VADER_sentiment_score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use VADER to analyze Reviews\n",
    "https://towardsdatascience.com/sentimental-analysis-using-vader-a3415fef7664"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to C:\\Users\\A Girl's\n",
      "[nltk_data]     Lenovo\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>stars</th>\n",
       "      <th>helpful_yes</th>\n",
       "      <th>helpful_no</th>\n",
       "      <th>text</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>I am interested in the flavoring components us...</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Boy, was I surprised when I got my Bryers home...</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key  stars  helpful_yes  helpful_no  \\\n",
       "0  0_breyers      1           11           0   \n",
       "1  0_breyers      1            7           0   \n",
       "2  0_breyers      1            8           0   \n",
       "3  0_breyers      1            4           0   \n",
       "4  0_breyers      5           21           2   \n",
       "\n",
       "                                                text  rating  \n",
       "0  I am interested in the flavoring components us...     4.1  \n",
       "1  Boy, was I surprised when I got my Bryers home...     4.1  \n",
       "2  I havent purchased this product in awhile and ...     4.1  \n",
       "3  The Natural Vanilla recipe change to include T...     4.1  \n",
       "4  I had the same issue with breyers. I finally f...     4.1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vader = pd.read_csv(\"Resources/helpful_clean_reviews_combined.csv\")\n",
    "# drop index\n",
    "df_vader = df_vader.drop([\"Unnamed: 0\"], axis=1)\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
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       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "         key                                               text  stars\n",
       "0  0_breyers  I am interested in the flavoring components us...      1\n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1\n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1\n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1\n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vader = df_vader.drop([\"helpful_yes\",\"helpful_no\",\"rating\"], axis=1)\n",
    "df_vader = df_vader.reindex([\"key\",\"text\",\"stars\"], axis=1)\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>text</th>\n",
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       "      <th>sentiment</th>\n",
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       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         key                                               text  stars  \\\n",
       "0  0_breyers  I am interested in the flavoring components us...      1   \n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1   \n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1   \n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1   \n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5   \n",
       "\n",
       "  sentiment  \n",
       "0       neg  \n",
       "1       neg  \n",
       "2       neg  \n",
       "3       neg  \n",
       "4       pos  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assign 'pos' for positive sentiment, 'neg' for negative\n",
    "# if stars 4 or higher, sentiment is positive\n",
    "\n",
    "def applyFunc(s):\n",
    "    if s >= 4:\n",
    "        return 'pos'\n",
    "    else:\n",
    "        return 'neg'\n",
    "\n",
    "# populate column        \n",
    "df_vader['sentiment'] = df_vader['stars'].apply(applyFunc)\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>vader_sentiment_score</th>\n",
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       "      <td>{'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...</td>\n",
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       "      <th>1</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>Boy, was I surprised when I got my Bryers home...</td>\n",
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       "      <td>I havent purchased this product in awhile and ...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "         key                                               text  stars  \\\n",
       "0  0_breyers  I am interested in the flavoring components us...      1   \n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1   \n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1   \n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1   \n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5   \n",
       "\n",
       "  sentiment                              vader_sentiment_score  \n",
       "0       neg  {'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...  \n",
       "1       neg  {'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...  \n",
       "2       neg  {'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...  \n",
       "3       neg  {'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...  \n",
       "4       pos  {'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create vader_sentiment_score column to hold polarity_score dictionaries\n",
    "df_vader['vader_sentiment_score'] = df_vader['text'].apply(lambda review: sid.polarity_scores(review))\n",
    "\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>vader_sentiment_score</th>\n",
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       "      <td>I am interested in the flavoring components us...</td>\n",
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       "      <td>neg</td>\n",
       "      <td>{'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...</td>\n",
       "      <td>0.9804</td>\n",
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       "      <th>1</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>Boy, was I surprised when I got my Bryers home...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...</td>\n",
       "      <td>-0.1553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...</td>\n",
       "      <td>0.7776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...</td>\n",
       "      <td>0.6124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key                                               text  stars  \\\n",
       "0  0_breyers  I am interested in the flavoring components us...      1   \n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1   \n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1   \n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1   \n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5   \n",
       "\n",
       "  sentiment                              vader_sentiment_score  compound  \n",
       "0       neg  {'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...    0.9804  \n",
       "1       neg  {'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...    0.2263  \n",
       "2       neg  {'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...   -0.1553  \n",
       "3       neg  {'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...    0.7776  \n",
       "4       pos  {'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...    0.6124  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create compound column to hold total score\n",
    "df_vader['compound']  = df_vader['vader_sentiment_score'].apply(lambda score_dict: score_dict['compound'])\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I am interested in the flavoring components us...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...</td>\n",
       "      <td>0.9804</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>Boy, was I surprised when I got my Bryers home...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...</td>\n",
       "      <td>0.2263</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...</td>\n",
       "      <td>-0.1553</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...</td>\n",
       "      <td>0.7776</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...</td>\n",
       "      <td>0.6124</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key                                               text  stars  \\\n",
       "0  0_breyers  I am interested in the flavoring components us...      1   \n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1   \n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1   \n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1   \n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5   \n",
       "\n",
       "  sentiment                              vader_sentiment_score  compound  \\\n",
       "0       neg  {'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...    0.9804   \n",
       "1       neg  {'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...    0.2263   \n",
       "2       neg  {'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...   -0.1553   \n",
       "3       neg  {'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...    0.7776   \n",
       "4       pos  {'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...    0.6124   \n",
       "\n",
       "  comp_score  \n",
       "0        pos  \n",
       "1        pos  \n",
       "2        neg  \n",
       "3        pos  \n",
       "4        pos  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine if overall sentiment is positive or negative\n",
    "# if compound value >0, then 'pos', otherwise its 'neg'\n",
    "df_vader['comp_score'] = df_vader['compound'].apply(lambda c: 'pos' if c >0 else 'neg')\n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 Vader Sentiment Scores\n",
      "Ranked by Compound Value\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>0.9804</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.8556</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.5826</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.8501</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2192</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>0.6884</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.9811</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2195</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>0.6885</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.8934</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.9865</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.9640</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.5093</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2200</th>\n",
       "      <td>39_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.6369</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2202</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.7804</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>38_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.7159</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.5562</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.7096</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2205</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.9042</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.8172</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2207</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.7445</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.5659</td>\n",
       "      <td>pos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             key  stars sentiment  compound comp_score\n",
       "0      0_breyers      1       neg    0.9804        pos\n",
       "2201        4_bj      5       pos    0.8556        pos\n",
       "2190  39_talenti      5       pos    0.5826        pos\n",
       "2191  39_talenti      5       pos    0.8501        pos\n",
       "2192  39_talenti      2       neg    0.6884        pos\n",
       "2193  39_talenti      5       pos    0.9811        pos\n",
       "2195  39_talenti      1       neg    0.6885        pos\n",
       "2196  39_talenti      5       pos    0.8934        pos\n",
       "2197  39_talenti      5       pos    0.9865        pos\n",
       "2198  39_talenti      5       pos    0.9640        pos\n",
       "2199  39_talenti      5       pos    0.5093        pos\n",
       "2200  39_talenti      5       pos    0.6369        pos\n",
       "2202        4_bj      5       pos    0.7804        pos\n",
       "2156  38_talenti      5       pos    0.7159        pos\n",
       "2203        4_bj      5       pos    0.5562        pos\n",
       "2204        4_bj      5       pos    0.7096        pos\n",
       "2205        4_bj      5       pos    0.9042        pos\n",
       "2206        4_bj      5       pos    0.8172        pos\n",
       "2207        4_bj      5       pos    0.7445        pos\n",
       "2208        4_bj      5       pos    0.5659        pos"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View VADER sentiment ranking\n",
    "\n",
    "vader_ranking = pd.DataFrame(df_vader, columns=['key','stars','sentiment','compound','comp_score'])\n",
    "vader_review_rank = vader_ranking.sort_values('comp_score', ascending=False)\n",
    "\n",
    "print(str('Top 20 Vader Sentiment Scores'))\n",
    "print(str('Ranked by Compound Value'))\n",
    "display(vader_review_rank[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bottom 20 Vader Sentiment Scores\n",
      "Ranked by Compound Value\n"
     ]
    },
    {
     "data": {
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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>key</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>53_hd</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>24_hd</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.9444</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3098</th>\n",
       "      <td>60_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.7323</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>12_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.8121</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>22_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.3400</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>12_breyers</td>\n",
       "      <td>2</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.2261</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>22_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.1761</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>34_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>-0.6113</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3091</th>\n",
       "      <td>60_breyers</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>-0.6074</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>34_talenti</td>\n",
       "      <td>4</td>\n",
       "      <td>pos</td>\n",
       "      <td>-0.2263</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>12_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.6800</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2686</th>\n",
       "      <td>5_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.6420</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2757</th>\n",
       "      <td>50_breyers</td>\n",
       "      <td>3</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.6652</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.4290</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.6601</td>\n",
       "      <td>neg</td>\n",
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       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>46_bj</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>-0.7580</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>34_talenti</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>12_breyers</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.9234</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>6_talenti</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.1952</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>34_bj</td>\n",
       "      <td>3</td>\n",
       "      <td>neg</td>\n",
       "      <td>-0.2411</td>\n",
       "      <td>neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             key  stars sentiment  compound comp_score\n",
       "2858       53_hd      5       pos    0.0000        neg\n",
       "1126       24_hd      2       neg   -0.9444        neg\n",
       "3098  60_breyers      1       neg   -0.7323        neg\n",
       "285   12_breyers      1       neg   -0.8121        neg\n",
       "1040  22_breyers      1       neg   -0.3400        neg\n",
       "288   12_breyers      2       neg   -0.2261        neg\n",
       "1041  22_breyers      1       neg   -0.1761        neg\n",
       "1847  34_talenti      5       pos   -0.6113        neg\n",
       "3091  60_breyers      5       pos   -0.6074        neg\n",
       "1848  34_talenti      4       pos   -0.2263        neg\n",
       "292   12_breyers      1       neg   -0.6800        neg\n",
       "2686   5_breyers      1       neg   -0.6420        neg\n",
       "2757  50_breyers      3       neg   -0.6652        neg\n",
       "2231        4_bj      1       neg   -0.4290        neg\n",
       "2230        4_bj      1       neg   -0.6601        neg\n",
       "2571       46_bj      5       pos   -0.7580        neg\n",
       "1854  34_talenti      5       pos    0.0000        neg\n",
       "299   12_breyers      1       neg   -0.9234        neg\n",
       "3081   6_talenti      1       neg   -0.1952        neg\n",
       "1788       34_bj      3       neg   -0.2411        neg"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(str('Bottom 20 Vader Sentiment Scores'))\n",
    "print(str('Ranked by Compound Value'))\n",
    "display(vader_review_rank[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>key</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>vader_sentiment_score</th>\n",
       "      <th>compound</th>\n",
       "      <th>comp_score</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I am interested in the flavoring components us...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...</td>\n",
       "      <td>0.9804</td>\n",
       "      <td>pos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>Boy, was I surprised when I got my Bryers home...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...</td>\n",
       "      <td>0.2263</td>\n",
       "      <td>pos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I havent purchased this product in awhile and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...</td>\n",
       "      <td>-0.1553</td>\n",
       "      <td>neg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>The Natural Vanilla recipe change to include T...</td>\n",
       "      <td>1</td>\n",
       "      <td>neg</td>\n",
       "      <td>{'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...</td>\n",
       "      <td>0.7776</td>\n",
       "      <td>pos</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>I had the same issue with breyers. I finally f...</td>\n",
       "      <td>5</td>\n",
       "      <td>pos</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...</td>\n",
       "      <td>0.6124</td>\n",
       "      <td>pos</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         key                                               text  stars  \\\n",
       "0  0_breyers  I am interested in the flavoring components us...      1   \n",
       "1  0_breyers  Boy, was I surprised when I got my Bryers home...      1   \n",
       "2  0_breyers  I havent purchased this product in awhile and ...      1   \n",
       "3  0_breyers  The Natural Vanilla recipe change to include T...      1   \n",
       "4  0_breyers  I had the same issue with breyers. I finally f...      5   \n",
       "\n",
       "  sentiment                              vader_sentiment_score  compound  \\\n",
       "0       neg  {'neg': 0.042, 'neu': 0.823, 'pos': 0.135, 'co...    0.9804   \n",
       "1       neg  {'neg': 0.0, 'neu': 0.957, 'pos': 0.043, 'comp...    0.2263   \n",
       "2       neg  {'neg': 0.128, 'neu': 0.757, 'pos': 0.115, 'co...   -0.1553   \n",
       "3       neg  {'neg': 0.027, 'neu': 0.844, 'pos': 0.129, 'co...    0.7776   \n",
       "4       pos  {'neg': 0.0, 'neu': 0.783, 'pos': 0.217, 'comp...    0.6124   \n",
       "\n",
       "  comp_score predicted  \n",
       "0        pos         0  \n",
       "1        pos         0  \n",
       "2        neg         1  \n",
       "3        pos         0  \n",
       "4        pos         1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate how well VADER performed with predicting the same sentiment as our original formula\n",
    "# this is a way to test how reliable our formula is\n",
    "\n",
    "# Create predicted column to hold 1 if predicted correctly, and 0 if otherwise\n",
    "df_vader['predicted'] =\"\"\n",
    "\n",
    "\n",
    "for s in range(len(df_vader)):\n",
    "    if (df_vader.sentiment[s] == 'pos') & (df_vader.comp_score[s] == 'pos'):\n",
    "        df_vader['predicted'][s] = 1\n",
    "\n",
    "    elif (df_vader.sentiment[s] == 'neg') & (df_vader.comp_score[s] == 'neg'):\n",
    "        df_vader['predicted'][s] = 1\n",
    "\n",
    "    else:\n",
    "        df_vader['predicted'][s] = 0\n",
    "         \n",
    "df_vader.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create VADER_sentiment_score\n",
    "#df_vader.to_csv(\"Resources/VADER_sentiment_score.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8221702252120503"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate overall prediction accuracy\n",
    "pred_rate_all = df_vader['predicted'].sum() / df_vader['predicted'].count()\n",
    "pred_rate_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3742690058479532"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create negative sentiment dataframe\n",
    "df_neg_sent = df_vader[df_vader['sentiment'] =='neg']\n",
    "\n",
    "# calculate negative rate prediction accuracy\n",
    "pred_rate_neg = df_neg_sent['predicted'].sum() / df_neg_sent['predicted'].count()\n",
    "pred_rate_neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9341864716636198"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create positive sentiment dataframe\n",
    "\n",
    "df_pos_sent = df_vader[df_vader['sentiment'] =='pos']\n",
    "\n",
    "# calculate positive rate prediction accuracy\n",
    "pred_rate_pos = df_pos_sent['predicted'].sum() / df_pos_sent['predicted'].count()\n",
    "pred_rate_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VADER Sentiment Intensity Analyzer Summary\n",
      "------------------------------------------\n",
      "VADER Predicted the sentiment for  82 % of all reviews combined correctly.\n",
      "VADER Predicted the sentiment for  37 % of negative reviews correctly.\n",
      "VADER Predicted the sentiment for  93 % of positive reviews correctly.\n"
     ]
    }
   ],
   "source": [
    "print ('VADER Sentiment Intensity Analyzer Summary')\n",
    "print ('------------------------------------------')\n",
    "print ('VADER Predicted the sentiment for ',round(pred_rate_all*100),'% of all reviews combined correctly.')\n",
    "print ('VADER Predicted the sentiment for ',round(pred_rate_neg*100),'% of negative reviews correctly.')\n",
    "print ('VADER Predicted the sentiment for ',round(pred_rate_pos*100),'% of positive reviews correctly.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Positive reviews are what we want as we hope the reviews will aide in identifying a new specialty flavor.  The VADER Sentiment Analyzer agrees with our formula for positive sentiment 93% of the time. That is GREAT!\n",
    "\n",
    "In theory, we should be able to calculate the average \"compound value\" for each key, followed by ranking the top 10 to see which products achieved the highest sentiment based on text alone. \n",
    "\n",
    "So lets see...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>compound</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>0.6124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>0.9502</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>0.4497</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          key  compound predicted\n",
       "4   0_breyers    0.6124         1\n",
       "56  0_breyers    0.9502         1\n",
       "68  0_breyers    0.4497         1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create df df_pos_sent_clean to focus only on relevant columns\n",
    "df_pos_sent_clean = pd.DataFrame(df_pos_sent, columns=['key','compound','predicted'])\n",
    "\n",
    "# filter df_pos_sent_clean down to the 93% positive prediction agreement\n",
    "df_pos_sent_clean = df_pos_sent_clean[df_pos_sent_clean['predicted'] ==1]\n",
    "df_pos_sent_clean.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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>compound</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0_breyers</th>\n",
       "      <td>0.670767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0_hd</th>\n",
       "      <td>0.760012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0_talenti</th>\n",
       "      <td>0.862295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_bj</th>\n",
       "      <td>0.754062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10_breyers</th>\n",
       "      <td>0.896914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8_bj</th>\n",
       "      <td>0.852022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8_hd</th>\n",
       "      <td>0.806029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8_talenti</th>\n",
       "      <td>0.801650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9_bj</th>\n",
       "      <td>0.822423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9_hd</th>\n",
       "      <td>0.799963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            compound\n",
       "key                 \n",
       "0_breyers   0.670767\n",
       "0_hd        0.760012\n",
       "0_talenti   0.862295\n",
       "10_bj       0.754062\n",
       "10_breyers  0.896914\n",
       "...              ...\n",
       "8_bj        0.852022\n",
       "8_hd        0.806029\n",
       "8_talenti   0.801650\n",
       "9_bj        0.822423\n",
       "9_hd        0.799963\n",
       "\n",
       "[183 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average compound rating and reveal top 10 \n",
    "df_key_avg = df_pos_sent_clean.groupby('key').mean()\n",
    "\n",
    "df_key_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_bj</td>\n",
       "      <td>Salted Caramel Core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_bj</td>\n",
       "      <td>Netflix &amp; Chilll'd™</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2_bj</td>\n",
       "      <td>Chip Happens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3_bj</td>\n",
       "      <td>Cannoli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>Gimme S’more!™</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    key                 name\n",
       "0  0_bj  Salted Caramel Core\n",
       "1  1_bj  Netflix & Chilll'd™\n",
       "2  2_bj         Chip Happens\n",
       "3  3_bj              Cannoli\n",
       "4  4_bj       Gimme S’more!™"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create products table with key and ice cream name\n",
    "products = pd.read_csv(\"Resources/products.csv\")\n",
    "products = products.drop([\"brand\",\"subhead\",\"description\",\"rating\",\"rating_count\",\"ingredients\"], axis=1)\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>name</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1_bj</td>\n",
       "      <td>Netflix &amp; Chilll'd™</td>\n",
       "      <td>0.821515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2_bj</td>\n",
       "      <td>Chip Happens</td>\n",
       "      <td>0.884748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4_bj</td>\n",
       "      <td>Gimme S’more!™</td>\n",
       "      <td>0.830148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5_bj</td>\n",
       "      <td>Peanut Butter Half Baked®</td>\n",
       "      <td>0.860443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6_bj</td>\n",
       "      <td>Berry Sweet Mascarpone</td>\n",
       "      <td>0.792571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>60_breyers</td>\n",
       "      <td>Chocolate Peanut Butter</td>\n",
       "      <td>0.851583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>61_breyers</td>\n",
       "      <td>Natural Vanilla Snack Cups 10ct</td>\n",
       "      <td>0.887400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>63_breyers</td>\n",
       "      <td>Chocolate Snack Cups 10ct</td>\n",
       "      <td>0.855750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>64_breyers</td>\n",
       "      <td>CINNABON®</td>\n",
       "      <td>0.693760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>65_breyers</td>\n",
       "      <td>CarbSmart™ Caramel Swirl Bar</td>\n",
       "      <td>0.730150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            key                             name  compound\n",
       "0          1_bj              Netflix & Chilll'd™  0.821515\n",
       "1          2_bj                     Chip Happens  0.884748\n",
       "2          4_bj                   Gimme S’more!™  0.830148\n",
       "3          5_bj        Peanut Butter Half Baked®  0.860443\n",
       "4          6_bj           Berry Sweet Mascarpone  0.792571\n",
       "..          ...                              ...       ...\n",
       "178  60_breyers          Chocolate Peanut Butter  0.851583\n",
       "179  61_breyers  Natural Vanilla Snack Cups 10ct  0.887400\n",
       "180  63_breyers        Chocolate Snack Cups 10ct  0.855750\n",
       "181  64_breyers                        CINNABON®  0.693760\n",
       "182  65_breyers     CarbSmart™ Caramel Swirl Bar  0.730150\n",
       "\n",
       "[183 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge products and key_avg dataframes to get ice cream name for each key\n",
    "df_key_avg = pd.merge(products, \n",
    "                      df_key_avg, \n",
    "                      on ='key', \n",
    "                      how ='inner')\n",
    "df_key_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Top 20 Ice Cream Flavors\n",
      "        Ranked by Average Compound Value\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>key</th>\n",
       "      <th>name</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5_breyers</td>\n",
       "      <td>Mint Chocolate Chip</td>\n",
       "      <td>0.950500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>17_breyers</td>\n",
       "      <td>Vanilla Caramel</td>\n",
       "      <td>0.942800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>11_breyers</td>\n",
       "      <td>French Vanilla</td>\n",
       "      <td>0.941680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>56_hd</td>\n",
       "      <td>Strawberry Waffle Cone HEAVEN Light Ice Cream</td>\n",
       "      <td>0.940275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>25_breyers</td>\n",
       "      <td>No Sugar Added Vanilla Chocolate Strawberry</td>\n",
       "      <td>0.936500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>50_breyers</td>\n",
       "      <td>Non-Dairy OREO® Cookies &amp; Cream</td>\n",
       "      <td>0.935789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>31_breyers</td>\n",
       "      <td>SNICKERS®</td>\n",
       "      <td>0.934600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>18_hd</td>\n",
       "      <td>Coconut Caramel Non-Dairy</td>\n",
       "      <td>0.930483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>24_hd</td>\n",
       "      <td>Coffee Vanilla Chocolate TRIO CRISPY LAYERS</td>\n",
       "      <td>0.929120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>15_talenti</td>\n",
       "      <td>COLD BREW COFFEE SORBETTO</td>\n",
       "      <td>0.928032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>65_hd</td>\n",
       "      <td>Vanilla Milk Chocolate Ice Cream Bar</td>\n",
       "      <td>0.923325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>29_hd</td>\n",
       "      <td>Dulce de Leche Ice Cream</td>\n",
       "      <td>0.919044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>55_hd</td>\n",
       "      <td>Strawberry Ice Cream</td>\n",
       "      <td>0.913781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>16_breyers</td>\n",
       "      <td>OREO® &amp; CHIPS AHOY!® 2in1</td>\n",
       "      <td>0.911600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>27_breyers</td>\n",
       "      <td>Raspberry Cheesecake Gelato Indulgences</td>\n",
       "      <td>0.910120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>48_hd</td>\n",
       "      <td>Rocky Road Ice Cream</td>\n",
       "      <td>0.909300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>49_hd</td>\n",
       "      <td>Rosé &amp; Cream Ice Cream</td>\n",
       "      <td>0.901633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>47_hd</td>\n",
       "      <td>Raspberry Sorbet</td>\n",
       "      <td>0.901217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>35_breyers</td>\n",
       "      <td>No Sugar Added Butter Pecan</td>\n",
       "      <td>0.901033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4_hd</td>\n",
       "      <td>Butter Pecan Ice Cream</td>\n",
       "      <td>0.900067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            key                                           name  compound\n",
       "146   5_breyers                            Mint Chocolate Chip  0.950500\n",
       "154  17_breyers                                Vanilla Caramel  0.942800\n",
       "150  11_breyers                                 French Vanilla  0.941680\n",
       "92        56_hd  Strawberry Waffle Cone HEAVEN Light Ice Cream  0.940275\n",
       "160  25_breyers    No Sugar Added Vanilla Chocolate Strawberry  0.936500\n",
       "172  50_breyers                Non-Dairy OREO® Cookies & Cream  0.935789\n",
       "164  31_breyers                                      SNICKERS®  0.934600\n",
       "62        18_hd                      Coconut Caramel Non-Dairy  0.930483\n",
       "67        24_hd    Coffee Vanilla Chocolate TRIO CRISPY LAYERS  0.929120\n",
       "116  15_talenti                      COLD BREW COFFEE SORBETTO  0.928032\n",
       "99        65_hd           Vanilla Milk Chocolate Ice Cream Bar  0.923325\n",
       "71        29_hd                       Dulce de Leche Ice Cream  0.919044\n",
       "91        55_hd                           Strawberry Ice Cream  0.913781\n",
       "153  16_breyers                      OREO® & CHIPS AHOY!® 2in1  0.911600\n",
       "162  27_breyers        Raspberry Cheesecake Gelato Indulgences  0.910120\n",
       "87        48_hd                           Rocky Road Ice Cream  0.909300\n",
       "88        49_hd                         Rosé & Cream Ice Cream  0.901633\n",
       "86        47_hd                               Raspberry Sorbet  0.901217\n",
       "166  35_breyers                    No Sugar Added Butter Pecan  0.901033\n",
       "50         4_hd                         Butter Pecan Ice Cream  0.900067"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View Top 20 Ice Cream Flavors\n",
    "\n",
    "df_key_avg = df_key_avg.sort_values('compound', ascending=False)\n",
    "\n",
    "print(str('        Top 20 Ice Cream Flavors'))\n",
    "print(str('        Ranked by Average Compound Value'))\n",
    "display(df_key_avg[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Bottom 20 Ice Cream Flavors\n",
      "        Ranked by Average Compound Value\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>name</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>41_talenti</td>\n",
       "      <td>STRAWBERRY HIBISCUS SORBETTO</td>\n",
       "      <td>0.718810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>26_hd</td>\n",
       "      <td>Cookies and Cream Ice Cream</td>\n",
       "      <td>0.717750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>68_hd</td>\n",
       "      <td>Whiskey Hazelnut Latte Ice Cream</td>\n",
       "      <td>0.716380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>64_hd</td>\n",
       "      <td>Vanilla Chocolate Chip Ice Cream</td>\n",
       "      <td>0.713725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>33_breyers</td>\n",
       "      <td>CarbSmart™ Almond Bar</td>\n",
       "      <td>0.708600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1_breyers</td>\n",
       "      <td>Homemade Vanilla</td>\n",
       "      <td>0.706433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>48_bj</td>\n",
       "      <td>Vanilla Caramel Fudge</td>\n",
       "      <td>0.705100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>64_breyers</td>\n",
       "      <td>CINNABON®</td>\n",
       "      <td>0.693760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0_breyers</td>\n",
       "      <td>Natural Vanilla</td>\n",
       "      <td>0.670767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>21_breyers</td>\n",
       "      <td>Vanilla Fudge Twirl</td>\n",
       "      <td>0.661625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>43_hd</td>\n",
       "      <td>Peppermint Bark Ice Cream</td>\n",
       "      <td>0.661473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>30_breyers</td>\n",
       "      <td>No Sugar Added Caramel Swirl</td>\n",
       "      <td>0.660550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>27_hd</td>\n",
       "      <td>Double Belgian Chocolate Chip Ice Cream</td>\n",
       "      <td>0.645620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>22_hd</td>\n",
       "      <td>Coffee Almond Crunch Ice Cream Bar</td>\n",
       "      <td>0.635870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>39_breyers</td>\n",
       "      <td>CarbSmart™ Fudge Bar</td>\n",
       "      <td>0.628600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>36_breyers</td>\n",
       "      <td>CarbSmart™ Vanilla Bar</td>\n",
       "      <td>0.624020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12_breyers</td>\n",
       "      <td>CarbSmart™ Vanilla</td>\n",
       "      <td>0.616800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>52_breyers</td>\n",
       "      <td>Vanilla Chocolate</td>\n",
       "      <td>0.612300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6_breyers</td>\n",
       "      <td>Vanilla Chocolate Strawberry</td>\n",
       "      <td>0.440400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2_breyers</td>\n",
       "      <td>Extra Creamy Vanilla</td>\n",
       "      <td>0.425900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            key                                     name  compound\n",
       "139  41_talenti             STRAWBERRY HIBISCUS SORBETTO  0.718810\n",
       "69        26_hd              Cookies and Cream Ice Cream  0.717750\n",
       "101       68_hd         Whiskey Hazelnut Latte Ice Cream  0.716380\n",
       "98        64_hd         Vanilla Chocolate Chip Ice Cream  0.713725\n",
       "165  33_breyers                    CarbSmart™ Almond Bar  0.708600\n",
       "142   1_breyers                         Homemade Vanilla  0.706433\n",
       "40        48_bj                    Vanilla Caramel Fudge  0.705100\n",
       "181  64_breyers                                CINNABON®  0.693760\n",
       "141   0_breyers                          Natural Vanilla  0.670767\n",
       "157  21_breyers                      Vanilla Fudge Twirl  0.661625\n",
       "83        43_hd                Peppermint Bark Ice Cream  0.661473\n",
       "163  30_breyers             No Sugar Added Caramel Swirl  0.660550\n",
       "70        27_hd  Double Belgian Chocolate Chip Ice Cream  0.645620\n",
       "66        22_hd       Coffee Almond Crunch Ice Cream Bar  0.635870\n",
       "168  39_breyers                     CarbSmart™ Fudge Bar  0.628600\n",
       "167  36_breyers                   CarbSmart™ Vanilla Bar  0.624020\n",
       "151  12_breyers                       CarbSmart™ Vanilla  0.616800\n",
       "173  52_breyers                        Vanilla Chocolate  0.612300\n",
       "147   6_breyers             Vanilla Chocolate Strawberry  0.440400\n",
       "143   2_breyers                     Extra Creamy Vanilla  0.425900"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View Bottom 20 Ice Cream Flavors\n",
    "print(str('        Bottom 20 Ice Cream Flavors'))\n",
    "print(str('        Ranked by Average Compound Value'))\n",
    "display(df_key_avg[-20:])"
   ]
  }
 ],
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