n",
       "Iced Cinnamon Almondmilk Macchiato                                0.025444   \n",
       "Iced Golden Ginger Drink                                          0.079276   \n",
       "Iced Caffè Latte                                                  0.000000   \n",
       "Iced Cascara Latte                                                0.106629   \n",
       "Caramelized Honey Latte                                           0.064853   \n",
       "Gingerbread Latte                                                 0.000000   \n",
       "Iced Coffee with Milk                                             0.460340   \n",
       "Iced Caramel Cloud Macchiato                                      0.127903   \n",
       "Iced Cinnamon Dolce Latte                                         1.000000   \n",
       "Iced Green Tea Latte                                              0.105322   \n",
       "Cinnamon Roll Frappuccino                                         0.106629   \n",
       "\n",
       "drink_name                                       Iced Green Tea Latte  \\\n",
       "drink_name                                                              \n",
       "Caffè Americano                                              0.000000   \n",
       "Caffè Mocha                                                  0.223331   \n",
       "Caramel Macchiato                                            0.015257   \n",
       "Cinnamon Dolce Latte                                         0.016238   \n",
       "Pumpkin Spice Latte                                          0.000000   \n",
       "White Chocolate Mocha                                        0.014079   \n",
       "Vanilla Latte                                                0.028476   \n",
       "Iced Caramel Macchiato                                       0.110831   \n",
       "Mocha Frappuccino                                            0.092188   \n",
       "Green Tea Latte                                              0.027874   \n",
       "Chai Tea Latte                                               0.024995   \n",
       "Java Chip Frappuccino                                        0.021725   \n",
       "Flat White                                                   0.021038   \n",
       "Espresso Con Panna                                           0.019661   \n",
       "Nitro Cold Brew                                              0.019036   \n",
       "Iced White Chocolate Mocha                                   0.018714   \n",
       "Salted Caramel Mocha                                         0.058157   \n",
       "Cold Brew Coffee                                             0.279194   \n",
       "Blonde Roast                                                 0.135892   \n",
       "Americano Misto                                              0.015621   \n",
       "Iced Caffè Americano                                         0.199272   \n",
       "Caramel Frappuccino                                          0.166272   \n",
       "Iced Chai Tea Latte                                          0.014719   \n",
       "Hazelnut Latte                                               0.013695   \n",
       "Vanilla Bean Frappuccino                                     0.011714   \n",
       "Vanilla Sweet Cream Cold Brew                                0.011385   \n",
       "Caramel Brulée Latte                                         0.011083   \n",
       "Toasted White Chocolate Mocha                                0.095621   \n",
       "Chestnut Praline Latte                                       0.152861   \n",
       "Iced Caffè Mocha                                             0.066914   \n",
       "Caramel Apple Spice                                          0.015811   \n",
       "Iced Caramel Macchiato with Coconut Milk                     0.061771   \n",
       "Iced White Chocolate Mocha with Raspberry Syrup              0.167099   \n",
       "Blonde Vanilla Latte                                         0.051687   \n",
       "Double Chocolaty Chip Frappuccino                            0.079412   \n",
       "Iced Pineapple Green Tea Infusion                            0.048843   \n",
       "Iced Peach Green Tea Infusion                                0.046806   \n",
       "Iced Cinnamon Almondmilk Macchiato                           0.081238   \n",
       "Iced Golden Ginger Drink                                     0.103981   \n",
       "Iced Caffè Latte                                             0.138663   \n",
       "Iced Cascara Latte                                           0.101627   \n",
       "Caramelized Honey Latte                                      0.000000   \n",
       "Gingerbread Latte                                            0.755010   \n",
       "Iced Coffee with Milk                                        0.155323   \n",
       "Iced Caramel Cloud Macchiato                                 0.077669   \n",
       "Iced Cinnamon Dolce Latte                                    0.105322   \n",
       "Iced Green Tea Latte                                         1.000000   \n",
       "Cinnamon Roll Frappuccino                                    0.101627   \n",
       "\n",
       "drink_name                                       Cinnamon Roll Frappuccino  \n",
       "drink_name                                                                  \n",
       "Caffè Americano                                                   0.000000  \n",
       "Caffè Mocha                                                       0.028579  \n",
       "Caramel Macchiato                                                 0.000000  \n",
       "Cinnamon Dolce Latte                                              0.000000  \n",
       "Pumpkin Spice Latte                                               0.000000  \n",
       "White Chocolate Mocha                                             0.000000  \n",
       "Vanilla Latte                                                     0.026033  \n",
       "Iced Caramel Macchiato                                            0.069354  \n",
       "Mocha Frappuccino                                                 0.057688  \n",
       "Green Tea Latte                                                   0.000000  \n",
       "Chai Tea Latte                                                    0.000000  \n",
       "Java Chip Frappuccino                                             0.000000  \n",
       "Flat White                                                        0.000000  \n",
       "Espresso Con Panna                                                0.000000  \n",
       "Nitro Cold Brew                                                   0.000000  \n",
       "Iced White Chocolate Mocha                                        0.000000  \n",
       "Salted Caramel Mocha                                              0.036392  \n",
       "Cold Brew Coffee                                                  0.033562  \n",
       "Blonde Roast                                                      0.032877  \n",
       "Americano Misto                                                   0.000000  \n",
       "Iced Caffè Americano                                              0.029882  \n",
       "Caramel Frappuccino                                               0.000000  \n",
       "Iced Chai Tea Latte                                               0.000000  \n",
       "Hazelnut Latte                                                    0.000000  \n",
       "Vanilla Bean Frappuccino                                          0.000000  \n",
       "Vanilla Sweet Cream Cold Brew                                     0.000000  \n",
       "Caramel Brulée Latte                                              0.079152  \n",
       "Toasted White Chocolate Mocha                                     0.000000  \n",
       "Chestnut Praline Latte                                            0.133193  \n",
       "Iced Caffè Mocha                                                  0.041872  \n",
       "Caramel Apple Spice                                               0.088307  \n",
       "Iced Caramel Macchiato with Coconut Milk                          0.038654  \n",
       "Iced White Chocolate Mocha with Raspberry Syrup                   0.393024  \n",
       "Blonde Vanilla Latte                                              0.032344  \n",
       "Double Chocolaty Chip Frappuccino                                 0.000000  \n",
       "Iced Pineapple Green Tea Infusion                                 0.030564  \n",
       "Iced Peach Green Tea Infusion                                     0.029289  \n",
       "Iced Cinnamon Almondmilk Macchiato                                0.140928  \n",
       "Iced Golden Ginger Drink                                          0.000000  \n",
       "Iced Caffè Latte                                                  0.126299  \n",
       "Iced Cascara Latte                                                0.864535  \n",
       "Caramelized Honey Latte                                           0.000000  \n",
       "Gingerbread Latte                                                 0.070984  \n",
       "Iced Coffee with Milk                                             0.000000  \n",
       "Iced Caramel Cloud Macchiato                                      0.048602  \n",
       "Iced Cinnamon Dolce Latte                                         0.106629  \n",
       "Iced Green Tea Latte                                              0.101627  \n",
       "Cinnamon Roll Frappuccino                                         1.000000  \n",
       "\n",
       "[48 rows x 48 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXTRA CODE\n",
    "cosine_sim_sorted = cosine_sim[np.argsort(-cosine_sim[:, 0]), :]\n",
    "cosine_sim_sorted = cosine_sim_sorted[:, np.argsort(-cosine_sim_sorted[0, :])]\n",
    "\n",
    "# Convert the cosine similarity matrix to a DataFrame and add the corresponding drink names\n",
    "drink_names = coffee_descriptions['drink_name']\n",
    "cosine_sim_df = pd.DataFrame(cosine_sim_sorted, columns=drink_names, index=drink_names)\n",
    "\n",
    "# Print the sorted cosine similarity matrix\n",
    "cosine_sim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e77a6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pymongo\n",
    "\n",
    "# Connect to database\n",
    "# client = pymongo.MongoClient('mongodb://localhost:27017/')\n",
    "\n",
    "# Init the database\n",
    "# db = client.coffee_db\n",
    "\n",
    "# Init the \"collection\" aka table named surveys in MongoDB\n",
    "# collection = db.surveys\n",
    "\n",
    "# surveys = collection.find()\n",
    "# surveys = list(surveys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa491a8",
   "metadata": {},
   "source": [
    "# Load Live Data and Manipulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59ef8a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>drinks_tried</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6441d4e5c45b3fa5015c3b22</td>\n",
       "      <td>[1, 1, None, None, 1, 1, None, None, 1, None, ...</td>\n",
       "      <td>[5, 3, N/A, N/A, 5, 4, N/A, N/A, 3, N/A, N/A, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6441dbd0c45b3fa5015c3b24</td>\n",
       "      <td>[None, None, 1, None, None, None, 1, 1, None, ...</td>\n",
       "      <td>[N/A, N/A, 5, N/A, N/A, N/A, 5, 5, N/A, N/A, 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6441dc2aac0426bc7e237adf</td>\n",
       "      <td>[None, None, 1, 1, 1, None, None, 1, None, 1, ...</td>\n",
       "      <td>[N/A, N/A, 5, 3, 5, N/A, N/A, 5, N/A, 4, N/A, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6441dd7cac0426bc7e237ae1</td>\n",
       "      <td>[None, None, 1, None, 1, 1, 1, 1, None, None, ...</td>\n",
       "      <td>[N/A, N/A, 5, N/A, 4, 4, 3, 3, N/A, N/A, 5, N/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6441e073dcd5e88df5170c5e</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, None, None, None, None, Non...</td>\n",
       "      <td>[5, 3, 3, 3, 1, 5, N/A, N/A, N/A, N/A, N/A, N/...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         _id  \\\n",
       "43  6441d4e5c45b3fa5015c3b22   \n",
       "44  6441dbd0c45b3fa5015c3b24   \n",
       "45  6441dc2aac0426bc7e237adf   \n",
       "46  6441dd7cac0426bc7e237ae1   \n",
       "47  6441e073dcd5e88df5170c5e   \n",
       "\n",
       "                                         drinks_tried  \\\n",
       "43  [1, 1, None, None, 1, 1, None, None, 1, None, ...   \n",
       "44  [None, None, 1, None, None, None, 1, 1, None, ...   \n",
       "45  [None, None, 1, 1, 1, None, None, 1, None, 1, ...   \n",
       "46  [None, None, 1, None, 1, 1, 1, 1, None, None, ...   \n",
       "47  [1, 1, 1, 1, 1, 1, None, None, None, None, Non...   \n",
       "\n",
       "                                              ratings  \n",
       "43  [5, 3, N/A, N/A, 5, 4, N/A, N/A, 3, N/A, N/A, ...  \n",
       "44  [N/A, N/A, 5, N/A, N/A, N/A, 5, 5, N/A, N/A, 3...  \n",
       "45  [N/A, N/A, 5, 3, 5, N/A, N/A, 5, N/A, 4, N/A, ...  \n",
       "46  [N/A, N/A, 5, N/A, 4, 4, 3, 3, N/A, N/A, 5, N/...  \n",
       "47  [5, 3, 3, 3, 1, 5, N/A, N/A, N/A, N/A, N/A, N/...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymongo import MongoClient\n",
    "cluster = MongoClient(f\"mongodb+srv://{user}:{password}@cluster0.nrodsk8.mongodb.net/coffee_db?retryWrites=true&w=majority\")\n",
    "db = cluster['coffee_db']\n",
    "surveys = db['surveys']\n",
    "# list(surveys.find())\n",
    "surveys = list(surveys.find())\n",
    "surveys = pd.DataFrame(surveys)\n",
    "surveys.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f820319a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the _id column\n",
    "users = [i for i in surveys['_id']]\n",
    "\n",
    "# clean the users\n",
    "clean_users = []\n",
    "count = 0\n",
    "for i in users:\n",
    "    new_id = \"user\" + str(count)\n",
    "    count += 1\n",
    "    clean_users.append(new_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e15ecc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean the ratings column\n",
    "ratings = [i for i in surveys['ratings']]\n",
    "\n",
    "clean_ratings = []\n",
    "for i in ratings:\n",
    "    clean_i = []\n",
    "    for j in i:\n",
    "        if j == 'N/A':\n",
    "            j = np.nan\n",
    "        if j == '1':\n",
    "            j = 1\n",
    "        if j == '2':\n",
    "            j = 2\n",
    "        if j == '3':\n",
    "            j = 3\n",
    "        if j == '4':\n",
    "            j = 4\n",
    "        if j == '5':\n",
    "            j = 5\n",
    "        clean_i.append(j)\n",
    "    clean_ratings.append(clean_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "203756a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create separate list of the drink names\n",
    "clean_drink_names = [i for i in coffee_descriptions['drink_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28484601",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Caffè Americano</th>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <th>Cinnamon Dolce Latte</th>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <th>White Chocolate Mocha</th>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <th>Green Tea Latte</th>\n",
       "      <th>...</th>\n",
       "      <th>Iced Golden Ginger Drink</th>\n",
       "      <th>Iced Caffè Latte</th>\n",
       "      <th>Iced Cascara Latte</th>\n",
       "      <th>Caramelized Honey Latte</th>\n",
       "      <th>Gingerbread Latte</th>\n",
       "      <th>Iced Coffee with Milk</th>\n",
       "      <th>Iced Caramel Cloud Macchiato</th>\n",
       "      <th>Iced Cinnamon Dolce Latte</th>\n",
       "      <th>Iced Green Tea Latte</th>\n",
       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Caffè Americano  Caffè Mocha  Caramel Macchiato  Cinnamon Dolce Latte  \\\n",
       "user0              NaN          3.0                NaN                   NaN   \n",
       "user1              NaN          NaN                5.0                   NaN   \n",
       "user2              NaN          NaN                3.0                   NaN   \n",
       "user3              NaN          NaN                NaN                   NaN   \n",
       "user4              NaN          NaN                2.0                   2.0   \n",
       "\n",
       "       Pumpkin Spice Latte  White Chocolate Mocha  Vanilla Latte  \\\n",
       "user0                  2.0                    NaN            2.0   \n",
       "user1                  NaN                    NaN            3.0   \n",
       "user2                  NaN                    NaN            NaN   \n",
       "user3                  NaN                    NaN            NaN   \n",
       "user4                  1.0                    NaN            NaN   \n",
       "\n",
       "       Iced Caramel Macchiato  Mocha Frappuccino  Green Tea Latte  ...  \\\n",
       "user0                     NaN                3.0              NaN  ...   \n",
       "user1                     NaN                NaN              NaN  ...   \n",
       "user2                     NaN                NaN              NaN  ...   \n",
       "user3                     3.0                NaN              NaN  ...   \n",
       "user4                     NaN                5.0              NaN  ...   \n",
       "\n",
       "       Iced Golden Ginger Drink  Iced Caffè Latte  Iced Cascara Latte  \\\n",
       "user0                       NaN               NaN                 2.0   \n",
       "user1                       NaN               NaN                 NaN   \n",
       "user2                       NaN               NaN                 NaN   \n",
       "user3                       NaN               NaN                 NaN   \n",
       "user4                       NaN               NaN                 NaN   \n",
       "\n",
       "       Caramelized Honey Latte  Gingerbread Latte  Iced Coffee with Milk  \\\n",
       "user0                      NaN                NaN                    NaN   \n",
       "user1                      NaN                NaN                    NaN   \n",
       "user2                      NaN                NaN                    NaN   \n",
       "user3                      NaN                NaN                    3.0   \n",
       "user4                      NaN                NaN                    NaN   \n",
       "\n",
       "       Iced Caramel Cloud Macchiato  Iced Cinnamon Dolce Latte  \\\n",
       "user0                           NaN                        NaN   \n",
       "user1                           NaN                        NaN   \n",
       "user2                           NaN                        NaN   \n",
       "user3                           NaN                        NaN   \n",
       "user4                           NaN                        NaN   \n",
       "\n",
       "       Iced Green Tea Latte  Cinnamon Roll Frappuccino  \n",
       "user0                   NaN                        3.0  \n",
       "user1                   NaN                        NaN  \n",
       "user2                   NaN                        NaN  \n",
       "user3                   4.0                        NaN  \n",
       "user4                   NaN                        NaN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create user-item DataFrame based on user ratings \n",
    "clean_ratings_df = pd.DataFrame(data= clean_ratings, index=clean_users, columns=clean_drink_names) \n",
    "clean_ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd58a1cf",
   "metadata": {},
   "source": [
    "### Now that we have transformed our data into a more accessible and easy-to-read format, we are equipped to perform various analyses and manipulations as needed. With this improved data format, we can conduct further analysis, such as calculating summary statistics, performing statistical tests, or creating visualizations, to gain deeper insights into the data. Additionally, the data is now in a format that is more receptive to data manipulation techniques, such as filtering, sorting, or aggregating, which can help us explore different angles of the data and extract meaningful information. This improved data format sets the stage for robust analysis and interpretation, enabling us to draw informed conclusions and make data-driven decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5efe7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>43</th>\n",
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       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3    4   5    6    7    8   9   ...  38  39   40  41  \\\n",
       "user0 NaN  3.0  NaN  NaN  2.0 NaN  2.0  NaN  3.0 NaN  ... NaN NaN  2.0 NaN   \n",
       "user1 NaN  NaN  5.0  NaN  NaN NaN  3.0  NaN  NaN NaN  ... NaN NaN  NaN NaN   \n",
       "user2 NaN  NaN  3.0  NaN  NaN NaN  NaN  NaN  NaN NaN  ... NaN NaN  NaN NaN   \n",
       "user3 NaN  NaN  NaN  NaN  NaN NaN  NaN  3.0  NaN NaN  ... NaN NaN  NaN NaN   \n",
       "user4 NaN  NaN  2.0  2.0  1.0 NaN  NaN  NaN  5.0 NaN  ... NaN NaN  NaN NaN   \n",
       "\n",
       "       42   43  44  45   46   47  \n",
       "user0 NaN  NaN NaN NaN  NaN  3.0  \n",
       "user1 NaN  NaN NaN NaN  NaN  NaN  \n",
       "user2 NaN  NaN NaN NaN  NaN  NaN  \n",
       "user3 NaN  3.0 NaN NaN  4.0  NaN  \n",
       "user4 NaN  NaN NaN NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## EXTRA CODE\n",
    "enumerated_drink_names = [i for i,j in enumerate(clean_drink_names)]\n",
    "enumerated_drink_names_df = pd.DataFrame(data= clean_ratings, index=clean_users, columns=enumerated_drink_names) \n",
    "enumerated_drink_names_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f650b14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks_tried = [i for i in surveys['drinks_tried']]\n",
    "\n",
    "# Clean the drinks_tried col\n",
    "clean_drinks_tried = []\n",
    "for i in drinks_tried:\n",
    "    clean_i = []\n",
    "    for j in i:\n",
    "        if j == None:\n",
    "            j = 0\n",
    "        if j == '1':\n",
    "            j = 1\n",
    "        clean_i.append(j)\n",
    "    clean_drinks_tried.append(clean_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1208b60b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Caffè Americano</th>\n",
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       "      <th>Caramel Macchiato</th>\n",
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       "      <th>Mocha Frappuccino</th>\n",
       "      <th>Green Tea Latte</th>\n",
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       "      <th>Cinnamon Roll Frappuccino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Caffè Americano  Caffè Mocha  Caramel Macchiato  Cinnamon Dolce Latte  \\\n",
       "user0                0            1                  0                     0   \n",
       "user1                0            0                  1                     0   \n",
       "user2                0            0                  1                     0   \n",
       "user3                0            0                  0                     0   \n",
       "user4                0            0                  1                     1   \n",
       "\n",
       "       Pumpkin Spice Latte  White Chocolate Mocha  Vanilla Latte  \\\n",
       "user0                    1                      0              1   \n",
       "user1                    0                      0              1   \n",
       "user2                    0                      0              0   \n",
       "user3                    0                      0              0   \n",
       "user4                    1                      0              0   \n",
       "\n",
       "       Iced Caramel Macchiato  Mocha Frappuccino  Green Tea Latte  ...  \\\n",
       "user0                       0                  1                0  ...   \n",
       "user1                       0                  0                0  ...   \n",
       "user2                       0                  0                0  ...   \n",
       "user3                       1                  0                0  ...   \n",
       "user4                       0                  1                0  ...   \n",
       "\n",
       "       Iced Golden Ginger Drink  Iced Caffè Latte  Iced Cascara Latte  \\\n",
       "user0                         0                 0                   1   \n",
       "user1                         0                 0                   0   \n",
       "user2                         0                 0                   0   \n",
       "user3                         0                 0                   0   \n",
       "user4                         0                 0                   0   \n",
       "\n",
       "       Caramelized Honey Latte  Gingerbread Latte  Iced Coffee with Milk  \\\n",
       "user0                        0                  0                      0   \n",
       "user1                        0                  0                      0   \n",
       "user2                        0                  0                      0   \n",
       "user3                        0                  0                      1   \n",
       "user4                        0                  0                      0   \n",
       "\n",
       "       Iced Caramel Cloud Macchiato  Iced Cinnamon Dolce Latte  \\\n",
       "user0                             0                          0   \n",
       "user1                             0                          0   \n",
       "user2                             0                          0   \n",
       "user3                             0                          0   \n",
       "user4                             0                          0   \n",
       "\n",
       "       Iced Green Tea Latte  Cinnamon Roll Frappuccino  \n",
       "user0                     0                          1  \n",
       "user1                     0                          0  \n",
       "user2                     0                          0  \n",
       "user3                     1                          0  \n",
       "user4                     0                          0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This DataFrame gives binary scores rather than 1-5 rating\n",
    "clean_drinks_tried_df = pd.DataFrame(data= clean_drinks_tried, index=clean_users, columns=clean_drink_names)\n",
    "# clean_drinks_tried_df.reset_index(inplace=True)\n",
    "clean_drinks_tried_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4a8689",
   "metadata": {},
   "source": [
    "## Change `clean_ratings_df` from wide format to long format (aka melt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b69d569",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose the Ratings DataFrame to swap rows and columns\n",
    "clean_ratings_df_transposed = clean_ratings_df.transpose()\n",
    "\n",
    "# Reset the index to convert it from an index to columns\n",
    "clean_ratings_df_transposed.reset_index(level=0, inplace=True)\n",
    "clean_ratings_df_transposed.rename(columns={'index': 'item_id'}, inplace=True)\n",
    "\n",
    "# Melt the DataFrame to convert it from wide to long format\n",
    "clean_ratings_df_transposed_melted = pd.melt(clean_ratings_df_transposed, id_vars='item_id', value_vars=[col for col in clean_ratings_df_transposed.columns if col != 'item_id'],\n",
    "                    var_name='user_id', value_name='rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94ebad71",
   "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>item_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caffè Americano</td>\n",
       "      <td>user0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caffè Mocha</td>\n",
       "      <td>user0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caramel Macchiato</td>\n",
       "      <td>user0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cinnamon Dolce Latte</td>\n",
       "      <td>user0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pumpkin Spice Latte</td>\n",
       "      <td>user0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>Iced Coffee with Milk</td>\n",
       "      <td>user47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>Iced Caramel Cloud Macchiato</td>\n",
       "      <td>user47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>Iced Cinnamon Dolce Latte</td>\n",
       "      <td>user47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>Iced Green Tea Latte</td>\n",
       "      <td>user47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>Cinnamon Roll Frappuccino</td>\n",
       "      <td>user47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2304 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           item_id user_id  rating\n",
       "0                  Caffè Americano   user0     NaN\n",
       "1                      Caffè Mocha   user0     3.0\n",
       "2                Caramel Macchiato   user0     NaN\n",
       "3             Cinnamon Dolce Latte   user0     NaN\n",
       "4              Pumpkin Spice Latte   user0     2.0\n",
       "...                            ...     ...     ...\n",
       "2299         Iced Coffee with Milk  user47     NaN\n",
       "2300  Iced Caramel Cloud Macchiato  user47     NaN\n",
       "2301     Iced Cinnamon Dolce Latte  user47     NaN\n",
       "2302          Iced Green Tea Latte  user47     NaN\n",
       "2303     Cinnamon Roll Frappuccino  user47     NaN\n",
       "\n",
       "[2304 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_ratings_df_transposed_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b589f9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0    0.364821\n",
       "5.0    0.283388\n",
       "4.0    0.237785\n",
       "2.0    0.068404\n",
       "1.0    0.045603\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_ratings_df_transposed_melted['rating'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ff3315",
   "metadata": {},
   "source": [
    "## Drinks with the highest average ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3ae6f28",
   "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>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Double Chocolaty Chip Frappuccino</th>\n",
       "      <td>4.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Brulée Latte</th>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Chip Frappuccino</th>\n",
       "      <td>4.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Frappuccino</th>\n",
       "      <td>4.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nitro Cold Brew</th>\n",
       "      <td>4.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     rating\n",
       "item_id                                    \n",
       "Double Chocolaty Chip Frappuccino  4.272727\n",
       "Caramel Brulée Latte               4.200000\n",
       "Java Chip Frappuccino              4.142857\n",
       "Caramel Frappuccino                4.142857\n",
       "Nitro Cold Brew                    4.142857"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_ratings_df_transposed_melted.groupby(['item_id']).mean().sort_values(by='rating', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3882fc",
   "metadata": {},
   "source": [
    "### Here we can see the drinks with the highest average ratings. Although, some of these drinks have only been tried by students a few times. To get a more accurate measure we should make a ratio to get a more accurate reading on which drinks are more popular. Lets take total rating score and divide it by the total times a drink was tried"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c86e1396",
   "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>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Chip Frappuccino</th>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Frappuccino</th>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Double Chocolaty Chip Frappuccino</th>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Americano</th>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rating\n",
       "item_id                                  \n",
       "Caramel Macchiato                    68.0\n",
       "Caffè Mocha                          60.0\n",
       "Java Chip Frappuccino                58.0\n",
       "Caramel Frappuccino                  58.0\n",
       "Mocha Frappuccino                    52.0\n",
       "Iced Caramel Macchiato               51.0\n",
       "Double Chocolaty Chip Frappuccino    47.0\n",
       "Vanilla Latte                        47.0\n",
       "Pumpkin Spice Latte                  46.0\n",
       "Caffè Americano                      45.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_ratings = clean_ratings_df_transposed_melted.groupby(['item_id']).sum().sort_values(by='rating', ascending=False)\n",
    "sum_ratings.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8fbf065",
   "metadata": {},
   "source": [
    "## Change `clean_drinks_tried_df` from wide format to long format (aka melt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "24983bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>tried</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caffè Americano</td>\n",
       "      <td>user0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caffè Mocha</td>\n",
       "      <td>user0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caramel Macchiato</td>\n",
       "      <td>user0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cinnamon Dolce Latte</td>\n",
       "      <td>user0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pumpkin Spice Latte</td>\n",
       "      <td>user0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2299</th>\n",
       "      <td>Iced Coffee with Milk</td>\n",
       "      <td>user47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2300</th>\n",
       "      <td>Iced Caramel Cloud Macchiato</td>\n",
       "      <td>user47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>Iced Cinnamon Dolce Latte</td>\n",
       "      <td>user47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2302</th>\n",
       "      <td>Iced Green Tea Latte</td>\n",
       "      <td>user47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2303</th>\n",
       "      <td>Cinnamon Roll Frappuccino</td>\n",
       "      <td>user47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2304 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           item_id user_id  tried\n",
       "0                  Caffè Americano   user0      0\n",
       "1                      Caffè Mocha   user0      1\n",
       "2                Caramel Macchiato   user0      0\n",
       "3             Cinnamon Dolce Latte   user0      0\n",
       "4              Pumpkin Spice Latte   user0      1\n",
       "...                            ...     ...    ...\n",
       "2299         Iced Coffee with Milk  user47      0\n",
       "2300  Iced Caramel Cloud Macchiato  user47      0\n",
       "2301     Iced Cinnamon Dolce Latte  user47      0\n",
       "2302          Iced Green Tea Latte  user47      0\n",
       "2303     Cinnamon Roll Frappuccino  user47      0\n",
       "\n",
       "[2304 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the Drinks Tried DataFrame to swap rows and columns\n",
    "clean_drinks_tried_df_transposed = clean_drinks_tried_df.transpose()\n",
    "\n",
    "# Reset the index to convert it from an index to columns\n",
    "clean_drinks_tried_df_transposed.reset_index(level=0, inplace=True)\n",
    "clean_drinks_tried_df_transposed.rename(columns={'index': 'item_id'}, inplace=True)\n",
    "\n",
    "# Melt the DataFrame to convert it from wide to long format\n",
    "clean_drinks_tried_df_melted = pd.melt(clean_drinks_tried_df_transposed, id_vars='item_id', value_vars=[col for col in clean_drinks_tried_df_transposed.columns if col != 'item_id'],\n",
    "                    var_name='user_id', value_name='tried')\n",
    "clean_drinks_tried_df_melted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a30d0ad3",
   "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>tried</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caramel Macchiato</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pumpkin Spice Latte</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Java Chip Frappuccino</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caramel Frappuccino</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanilla Latte</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iced Caramel Macchiato</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mocha Frappuccino</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Americano</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Double Chocolaty Chip Frappuccino</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   tried\n",
       "item_id                                 \n",
       "Caramel Macchiato                     18\n",
       "Caffè Mocha                           17\n",
       "Pumpkin Spice Latte                   15\n",
       "Java Chip Frappuccino                 14\n",
       "Caramel Frappuccino                   14\n",
       "Vanilla Latte                         13\n",
       "Iced Caramel Macchiato                13\n",
       "Mocha Frappuccino                     13\n",
       "Caffè Americano                       12\n",
       "Double Chocolaty Chip Frappuccino     11"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_tried = clean_drinks_tried_df_melted.groupby(['item_id']).sum().sort_values(by='tried', ascending=False)\n",
    "sum_tried.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d506d1cf",
   "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>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Americano Misto</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Roast</th>\n",
       "      <td>3.285714</td>\n",
       "      <td>7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blonde Vanilla Latte</th>\n",
       "      <td>3.400000</td>\n",
       "      <td>5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Americano</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caffè Mocha</th>\n",
       "      <td>3.529412</td>\n",
       "      <td>17</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        result  tried  rating  price\n",
       "item_id                                             \n",
       "Americano Misto       3.666667      3    11.0   3.75\n",
       "Blonde Roast          3.285714      7    23.0   2.45\n",
       "Blonde Vanilla Latte  3.400000      5    17.0   4.55\n",
       "Caffè Americano       3.750000     12    45.0   3.60\n",
       "Caffè Mocha           3.529412     17    60.0   4.45"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new rating based on the ratio of a drinks rating to the amount of times it was tried\n",
    "rating_tried_ratio = pd.DataFrame({'result': sum_ratings['rating'].div(sum_tried['tried']),\n",
    "                                   'tried': sum_tried['tried'],\n",
    "                                   'rating': sum_ratings['rating'],\n",
    "                                   'price': coffee_prices['price']\n",
    "                                  })\n",
    "rating_tried_ratio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c7617382",
   "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>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.453379</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>23.312500</td>\n",
       "      <td>4.459375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.516301</td>\n",
       "      <td>4.708526</td>\n",
       "      <td>18.176623</td>\n",
       "      <td>0.637620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.873626</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.272727</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          result      tried     rating      price\n",
       "count  48.000000  48.000000  48.000000  48.000000\n",
       "mean    3.453379   6.500000  23.312500   4.459375\n",
       "std     0.516301   4.708526  18.176623   0.637620\n",
       "min     2.000000   1.000000   3.000000   2.450000\n",
       "25%     3.000000   2.000000   8.000000   3.950000\n",
       "50%     3.500000   5.500000  18.500000   4.750000\n",
       "75%     3.873626   9.000000  32.500000   4.950000\n",
       "max     4.272727  18.000000  68.000000   5.250000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_tried_ratio.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87aab209",
   "metadata": {},
   "source": [
    "# Analysis Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ffe65e84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean rating: 3.6449511400651464\n",
      "Median rating: 4.0\n",
      "Standard deviation of ratings: 1.1000589322702898\n"
     ]
    }
   ],
   "source": [
    "# Compute basic summary statistics of the ratings\n",
    "# print(clean_ratings_df_transposed_melted['rating'].describe())\n",
    "\n",
    "# Calculate summary statistics of ratings\n",
    "mean_rating = clean_ratings_df_transposed_melted['rating'].mean()\n",
    "median_rating = clean_ratings_df_transposed_melted['rating'].median()\n",
    "std_rating = clean_ratings_df_transposed_melted['rating'].std()\n",
    "print('Mean rating:', mean_rating)\n",
    "print('Median rating:', median_rating)\n",
    "print('Standard deviation of ratings:', std_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42b46fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(clean_ratings_df_transposed_melted['rating'], bins=10, kde=True)\n",
    "plt.xlabel('Ratings')\n",
    "plt.ylabel('Frequency')\n",
    "plt.suptitle('Frequency Distribution of Ratings', weight= 'bold').set_fontsize(20)\n",
    "plt.title('Distribution of Ratings')\n",
    "plt.show()\n",
    "\n",
    "# Create a boxplot of ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.boxplot(clean_ratings_df_transposed_melted['rating'])\n",
    "plt.xlabel('Ratings')\n",
    "plt.title('Boxplot of Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0d558fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a QQ plot of ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "stats.probplot(clean_ratings_df_transposed_melted['rating'], plot=plt)\n",
    "plt.xlabel('Theoretical Quantiles')\n",
    "plt.ylabel('Sample Quantiles')\n",
    "plt.title('QQ Plot of Ratings')\n",
    "plt.show()\n",
    "\n",
    "# Create a kernel density plot of ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.kdeplot(clean_ratings_df_transposed_melted['rating'], fill=True)\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Density')\n",
    "plt.suptitle('Distribution of Ratings', weight ='bold').set_fontsize(20)\n",
    "plt.title('Kernel Density Plot of Ratings')\n",
    "plt.show()\n",
    "\n",
    "# Create a violin plot of ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.violinplot(clean_ratings_df_transposed_melted['rating'])\n",
    "plt.xlabel('Ratings')\n",
    "plt.title('Violin Plot of Ratings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5a141c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create a swarm plot of ratings with item ID as hue\n",
    "sns.swarmplot(x='item_id', y='rating', data=clean_ratings_df_transposed_melted, hue='user_id', dodge=True, size=4)\n",
    "plt.xlabel('Item ID')\n",
    "plt.ylabel('Rating')\n",
    "plt.title('Swarm Plot of Ratings by Item ID')\n",
    "plt.xticks(rotation=90) # Rotate x-axis labels if needed\n",
    "plt.legend([], [], frameon=False) # Hide legend\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab5583d",
   "metadata": {},
   "source": [
    "### As evident from the swarm plot, the data points exhibit a higher density within the rating scores of 3 to 5. This indicates that a significant portion of the data falls within this range, suggesting that customers tend to rate the coffee drinks relatively positively, with the majority of ratings clustered in the higher end of the scale. This observation may suggest that the coffee drinks are generally well-received by customers, as reflected by the higher density of data points in the higher rating scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5037adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the data from the DataFrame\n",
    "result = rating_tried_ratio['result']\n",
    "tried = rating_tried_ratio['tried']\n",
    "rating = rating_tried_ratio['rating']\n",
    "price =  rating_tried_ratio['price']\n",
    "\n",
    "# Create a scatterplot\n",
    "plt.scatter(tried, rating, c=result, cmap='viridis', edgecolors='black', alpha=0.8)\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Number of Times Tried')\n",
    "plt.ylabel('Total Ratings')\n",
    "plt.title('Rating Tried Ratio')\n",
    "\n",
    "# Add a colorbar\n",
    "cbar = plt.colorbar()\n",
    "cbar.set_label('Rating Tried Ratio')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1160a93",
   "metadata": {},
   "source": [
    "### We can observe a strong positive correlation between customer ratings of Starbucks coffee drinks and the number of times a particular drink has been tried. In this analysis, ratings serve as the dependent variable, while the frequency of trying a specific drink acts as the regressor variable. The data suggests that as customers try a particular drink more frequently, their ratings tend to be higher, indicating a positive relationship between drink trial frequency and customer ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb6b4fd",
   "metadata": {},
   "source": [
    "## Most Popular Drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "379deb58",
   "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>item_id</th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caffè Americano</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iced Caffè Mocha</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caffè Mocha</td>\n",
       "      <td>3.529412</td>\n",
       "      <td>17</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iced Caffè Americano</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iced Caffè Latte</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                item_id    result  tried  rating  price\n",
       "0       Caffè Americano  3.750000     12    45.0   3.60\n",
       "1      Iced Caffè Mocha  3.600000      5    18.0   4.75\n",
       "2           Caffè Mocha  3.529412     17    60.0   4.45\n",
       "3  Iced Caffè Americano  3.500000      4    14.0   3.75\n",
       "4      Iced Caffè Latte  3.500000      4    14.0   3.75"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caffè_ratio = rating_tried_ratio.loc[rating_tried_ratio.index.str.contains(\"Caffè\")].sort_values(by='result', ascending=False).reset_index()\n",
    "caffè_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "32f0e1e2",
   "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>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.575882</td>\n",
       "      <td>8.40000</td>\n",
       "      <td>30.200000</td>\n",
       "      <td>4.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.105563</td>\n",
       "      <td>5.85662</td>\n",
       "      <td>21.099763</td>\n",
       "      <td>0.507937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.529412</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>4.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         result     tried     rating     price\n",
       "count  5.000000   5.00000   5.000000  5.000000\n",
       "mean   3.575882   8.40000  30.200000  4.060000\n",
       "std    0.105563   5.85662  21.099763  0.507937\n",
       "min    3.500000   4.00000  14.000000  3.600000\n",
       "25%    3.500000   4.00000  14.000000  3.750000\n",
       "50%    3.529412   5.00000  18.000000  3.750000\n",
       "75%    3.600000  12.00000  45.000000  4.450000\n",
       "max    3.750000  17.00000  60.000000  4.750000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caffè_ratio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "029531d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>item_id</th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iced White Chocolate Mocha with Raspberry Syrup</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mocha Frappuccino</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Salted Caramel Mocha</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iced Caffè Mocha</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caffè Mocha</td>\n",
       "      <td>3.529412</td>\n",
       "      <td>17</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>White Chocolate Mocha</td>\n",
       "      <td>3.454545</td>\n",
       "      <td>11</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toasted White Chocolate Mocha</td>\n",
       "      <td>3.444444</td>\n",
       "      <td>9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Iced White Chocolate Mocha</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           item_id    result  tried  rating  \\\n",
       "0  Iced White Chocolate Mocha with Raspberry Syrup  4.000000      2     8.0   \n",
       "1                                Mocha Frappuccino  4.000000     13    52.0   \n",
       "2                             Salted Caramel Mocha  3.857143      7    27.0   \n",
       "3                                 Iced Caffè Mocha  3.600000      5    18.0   \n",
       "4                                      Caffè Mocha  3.529412     17    60.0   \n",
       "5                            White Chocolate Mocha  3.454545     11    38.0   \n",
       "6                    Toasted White Chocolate Mocha  3.444444      9    31.0   \n",
       "7                       Iced White Chocolate Mocha  3.142857      7    22.0   \n",
       "\n",
       "   price  \n",
       "0   5.25  \n",
       "1   4.95  \n",
       "2   4.95  \n",
       "3   4.75  \n",
       "4   4.45  \n",
       "5   4.75  \n",
       "6   4.95  \n",
       "7   4.95  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mocha_ratio = rating_tried_ratio.loc[rating_tried_ratio.index.str.contains(\"Mocha\")].sort_values(by='result', ascending=False).reset_index()\n",
    "mocha_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aaa90a73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.628550</td>\n",
       "      <td>8.875000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>4.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.302116</td>\n",
       "      <td>4.733996</td>\n",
       "      <td>17.39458</td>\n",
       "      <td>0.231455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.142857</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>4.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.452020</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.564706</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.892857</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>41.50000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         result      tried    rating     price\n",
       "count  8.000000   8.000000   8.00000  8.000000\n",
       "mean   3.628550   8.875000  32.00000  4.875000\n",
       "std    0.302116   4.733996  17.39458  0.231455\n",
       "min    3.142857   2.000000   8.00000  4.450000\n",
       "25%    3.452020   6.500000  21.00000  4.750000\n",
       "50%    3.564706   8.000000  29.00000  4.950000\n",
       "75%    3.892857  11.500000  41.50000  4.950000\n",
       "max    4.000000  17.000000  60.00000  5.250000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mocha_ratio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c10304d5",
   "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>item_id</th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Caramel Brulée Latte</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caramel Frappuccino</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>14</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iced Caramel Macchiato with Coconut Milk</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iced Caramel Macchiato</td>\n",
       "      <td>3.923077</td>\n",
       "      <td>13</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salted Caramel Mocha</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Caramel Macchiato</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>18</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Iced Caramel Cloud Macchiato</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Caramel Apple Spice</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Caramelized Honey Latte</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    item_id    result  tried  rating  price\n",
       "0                      Caramel Brulée Latte  4.200000      5    21.0   4.95\n",
       "1                       Caramel Frappuccino  4.142857     14    58.0   4.95\n",
       "2  Iced Caramel Macchiato with Coconut Milk  4.000000      2     8.0   4.95\n",
       "3                    Iced Caramel Macchiato  3.923077     13    51.0   4.95\n",
       "4                      Salted Caramel Mocha  3.857143      7    27.0   4.95\n",
       "5                         Caramel Macchiato  3.777778     18    68.0   4.75\n",
       "6              Iced Caramel Cloud Macchiato  3.500000      2     7.0   4.95\n",
       "7                       Caramel Apple Spice  3.000000      1     3.0   3.65\n",
       "8                   Caramelized Honey Latte  3.000000      1     3.0   4.75"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caramel_ratio = rating_tried_ratio.loc[rating_tried_ratio.index.str.contains(\"Caramel\")].sort_values(by='result', ascending=False).reset_index()\n",
    "caramel_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "78c154de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.711206</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>27.333333</td>\n",
       "      <td>4.761111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.452147</td>\n",
       "      <td>6.442049</td>\n",
       "      <td>25.411612</td>\n",
       "      <td>0.425572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.857143</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.200000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         result      tried     rating     price\n",
       "count  9.000000   9.000000   9.000000  9.000000\n",
       "mean   3.711206   7.000000  27.333333  4.761111\n",
       "std    0.452147   6.442049  25.411612  0.425572\n",
       "min    3.000000   1.000000   3.000000  3.650000\n",
       "25%    3.500000   2.000000   7.000000  4.750000\n",
       "50%    3.857143   5.000000  21.000000  4.950000\n",
       "75%    4.000000  13.000000  51.000000  4.950000\n",
       "max    4.200000  18.000000  68.000000  4.950000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caramel_ratio.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "26e5e3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Double Chocolaty Chip Frappuccino</td>\n",
       "      <td>4.272727</td>\n",
       "      <td>11</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caramel Frappuccino</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>14</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java Chip Frappuccino</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>14</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mocha Frappuccino</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cinnamon Roll Frappuccino</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vanilla Bean Frappuccino</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             item_id    result  tried  rating  price\n",
       "0  Double Chocolaty Chip Frappuccino  4.272727     11    47.0   4.95\n",
       "1                Caramel Frappuccino  4.142857     14    58.0   4.95\n",
       "2              Java Chip Frappuccino  4.142857     14    58.0   5.25\n",
       "3                  Mocha Frappuccino  4.000000     13    52.0   4.95\n",
       "4          Cinnamon Roll Frappuccino  3.000000      2     6.0   4.95\n",
       "5           Vanilla Bean Frappuccino  2.875000      8    23.0   4.75"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frap_ratio = rating_tried_ratio.loc[rating_tried_ratio.index.str.contains(\"Frappuccino\")].sort_values(by='result', ascending=False).reset_index()\n",
    "frap_ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "018cc993",
   "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>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.738907</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>40.666667</td>\n",
       "      <td>4.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.627982</td>\n",
       "      <td>4.676181</td>\n",
       "      <td>21.369761</td>\n",
       "      <td>0.160208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.071429</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.142857</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>56.500000</td>\n",
       "      <td>4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.272727</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>5.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         result      tried     rating     price\n",
       "count  6.000000   6.000000   6.000000  6.000000\n",
       "mean   3.738907  10.333333  40.666667  4.966667\n",
       "std    0.627982   4.676181  21.369761  0.160208\n",
       "min    2.875000   2.000000   6.000000  4.750000\n",
       "25%    3.250000   8.750000  29.000000  4.950000\n",
       "50%    4.071429  12.000000  49.500000  4.950000\n",
       "75%    4.142857  13.750000  56.500000  4.950000\n",
       "max    4.272727  14.000000  58.000000  5.250000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frap_ratio.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df11c09",
   "metadata": {},
   "source": [
    "### While comparing drinks, Caffè style drinks have the lowest variability with a standard deviation of 0.10 between the ratio ratings vs. times tried letting us know customers have more consistent ratings with Caffè drinks. Frappuccino's have more inconsistency and the highest standard deviation when rating these style drinks. NJIT students apparently have a sweet tooth because Caramel and Mocha style drinks are the most commonly tried drink. However, we don't have a lot of data either so these results can change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "43896934",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\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>item_id</th>\n",
       "      <th>result</th>\n",
       "      <th>tried</th>\n",
       "      <th>rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Double Chocolaty Chip Frappuccino</td>\n",
       "      <td>4.272727</td>\n",
       "      <td>11</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Caramel Frappuccino</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>14</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Java Chip Frappuccino</td>\n",
       "      <td>4.142857</td>\n",
       "      <td>14</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mocha Frappuccino</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>52.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iced Caramel Macchiato</td>\n",
       "      <td>3.923077</td>\n",
       "      <td>13</td>\n",
       "      <td>51.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Caramel Macchiato</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>18</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Caffè Americano</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>12</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vanilla Latte</td>\n",
       "      <td>3.615385</td>\n",
       "      <td>13</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Caffè Mocha</td>\n",
       "      <td>3.529412</td>\n",
       "      <td>17</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>White Chocolate Mocha</td>\n",
       "      <td>3.454545</td>\n",
       "      <td>11</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pumpkin Spice Latte</td>\n",
       "      <td>3.066667</td>\n",
       "      <td>15</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              item_id    result  tried  rating  price\n",
       "0   Double Chocolaty Chip Frappuccino  4.272727     11    47.0   4.95\n",
       "1                 Caramel Frappuccino  4.142857     14    58.0   4.95\n",
       "2               Java Chip Frappuccino  4.142857     14    58.0   5.25\n",
       "3                   Mocha Frappuccino  4.000000     13    52.0   4.95\n",
       "4              Iced Caramel Macchiato  3.923077     13    51.0   4.95\n",
       "5                   Caramel Macchiato  3.777778     18    68.0   4.75\n",
       "6                     Caffè Americano  3.750000     12    45.0   3.60\n",
       "7                       Vanilla Latte  3.615385     13    47.0   4.55\n",
       "8                         Caffè Mocha  3.529412     17    60.0   4.45\n",
       "9               White Chocolate Mocha  3.454545     11    38.0   4.75\n",
       "10                Pumpkin Spice Latte  3.066667     15    46.0   4.95"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_popular_drinks = rating_tried_ratio.loc[rating_tried_ratio['tried'] >= 10].sort_values(by='result', ascending=False).reset_index()\n",
    "most_popular_drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20287588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_palette = sns.color_palette(\"dark:#5A9_r\", len(most_popular_drinks))\n",
    "\n",
    "# Create the bar plot with the green palette\n",
    "sns.barplot(x=most_popular_drinks['result'], y=most_popular_drinks['item_id'], edgecolor='black', palette=green_palette)\n",
    "plt.suptitle('NJIT Students Most Popular Drinks', weight='bold', color='black', fontsize=20)\n",
    "plt.title('Data Scraped from MongoDB', fontsize=12)\n",
    "plt.xlabel('Ratings/Times Tried Ratio', weight='bold', color='black')\n",
    "plt.ylabel('Drink Name', weight='bold', color='black')\n",
    "# plt.gca().invert_xaxis()\n",
    "plt.xticks(color='black')\n",
    "plt.yticks(color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b585ffe7",
   "metadata": {},
   "source": [
    "### Something to think about is because it is currently April and it's starting to get warmer is this just a trend? Frappuccino drinks are blended with ice and are usually a refreshing drink when it's warmer out. Maybe this chart will change as the seasons change... "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d591bb2",
   "metadata": {},
   "source": [
    "# Recommendation Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1a819d",
   "metadata": {},
   "source": [
    "### User-Based Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c0b55474",
   "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>user0</th>\n",
       "      <th>user1</th>\n",
       "      <th>user2</th>\n",
       "      <th>user3</th>\n",
       "      <th>user4</th>\n",
       "      <th>user5</th>\n",
       "      <th>user6</th>\n",
       "      <th>user7</th>\n",
       "      <th>user8</th>\n",
       "      <th>user9</th>\n",
       "      <th>...</th>\n",
       "      <th>user38</th>\n",
       "      <th>user39</th>\n",
       "      <th>user40</th>\n",
       "      <th>user41</th>\n",
       "      <th>user42</th>\n",
       "      <th>user43</th>\n",
       "      <th>user44</th>\n",
       "      <th>user45</th>\n",
       "      <th>user46</th>\n",
       "      <th>user47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073577</td>\n",
       "      <td>0.266835</td>\n",
       "      <td>0.161018</td>\n",
       "      <td>0.371223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.595621</td>\n",
       "      <td>0.485582</td>\n",
       "      <td>0.532906</td>\n",
       "      <td>0.116335</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260133</td>\n",
       "      <td>0.144296</td>\n",
       "      <td>0.234420</td>\n",
       "      <td>0.147153</td>\n",
       "      <td>0.268001</td>\n",
       "      <td>0.437890</td>\n",
       "      <td>0.305711</td>\n",
       "      <td>0.129799</td>\n",
       "      <td>0.199422</td>\n",
       "      <td>0.300203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>0.073577</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.261116</td>\n",
       "      <td>0.210090</td>\n",
       "      <td>0.370681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212132</td>\n",
       "      <td>0.271607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.353009</td>\n",
       "      <td>0.660384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.119208</td>\n",
       "      <td>0.138675</td>\n",
       "      <td>0.586588</td>\n",
       "      <td>0.356033</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>0.144005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>0.266835</td>\n",
       "      <td>0.261116</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.310853</td>\n",
       "      <td>0.553912</td>\n",
       "      <td>0.315205</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.373110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.271578</td>\n",
       "      <td>0.382920</td>\n",
       "      <td>0.125630</td>\n",
       "      <td>0.146427</td>\n",
       "      <td>0.075204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>0.161018</td>\n",
       "      <td>0.210090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.266223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.226096</td>\n",
       "      <td>0.414253</td>\n",
       "      <td>0.144418</td>\n",
       "      <td>0.480129</td>\n",
       "      <td>0.323985</td>\n",
       "      <td>0.126058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>0.371223</td>\n",
       "      <td>0.370681</td>\n",
       "      <td>0.494709</td>\n",
       "      <td>0.259588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353030</td>\n",
       "      <td>0.567908</td>\n",
       "      <td>0.290852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.145393</td>\n",
       "      <td>0.608401</td>\n",
       "      <td>0.074136</td>\n",
       "      <td>0.245490</td>\n",
       "      <td>0.436936</td>\n",
       "      <td>0.280857</td>\n",
       "      <td>0.285351</td>\n",
       "      <td>0.374162</td>\n",
       "      <td>0.213519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user0     user1     user2     user3     user4  user5     user6  \\\n",
       "user0  1.000000  0.073577  0.266835  0.161018  0.371223    0.0  0.595621   \n",
       "user1  0.073577  1.000000  0.261116  0.210090  0.370681    0.0  0.000000   \n",
       "user2  0.266835  0.261116  1.000000  0.000000  0.494709    0.0  0.310853   \n",
       "user3  0.161018  0.210090  0.000000  1.000000  0.259588    0.0  0.000000   \n",
       "user4  0.371223  0.370681  0.494709  0.259588  1.000000    0.0  0.353030   \n",
       "\n",
       "          user7     user8     user9  ...    user38    user39    user40  \\\n",
       "user0  0.485582  0.532906  0.116335  ...  0.260133  0.144296  0.234420   \n",
       "user1  0.212132  0.271607  0.000000  ...  0.000000  0.353009  0.660384   \n",
       "user2  0.553912  0.315205  0.000000  ...  0.000000  0.000000  0.373110   \n",
       "user3  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.266223   \n",
       "user4  0.567908  0.290852  0.000000  ...  0.000000  0.145393  0.608401   \n",
       "\n",
       "         user41    user42    user43    user44    user45    user46    user47  \n",
       "user0  0.147153  0.268001  0.437890  0.305711  0.129799  0.199422  0.300203  \n",
       "user1  0.000000  0.119208  0.138675  0.586588  0.356033  0.605634  0.144005  \n",
       "user2  0.000000  0.000000  0.271578  0.382920  0.125630  0.146427  0.075204  \n",
       "user3  0.000000  0.226096  0.414253  0.144418  0.480129  0.323985  0.126058  \n",
       "user4  0.074136  0.245490  0.436936  0.280857  0.285351  0.374162  0.213519  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_ratings_cosine_similarity = cosine_similarity(clean_ratings_df.fillna(0))\n",
    "# clean_ratings_cosine_similarity.reset_index(inplace=True)\n",
    "\n",
    "# Create a dataframe with the cosine similarity matrix\n",
    "clean_ratings_cosine_similarity_df = pd.DataFrame(clean_ratings_cosine_similarity, columns=clean_users, index=clean_users)\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "clean_ratings_cosine_similarity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9fc5a11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([46, 21, 20, 19, 18], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the rows of the cosine similarity matrix based on the similarity scores in descending order\n",
    "sorted_indices = np.argsort(clean_ratings_cosine_similarity, axis=1)[:, ::-1]\n",
    "\n",
    "# Specify the number of top similar users to select for each user\n",
    "top_n = 5\n",
    "\n",
    "# Select the top N similar users for each user from the cosine similarity matrix\n",
    "similar_users = {}\n",
    "for i, indices in enumerate(sorted_indices):\n",
    "    # Exclude the user itself from the similar users\n",
    "    similar_users[i] = indices[1:top_n+1]\n",
    "\n",
    "# You can now access the similar users for each user in the `similar_users` dictionary.\n",
    "# For example, to get the similar users for user with index 0:\n",
    "similar_users_for_user_0 = similar_users[5]\n",
    "similar_users_for_user_0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86ca9ee",
   "metadata": {},
   "source": [
    "### In this example, we use argsort() function from NumPy to sort the rows of the cosine similarity matrix in descending order, which gives us the indices of the most similar users for each user. We then select the top N similar users (excluding the user itself) for each user by slicing the sorted indices. Finally, we store the similar users in a dictionary, where the keys are the user indices and the values are arrays of indices representing the similar users. You can customize the value of top_n to select a different number of similar users based on your specific needs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e998582a",
   "metadata": {},
   "source": [
    "# User-Based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa6484f",
   "metadata": {},
   "source": [
    "### Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2cdcbecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mocha Frappuccino', 'Iced White Chocolate Mocha',\n",
       "       'Vanilla Sweet Cream Cold Brew', 'Double Chocolaty Chip Frappuccino',\n",
       "       'Salted Caramel Mocha'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_recommendations(top_n=5):\n",
    "\n",
    "    target_user = clean_ratings_df[-1:].index[0]\n",
    "    \n",
    "    # Extract the similarity scores for the target user from the cosine similarity matrix\n",
    "    similarity_scores = clean_ratings_cosine_similarity_df[target_user]\n",
    "\n",
    "    # Sort the similarity scores in descending order and select the top N similar users\n",
    "    similar_users_indices = np.argsort(similarity_scores)[::-1][1:top_n+1]\n",
    "\n",
    "    # Get the ratings of similar users for items that the target user has not rated\n",
    "    similar_users_ratings = clean_ratings_df.iloc[similar_users_indices]\n",
    "    target_user_ratings = clean_ratings_df.loc[target_user]\n",
    "\n",
    "    # Filter out items that the target user has already rated\n",
    "    unrated_items_mask = target_user_ratings.isna()\n",
    "    similar_users_ratings = similar_users_ratings.loc[:, unrated_items_mask]\n",
    "\n",
    "    # Calculate the weighted average of the ratings of similar users using the similarity scores as weights\n",
    "    similarity_scores = similarity_scores[similar_users_indices]\n",
    "    weighted_avg_ratings = similar_users_ratings.mul(similarity_scores, axis=0).sum() / similarity_scores.sum()\n",
    "\n",
    "    # Sort the weighted average ratings in descending order to get the top-rated items\n",
    "    recommended_items = weighted_avg_ratings.sort_values(ascending=False)\n",
    "\n",
    "    # You can now access the recommended items for the target user in the `recommended_items` Series.\n",
    "    # For example, to get the top N recommended items for the target user:\n",
    "    top_n_recommended_items = recommended_items.head(top_n)\n",
    "    # print(top_n_recommended_items)\n",
    "\n",
    "    ###### EXTRA CODE for MAE or other evaluation metric ##########\n",
    "    # Extract the actual ratings for the recommended items\n",
    "    # actual_ratings = clean_ratings_df.fillna(0).loc[f\"user{target_user}\", top_n_recommended_items.index]\n",
    "\n",
    "    # Extract the predicted ratings (weighted average ratings) for the recommended items\n",
    "    # predicted_ratings = top_n_recommended_items.fillna(0).values\n",
    "\n",
    "\n",
    "    return top_n_recommended_items.keys()\n",
    "\n",
    "generate_recommendations(top_n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d25916",
   "metadata": {},
   "source": [
    "### The code uses a user-based collaborative filtering model to generate recommendations for a target user. It determines the target user as the last user who completed the survey in the database. It extracts similarity scores between the target user and all other users from a cosine similarity matrix. The top N similar users are selected based on the sorted similarity scores. The ratings of similar users for items not rated by the target user are retrieved and weighted by the similarity scores. The weighted average ratings are then used to recommend top-rated items for the target user, excluding items already rated by the target user. The final recommendations are stored in the top_n_recommended_items Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a2d37bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mocha Frappuccino                    3.182847\n",
       "Iced White Chocolate Mocha           3.003784\n",
       "Vanilla Sweet Cream Cold Brew        2.634478\n",
       "Double Chocolaty Chip Frappuccino    2.568923\n",
       "Salted Caramel Mocha                 2.482123\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_user = clean_ratings_df[-1:].index[0]\n",
    "# target_user\n",
    "# Extract the similarity scores for the target user from the cosine similarity matrix\n",
    "similarity_scores = clean_ratings_cosine_similarity_df[target_user]\n",
    "\n",
    "similar_users_indices = np.argsort(similarity_scores)[::-1][1:6]\n",
    "# similar_users_indices\n",
    "similar_users_ratings = clean_ratings_df.iloc[similar_users_indices]\n",
    "target_user_ratings = clean_ratings_df.loc[target_user]\n",
    "\n",
    "unrated_items_mask = target_user_ratings.isna()\n",
    "similar_users_ratings = similar_users_ratings.loc[:, unrated_items_mask]\n",
    "\n",
    "similarity_scores = similarity_scores[similar_users_indices]\n",
    "weighted_avg_ratings = similar_users_ratings.mul(similarity_scores, axis=0).sum() / similarity_scores.sum()\n",
    "weighted_avg_ratings.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d511ef5d",
   "metadata": {},
   "source": [
    "# Performance Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe9b076",
   "metadata": {},
   "source": [
    "### Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e936eda2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user0</th>\n",
       "      <th>user1</th>\n",
       "      <th>user2</th>\n",
       "      <th>user3</th>\n",
       "      <th>user4</th>\n",
       "      <th>user5</th>\n",
       "      <th>user6</th>\n",
       "      <th>user7</th>\n",
       "      <th>user8</th>\n",
       "      <th>user9</th>\n",
       "      <th>...</th>\n",
       "      <th>user38</th>\n",
       "      <th>user39</th>\n",
       "      <th>user40</th>\n",
       "      <th>user41</th>\n",
       "      <th>user42</th>\n",
       "      <th>user43</th>\n",
       "      <th>user44</th>\n",
       "      <th>user45</th>\n",
       "      <th>user46</th>\n",
       "      <th>user47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.020833</td>\n",
       "      <td>1.354167</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.020833</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.770833</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>1.062500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.354167</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>1.229167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>1.020833</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.520833</td>\n",
       "      <td>1.291667</td>\n",
       "      <td>0.979167</td>\n",
       "      <td>1.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>1.354167</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.104167</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.979167</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770833</td>\n",
       "      <td>0.770833</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.791667</td>\n",
       "      <td>0.979167</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>1.020833</td>\n",
       "      <td>1.604167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>1.104167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.729167</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>0.645833</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.770833</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.354167</td>\n",
       "      <td>1.041667</td>\n",
       "      <td>1.416667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user0     user1     user2     user3     user4     user5     user6  \\\n",
       "user0  0.000000  1.062500  1.020833  1.354167  1.125000  0.895833  0.729167   \n",
       "user1  1.062500  0.000000  0.458333  0.750000  0.729167  0.250000  0.708333   \n",
       "user2  1.020833  0.458333  0.000000  1.000000  0.645833  0.333333  0.583333   \n",
       "user3  1.354167  0.750000  1.000000  0.000000  1.104167  0.666667  1.125000   \n",
       "user4  1.125000  0.729167  0.645833  1.104167  0.000000  0.729167  0.854167   \n",
       "\n",
       "          user7     user8     user9  ...    user38    user39    user40  \\\n",
       "user0  0.812500  0.750000  0.937500  ...  0.875000  0.916667  1.333333   \n",
       "user1  0.583333  0.437500  0.375000  ...  0.354167  0.229167  0.562500   \n",
       "user2  0.416667  0.479167  0.458333  ...  0.437500  0.437500  0.812500   \n",
       "user3  1.125000  0.979167  0.791667  ...  0.770833  0.770833  1.187500   \n",
       "user4  0.645833  0.791667  0.854167  ...  0.833333  0.750000  0.666667   \n",
       "\n",
       "         user41    user42    user43    user44    user45    user46    user47  \n",
       "user0  1.020833  1.187500  1.770833  1.000000  1.770833  1.375000  1.458333  \n",
       "user1  0.500000  0.791667  2.000000  0.354167  1.000000  0.520833  1.229167  \n",
       "user2  0.583333  1.000000  1.875000  0.520833  1.291667  0.979167  1.312500  \n",
       "user3  0.916667  1.000000  1.791667  0.979167  0.958333  1.020833  1.604167  \n",
       "user4  0.895833  1.062500  1.770833  0.833333  1.354167  1.041667  1.416667  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_mae_scores = []\n",
    "for i in range(0, len(clean_ratings_df)):\n",
    "    user_a = clean_ratings_df.fillna(0).loc[f\"user{i}\"].values\n",
    "    user_a_mae = []\n",
    "    for j in range(0, len(clean_ratings_df)):\n",
    "        user_b = clean_ratings_df.fillna(0).loc[f\"user{j}\"].values\n",
    "        mae = mean_absolute_error(user_a,user_b)\n",
    "        user_a_mae.append(mae)\n",
    "    user_mae_scores.append(user_a_mae)\n",
    "user_mae_scores\n",
    "\n",
    "# Create a dataframe with the cosine similarity matrix\n",
    "clean_mae_df = pd.DataFrame(user_mae_scores, columns=clean_users, index=clean_users)\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "clean_mae_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa75d91",
   "metadata": {},
   "source": [
    "### According to [Su, X. & Khoshgoftaar, T. M.] the most widely used metric in collaborative filtering research literature is Mean Absolute Error (MAE), which computes the average of the absolute difference between the predictions and true ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6996b746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36, 39, 31,  5, 17], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume you have a pandas DataFrame called `user_mae_scores` that contains the Mean Absolute Error values\n",
    "# among users, where rows represent users and columns represent users, and the values are the Mean Absolute Error scores.\n",
    "\n",
    "# Sort the rows of the user_mae_scores based on the similarity scores in descending order\n",
    "sorted_indices = np.argsort(user_mae_scores, axis=1)[:, :]\n",
    "\n",
    "# Specify the number of top similar users to select for each user\n",
    "top_n = 5\n",
    "\n",
    "# Select the top N similar users for each user from the cosine similarity matrix\n",
    "similar_users = {}\n",
    "for i, indices in enumerate(sorted_indices):\n",
    "    # Exclude the user itself from the similar users\n",
    "    similar_users[i] = indices[1:top_n+1]\n",
    "\n",
    "# You can now access the similar users for each user in the `similar_users` dictionary.\n",
    "# For example, to get the similar users for user with index 0:\n",
    "similar_users_for_user_0 = similar_users[1]\n",
    "similar_users_for_user_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8d5b559",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorted_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b825cc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Create a heatmap for MAE matrix\n",
    "sns.heatmap(user_mae_scores, ax=axes[0], cmap='coolwarm', cbar=True)\n",
    "axes[0].set_title('User-User Mean Absolute Error Matrix')\n",
    "axes[0].set_xlabel('User Index')\n",
    "axes[0].set_ylabel('User Index')\n",
    "\n",
    "# Create a heatmap for cosine similarity matrix\n",
    "sns.heatmap(clean_ratings_cosine_similarity, ax=axes[1], cmap='coolwarm', cbar=True)\n",
    "axes[1].set_title('User-User Cosine Similarity Matrix')\n",
    "axes[1].set_xlabel('User Index')\n",
    "axes[1].set_ylabel('User Index')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dba4b16",
   "metadata": {},
   "source": [
    "### Since I couldn't collect as much data as I had hoped, you can still analyze the heat maps to compare the MAE matrix (left) with the cosine similarity user matrix (right). As mentioned earlier, a lower MAE value indicates better prediction, while a higher cosine similarity score also indicates better prediction. Despite the heat maps appearing as rectangles rather than squares, you can observe that these two matrices are symmetric. The main objective here is to identify any patterns in the data. One noticeable pattern is between user 18 and user 38, where the MAE heat map shows a dark blue indicating lower MAE scores, while the cosine similarity heat map shows light orange to red. This indicates that our model may be working well in that particular area."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2367a02b",
   "metadata": {},
   "source": [
    "### Root Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8768ed45",
   "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>user0</th>\n",
       "      <th>user1</th>\n",
       "      <th>user2</th>\n",
       "      <th>user3</th>\n",
       "      <th>user4</th>\n",
       "      <th>user5</th>\n",
       "      <th>user6</th>\n",
       "      <th>user7</th>\n",
       "      <th>user8</th>\n",
       "      <th>user9</th>\n",
       "      <th>...</th>\n",
       "      <th>user38</th>\n",
       "      <th>user39</th>\n",
       "      <th>user40</th>\n",
       "      <th>user41</th>\n",
       "      <th>user42</th>\n",
       "      <th>user43</th>\n",
       "      <th>user44</th>\n",
       "      <th>user45</th>\n",
       "      <th>user46</th>\n",
       "      <th>user47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.887459</td>\n",
       "      <td>1.761865</td>\n",
       "      <td>2.086664</td>\n",
       "      <td>1.859659</td>\n",
       "      <td>1.664582</td>\n",
       "      <td>1.406829</td>\n",
       "      <td>1.587713</td>\n",
       "      <td>1.428869</td>\n",
       "      <td>1.713914</td>\n",
       "      <td>...</td>\n",
       "      <td>1.632993</td>\n",
       "      <td>1.670828</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>1.820027</td>\n",
       "      <td>1.941863</td>\n",
       "      <td>2.673169</td>\n",
       "      <td>1.814295</td>\n",
       "      <td>2.520747</td>\n",
       "      <td>2.207940</td>\n",
       "      <td>2.273030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>1.887459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.338532</td>\n",
       "      <td>1.670828</td>\n",
       "      <td>1.587713</td>\n",
       "      <td>1.020621</td>\n",
       "      <td>1.755942</td>\n",
       "      <td>1.581139</td>\n",
       "      <td>1.299038</td>\n",
       "      <td>1.207615</td>\n",
       "      <td>...</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.968246</td>\n",
       "      <td>1.330727</td>\n",
       "      <td>1.443376</td>\n",
       "      <td>1.744037</td>\n",
       "      <td>2.979094</td>\n",
       "      <td>1.145644</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.450575</td>\n",
       "      <td>2.222049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>1.761865</td>\n",
       "      <td>1.338532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.947220</td>\n",
       "      <td>1.479020</td>\n",
       "      <td>1.172604</td>\n",
       "      <td>1.541104</td>\n",
       "      <td>1.258306</td>\n",
       "      <td>1.346291</td>\n",
       "      <td>1.338532</td>\n",
       "      <td>...</td>\n",
       "      <td>1.376893</td>\n",
       "      <td>1.282900</td>\n",
       "      <td>1.713914</td>\n",
       "      <td>1.554563</td>\n",
       "      <td>1.936492</td>\n",
       "      <td>2.857738</td>\n",
       "      <td>1.436141</td>\n",
       "      <td>2.291288</td>\n",
       "      <td>2.015564</td>\n",
       "      <td>2.349645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>2.086664</td>\n",
       "      <td>1.670828</td>\n",
       "      <td>1.947220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.952562</td>\n",
       "      <td>1.554563</td>\n",
       "      <td>2.111477</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>1.920286</td>\n",
       "      <td>1.683251</td>\n",
       "      <td>...</td>\n",
       "      <td>1.713914</td>\n",
       "      <td>1.639360</td>\n",
       "      <td>2.015564</td>\n",
       "      <td>1.859659</td>\n",
       "      <td>1.925703</td>\n",
       "      <td>2.700309</td>\n",
       "      <td>1.931105</td>\n",
       "      <td>1.936492</td>\n",
       "      <td>1.973787</td>\n",
       "      <td>2.470661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>1.859659</td>\n",
       "      <td>1.587713</td>\n",
       "      <td>1.479020</td>\n",
       "      <td>1.952562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.652019</td>\n",
       "      <td>1.761865</td>\n",
       "      <td>1.450575</td>\n",
       "      <td>1.707825</td>\n",
       "      <td>1.773650</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802776</td>\n",
       "      <td>1.658312</td>\n",
       "      <td>1.513825</td>\n",
       "      <td>1.876388</td>\n",
       "      <td>1.963203</td>\n",
       "      <td>2.673169</td>\n",
       "      <td>1.837117</td>\n",
       "      <td>2.286737</td>\n",
       "      <td>1.947220</td>\n",
       "      <td>2.397916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user0     user1     user2     user3     user4     user5     user6  \\\n",
       "user0  0.000000  1.887459  1.761865  2.086664  1.859659  1.664582  1.406829   \n",
       "user1  1.887459  0.000000  1.338532  1.670828  1.587713  1.020621  1.755942   \n",
       "user2  1.761865  1.338532  0.000000  1.947220  1.479020  1.172604  1.541104   \n",
       "user3  2.086664  1.670828  1.947220  0.000000  1.952562  1.554563  2.111477   \n",
       "user4  1.859659  1.587713  1.479020  1.952562  0.000000  1.652019  1.761865   \n",
       "\n",
       "          user7     user8     user9  ...    user38    user39    user40  \\\n",
       "user0  1.587713  1.428869  1.713914  ...  1.632993  1.670828  2.121320   \n",
       "user1  1.581139  1.299038  1.207615  ...  1.250000  0.968246  1.330727   \n",
       "user2  1.258306  1.346291  1.338532  ...  1.376893  1.282900  1.713914   \n",
       "user3  2.121320  1.920286  1.683251  ...  1.713914  1.639360  2.015564   \n",
       "user4  1.450575  1.707825  1.773650  ...  1.802776  1.658312  1.513825   \n",
       "\n",
       "         user41    user42    user43    user44    user45    user46    user47  \n",
       "user0  1.820027  1.941863  2.673169  1.814295  2.520747  2.207940  2.273030  \n",
       "user1  1.443376  1.744037  2.979094  1.145644  2.000000  1.450575  2.222049  \n",
       "user2  1.554563  1.936492  2.857738  1.436141  2.291288  2.015564  2.349645  \n",
       "user3  1.859659  1.925703  2.700309  1.931105  1.936492  1.973787  2.470661  \n",
       "user4  1.876388  1.963203  2.673169  1.837117  2.286737  1.947220  2.397916  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rmse_scores = []\n",
    "for i in range(0, len(clean_ratings_df)):\n",
    "    \n",
    "    # Extract the actual ratings from the DataFrames as numpy arrays\n",
    "    user_a = clean_ratings_df.fillna(0).loc[f\"user{i}\"].values\n",
    "    user_a_rmse = []\n",
    "    \n",
    "    for j in range(0, len(clean_ratings_df)):\n",
    "        \n",
    "        # Extract the predicted ratings from the DataFrames as numpy arrays\n",
    "        user_b = clean_ratings_df.fillna(0).loc[f\"user{j}\"].values\n",
    "        \n",
    "        # Calculate the squared error between actual and predicted ratings\n",
    "        squared_error = (user_a - user_b) ** 2\n",
    "        \n",
    "        # Calculate the mean of squared errors\n",
    "        mse = np.mean(squared_error)\n",
    "        \n",
    "        # Calculate the RMSE by taking the square root of MSE\n",
    "        rmse = np.sqrt(mse)\n",
    "        user_a_rmse.append(rmse)\n",
    "    user_rmse_scores.append(user_a_rmse)\n",
    "\n",
    "# Create a dataframe with the cosine similarity matrix\n",
    "clean_rmse_df = pd.DataFrame(user_rmse_scores, columns=clean_users, index=clean_users)\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "clean_rmse_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef12f3c8",
   "metadata": {},
   "source": [
    "###  Also according to [Su, X. & Khoshgoftaar, T. M.] The Root Squared Mean Error (RMSE) is becoming popular because it is the Netflix prize metric for movie recommendation performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eedcb233",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean_rmse_df.iloc[1, :].values.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "188b162b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots side by side\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# Create a heatmap for MAE matrix\n",
    "sns.heatmap(user_rmse_scores, ax=axes[0], cmap='coolwarm', cbar=True)\n",
    "axes[0].set_title('User-User Root Square Mean Error Matrix')\n",
    "axes[0].set_xlabel('User Index')\n",
    "axes[0].set_ylabel('User Index')\n",
    "\n",
    "# Create a heatmap for cosine similarity matrix\n",
    "sns.heatmap(clean_ratings_cosine_similarity, ax=axes[1], cmap='coolwarm', cbar=True)\n",
    "axes[1].set_title('User-User Cosine Similarity Matrix')\n",
    "axes[1].set_xlabel('User Index')\n",
    "axes[1].set_ylabel('User Index')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a6745e",
   "metadata": {},
   "source": [
    "### Similar to MAE, RMSE is also evaluated with lower scores indicating better predictions. Upon examining both heat maps, it is evident that there are patterns for user 18 in comparison with user 34 and user 38, suggesting that our models are performing well in those areas. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25584a4",
   "metadata": {},
   "source": [
    "### Item Based Content Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "26cc1359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for User user47: ['Iced Golden Ginger Drink', 'Iced Cascara Latte', 'Green Tea Latte', 'Chestnut Praline Latte', 'Iced Chai Tea Latte', 'Iced Coffee with Milk', 'Nitro Cold Brew', 'Iced White Chocolate Mocha with Raspberry Syrup', 'Iced Caffè Mocha', 'Flat White', 'Vanilla Bean Frappuccino', 'Iced Green Tea Latte', 'Double Chocolaty Chip Frappuccino', 'Vanilla Latte', 'Iced White Chocolate Mocha', 'Iced Caramel Macchiato', 'Iced Cinnamon Dolce Latte', 'Iced Caramel Cloud Macchiato', 'Iced Cinnamon Almondmilk Macchiato', 'Caramelized Honey Latte', 'Mocha Frappuccino']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import random\n",
    "\n",
    "# Replace NaN values with 0 (or any other suitable value)\n",
    "# clean_ratings_df.fillna(0, inplace=True)\n",
    "\n",
    "# Compute item-item similarity using cosine similarity\n",
    "item_similarity = cosine_similarity(clean_ratings_df.fillna(0))\n",
    "# Convert the similarity matrix to a Pandas DataFrame\n",
    "item_similarity_df = pd.DataFrame(item_similarity, index=clean_ratings_df.columns, columns=clean_ratings_df.columns)\n",
    "\n",
    "def get_top_similar_items(item_id, similarity_matrix, n=3):\n",
    "    # Get the similarity scores for the given item\n",
    "    item_similarity_scores = similarity_matrix[item_id]\n",
    "\n",
    "    # Sort the similarity scores in descending order\n",
    "    item_similarity_scores_sorted = item_similarity_scores.sort_values(ascending=False)\n",
    "\n",
    "    # Get the top n similar items\n",
    "    similar_items = item_similarity_scores_sorted.head(n).index\n",
    "\n",
    "    return similar_items\n",
    "\n",
    "# Function to make recommendations for a given user\n",
    "def make_recommendations(user_id, similarity_matrix, ratings_df, n=3):\n",
    "    # Get the ratings given by the user\n",
    "    user_ratings = ratings_df.loc[user_id]\n",
    "\n",
    "    # Get the items already rated by the user\n",
    "    rated_items = user_ratings[user_ratings > 0].index\n",
    "\n",
    "    # Initialize an empty list to store recommendations\n",
    "    recommendations = []\n",
    "\n",
    "    # Loop through the rated items\n",
    "    for item_id in rated_items:\n",
    "        # Get the top N similar items for the rated item\n",
    "        similar_items = get_top_similar_items(item_id, similarity_matrix, n)\n",
    "\n",
    "        # Add the similar items to the recommendations list\n",
    "        recommendations.extend(similar_items)\n",
    "\n",
    "    # Filter out the items already rated by the user\n",
    "    recommendations = list(set(recommendations) - set(rated_items))\n",
    "    \n",
    "    # Randomly choose n items from the list \n",
    "#     recommendations = random.sample(recommendations, n) \n",
    "\n",
    "    return recommendations\n",
    "\n",
    "# Example usage:\n",
    "# Make recommendations for user with user_id=1, using item-item similarity matrix\n",
    "user_id = clean_ratings_df[-1:].index[0]\n",
    "similarity_matrix = item_similarity_df # Use the converted DataFrame\n",
    "recommendations = make_recommendations(user_id, similarity_matrix, clean_ratings_df, n=5)\n",
    "print(\"Recommendations for User {}: {}\".format(user_id, recommendations))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc694295",
   "metadata": {},
   "source": [
    "###  The only problem with the current model is that it does not provide rankings or prioritize the recommended items. The recommendations are simply based on the top N similar items for each item rated by the user, without considering the overall relevance or importance of those items. This can result in recommendations that may not be truly relevant or valuable to the user. On the other hand, the User-based Collaborative Filtering Model, which relies on ratings given by users, and the Item-based Content Filtering Model, which uses cosine similarities of item descriptions, are often more sought after. These models take into account the relationships between users and items, and can provide more personalized and relevant recommendations by considering the overall preferences and interests of the users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560a6a4e",
   "metadata": {},
   "source": [
    "### Adjusted Cosine Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d57757cf",
   "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>user0</th>\n",
       "      <th>user1</th>\n",
       "      <th>user2</th>\n",
       "      <th>user3</th>\n",
       "      <th>user4</th>\n",
       "      <th>user5</th>\n",
       "      <th>user6</th>\n",
       "      <th>user7</th>\n",
       "      <th>user8</th>\n",
       "      <th>user9</th>\n",
       "      <th>...</th>\n",
       "      <th>user38</th>\n",
       "      <th>user39</th>\n",
       "      <th>user40</th>\n",
       "      <th>user41</th>\n",
       "      <th>user42</th>\n",
       "      <th>user43</th>\n",
       "      <th>user44</th>\n",
       "      <th>user45</th>\n",
       "      <th>user46</th>\n",
       "      <th>user47</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.071281</td>\n",
       "      <td>0.140892</td>\n",
       "      <td>-0.091640</td>\n",
       "      <td>0.176763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.529869</td>\n",
       "      <td>0.393748</td>\n",
       "      <td>0.473843</td>\n",
       "      <td>0.014655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218768</td>\n",
       "      <td>0.044292</td>\n",
       "      <td>-0.018404</td>\n",
       "      <td>0.018758</td>\n",
       "      <td>0.046312</td>\n",
       "      <td>0.136787</td>\n",
       "      <td>0.170673</td>\n",
       "      <td>-0.200155</td>\n",
       "      <td>-0.037340</td>\n",
       "      <td>0.028773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>-0.071281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.206001</td>\n",
       "      <td>0.119934</td>\n",
       "      <td>0.301805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.085561</td>\n",
       "      <td>0.146135</td>\n",
       "      <td>0.218676</td>\n",
       "      <td>-0.049869</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036852</td>\n",
       "      <td>0.320007</td>\n",
       "      <td>0.636328</td>\n",
       "      <td>-0.063830</td>\n",
       "      <td>0.015153</td>\n",
       "      <td>-0.028266</td>\n",
       "      <td>0.553671</td>\n",
       "      <td>0.277048</td>\n",
       "      <td>0.571444</td>\n",
       "      <td>0.020277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>0.140892</td>\n",
       "      <td>0.206001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.140752</td>\n",
       "      <td>0.429198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.241904</td>\n",
       "      <td>0.509990</td>\n",
       "      <td>0.256634</td>\n",
       "      <td>-0.058525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043249</td>\n",
       "      <td>-0.060573</td>\n",
       "      <td>0.284488</td>\n",
       "      <td>-0.074909</td>\n",
       "      <td>-0.142263</td>\n",
       "      <td>0.118869</td>\n",
       "      <td>0.322551</td>\n",
       "      <td>-0.023708</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>-0.088388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>-0.091640</td>\n",
       "      <td>0.119934</td>\n",
       "      <td>-0.140752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.160767</td>\n",
       "      <td>-0.158969</td>\n",
       "      <td>-0.136962</td>\n",
       "      <td>-0.093701</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069244</td>\n",
       "      <td>-0.096981</td>\n",
       "      <td>0.085403</td>\n",
       "      <td>-0.119934</td>\n",
       "      <td>0.049825</td>\n",
       "      <td>0.196955</td>\n",
       "      <td>0.011227</td>\n",
       "      <td>0.336198</td>\n",
       "      <td>0.173955</td>\n",
       "      <td>-0.125185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>0.176763</td>\n",
       "      <td>0.301805</td>\n",
       "      <td>0.429198</td>\n",
       "      <td>0.086730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.248793</td>\n",
       "      <td>0.502717</td>\n",
       "      <td>0.195439</td>\n",
       "      <td>-0.097091</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.071749</td>\n",
       "      <td>0.064888</td>\n",
       "      <td>0.508479</td>\n",
       "      <td>-0.039057</td>\n",
       "      <td>0.067432</td>\n",
       "      <td>0.219607</td>\n",
       "      <td>0.166846</td>\n",
       "      <td>0.077699</td>\n",
       "      <td>0.230327</td>\n",
       "      <td>-0.020464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user0     user1     user2     user3     user4  user5     user6  \\\n",
       "user0  1.000000 -0.071281  0.140892 -0.091640  0.176763    0.0  0.529869   \n",
       "user1 -0.071281  1.000000  0.206001  0.119934  0.301805    0.0 -0.085561   \n",
       "user2  0.140892  0.206001  1.000000 -0.140752  0.429198    0.0  0.241904   \n",
       "user3 -0.091640  0.119934 -0.140752  1.000000  0.086730    0.0 -0.160767   \n",
       "user4  0.176763  0.301805  0.429198  0.086730  1.000000    0.0  0.248793   \n",
       "\n",
       "          user7     user8     user9  ...    user38    user39    user40  \\\n",
       "user0  0.393748  0.473843  0.014655  ...  0.218768  0.044292 -0.018404   \n",
       "user1  0.146135  0.218676 -0.049869  ... -0.036852  0.320007  0.636328   \n",
       "user2  0.509990  0.256634 -0.058525  ... -0.043249 -0.060573  0.284488   \n",
       "user3 -0.158969 -0.136962 -0.093701  ... -0.069244 -0.096981  0.085403   \n",
       "user4  0.502717  0.195439 -0.097091  ... -0.071749  0.064888  0.508479   \n",
       "\n",
       "         user41    user42    user43    user44    user45    user46    user47  \n",
       "user0  0.018758  0.046312  0.136787  0.170673 -0.200155 -0.037340  0.028773  \n",
       "user1 -0.063830  0.015153 -0.028266  0.553671  0.277048  0.571444  0.020277  \n",
       "user2 -0.074909 -0.142263  0.118869  0.322551 -0.023708  0.029973 -0.088388  \n",
       "user3 -0.119934  0.049825  0.196955  0.011227  0.336198  0.173955 -0.125185  \n",
       "user4 -0.039057  0.067432  0.219607  0.166846  0.077699  0.230327 -0.020464  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean of each row and put it in a new col called mean\n",
    "# clean_ratings_df[\"mean\"] = clean_ratings_df.mean(axis=1)\n",
    "\n",
    "# Centered Cosine calculation\n",
    "# Find the mean of each row and subtract each val in the row by the row mean\n",
    "\n",
    "centered_cosine_df = clean_ratings_df.fillna(0).sub(clean_ratings_df.fillna(0).mean(axis=1), axis=0)\n",
    "\n",
    "centered_cosine_similarity = cosine_similarity(centered_cosine_df)\n",
    "\n",
    "# Create a dataframe with the cosine similarity matrix\n",
    "centered_cosine_similarity_df = pd.DataFrame(centered_cosine_similarity, columns=clean_users, index=clean_users)\n",
    "\n",
    "# Print the cosine similarity matrix\n",
    "centered_cosine_similarity_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d8ce77",
   "metadata": {},
   "source": [
    "###  The code calculates the mean of each row in the clean_ratings_df dataframe and then subtracts this mean value from each element in the corresponding row of the new centered_cosine_df dataframe. This process is known as centering the data, as it shifts the values in each row so that the mean of the row becomes 0. This can be useful in certain similarity calculations, such as cosine similarity, to remove any bias or baseline effects from the data before performing similarity comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1845da33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([46, 21, 20, 19, 18], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume you have a pandas DataFrame called `cosine_similarity_matrix` that contains the cosine similarity values\n",
    "# among users, where rows represent users and columns represent users, and the values are the cosine similarity scores.\n",
    "\n",
    "# Sort the rows of the cosine similarity matrix based on the similarity scores in descending order\n",
    "sorted_indices = np.argsort(centered_cosine_similarity, axis=1)[:, ::-1]\n",
    "\n",
    "# Specify the number of top similar users to select for each user\n",
    "top_n = 5\n",
    "\n",
    "# Select the top N similar users for each user from the cosine similarity matrix\n",
    "similar_users = {}\n",
    "for i, indices in enumerate(sorted_indices):\n",
    "    # Exclude the user itself from the similar users\n",
    "    similar_users[i] = indices[1:top_n+1]\n",
    "\n",
    "# You can now access the similar users for each user in the `similar_users` dictionary.\n",
    "# For example, to get the similar users for user with index 0:\n",
    "similar_users_for_user_0 = similar_users[31]\n",
    "similar_users_for_user_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9a20d578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Salted Caramel Mocha             1.110114\n",
       "Iced White Chocolate Mocha       1.110114\n",
       "Vanilla Sweet Cream Cold Brew    0.986306\n",
       "Mocha Frappuccino                0.949215\n",
       "Iced Caramel Macchiato           0.591784\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the target user for whom you want to generate recommendations\n",
    "target_user = clean_ratings_df[-1:].index[0]\n",
    "\n",
    "# Specify the number of top similar users to use for generating recommendations\n",
    "top_n = 5\n",
    "\n",
    "# Extract the similarity scores for the target user from the cosine similarity matrix\n",
    "similarity_scores = centered_cosine_similarity_df[target_user]\n",
    "\n",
    "# Sort the similarity scores in descending order and select the top N similar users\n",
    "similar_users_indices = np.argsort(similarity_scores)[::-1][1:top_n+1]\n",
    "\n",
    "# Get the ratings of similar users for items that the target user has not rated\n",
    "similar_users_ratings = clean_ratings_df.iloc[similar_users_indices]\n",
    "target_user_ratings = clean_ratings_df.loc[target_user]\n",
    "\n",
    "# # Filter out items that the target user has already rated\n",
    "unrated_items_mask = target_user_ratings.isna()\n",
    "similar_users_ratings = similar_users_ratings.loc[:, unrated_items_mask]\n",
    "\n",
    "# Calculate the weighted average of the ratings of similar users using the similarity scores as weights\n",
    "similarity_scores = similarity_scores[similar_users_indices]\n",
    "weighted_avg_ratings = similar_users_ratings.mul(similarity_scores, axis=0).sum() / similarity_scores.sum()\n",
    "\n",
    "# Sort the weighted average ratings in descending order to get the top-rated items\n",
    "recommended_items = weighted_avg_ratings.sort_values(ascending=False)\n",
    "\n",
    "# You can now access the recommended items for the target user in the `recommended_items` Series.\n",
    "# For example, to get the top N recommended items for the target user:\n",
    "top_n_recommended_items = recommended_items.head()\n",
    "top_n_recommended_items"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb47f430",
   "metadata": {},
   "source": [
    "###  Even though we used the centered cosine similarity which had different scores than the model we used in deployment, we still got the same Top_N drink recommendations. However, they are in a different order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6aac72f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with two subplots side by side\n",
    "fig, axes = plt.subplots(2, 2, figsize=(12, 12))\n",
    "\n",
    "# Create a heatmap for MAE matrix\n",
    "sns.heatmap(user_mae_scores, ax=axes[0][0], cmap='coolwarm', cbar=True)\n",
    "axes[0][0].set_title('User-User Mean Absolute Error Matrix')\n",
    "plt.setp( axes[0][0].get_xticklabels(), visible=False)\n",
    "# axes[0][0].set_xlabel('User Index')\n",
    "axes[0][0].set_ylabel('User Index')\n",
    "\n",
    "# Create a heatmap for cosine similarity matrix\n",
    "sns.heatmap(user_rmse_scores, ax=axes[1][0], cmap='coolwarm', cbar=True)\n",
    "axes[1][0].set_title('User-User Root Square Mean Error Matrix ')\n",
    "axes[1][0].set_xlabel('User Index')\n",
    "axes[1][0].set_ylabel('User Index')\n",
    "\n",
    "# Create a heatmap for RMSE matrix\n",
    "sns.heatmap(centered_cosine_similarity_df, ax=axes[0][1], cmap='coolwarm', cbar=True)\n",
    "axes[0][1].set_title('User-User Centered Cosine Similarity Matrix')\n",
    "# axes[0][1].set_xlabel('User Index')\n",
    "# axes[0][1].set_ylabel('User Index')\n",
    "plt.setp( axes[0][1].get_xticklabels(), visible=False)\n",
    "\n",
    "\n",
    "# Create a heatmap for cosine similarity matrix\n",
    "sns.heatmap(centered_cosine_similarity_df, ax=axes[1][1], cmap='coolwarm', cbar=True)\n",
    "axes[1][1].set_title('User-User Centered Cosine Similarity Matrix')\n",
    "axes[1][1].set_xlabel('User Index')\n",
    "axes[1][1].set_ylabel('User Index')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8022865b",
   "metadata": {},
   "source": [
    "###  Upon examining both evaluation maps with the centered cosine similarity maps, it is evident that there are patterns for user 3 and user 12, user 9 in comparison with user 27, and again with user 18 in comparison with user 34 and user 38. This suggests that this model would also perform well in deployment and could be used if desired. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a39ba4",
   "metadata": {},
   "source": [
    "# References\n",
    "Bell, R. M., Koren, Y., & Volinsky, C. (2010). The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems (TMIS), 1(1), 1-19.\n",
    "\n",
    "Elahi, M., Ricci, F., & Rubens, N. (2014). Active Learning in Collaborative Filtering Recommender Systems.\n",
    "\n",
    "Jannach, D., & Jugovac, M. (2016). Measuring the Business Value of Recommender Systems. University of Klagenfurt, TU Dortmund.\n",
    "\n",
    "Real Python. (n.d.). How to Build a Recommendation Engine with Collaborative Filtering. Retrieved from https://realpython.com/build-recommendation-engine-collaborative-filtering/\n",
    "\n",
    "Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b402b00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preferred_drinks.drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "58a46e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# surveys.drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be35a0cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonAutomation]",
   "language": "python",
   "name": "conda-env-PythonAutomation-py"
  },
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    "version": 3
   },
   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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 },
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}