Amazon_review_word_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I didn't put any google trends data here since we are only exploring amazon reviews in this notebook.\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "    \n",
    "    \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def GetMergedAmazonDataFrames():\n",
    "#     files_to_use = [r'data\\AirPods Pro.csv', r'data\\Elite 75t.csv',r'data\\Free.csv',\n",
    "#                    r'data\\Galaxy Buds Plus.csv',r'data\\M-200.csv',r'data\\NC700.csv',\n",
    "#                    r'data\\Soundcore Liberty 2 Pro.csv',r'data\\Soundcore Liberty Air 2.csv',r'data\\Soundcore Life Q20.csv',\n",
    "#                    r'data\\WF-1000XM3.csv']\n",
    "    files_to_use = [r'data\\AirPods Pro.csv', r'data\\Elite 75t.csv',r'data\\Free.csv',\n",
    "                   r'data\\Galaxy Buds Plus.csv',r'data\\NC700.csv',\n",
    "                   r'data\\Soundcore Liberty 2 Pro.csv',r'data\\Soundcore Liberty Air 2.csv',r'data\\Soundcore Life Q20.csv',\n",
    "                   r'data\\WF-1000XM3.csv']\n",
    "    \n",
    "    amzn_df = pd.read_csv(files_to_use[0],  parse_dates=False)\n",
    "    for file in files_to_use[1:]:\n",
    "        df = pd.read_csv(file,  parse_dates=False)\n",
    "#         amzn_df = pd.merge(amzn_df, df, how='outer',on='review_date')\n",
    "        amzn_df = pd.concat([amzn_df,df],sort=False)\n",
    "        \n",
    "    amzn_df['review_date'] = pd.to_datetime(amzn_df['review_date'], format='%Y-%m-%d')\n",
    "    amzn_df = amzn_df[['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date', 'review_title', 'review_text']]\n",
    "    return amzn_df\n",
    "    \n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def GetAmazonTextReviewsDf():\n",
    "    ### This function is used to prepare \n",
    "    #   the data frame to word and text analysis.. ####\n",
    "    amzn_df = GetMergedAmazonDataFrames()\n",
    "    columns = ['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date', 'review_title', 'review_text']\n",
    "    \n",
    "    amzn_df=amzn_df[columns]\n",
    "    return amzn_df\n",
    "#     amzn_df['totalReviews'] =\n",
    "    \n",
    "def GetAmazonNumericalReviewsDf():\n",
    "    amzn_df = GetMergedAmazonDataFrames()\n",
    "    columns = ['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date']\n",
    "    \n",
    "    amzn_df=amzn_df[columns]\n",
    "    # split into two data sets for purchased and not purchased.\n",
    "    amzn_purchased = amzn_df.where(amzn_df['verified_purchase']== 1).dropna()\n",
    "    amzn_notPurchased = amzn_df.where(amzn_df['verified_purchase']== 0).dropna()\n",
    "    \n",
    "    tempdf = None\n",
    "    for product in amzn_purchased['ProductName'].unique():\n",
    "        df = amzn_purchased.where(amzn_purchased['ProductName'] == product).dropna()\n",
    "        \n",
    "        manufacturer = df['Manufacturer'].unique()[0]\n",
    "        df= df[['review_rating','review_date']]\n",
    "        \n",
    "        dfCount = df\n",
    "        df = df.set_index('review_date').resample('W-SAT').mean().reset_index()#.agg(['mean','count']).reset_index()\n",
    "#         df['review_rating'] = df['review_rating'].fillna(0)\n",
    "        df['review_rating_norm'] = Normalize(df['review_rating'].fillna(df['review_rating'].mean()))\n",
    "        df['ProductName'] = df['review_rating'].apply(lambda x: product)\n",
    "        df['Manufacturer'] = df['review_rating'].apply(lambda x: manufacturer)\n",
    "        df['verified_purchase'] = df['review_rating'].apply(lambda x: '1')\n",
    "        df['count'] = dfCount[['review_rating','review_date']].set_index('review_date').resample('W-SAT').count().reset_index()['review_rating']\n",
    "#         print(df.head())\n",
    "        if tempdf is None:\n",
    "            tempdf = df\n",
    "        else:\n",
    "            tempdf = pd.concat([tempdf, df])\n",
    "            \n",
    "    \n",
    "    for product in amzn_notPurchased['ProductName'].unique():\n",
    "        df = amzn_notPurchased.where(amzn_notPurchased['ProductName'] == product).dropna()\n",
    "        \n",
    "        manufacturer = df['Manufacturer'].unique()[0]\n",
    "        df= df[['review_rating','review_date']]\n",
    "        \n",
    "        dfCount = df\n",
    "        \n",
    "        \n",
    "        df = df.set_index('review_date').resample('W-SAT').mean().reset_index()#.agg(['mean','count']).reset_index()\n",
    "#         df['review_rating'] = df['review_rating'].fillna(0)\n",
    "        df['review_rating_norm'] = Normalize(df['review_rating'].fillna(df['review_rating'].mean()))\n",
    "        df['ProductName'] = df['review_rating'].apply(lambda x: product)\n",
    "        df['Manufacturer'] = df['review_rating'].apply(lambda x: manufacturer)\n",
    "        df['verified_purchase'] = df['review_rating'].apply(lambda x: '0')\n",
    "        df['count'] = dfCount[['review_rating','review_date']].set_index('review_date').resample('W-SAT').count().reset_index()['review_rating']\n",
    "        \n",
    "#         print(df.head())\n",
    "        if tempdf is None:\n",
    "            tempdf = df\n",
    "        else:\n",
    "            tempdf = pd.concat([tempdf, df])\n",
    "            \n",
    "    return tempdf\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Math functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Normalize(array):\n",
    "    return array / np.linalg.norm(array, ord=1)\n",
    "    \n",
    "# Sav-golay filter for averaging.\n",
    "def savGolay(array, window = 9, order =7):\n",
    "    from scipy.ndimage import convolve1d\n",
    "    from scipy.signal import savgol_coeffs, savgol_filter\n",
    "    from scipy.signal._savitzky_golay import _polyder\n",
    "    return savgol_filter(array, window, order)\n",
    "   \n",
    "# Simple Moving Average used in financial data.\n",
    "def SMA(pandas_df_daily,columnName, dayWindowSize=20):\n",
    "    daily_close_df = pandas_df_daily[columnName].copy()\n",
    "    return daily_close_df.rolling(dayWindowSize).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions for working with words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\AngryGamers\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package tagsets to\n",
      "[nltk_data]     C:\\Users\\AngryGamers\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package tagsets is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('tagsets')\n",
    "\n",
    "def TokenizeText(text):\n",
    "    from nltk.tokenize import RegexpTokenizer\n",
    "    tokenizer = RegexpTokenizer(r'\\w+')\n",
    "#     text = \"This is my text. It includes commas, question marks? and other stuff. Also U.S..\".lower()\n",
    "    tokens = tokenizer.tokenize(text)\n",
    "    return tokens\n",
    "\n",
    "def GetWordsAndTheirTags(text):\n",
    "    text = CleanText(text)\n",
    "    tokens = TokenizeText(text)\n",
    "    return nltk.pos_tag(tokens)\n",
    "\n",
    "def CleanText(text):\n",
    "    text=str(text)\n",
    "    text= text.lower().replace(\"wouldn\\'t\",\"would not\").replace('canceling','cancelling')\n",
    "    return text\n",
    "def helperGetWordsWithTokenType(text, t):   \n",
    "# #     print(nltk.help.upenn_tagset('PRP$'))\n",
    "# #     print(text)\n",
    "#     text = CleanText(text)\n",
    "#     tokens = TokenizeText(text)\n",
    "# #     for duo in nltk.pos_tag(tokens):\n",
    "# #         if duo[1] =='JJ': # adjectives\n",
    "# #             print(duo)           \n",
    "#     return [x[0] for x in nltk.pos_tag(tokens) if x[1]=='JJ']\n",
    "    tokens = TokenizeText(text)          \n",
    "    return [x[0] for x in nltk.pos_tag(tokens) if x[1] in t]\n",
    "    \n",
    "def GetAdjectivesFromText(text):\n",
    "    text = CleanText(text)\n",
    "    return helperGetWordsWithTokenType(text,['JJ'])\n",
    "\n",
    "def GetNounsFromText(text):\n",
    "    text = CleanText(text)\n",
    "    return helperGetWordsWithTokenType(text,['NN','NNS'])\n",
    "\n",
    "def GetVerbssFromText(text):\n",
    "    text = CleanText(text)\n",
    "    # Verbs are tricky... we have to add a 'to' before every word in the text, so we can convert words to verbs\n",
    "    # this will return any words that can be interpreted as a verb even if not used in the text that way.\n",
    "    # it's difficult to know by the text alone if a word is a verb or not, so this will force it to a verb.\n",
    "    \n",
    "    tokens = TokenizeText(text)   \n",
    "    \n",
    "    words = [ 'to '+ x for x in tokens]\n",
    "#     print(' '.join(words))\n",
    "    return helperGetWordsWithTokenType(' '.join(words),['VBZ','VB'])\n",
    "\n",
    "def GetWordPairs(text):\n",
    "    text = CleanText(text)\n",
    "    tokens = TokenizeText(text)\n",
    "    \n",
    "    \n",
    "    STOPWORDS = ['of' ,'the', 'i', 'on', 'and', 'm', 'it' ,'s', 've', 'if' ,'to', 'don' ,'t' ,'are' ,'they'\\\n",
    "                 ,'not', 'but','them' ,'do' ,'then' ,'you' ,'me' \\\n",
    "                 ,'these' ,'a' ,'is' ,'for', 'can', 'you' ,'this' ,'my', 'we' ,'can', 'have', 'than', 'with']\n",
    "    \n",
    "    \n",
    "    from nltk.stem import PorterStemmer\n",
    "#     from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "\n",
    "    ps = PorterStemmer()\n",
    "\n",
    "    tokens = [ ps.stem(elem) for elem in tokens if elem not in STOPWORDS]\n",
    "    \n",
    "#     tokens= GetAdjectivesFromText(text)\n",
    "    #we need to create a new array with pairs of the words in the tokens array.\n",
    "    if len(tokens)< 2:\n",
    "        return []\n",
    "    \n",
    "    pairs = []\n",
    "    for i in range(0,len(tokens)-1,1):\n",
    "        firstWord = tokens[i]\n",
    "        secondWord = tokens[i+1]\n",
    "        pairs.append(set([firstWord,secondWord]))\n",
    "    #     print(set([firstWord,secondWord]))\n",
    "#     print(pairs)\n",
    "    return pairs\n",
    "\n",
    "def GetWordTriplets(text):\n",
    "    text = CleanText(text)\n",
    "    tokens = TokenizeText(text)\n",
    "    \n",
    "    #we need to create a new array with pairs of the words in the tokens array.\n",
    "\n",
    "    pairs = []\n",
    "    for i in range(0,len(tokens)-2,1):\n",
    "        firstWord = tokens[i]\n",
    "        secondWord = tokens[i+1]\n",
    "        thirdWord = tokens[i+2]\n",
    "        pairs.append(set([firstWord,secondWord,thirdWord]))\n",
    "    #     print(set([firstWord,secondWord]))\n",
    "#     print(pairs)\n",
    "    return pairs\n",
    "\n",
    "from collections import defaultdict\n",
    "def count_frequency(word_list):\n",
    "    \"\"\" Function input: list object contains strings of individual words.\n",
    "        Function output: defaultdict() object containing {word, frequency} pairs. \"\"\"\n",
    "\n",
    "    freq_counts = defaultdict(int)\n",
    "    \n",
    "    for word in word_list:\n",
    "        freq_counts[word] += 1\n",
    "\n",
    "    return freq_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GetVerbssFromText(\"This is my hate text. It includes commas, question marks? dog and other stuff. Also U.S..wouldn't it be great\")\n",
    "# GetAdjectivesFromText(\"This is my text. It includes commas, question marks? and other stuff. Also U.S..\".lower())\n",
    "# #     print(duo[1])\n",
    "# # tokens\n",
    "# GetWordsAndTheirTags(\"This is my hate text. It includes commas, question marks? dog and other stuff. Also U.S..wouldn't it be great\")\n",
    "\n",
    "# text = CleanText(\"This is my hate text. It includes commas, question marks? dog and other stuff. Also U.S..wouldn't it be great\")\n",
    "# tokens = TokenizeText(text)  \n",
    "\n",
    "# pairs = []\n",
    "# for i in range(0,len(tokens)-1,1):\n",
    "#     firstWord = tokens[i]\n",
    "#     secondWord = tokens[i+1]\n",
    "#     pairs.append(set([firstWord,secondWord]))\n",
    "# #     print(set([firstWord,secondWord]))\n",
    "# print(pairs)\n",
    "# # tokens.rotate(1)\n",
    "# GetWordPairs(\"This is my hate text. It includes commas, question marks? dog and other stuff. Also U.S..wouldn't it be great\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functions to tweak pandas data frames.\n",
    "def AddAdjectiveNounsVerbsToDF(df, textColumn):\n",
    "    df = df.copy()\n",
    "#     amzn_df['text_adjectives'] =amzn_df['review_text'].apply(lambda x: GetAdjectivesFromText(str(x).lower()))\n",
    "#     amzn_df['text_nouns'] =amzn_df['review_text'].apply(lambda x: GetNounsFromText(str(x).lower()))\n",
    "#     amzn_df['text_verbs'] =amzn_df['review_text'].apply(lambda x: GetVerbssFromText(str(x).lower()))\n",
    "    df['text_adjectives'] =df[textColumn].apply(lambda x: GetAdjectivesFromText(str(x).lower()))\n",
    "    df['text_adjectives'] = df['text_adjectives'].apply(lambda row: set([  x for x in row if(len(x) >1)]) )\n",
    "    \n",
    "    df['text_nouns'] =df[textColumn].apply(lambda x: GetNounsFromText(str(x).lower()))\n",
    "    df['text_nouns'] = df['text_nouns'].apply(lambda row: set([  x for x in row if(len(x) >1)]) )\n",
    "    \n",
    "    df['text_verbs'] =df[textColumn].apply(lambda x: GetVerbssFromText(str(x).lower()))\n",
    "    df['text_verbs'] = df['text_verbs'].apply(lambda row: set([  x for x in row if(len(x) >1)]) )\n",
    "    \n",
    "    # create word pairs.\n",
    "    df['text_pairs'] = df[textColumn].apply(lambda row: GetWordPairs(row))\n",
    "    df['text_triplets'] = df[textColumn].apply(lambda row: GetWordTriplets(row))\n",
    "    return df\n",
    "\n",
    "def GetDataframeOfWords(pandasdf, textColumn):\n",
    "#     pandasdf['words'] = CreateListForColumn(pandasdf, textColumn)\n",
    "    pandasdf['words']=pandasdf[textColumn]\n",
    "    wordArray = pandasdf[pandasdf['words'].map(lambda d: len(d)) > 0]['words'].values\n",
    "    words = []\n",
    "    for wordlist in wordArray:\n",
    "        words.extend(wordlist)\n",
    "    wordDictionary = count_frequency(words)\n",
    "    \n",
    "    df = pd.DataFrame.from_dict(wordDictionary, orient='index',\n",
    "                       columns=['count']).reset_index()\n",
    "    df = df.rename(columns={\"index\": \"word\"})\n",
    "    df = df.sort_values(by=['count'], ascending=False)\n",
    "    \n",
    "    totalWords = df['count'].sum()\n",
    "    df['usage'] = df['count']/totalWords\n",
    "#     df = df.set_index('word')\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Manufacturer</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>review_rating</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_title</th>\n",
       "      <th>review_text</th>\n",
       "      <th>text_adjectives</th>\n",
       "      <th>text_nouns</th>\n",
       "      <th>text_verbs</th>\n",
       "      <th>text_pairs</th>\n",
       "      <th>text_triplets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Airpods Pro deliver😎</td>\n",
       "      <td>This airpods pro do exactly what apple said th...</td>\n",
       "      <td>{many, different, new}</td>\n",
       "      <td>{airpods, buds, size, put, thats, alot, noise,...</td>\n",
       "      <td>{noise, transperancy, resistant, do, tell, com...</td>\n",
       "      <td>[{pro, airpod}, {pro, exactli}, {what, exactli...</td>\n",
       "      <td>[{airpods, pro, this}, {airpods, pro, do}, {pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>List of Features/Changes compared to Airpods (v2)</td>\n",
       "      <td>Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...</td>\n",
       "      <td>{black, replacement, old, noise, non, likely, ...</td>\n",
       "      <td>{iphone, range, mode, ease, users, non, noise,...</td>\n",
       "      <td>{iphone, play, feel, range, mode, macbook, eas...</td>\n",
       "      <td>[{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...</td>\n",
       "      <td>[{iphone, 11, like}, {pro, iphone, 11}, {pro, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Cool but expensive</td>\n",
       "      <td>First review yeh heard about these excited but...</td>\n",
       "      <td>{excited}</td>\n",
       "      <td>{airpods, review, dollars}</td>\n",
       "      <td>{less, first, yeh, heard, review, like, get}</td>\n",
       "      <td>[{review, first}, {review, yeh}, {heard, yeh},...</td>\n",
       "      <td>[{yeh, review, first}, {heard, review, yeh}, {...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Lit</td>\n",
       "      <td>They fire</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{fire}</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>Saved my marriage</td>\n",
       "      <td>With the new transparency mode, I can listen t...</td>\n",
       "      <td>{new}</td>\n",
       "      <td>{street, music, mode, cook, car, boys, transpa...</td>\n",
       "      <td>{street, listen, be, mode, cook, ran, boys, tr...</td>\n",
       "      <td>[{new, transpar}, {transpar, mode}, {listen, m...</td>\n",
       "      <td>[{with, the, new}, {transparency, new, the}, {...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Manufacturer  ProductName  review_rating  verified_purchase review_date  \\\n",
       "0        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "1        Apple  AirPods Pro              4                  0  2019-10-30   \n",
       "2        Apple  AirPods Pro              3                  0  2019-10-30   \n",
       "3        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "4        Apple  AirPods Pro              5                  1  2019-10-31   \n",
       "\n",
       "                                        review_title  \\\n",
       "0                               Airpods Pro deliver😎   \n",
       "1  List of Features/Changes compared to Airpods (v2)   \n",
       "2                                 Cool but expensive   \n",
       "3                                                Lit   \n",
       "4                                  Saved my marriage   \n",
       "\n",
       "                                         review_text  \\\n",
       "0  This airpods pro do exactly what apple said th...   \n",
       "1  Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...   \n",
       "2  First review yeh heard about these excited but...   \n",
       "3                                          They fire   \n",
       "4  With the new transparency mode, I can listen t...   \n",
       "\n",
       "                                     text_adjectives  \\\n",
       "0                             {many, different, new}   \n",
       "1  {black, replacement, old, noise, non, likely, ...   \n",
       "2                                          {excited}   \n",
       "3                                                 {}   \n",
       "4                                              {new}   \n",
       "\n",
       "                                          text_nouns  \\\n",
       "0  {airpods, buds, size, put, thats, alot, noise,...   \n",
       "1  {iphone, range, mode, ease, users, non, noise,...   \n",
       "2                         {airpods, review, dollars}   \n",
       "3                                                 {}   \n",
       "4  {street, music, mode, cook, car, boys, transpa...   \n",
       "\n",
       "                                          text_verbs  \\\n",
       "0  {noise, transperancy, resistant, do, tell, com...   \n",
       "1  {iphone, play, feel, range, mode, macbook, eas...   \n",
       "2       {less, first, yeh, heard, review, like, get}   \n",
       "3                                             {fire}   \n",
       "4  {street, listen, be, mode, cook, ran, boys, tr...   \n",
       "\n",
       "                                          text_pairs  \\\n",
       "0  [{pro, airpod}, {pro, exactli}, {what, exactli...   \n",
       "1  [{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...   \n",
       "2  [{review, first}, {review, yeh}, {heard, yeh},...   \n",
       "3                                                 []   \n",
       "4  [{new, transpar}, {transpar, mode}, {listen, m...   \n",
       "\n",
       "                                       text_triplets  \n",
       "0  [{airpods, pro, this}, {airpods, pro, do}, {pr...  \n",
       "1  [{iphone, 11, like}, {pro, iphone, 11}, {pro, ...  \n",
       "2  [{yeh, review, first}, {heard, review, yeh}, {...  \n",
       "3                                                 []  \n",
       "4  [{with, the, new}, {transparency, new, the}, {...  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amzn_df = GetAmazonTextReviewsDf()\n",
    "amzn_df = AddAdjectiveNounsVerbsToDF(amzn_df,'review_text')\n",
    "amzn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Manufacturer</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>review_rating</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_title</th>\n",
       "      <th>review_text</th>\n",
       "      <th>text_adjectives</th>\n",
       "      <th>text_nouns</th>\n",
       "      <th>text_verbs</th>\n",
       "      <th>text_pairs</th>\n",
       "      <th>text_triplets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Airpods Pro deliver😎</td>\n",
       "      <td>This airpods pro do exactly what apple said th...</td>\n",
       "      <td>{many, different, new}</td>\n",
       "      <td>{airpods, buds, size, put, thats, alot, noise,...</td>\n",
       "      <td>{noise, transperancy, resistant, do, tell, com...</td>\n",
       "      <td>[{pro, airpod}, {pro, exactli}, {what, exactli...</td>\n",
       "      <td>[{airpods, pro, this}, {airpods, pro, do}, {pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>List of Features/Changes compared to Airpods (v2)</td>\n",
       "      <td>Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...</td>\n",
       "      <td>{black, replacement, old, noise, non, likely, ...</td>\n",
       "      <td>{iphone, range, mode, ease, users, non, noise,...</td>\n",
       "      <td>{iphone, play, feel, range, mode, macbook, eas...</td>\n",
       "      <td>[{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...</td>\n",
       "      <td>[{iphone, 11, like}, {pro, iphone, 11}, {pro, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Cool but expensive</td>\n",
       "      <td>First review yeh heard about these excited but...</td>\n",
       "      <td>{excited}</td>\n",
       "      <td>{airpods, review, dollars}</td>\n",
       "      <td>{less, first, yeh, heard, review, like, get}</td>\n",
       "      <td>[{review, first}, {review, yeh}, {heard, yeh},...</td>\n",
       "      <td>[{yeh, review, first}, {heard, review, yeh}, {...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Lit</td>\n",
       "      <td>They fire</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{fire}</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>Saved my marriage</td>\n",
       "      <td>With the new transparency mode, I can listen t...</td>\n",
       "      <td>{new}</td>\n",
       "      <td>{street, music, mode, cook, car, boys, transpa...</td>\n",
       "      <td>{street, listen, be, mode, cook, ran, boys, tr...</td>\n",
       "      <td>[{new, transpar}, {transpar, mode}, {listen, m...</td>\n",
       "      <td>[{with, the, new}, {transparency, new, the}, {...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Manufacturer  ProductName  review_rating  verified_purchase review_date  \\\n",
       "0        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "1        Apple  AirPods Pro              4                  0  2019-10-30   \n",
       "2        Apple  AirPods Pro              3                  0  2019-10-30   \n",
       "3        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "4        Apple  AirPods Pro              5                  1  2019-10-31   \n",
       "\n",
       "                                        review_title  \\\n",
       "0                               Airpods Pro deliver😎   \n",
       "1  List of Features/Changes compared to Airpods (v2)   \n",
       "2                                 Cool but expensive   \n",
       "3                                                Lit   \n",
       "4                                  Saved my marriage   \n",
       "\n",
       "                                         review_text  \\\n",
       "0  This airpods pro do exactly what apple said th...   \n",
       "1  Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...   \n",
       "2  First review yeh heard about these excited but...   \n",
       "3                                          They fire   \n",
       "4  With the new transparency mode, I can listen t...   \n",
       "\n",
       "                                     text_adjectives  \\\n",
       "0                             {many, different, new}   \n",
       "1  {black, replacement, old, noise, non, likely, ...   \n",
       "2                                          {excited}   \n",
       "3                                                 {}   \n",
       "4                                              {new}   \n",
       "\n",
       "                                          text_nouns  \\\n",
       "0  {airpods, buds, size, put, thats, alot, noise,...   \n",
       "1  {iphone, range, mode, ease, users, non, noise,...   \n",
       "2                         {airpods, review, dollars}   \n",
       "3                                                 {}   \n",
       "4  {street, music, mode, cook, car, boys, transpa...   \n",
       "\n",
       "                                          text_verbs  \\\n",
       "0  {noise, transperancy, resistant, do, tell, com...   \n",
       "1  {iphone, play, feel, range, mode, macbook, eas...   \n",
       "2       {less, first, yeh, heard, review, like, get}   \n",
       "3                                             {fire}   \n",
       "4  {street, listen, be, mode, cook, ran, boys, tr...   \n",
       "\n",
       "                                          text_pairs  \\\n",
       "0  [{pro, airpod}, {pro, exactli}, {what, exactli...   \n",
       "1  [{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...   \n",
       "2  [{review, first}, {review, yeh}, {heard, yeh},...   \n",
       "3                                                 []   \n",
       "4  [{new, transpar}, {transpar, mode}, {listen, m...   \n",
       "\n",
       "                                       text_triplets  \n",
       "0  [{airpods, pro, this}, {airpods, pro, do}, {pr...  \n",
       "1  [{iphone, 11, like}, {pro, iphone, 11}, {pro, ...  \n",
       "2  [{yeh, review, first}, {heard, review, yeh}, {...  \n",
       "3                                                 []  \n",
       "4  [{with, the, new}, {transparency, new, the}, {...  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amzn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.mikulskibartosz.name/how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/\n",
    "\n",
    "# GetDataframeOfWords(amzn_df,'text_adjectives')\n",
    "# GetDataframeOfWords(amzn_df,'text_verbs')\n",
    "\n",
    "\n",
    "# GetDataframeOfWords(amzn_df[:50],'text_pairs')\n",
    "# from collections import Counter\n",
    "# Counter(amzn_df[:50]['text_pairs'])\n",
    "\n",
    "# amzn_df[:50]['text_pairs'].value_counts()\n",
    "\n",
    "# data = amzn_df[40:50][['review_date','verified_purchase','text_pairs']]\n",
    "# data = amzn_df[40:50]\n",
    "\n",
    "# data['text_pairs']#.apply(lambda x: x[0])\n",
    "\n",
    "# data=data.reset_index()\n",
    "# # data.where(data['text_pairs'])\n",
    "\n",
    "# ### This is a bit tricky what this is doing...###\n",
    "# #  we create a new temp column, which is where we get the values from that we'll be working with.\n",
    "# #  take the column and dumping that into it's own data frame, which makes a bunch of columns. (from array to df)\n",
    "# #  merge the the new dataframe with all of our many columns back into the original data frame with columns we want to keep.\n",
    "# #  we remove the temp column. This temp column would melt into our final column we are making...\n",
    "# #     the issue I had was without removing the column I could get pairs merged with other pairs, wo they ended up being \n",
    "# #     sets of 3+ merged with who knows what, which isn't what I wanted.\n",
    "# #  there is a variable column that's created in this process which we can remove.\n",
    "# data[\"tempPairs\"] = data[\"text_pairs\"]\n",
    "# data = data['text_pairs'].apply(pd.Series)\\\n",
    "#     .merge(data, right_index = True, left_index = True) \\\n",
    "#     .drop([\"tempPairs\"], axis = 1)\\\n",
    "#     .drop([\"text_pairs\"], axis = 1)\\\n",
    "#     .melt(id_vars = ['review_date', 'verified_purchase'], value_name = \"pairs\") \\\n",
    "#     .drop(\"variable\", axis = 1)\n",
    "\n",
    "# data = amzn_df[['review_date','verified_purchase','text_pairs']]\n",
    "# data = GetReviewPairsCounts(data, 'text_pairs',['review_date', 'verified_purchase'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will cover analysis of only single words found in the list. We will take a top down approach.<br><br>\n",
    "<ul>\n",
    "    <li> high reviews are above 3 and low reviews are less than 3, what are the unique words between the high and low?</li>\n",
    "    <li> what are the unique words between the purchased and not purchased?</li>\n",
    "    <li> what are the unique words between the products?</li>\n",
    "\n",
    "\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
      "       'review_date', 'review_title', 'review_text', 'text_adjectives',\n",
      "       'text_nouns', 'text_verbs', 'text_pairs', 'text_triplets'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>Manufacturer</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>review_rating</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_title</th>\n",
       "      <th>review_text</th>\n",
       "      <th>text_adjectives</th>\n",
       "      <th>text_nouns</th>\n",
       "      <th>text_verbs</th>\n",
       "      <th>text_pairs</th>\n",
       "      <th>text_triplets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Airpods Pro deliver😎</td>\n",
       "      <td>This airpods pro do exactly what apple said th...</td>\n",
       "      <td>{many, different, new}</td>\n",
       "      <td>{airpods, buds, size, put, thats, alot, noise,...</td>\n",
       "      <td>{noise, transperancy, resistant, do, tell, com...</td>\n",
       "      <td>[{pro, airpod}, {pro, exactli}, {what, exactli...</td>\n",
       "      <td>[{airpods, pro, this}, {airpods, pro, do}, {pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>List of Features/Changes compared to Airpods (v2)</td>\n",
       "      <td>Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...</td>\n",
       "      <td>{black, replacement, old, noise, non, likely, ...</td>\n",
       "      <td>{iphone, range, mode, ease, users, non, noise,...</td>\n",
       "      <td>{iphone, play, feel, range, mode, macbook, eas...</td>\n",
       "      <td>[{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...</td>\n",
       "      <td>[{iphone, 11, like}, {pro, iphone, 11}, {pro, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Cool but expensive</td>\n",
       "      <td>First review yeh heard about these excited but...</td>\n",
       "      <td>{excited}</td>\n",
       "      <td>{airpods, review, dollars}</td>\n",
       "      <td>{less, first, yeh, heard, review, like, get}</td>\n",
       "      <td>[{review, first}, {review, yeh}, {heard, yeh},...</td>\n",
       "      <td>[{yeh, review, first}, {heard, review, yeh}, {...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>Lit</td>\n",
       "      <td>They fire</td>\n",
       "      <td>{}</td>\n",
       "      <td>{}</td>\n",
       "      <td>{fire}</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>AirPods Pro</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>Saved my marriage</td>\n",
       "      <td>With the new transparency mode, I can listen t...</td>\n",
       "      <td>{new}</td>\n",
       "      <td>{street, music, mode, cook, car, boys, transpa...</td>\n",
       "      <td>{street, listen, be, mode, cook, ran, boys, tr...</td>\n",
       "      <td>[{new, transpar}, {transpar, mode}, {listen, m...</td>\n",
       "      <td>[{with, the, new}, {transparency, new, the}, {...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Manufacturer  ProductName  review_rating  verified_purchase review_date  \\\n",
       "0        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "1        Apple  AirPods Pro              4                  0  2019-10-30   \n",
       "2        Apple  AirPods Pro              3                  0  2019-10-30   \n",
       "3        Apple  AirPods Pro              5                  0  2019-10-30   \n",
       "4        Apple  AirPods Pro              5                  1  2019-10-31   \n",
       "\n",
       "                                        review_title  \\\n",
       "0                               Airpods Pro deliver😎   \n",
       "1  List of Features/Changes compared to Airpods (v2)   \n",
       "2                                 Cool but expensive   \n",
       "3                                                Lit   \n",
       "4                                  Saved my marriage   \n",
       "\n",
       "                                         review_text  \\\n",
       "0  This airpods pro do exactly what apple said th...   \n",
       "1  Like iphone 11 Pro, Macbook Pro, Airpad Pro; t...   \n",
       "2  First review yeh heard about these excited but...   \n",
       "3                                          They fire   \n",
       "4  With the new transparency mode, I can listen t...   \n",
       "\n",
       "                                     text_adjectives  \\\n",
       "0                             {many, different, new}   \n",
       "1  {black, replacement, old, noise, non, likely, ...   \n",
       "2                                          {excited}   \n",
       "3                                                 {}   \n",
       "4                                              {new}   \n",
       "\n",
       "                                          text_nouns  \\\n",
       "0  {airpods, buds, size, put, thats, alot, noise,...   \n",
       "1  {iphone, range, mode, ease, users, non, noise,...   \n",
       "2                         {airpods, review, dollars}   \n",
       "3                                                 {}   \n",
       "4  {street, music, mode, cook, car, boys, transpa...   \n",
       "\n",
       "                                          text_verbs  \\\n",
       "0  {noise, transperancy, resistant, do, tell, com...   \n",
       "1  {iphone, play, feel, range, mode, macbook, eas...   \n",
       "2       {less, first, yeh, heard, review, like, get}   \n",
       "3                                             {fire}   \n",
       "4  {street, listen, be, mode, cook, ran, boys, tr...   \n",
       "\n",
       "                                          text_pairs  \\\n",
       "0  [{pro, airpod}, {pro, exactli}, {what, exactli...   \n",
       "1  [{iphon, like}, {iphon, 11}, {pro, 11}, {pro, ...   \n",
       "2  [{review, first}, {review, yeh}, {heard, yeh},...   \n",
       "3                                                 []   \n",
       "4  [{new, transpar}, {transpar, mode}, {listen, m...   \n",
       "\n",
       "                                       text_triplets  \n",
       "0  [{airpods, pro, this}, {airpods, pro, do}, {pr...  \n",
       "1  [{iphone, 11, like}, {pro, iphone, 11}, {pro, ...  \n",
       "2  [{yeh, review, first}, {heard, review, yeh}, {...  \n",
       "3                                                 []  \n",
       "4  [{with, the, new}, {transparency, new, the}, {...  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(amzn_df.columns)\n",
    "amzn_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def SingleWords_df(df):\n",
    "    data = df.copy()\n",
    "    data = data[['verified_purchase','review_date', 'text_adjectives']]\n",
    "    # highAdjectivesRanking['text_adjectives'] = highAdjectivesRanking['text_adjectives'].apply(lambda x: repr(x))\n",
    "    data[\"tempPairs\"] = data[\"text_adjectives\"]\n",
    "    data = data['text_adjectives'].apply(lambda x: pd.Series(list(x)))\\\n",
    "        .merge(data, right_index = True, left_index = True) \\\n",
    "        .drop([\"tempPairs\"], axis = 1)\\\n",
    "        .drop([\"text_adjectives\"], axis = 1)\\\n",
    "        .melt(id_vars = ['review_date', 'verified_purchase'], value_name = \"words\") \\\n",
    "        .drop(\"variable\", axis = 1).dropna()\n",
    "    \n",
    "    \n",
    "    # count should be a ratio of the current elem/total sets. NOTE: can't really count them here...value too small.\n",
    "    totalReview= len(df['verified_purchase'])\n",
    "    data['count'] = data['verified_purchase'].apply(lambda x: 1/totalReview)\n",
    "    \n",
    "    return data\n",
    "\n",
    "\n",
    "def GetHighAndLowRankingUniqueWords(df, wordColumn):\n",
    "    lowAdjectivesRanking = df.where(df['review_rating'] < 2).dropna()\n",
    "    lowAdjectivesRanking = SingleWords_df(lowAdjectivesRanking)\n",
    "\n",
    "    highAdjectivesRanking = df.where(df['review_rating'] > 4).dropna()\n",
    "    highAdjectivesRanking = SingleWords_df(highAdjectivesRanking)\n",
    "    \n",
    "    adjCount_temp = highAdjectivesRanking[[wordColumn,'count']].groupby([wordColumn]).sum().sort_values(by='count',ascending=False).reset_index()\n",
    "    adjCount_temp2 = lowAdjectivesRanking[[wordColumn,'count']].groupby([wordColumn]).sum().sort_values(by='count',ascending=False).reset_index()\n",
    "\n",
    "    intersection = adjCount_temp.merge(adjCount_temp2, left_on=wordColumn, right_on=wordColumn, how='inner')\n",
    "\n",
    "    # highAdjectivesRanking['words'].isin(intersection['words'].unique())\n",
    "    top_adj_words_df = highAdjectivesRanking[~highAdjectivesRanking[wordColumn].isin(intersection[wordColumn].unique())].groupby(wordColumn).sum().sort_values(by='count',ascending=False)\n",
    "    lower_adj_words_df = lowAdjectivesRanking[~lowAdjectivesRanking[wordColumn].isin(intersection[wordColumn].unique())].groupby(wordColumn).sum().sort_values(by='count',ascending=False)\n",
    "\n",
    "    #NOTE: the intersection or ratings 2-4 don't have any good unique words to use, so by using the extremes we can find more uniqueness.\n",
    "    # intersection\n",
    "    df = lower_adj_words_df.reset_index().reset_index()[['index',wordColumn]]#.head()\n",
    "    top_adj_words_df=top_adj_words_df.reset_index().reset_index()[['index',wordColumn]]#.head()\n",
    "\n",
    "    # these include purchased and not purchased reviews and all products.\n",
    "    onevfiveStarReviewWords_df = df.merge(top_adj_words_df, left_on='index', right_on='index', how='inner')\n",
    "    onevfiveStarReviewWords_df = onevfiveStarReviewWords_df.rename(columns={wordColumn+'_x':'oneStarWords', wordColumn+'_y':'fiveStarWords','index':'rank'})\n",
    "    return onevfiveStarReviewWords_df\n",
    "    \n",
    "# This function will get the words in the highest and lowest star reviews for each product.\n",
    "def GetProductUniqueWords(df):\n",
    "    products = df['ProductName'].unique()\n",
    "    \n",
    "    temp_df = None\n",
    "    for elem in products:\n",
    "        p_df = df.where(df['ProductName'] == elem)\n",
    "        \n",
    "        p_df = GetHighAndLowRankingUniqueWords(p_df, 'words')\n",
    "        # now we have a data set of the top and lowest words for a specific product\n",
    "        # let's rename oneStarWords to the productNameTop,productNameLowest\n",
    "        p_df = p_df.rename(columns={'oneStarWords':elem+'_worst','fiveStarWords':elem+'_best'})\n",
    "        \n",
    "        if temp_df is None:\n",
    "            temp_df = p_df\n",
    "        else:\n",
    "            temp_df = temp_df.merge(p_df, left_on='rank', right_on='rank', how='inner')\n",
    "        \n",
    "    return temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\angrygamers\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  \n"
     ]
    },
    {
     "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>rank</th>\n",
       "      <th>oneStarWords</th>\n",
       "      <th>fiveStarWords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>supposed</td>\n",
       "      <td>phenomenal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>weren</td>\n",
       "      <td>skeptical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>pathetic</td>\n",
       "      <td>lightweight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>email</td>\n",
       "      <td>affordable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>amd</td>\n",
       "      <td>snug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>compitable</td>\n",
       "      <td>audiophile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>unintelligible</td>\n",
       "      <td>durable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>uninstalling</td>\n",
       "      <td>inexpensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>flashlight</td>\n",
       "      <td>powerful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>systemic</td>\n",
       "      <td>unique</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank    oneStarWords fiveStarWords\n",
       "0     0        supposed    phenomenal\n",
       "1     1           weren     skeptical\n",
       "2     2        pathetic   lightweight\n",
       "3     3           email    affordable\n",
       "4     4             amd          snug\n",
       "5     5      compitable    audiophile\n",
       "6     6  unintelligible       durable\n",
       "7     7    uninstalling   inexpensive\n",
       "8     8      flashlight      powerful\n",
       "9     9        systemic        unique"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "onevfiveStarReviewWords_df = GetHighAndLowRankingUniqueWords(amzn_df, 'words')\n",
    "onevfiveStarReviewWords_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\angrygamers\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['rank', 'AirPods Pro_worst', 'AirPods Pro_best', 'Elite 75t_worst',\n",
      "       'Elite 75t_best', 'Free_worst', 'Free_best', 'Galaxy Buds Plus_worst',\n",
      "       'Galaxy Buds Plus_best', 'NC700_worst', 'NC700_best',\n",
      "       'Soundcore Liberty 2 Pro_worst', 'Soundcore Liberty 2 Pro_best',\n",
      "       'Soundcore Liberty Air 2_worst', 'Soundcore Liberty Air 2_best',\n",
      "       'Soundcore Life Q20_worst', 'Soundcore Life Q20_best',\n",
      "       'WF-1000XM3_worst', 'WF-1000XM3_best'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>rank</th>\n",
       "      <th>AirPods Pro_worst</th>\n",
       "      <th>AirPods Pro_best</th>\n",
       "      <th>Elite 75t_worst</th>\n",
       "      <th>Elite 75t_best</th>\n",
       "      <th>Free_worst</th>\n",
       "      <th>Free_best</th>\n",
       "      <th>Galaxy Buds Plus_worst</th>\n",
       "      <th>Galaxy Buds Plus_best</th>\n",
       "      <th>NC700_worst</th>\n",
       "      <th>NC700_best</th>\n",
       "      <th>Soundcore Liberty 2 Pro_worst</th>\n",
       "      <th>Soundcore Liberty 2 Pro_best</th>\n",
       "      <th>Soundcore Liberty Air 2_worst</th>\n",
       "      <th>Soundcore Liberty Air 2_best</th>\n",
       "      <th>Soundcore Life Q20_worst</th>\n",
       "      <th>Soundcore Life Q20_best</th>\n",
       "      <th>WF-1000XM3_worst</th>\n",
       "      <th>WF-1000XM3_best</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>empty</td>\n",
       "      <td>awesome</td>\n",
       "      <td>disappointing</td>\n",
       "      <td>amazing</td>\n",
       "      <td>horrible</td>\n",
       "      <td>little</td>\n",
       "      <td>videos</td>\n",
       "      <td>long</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>super</td>\n",
       "      <td>add</td>\n",
       "      <td>different</td>\n",
       "      <td>user</td>\n",
       "      <td>awesome</td>\n",
       "      <td>unable</td>\n",
       "      <td>little</td>\n",
       "      <td>defective</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>disappointing</td>\n",
       "      <td>incredible</td>\n",
       "      <td>white</td>\n",
       "      <td>awesome</td>\n",
       "      <td>unable</td>\n",
       "      <td>usb</td>\n",
       "      <td>algo</td>\n",
       "      <td>awesome</td>\n",
       "      <td>disappointing</td>\n",
       "      <td>outstanding</td>\n",
       "      <td>galaxy</td>\n",
       "      <td>awesome</td>\n",
       "      <td>finicky</td>\n",
       "      <td>excellent</td>\n",
       "      <td>unusable</td>\n",
       "      <td>awesome</td>\n",
       "      <td>unusable</td>\n",
       "      <td>impressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>impossible</td>\n",
       "      <td>true</td>\n",
       "      <td>horrible</td>\n",
       "      <td>impressive</td>\n",
       "      <td>stop</td>\n",
       "      <td>perfect</td>\n",
       "      <td>sonido</td>\n",
       "      <td>clear</td>\n",
       "      <td>defective</td>\n",
       "      <td>large</td>\n",
       "      <td>unchanged</td>\n",
       "      <td>overall</td>\n",
       "      <td>awful</td>\n",
       "      <td>overall</td>\n",
       "      <td>worked</td>\n",
       "      <td>solid</td>\n",
       "      <td>weak</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>unbearable</td>\n",
       "      <td>top</td>\n",
       "      <td>unable</td>\n",
       "      <td>hearthrough</td>\n",
       "      <td>spotty</td>\n",
       "      <td>overall</td>\n",
       "      <td>pair</td>\n",
       "      <td>able</td>\n",
       "      <td>unable</td>\n",
       "      <td>clean</td>\n",
       "      <td>unable</td>\n",
       "      <td>amazing</td>\n",
       "      <td>basic</td>\n",
       "      <td>amazing</td>\n",
       "      <td>likely</td>\n",
       "      <td>expensive</td>\n",
       "      <td>worthless</td>\n",
       "      <td>solid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>numerous</td>\n",
       "      <td>pleased</td>\n",
       "      <td>dead</td>\n",
       "      <td>passive</td>\n",
       "      <td>alive</td>\n",
       "      <td>expensive</td>\n",
       "      <td>panasonic</td>\n",
       "      <td>overall</td>\n",
       "      <td>serious</td>\n",
       "      <td>favorite</td>\n",
       "      <td>purple</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>perfect</td>\n",
       "      <td>useless</td>\n",
       "      <td>impressed</td>\n",
       "      <td>youtube</td>\n",
       "      <td>helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>unable</td>\n",
       "      <td>solid</td>\n",
       "      <td>sudden</td>\n",
       "      <td>slight</td>\n",
       "      <td>scant</td>\n",
       "      <td>high</td>\n",
       "      <td>pasa</td>\n",
       "      <td>previous</td>\n",
       "      <td>frequent</td>\n",
       "      <td>crystal</td>\n",
       "      <td>professiinal</td>\n",
       "      <td>liberty</td>\n",
       "      <td>tech</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>amplified</td>\n",
       "      <td>build</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>minor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>existent</td>\n",
       "      <td>advertised</td>\n",
       "      <td>rid</td>\n",
       "      <td>light</td>\n",
       "      <td>oversized</td>\n",
       "      <td>battery</td>\n",
       "      <td>popular</td>\n",
       "      <td>excellent</td>\n",
       "      <td>loyal</td>\n",
       "      <td>pretty</td>\n",
       "      <td>precautionary</td>\n",
       "      <td>wireless</td>\n",
       "      <td>afterwhich</td>\n",
       "      <td>wireless</td>\n",
       "      <td>percussive</td>\n",
       "      <td>battery</td>\n",
       "      <td>audible</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>visible</td>\n",
       "      <td>outstanding</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>crisp</td>\n",
       "      <td>pairs</td>\n",
       "      <td>noise</td>\n",
       "      <td>present</td>\n",
       "      <td>perfect</td>\n",
       "      <td>tech</td>\n",
       "      <td>rich</td>\n",
       "      <td>ocean</td>\n",
       "      <td>small</td>\n",
       "      <td>post</td>\n",
       "      <td>true</td>\n",
       "      <td>se</td>\n",
       "      <td>own</td>\n",
       "      <td>re</td>\n",
       "      <td>superior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>firmware</td>\n",
       "      <td>everyday</td>\n",
       "      <td>sad</td>\n",
       "      <td>intuitive</td>\n",
       "      <td>paypal</td>\n",
       "      <td>impressed</td>\n",
       "      <td>purist</td>\n",
       "      <td>battery</td>\n",
       "      <td>frustrating</td>\n",
       "      <td>alexa</td>\n",
       "      <td>nasty</td>\n",
       "      <td>big</td>\n",
       "      <td>spent</td>\n",
       "      <td>pleased</td>\n",
       "      <td>read</td>\n",
       "      <td>ambient</td>\n",
       "      <td>friendly</td>\n",
       "      <td>convenient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>willing</td>\n",
       "      <td>hear</td>\n",
       "      <td>unfortunate</td>\n",
       "      <td>real</td>\n",
       "      <td>potential</td>\n",
       "      <td>compact</td>\n",
       "      <td>qi</td>\n",
       "      <td>active</td>\n",
       "      <td>horrendous</td>\n",
       "      <td>phenomenal</td>\n",
       "      <td>muted</td>\n",
       "      <td>incredible</td>\n",
       "      <td>scratched</td>\n",
       "      <td>black</td>\n",
       "      <td>petite</td>\n",
       "      <td>incredible</td>\n",
       "      <td>unintelligible</td>\n",
       "      <td>hear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>soooo</td>\n",
       "      <td>skeptical</td>\n",
       "      <td>current</td>\n",
       "      <td>pro</td>\n",
       "      <td>recorded</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>que</td>\n",
       "      <td>many</td>\n",
       "      <td>slow</td>\n",
       "      <td>pricey</td>\n",
       "      <td>lg</td>\n",
       "      <td>several</td>\n",
       "      <td>roav</td>\n",
       "      <td>favorite</td>\n",
       "      <td>persistent</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>sad</td>\n",
       "      <td>skeptical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>sooooooo</td>\n",
       "      <td>phenomenal</td>\n",
       "      <td>tinny</td>\n",
       "      <td>glad</td>\n",
       "      <td>robotic</td>\n",
       "      <td>solid</td>\n",
       "      <td>retail</td>\n",
       "      <td>high</td>\n",
       "      <td>broken</td>\n",
       "      <td>impressive</td>\n",
       "      <td>later</td>\n",
       "      <td>able</td>\n",
       "      <td>reconnect</td>\n",
       "      <td>white</td>\n",
       "      <td>outlier</td>\n",
       "      <td>impressive</td>\n",
       "      <td>ios</td>\n",
       "      <td>waterproof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>sent</td>\n",
       "      <td>similar</td>\n",
       "      <td>quiet</td>\n",
       "      <td>outstanding</td>\n",
       "      <td>split</td>\n",
       "      <td>convenient</td>\n",
       "      <td>ridiculous</td>\n",
       "      <td>amazing</td>\n",
       "      <td>special</td>\n",
       "      <td>lightweight</td>\n",
       "      <td>interested</td>\n",
       "      <td>own</td>\n",
       "      <td>read</td>\n",
       "      <td>usb</td>\n",
       "      <td>sloppy</td>\n",
       "      <td>affordable</td>\n",
       "      <td>usage</td>\n",
       "      <td>correct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>squeak</td>\n",
       "      <td>ambient</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>tiny</td>\n",
       "      <td>sketchy</td>\n",
       "      <td>light</td>\n",
       "      <td>serial</td>\n",
       "      <td>fit</td>\n",
       "      <td>disable</td>\n",
       "      <td>battery</td>\n",
       "      <td>info</td>\n",
       "      <td>top</td>\n",
       "      <td>pair</td>\n",
       "      <td>call</td>\n",
       "      <td>operable</td>\n",
       "      <td>top</td>\n",
       "      <td>vague</td>\n",
       "      <td>complete</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>scratchy</td>\n",
       "      <td>past</td>\n",
       "      <td>unwilling</td>\n",
       "      <td>favorite</td>\n",
       "      <td>misleading</td>\n",
       "      <td>cheap</td>\n",
       "      <td>sms</td>\n",
       "      <td>extra</td>\n",
       "      <td>garbled</td>\n",
       "      <td>superb</td>\n",
       "      <td>headphones</td>\n",
       "      <td>worth</td>\n",
       "      <td>podcast</td>\n",
       "      <td>glad</td>\n",
       "      <td>nonexistant</td>\n",
       "      <td>quick</td>\n",
       "      <td>supposed</td>\n",
       "      <td>resistant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>rubbery</td>\n",
       "      <td>average</td>\n",
       "      <td>certain</td>\n",
       "      <td>customizable</td>\n",
       "      <td>suggested</td>\n",
       "      <td>outside</td>\n",
       "      <td>sony</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>inferior</td>\n",
       "      <td>possible</td>\n",
       "      <td>half</td>\n",
       "      <td>heavy</td>\n",
       "      <td>strange</td>\n",
       "      <td>hard</td>\n",
       "      <td>near</td>\n",
       "      <td>noisy</td>\n",
       "      <td>provided</td>\n",
       "      <td>sleek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>refurbished</td>\n",
       "      <td>personal</td>\n",
       "      <td>green</td>\n",
       "      <td>outside</td>\n",
       "      <td>technological</td>\n",
       "      <td>quick</td>\n",
       "      <td>outright</td>\n",
       "      <td>call</td>\n",
       "      <td>unknown</td>\n",
       "      <td>cheap</td>\n",
       "      <td>functional</td>\n",
       "      <td>extra</td>\n",
       "      <td>overblown</td>\n",
       "      <td>incredible</td>\n",
       "      <td>mp3</td>\n",
       "      <td>constant</td>\n",
       "      <td>programmable</td>\n",
       "      <td>pleased</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>stayed</td>\n",
       "      <td>loose</td>\n",
       "      <td>systemic</td>\n",
       "      <td>snug</td>\n",
       "      <td>unhappy</td>\n",
       "      <td>white</td>\n",
       "      <td>tech</td>\n",
       "      <td>single</td>\n",
       "      <td>confident</td>\n",
       "      <td>crisp</td>\n",
       "      <td>amd</td>\n",
       "      <td>due</td>\n",
       "      <td>noisier</td>\n",
       "      <td>such</td>\n",
       "      <td>sized</td>\n",
       "      <td>white</td>\n",
       "      <td>supporting</td>\n",
       "      <td>additional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>sizes</td>\n",
       "      <td>lightweight</td>\n",
       "      <td>give</td>\n",
       "      <td>touch</td>\n",
       "      <td>unparalleled</td>\n",
       "      <td>wrong</td>\n",
       "      <td>third</td>\n",
       "      <td>super</td>\n",
       "      <td>choppy</td>\n",
       "      <td>daily</td>\n",
       "      <td>frustrated</td>\n",
       "      <td>eq</td>\n",
       "      <td>neo</td>\n",
       "      <td>personal</td>\n",
       "      <td>staticky</td>\n",
       "      <td>comparable</td>\n",
       "      <td>sudden</td>\n",
       "      <td>phenomenal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>received</td>\n",
       "      <td>satisfied</td>\n",
       "      <td>annoying</td>\n",
       "      <td>phenomenal</td>\n",
       "      <td>unreliable</td>\n",
       "      <td>audio</td>\n",
       "      <td>tolerable</td>\n",
       "      <td>wireless</td>\n",
       "      <td>known</td>\n",
       "      <td>exceptional</td>\n",
       "      <td>forum</td>\n",
       "      <td>favorite</td>\n",
       "      <td>miss</td>\n",
       "      <td>battery</td>\n",
       "      <td>snap</td>\n",
       "      <td>surprised</td>\n",
       "      <td>phasing</td>\n",
       "      <td>tight</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank AirPods Pro_worst AirPods Pro_best Elite 75t_worst Elite 75t_best  \\\n",
       "0      0             empty          awesome   disappointing        amazing   \n",
       "1      1     disappointing       incredible           white        awesome   \n",
       "2      2        impossible             true        horrible     impressive   \n",
       "3      3        unbearable              top          unable    hearthrough   \n",
       "4      4          numerous          pleased            dead        passive   \n",
       "5      5            unable            solid          sudden         slight   \n",
       "6      6          existent       advertised             rid          light   \n",
       "7      7           visible      outstanding    unacceptable          crisp   \n",
       "8      8          firmware         everyday             sad      intuitive   \n",
       "9      9           willing             hear     unfortunate           real   \n",
       "10    10             soooo        skeptical         current            pro   \n",
       "11    11          sooooooo       phenomenal           tinny           glad   \n",
       "12    12              sent          similar           quiet    outstanding   \n",
       "13    13            squeak          ambient      unreliable           tiny   \n",
       "14    14          scratchy             past       unwilling       favorite   \n",
       "15    15           rubbery          average         certain   customizable   \n",
       "16    16       refurbished         personal           green        outside   \n",
       "17    17            stayed            loose        systemic           snug   \n",
       "18    18             sizes      lightweight            give          touch   \n",
       "19    19          received        satisfied        annoying     phenomenal   \n",
       "\n",
       "       Free_worst   Free_best Galaxy Buds Plus_worst Galaxy Buds Plus_best  \\\n",
       "0        horrible      little                 videos                  long   \n",
       "1          unable         usb                   algo               awesome   \n",
       "2            stop     perfect                 sonido                 clear   \n",
       "3          spotty     overall                   pair                  able   \n",
       "4           alive   expensive              panasonic               overall   \n",
       "5           scant        high                   pasa              previous   \n",
       "6       oversized     battery                popular             excellent   \n",
       "7           pairs       noise                present               perfect   \n",
       "8          paypal   impressed                 purist               battery   \n",
       "9       potential     compact                     qi                active   \n",
       "10       recorded   fantastic                    que                  many   \n",
       "11        robotic       solid                 retail                  high   \n",
       "12          split  convenient             ridiculous               amazing   \n",
       "13        sketchy       light                 serial                   fit   \n",
       "14     misleading       cheap                    sms                 extra   \n",
       "15      suggested     outside                   sony             fantastic   \n",
       "16  technological       quick               outright                  call   \n",
       "17        unhappy       white                   tech                single   \n",
       "18   unparalleled       wrong                  third                 super   \n",
       "19     unreliable       audio              tolerable              wireless   \n",
       "\n",
       "      NC700_worst   NC700_best Soundcore Liberty 2 Pro_worst  \\\n",
       "0    unacceptable        super                           add   \n",
       "1   disappointing  outstanding                        galaxy   \n",
       "2       defective        large                     unchanged   \n",
       "3          unable        clean                        unable   \n",
       "4         serious     favorite                        purple   \n",
       "5        frequent      crystal                  professiinal   \n",
       "6           loyal       pretty                 precautionary   \n",
       "7            tech         rich                         ocean   \n",
       "8     frustrating        alexa                         nasty   \n",
       "9      horrendous   phenomenal                         muted   \n",
       "10           slow       pricey                            lg   \n",
       "11         broken   impressive                         later   \n",
       "12        special  lightweight                    interested   \n",
       "13        disable      battery                          info   \n",
       "14        garbled       superb                    headphones   \n",
       "15       inferior     possible                          half   \n",
       "16        unknown        cheap                    functional   \n",
       "17      confident        crisp                           amd   \n",
       "18         choppy        daily                    frustrated   \n",
       "19          known  exceptional                         forum   \n",
       "\n",
       "   Soundcore Liberty 2 Pro_best Soundcore Liberty Air 2_worst  \\\n",
       "0                     different                          user   \n",
       "1                       awesome                       finicky   \n",
       "2                       overall                         awful   \n",
       "3                       amazing                         basic   \n",
       "4                     fantastic                  unacceptable   \n",
       "5                       liberty                          tech   \n",
       "6                      wireless                    afterwhich   \n",
       "7                         small                          post   \n",
       "8                           big                         spent   \n",
       "9                    incredible                     scratched   \n",
       "10                      several                          roav   \n",
       "11                         able                     reconnect   \n",
       "12                          own                          read   \n",
       "13                          top                          pair   \n",
       "14                        worth                       podcast   \n",
       "15                        heavy                       strange   \n",
       "16                        extra                     overblown   \n",
       "17                          due                       noisier   \n",
       "18                           eq                           neo   \n",
       "19                     favorite                          miss   \n",
       "\n",
       "   Soundcore Liberty Air 2_best Soundcore Life Q20_worst  \\\n",
       "0                       awesome                   unable   \n",
       "1                     excellent                 unusable   \n",
       "2                       overall                   worked   \n",
       "3                       amazing                   likely   \n",
       "4                       perfect                  useless   \n",
       "5                     fantastic                amplified   \n",
       "6                      wireless               percussive   \n",
       "7                          true                       se   \n",
       "8                       pleased                     read   \n",
       "9                         black                   petite   \n",
       "10                     favorite               persistent   \n",
       "11                        white                  outlier   \n",
       "12                          usb                   sloppy   \n",
       "13                         call                 operable   \n",
       "14                         glad              nonexistant   \n",
       "15                         hard                     near   \n",
       "16                   incredible                      mp3   \n",
       "17                         such                    sized   \n",
       "18                     personal                 staticky   \n",
       "19                      battery                     snap   \n",
       "\n",
       "   Soundcore Life Q20_best WF-1000XM3_worst WF-1000XM3_best  \n",
       "0                   little        defective           black  \n",
       "1                  awesome         unusable       impressed  \n",
       "2                    solid             weak         android  \n",
       "3                expensive        worthless           solid  \n",
       "4                impressed          youtube         helpful  \n",
       "5                    build     unacceptable           minor  \n",
       "6                  battery          audible          strong  \n",
       "7                      own               re        superior  \n",
       "8                  ambient         friendly      convenient  \n",
       "9               incredible   unintelligible            hear  \n",
       "10               satisfied              sad       skeptical  \n",
       "11              impressive              ios      waterproof  \n",
       "12              affordable            usage         correct  \n",
       "13                     top            vague        complete  \n",
       "14                   quick         supposed       resistant  \n",
       "15                   noisy         provided           sleek  \n",
       "16                constant     programmable         pleased  \n",
       "17                   white       supporting      additional  \n",
       "18              comparable           sudden      phenomenal  \n",
       "19               surprised          phasing           tight  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "productSingleWords = GetProductUniqueWords(amzn_df).head(20)\n",
    "print(productSingleWords.columns)\n",
    "\n",
    "productSingleWords\n",
    "#TODO: create a high and low df of the table below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>AirPods Pro_worst</th>\n",
       "      <th>Elite 75t_worst</th>\n",
       "      <th>Free_worst</th>\n",
       "      <th>Galaxy Buds Plus_worst</th>\n",
       "      <th>NC700_worst</th>\n",
       "      <th>Soundcore Liberty 2 Pro_worst</th>\n",
       "      <th>Soundcore Liberty Air 2_worst</th>\n",
       "      <th>Soundcore Life Q20_worst</th>\n",
       "      <th>WF-1000XM3_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>empty</td>\n",
       "      <td>disappointing</td>\n",
       "      <td>horrible</td>\n",
       "      <td>videos</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>add</td>\n",
       "      <td>user</td>\n",
       "      <td>unable</td>\n",
       "      <td>defective</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>disappointing</td>\n",
       "      <td>white</td>\n",
       "      <td>unable</td>\n",
       "      <td>algo</td>\n",
       "      <td>disappointing</td>\n",
       "      <td>galaxy</td>\n",
       "      <td>finicky</td>\n",
       "      <td>unusable</td>\n",
       "      <td>unusable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>impossible</td>\n",
       "      <td>horrible</td>\n",
       "      <td>stop</td>\n",
       "      <td>sonido</td>\n",
       "      <td>defective</td>\n",
       "      <td>unchanged</td>\n",
       "      <td>awful</td>\n",
       "      <td>worked</td>\n",
       "      <td>weak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unbearable</td>\n",
       "      <td>unable</td>\n",
       "      <td>spotty</td>\n",
       "      <td>pair</td>\n",
       "      <td>unable</td>\n",
       "      <td>unable</td>\n",
       "      <td>basic</td>\n",
       "      <td>likely</td>\n",
       "      <td>worthless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>numerous</td>\n",
       "      <td>dead</td>\n",
       "      <td>alive</td>\n",
       "      <td>panasonic</td>\n",
       "      <td>serious</td>\n",
       "      <td>purple</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>useless</td>\n",
       "      <td>youtube</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>unable</td>\n",
       "      <td>sudden</td>\n",
       "      <td>scant</td>\n",
       "      <td>pasa</td>\n",
       "      <td>frequent</td>\n",
       "      <td>professiinal</td>\n",
       "      <td>tech</td>\n",
       "      <td>amplified</td>\n",
       "      <td>unacceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>existent</td>\n",
       "      <td>rid</td>\n",
       "      <td>oversized</td>\n",
       "      <td>popular</td>\n",
       "      <td>loyal</td>\n",
       "      <td>precautionary</td>\n",
       "      <td>afterwhich</td>\n",
       "      <td>percussive</td>\n",
       "      <td>audible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>visible</td>\n",
       "      <td>unacceptable</td>\n",
       "      <td>pairs</td>\n",
       "      <td>present</td>\n",
       "      <td>tech</td>\n",
       "      <td>ocean</td>\n",
       "      <td>post</td>\n",
       "      <td>se</td>\n",
       "      <td>re</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>firmware</td>\n",
       "      <td>sad</td>\n",
       "      <td>paypal</td>\n",
       "      <td>purist</td>\n",
       "      <td>frustrating</td>\n",
       "      <td>nasty</td>\n",
       "      <td>spent</td>\n",
       "      <td>read</td>\n",
       "      <td>friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>willing</td>\n",
       "      <td>unfortunate</td>\n",
       "      <td>potential</td>\n",
       "      <td>qi</td>\n",
       "      <td>horrendous</td>\n",
       "      <td>muted</td>\n",
       "      <td>scratched</td>\n",
       "      <td>petite</td>\n",
       "      <td>unintelligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AirPods Pro_worst Elite 75t_worst Free_worst Galaxy Buds Plus_worst  \\\n",
       "0             empty   disappointing   horrible                 videos   \n",
       "1     disappointing           white     unable                   algo   \n",
       "2        impossible        horrible       stop                 sonido   \n",
       "3        unbearable          unable     spotty                   pair   \n",
       "4          numerous            dead      alive              panasonic   \n",
       "5            unable          sudden      scant                   pasa   \n",
       "6          existent             rid  oversized                popular   \n",
       "7           visible    unacceptable      pairs                present   \n",
       "8          firmware             sad     paypal                 purist   \n",
       "9           willing     unfortunate  potential                     qi   \n",
       "\n",
       "     NC700_worst Soundcore Liberty 2 Pro_worst Soundcore Liberty Air 2_worst  \\\n",
       "0   unacceptable                           add                          user   \n",
       "1  disappointing                        galaxy                       finicky   \n",
       "2      defective                     unchanged                         awful   \n",
       "3         unable                        unable                         basic   \n",
       "4        serious                        purple                  unacceptable   \n",
       "5       frequent                  professiinal                          tech   \n",
       "6          loyal                 precautionary                    afterwhich   \n",
       "7           tech                         ocean                          post   \n",
       "8    frustrating                         nasty                         spent   \n",
       "9     horrendous                         muted                     scratched   \n",
       "\n",
       "  Soundcore Life Q20_worst WF-1000XM3_worst  \n",
       "0                   unable        defective  \n",
       "1                 unusable         unusable  \n",
       "2                   worked             weak  \n",
       "3                   likely        worthless  \n",
       "4                  useless          youtube  \n",
       "5                amplified     unacceptable  \n",
       "6               percussive          audible  \n",
       "7                       se               re  \n",
       "8                     read         friendly  \n",
       "9                   petite   unintelligible  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worstColumns = ['AirPods Pro_worst', 'Elite 75t_worst',\n",
    "       'Free_worst', 'Galaxy Buds Plus_worst','NC700_worst',\n",
    "       'Soundcore Liberty 2 Pro_worst', 'Soundcore Liberty Air 2_worst',\n",
    "       'Soundcore Life Q20_worst',\n",
    "       'WF-1000XM3_worst']\n",
    "\n",
    "bestColumns = ['AirPods Pro_best', 'Elite 75t_best','Free_best',\n",
    "       'Galaxy Buds Plus_best', 'NC700_best','Soundcore Liberty 2 Pro_best',\n",
    "       'Soundcore Liberty Air 2_best','Soundcore Life Q20_best',\n",
    "       'WF-1000XM3_best']\n",
    "\n",
    "worstSingleProductWords = productSingleWords[worstColumns]\n",
    "worstSingleProductWords[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>AirPods Pro_worst</th>\n",
      "      <th>Elite 75t_worst</th>\n",
      "      <th>Free_worst</th>\n",
      "      <th>Galaxy Buds Plus_worst</th>\n",
      "      <th>NC700_worst</th>\n",
      "      <th>Soundcore Liberty 2 Pro_worst</th>\n",
      "      <th>Soundcore Liberty Air 2_worst</th>\n",
      "      <th>Soundcore Life Q20_worst</th>\n",
      "      <th>WF-1000XM3_worst</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>0</th>\n",
      "      <td>empty</td>\n",
      "      <td>disappointing</td>\n",
      "      <td>horrible</td>\n",
      "      <td>videos</td>\n",
      "      <td>unacceptable</td>\n",
      "      <td>add</td>\n",
      "      <td>user</td>\n",
      "      <td>unable</td>\n",
      "      <td>defective</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>1</th>\n",
      "      <td>disappointing</td>\n",
      "      <td>white</td>\n",
      "      <td>unable</td>\n",
      "      <td>algo</td>\n",
      "      <td>disappointing</td>\n",
      "      <td>galaxy</td>\n",
      "      <td>finicky</td>\n",
      "      <td>unusable</td>\n",
      "      <td>unusable</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>2</th>\n",
      "      <td>impossible</td>\n",
      "      <td>horrible</td>\n",
      "      <td>stop</td>\n",
      "      <td>sonido</td>\n",
      "      <td>defective</td>\n",
      "      <td>unchanged</td>\n",
      "      <td>awful</td>\n",
      "      <td>worked</td>\n",
      "      <td>weak</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>3</th>\n",
      "      <td>unbearable</td>\n",
      "      <td>unable</td>\n",
      "      <td>spotty</td>\n",
      "      <td>pair</td>\n",
      "      <td>unable</td>\n",
      "      <td>unable</td>\n",
      "      <td>basic</td>\n",
      "      <td>likely</td>\n",
      "      <td>worthless</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>4</th>\n",
      "      <td>numerous</td>\n",
      "      <td>dead</td>\n",
      "      <td>alive</td>\n",
      "      <td>panasonic</td>\n",
      "      <td>serious</td>\n",
      "      <td>purple</td>\n",
      "      <td>unacceptable</td>\n",
      "      <td>useless</td>\n",
      "      <td>youtube</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>5</th>\n",
      "      <td>unable</td>\n",
      "      <td>sudden</td>\n",
      "      <td>scant</td>\n",
      "      <td>pasa</td>\n",
      "      <td>frequent</td>\n",
      "      <td>professiinal</td>\n",
      "      <td>tech</td>\n",
      "      <td>amplified</td>\n",
      "      <td>unacceptable</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>6</th>\n",
      "      <td>existent</td>\n",
      "      <td>rid</td>\n",
      "      <td>oversized</td>\n",
      "      <td>popular</td>\n",
      "      <td>loyal</td>\n",
      "      <td>precautionary</td>\n",
      "      <td>afterwhich</td>\n",
      "      <td>percussive</td>\n",
      "      <td>audible</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>7</th>\n",
      "      <td>visible</td>\n",
      "      <td>unacceptable</td>\n",
      "      <td>pairs</td>\n",
      "      <td>present</td>\n",
      "      <td>tech</td>\n",
      "      <td>ocean</td>\n",
      "      <td>post</td>\n",
      "      <td>se</td>\n",
      "      <td>re</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>8</th>\n",
      "      <td>firmware</td>\n",
      "      <td>sad</td>\n",
      "      <td>paypal</td>\n",
      "      <td>purist</td>\n",
      "      <td>frustrating</td>\n",
      "      <td>nasty</td>\n",
      "      <td>spent</td>\n",
      "      <td>read</td>\n",
      "      <td>friendly</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>9</th>\n",
      "      <td>willing</td>\n",
      "      <td>unfortunate</td>\n",
      "      <td>potential</td>\n",
      "      <td>qi</td>\n",
      "      <td>horrendous</td>\n",
      "      <td>muted</td>\n",
      "      <td>scratched</td>\n",
      "      <td>petite</td>\n",
      "      <td>unintelligible</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "html = worstSingleProductWords[:10].to_html()\n",
    "print(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>AirPods Pro_best</th>\n",
       "      <th>Elite 75t_best</th>\n",
       "      <th>Free_best</th>\n",
       "      <th>Galaxy Buds Plus_best</th>\n",
       "      <th>NC700_best</th>\n",
       "      <th>Soundcore Liberty 2 Pro_best</th>\n",
       "      <th>Soundcore Liberty Air 2_best</th>\n",
       "      <th>Soundcore Life Q20_best</th>\n",
       "      <th>WF-1000XM3_best</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>awesome</td>\n",
       "      <td>amazing</td>\n",
       "      <td>little</td>\n",
       "      <td>long</td>\n",
       "      <td>super</td>\n",
       "      <td>different</td>\n",
       "      <td>awesome</td>\n",
       "      <td>little</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>incredible</td>\n",
       "      <td>awesome</td>\n",
       "      <td>usb</td>\n",
       "      <td>awesome</td>\n",
       "      <td>outstanding</td>\n",
       "      <td>awesome</td>\n",
       "      <td>excellent</td>\n",
       "      <td>awesome</td>\n",
       "      <td>impressed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>true</td>\n",
       "      <td>impressive</td>\n",
       "      <td>perfect</td>\n",
       "      <td>clear</td>\n",
       "      <td>large</td>\n",
       "      <td>overall</td>\n",
       "      <td>overall</td>\n",
       "      <td>solid</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>top</td>\n",
       "      <td>hearthrough</td>\n",
       "      <td>overall</td>\n",
       "      <td>able</td>\n",
       "      <td>clean</td>\n",
       "      <td>amazing</td>\n",
       "      <td>amazing</td>\n",
       "      <td>expensive</td>\n",
       "      <td>solid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pleased</td>\n",
       "      <td>passive</td>\n",
       "      <td>expensive</td>\n",
       "      <td>overall</td>\n",
       "      <td>favorite</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>perfect</td>\n",
       "      <td>impressed</td>\n",
       "      <td>helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>solid</td>\n",
       "      <td>slight</td>\n",
       "      <td>high</td>\n",
       "      <td>previous</td>\n",
       "      <td>crystal</td>\n",
       "      <td>liberty</td>\n",
       "      <td>fantastic</td>\n",
       "      <td>build</td>\n",
       "      <td>minor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>advertised</td>\n",
       "      <td>light</td>\n",
       "      <td>battery</td>\n",
       "      <td>excellent</td>\n",
       "      <td>pretty</td>\n",
       "      <td>wireless</td>\n",
       "      <td>wireless</td>\n",
       "      <td>battery</td>\n",
       "      <td>strong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>outstanding</td>\n",
       "      <td>crisp</td>\n",
       "      <td>noise</td>\n",
       "      <td>perfect</td>\n",
       "      <td>rich</td>\n",
       "      <td>small</td>\n",
       "      <td>true</td>\n",
       "      <td>own</td>\n",
       "      <td>superior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>everyday</td>\n",
       "      <td>intuitive</td>\n",
       "      <td>impressed</td>\n",
       "      <td>battery</td>\n",
       "      <td>alexa</td>\n",
       "      <td>big</td>\n",
       "      <td>pleased</td>\n",
       "      <td>ambient</td>\n",
       "      <td>convenient</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hear</td>\n",
       "      <td>real</td>\n",
       "      <td>compact</td>\n",
       "      <td>active</td>\n",
       "      <td>phenomenal</td>\n",
       "      <td>incredible</td>\n",
       "      <td>black</td>\n",
       "      <td>incredible</td>\n",
       "      <td>hear</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AirPods Pro_best Elite 75t_best  Free_best Galaxy Buds Plus_best  \\\n",
       "0          awesome        amazing     little                  long   \n",
       "1       incredible        awesome        usb               awesome   \n",
       "2             true     impressive    perfect                 clear   \n",
       "3              top    hearthrough    overall                  able   \n",
       "4          pleased        passive  expensive               overall   \n",
       "5            solid         slight       high              previous   \n",
       "6       advertised          light    battery             excellent   \n",
       "7      outstanding          crisp      noise               perfect   \n",
       "8         everyday      intuitive  impressed               battery   \n",
       "9             hear           real    compact                active   \n",
       "\n",
       "    NC700_best Soundcore Liberty 2 Pro_best Soundcore Liberty Air 2_best  \\\n",
       "0        super                    different                      awesome   \n",
       "1  outstanding                      awesome                    excellent   \n",
       "2        large                      overall                      overall   \n",
       "3        clean                      amazing                      amazing   \n",
       "4     favorite                    fantastic                      perfect   \n",
       "5      crystal                      liberty                    fantastic   \n",
       "6       pretty                     wireless                     wireless   \n",
       "7         rich                        small                         true   \n",
       "8        alexa                          big                      pleased   \n",
       "9   phenomenal                   incredible                        black   \n",
       "\n",
       "  Soundcore Life Q20_best WF-1000XM3_best  \n",
       "0                  little           black  \n",
       "1                 awesome       impressed  \n",
       "2                   solid         android  \n",
       "3               expensive           solid  \n",
       "4               impressed         helpful  \n",
       "5                   build           minor  \n",
       "6                 battery          strong  \n",
       "7                     own        superior  \n",
       "8                 ambient      convenient  \n",
       "9              incredible            hear  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bestSingleProductWords = productSingleWords[bestColumns]\n",
    "bestSingleProductWords[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>AirPods Pro_best</th>\n",
      "      <th>Elite 75t_best</th>\n",
      "      <th>Free_best</th>\n",
      "      <th>Galaxy Buds Plus_best</th>\n",
      "      <th>NC700_best</th>\n",
      "      <th>Soundcore Liberty 2 Pro_best</th>\n",
      "      <th>Soundcore Liberty Air 2_best</th>\n",
      "      <th>Soundcore Life Q20_best</th>\n",
      "      <th>WF-1000XM3_best</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>0</th>\n",
      "      <td>awesome</td>\n",
      "      <td>amazing</td>\n",
      "      <td>little</td>\n",
      "      <td>long</td>\n",
      "      <td>super</td>\n",
      "      <td>different</td>\n",
      "      <td>awesome</td>\n",
      "      <td>little</td>\n",
      "      <td>black</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>1</th>\n",
      "      <td>incredible</td>\n",
      "      <td>awesome</td>\n",
      "      <td>usb</td>\n",
      "      <td>awesome</td>\n",
      "      <td>outstanding</td>\n",
      "      <td>awesome</td>\n",
      "      <td>excellent</td>\n",
      "      <td>awesome</td>\n",
      "      <td>impressed</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>2</th>\n",
      "      <td>true</td>\n",
      "      <td>impressive</td>\n",
      "      <td>perfect</td>\n",
      "      <td>clear</td>\n",
      "      <td>large</td>\n",
      "      <td>overall</td>\n",
      "      <td>overall</td>\n",
      "      <td>solid</td>\n",
      "      <td>android</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>3</th>\n",
      "      <td>top</td>\n",
      "      <td>hearthrough</td>\n",
      "      <td>overall</td>\n",
      "      <td>able</td>\n",
      "      <td>clean</td>\n",
      "      <td>amazing</td>\n",
      "      <td>amazing</td>\n",
      "      <td>expensive</td>\n",
      "      <td>solid</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>4</th>\n",
      "      <td>pleased</td>\n",
      "      <td>passive</td>\n",
      "      <td>expensive</td>\n",
      "      <td>overall</td>\n",
      "      <td>favorite</td>\n",
      "      <td>fantastic</td>\n",
      "      <td>perfect</td>\n",
      "      <td>impressed</td>\n",
      "      <td>helpful</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>5</th>\n",
      "      <td>solid</td>\n",
      "      <td>slight</td>\n",
      "      <td>high</td>\n",
      "      <td>previous</td>\n",
      "      <td>crystal</td>\n",
      "      <td>liberty</td>\n",
      "      <td>fantastic</td>\n",
      "      <td>build</td>\n",
      "      <td>minor</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>6</th>\n",
      "      <td>advertised</td>\n",
      "      <td>light</td>\n",
      "      <td>battery</td>\n",
      "      <td>excellent</td>\n",
      "      <td>pretty</td>\n",
      "      <td>wireless</td>\n",
      "      <td>wireless</td>\n",
      "      <td>battery</td>\n",
      "      <td>strong</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>7</th>\n",
      "      <td>outstanding</td>\n",
      "      <td>crisp</td>\n",
      "      <td>noise</td>\n",
      "      <td>perfect</td>\n",
      "      <td>rich</td>\n",
      "      <td>small</td>\n",
      "      <td>true</td>\n",
      "      <td>own</td>\n",
      "      <td>superior</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>8</th>\n",
      "      <td>everyday</td>\n",
      "      <td>intuitive</td>\n",
      "      <td>impressed</td>\n",
      "      <td>battery</td>\n",
      "      <td>alexa</td>\n",
      "      <td>big</td>\n",
      "      <td>pleased</td>\n",
      "      <td>ambient</td>\n",
      "      <td>convenient</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>9</th>\n",
      "      <td>hear</td>\n",
      "      <td>real</td>\n",
      "      <td>compact</td>\n",
      "      <td>active</td>\n",
      "      <td>phenomenal</td>\n",
      "      <td>incredible</td>\n",
      "      <td>black</td>\n",
      "      <td>incredible</td>\n",
      "      <td>hear</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "html = bestSingleProductWords[:10].to_html()\n",
    "print(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\angrygamers\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:6: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  \n"
     ]
    },
    {
     "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>words</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phenomenal</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0.037319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>skeptical</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.024517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lightweight</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.020048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>affordable</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.018478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>snug</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.017874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>demanded</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>decided</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>db</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>sportsound</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>ears</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1954 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            words  verified_purchase     count\n",
       "0      phenomenal              269.0  0.037319\n",
       "1       skeptical              175.0  0.024517\n",
       "2     lightweight              142.0  0.020048\n",
       "3      affordable              143.0  0.018478\n",
       "4            snug              124.0  0.017874\n",
       "...           ...                ...       ...\n",
       "1949     demanded                1.0  0.000121\n",
       "1950      decided                1.0  0.000121\n",
       "1951           db                1.0  0.000121\n",
       "1952   sportsound                1.0  0.000121\n",
       "1953         ears                1.0  0.000121\n",
       "\n",
       "[1954 rows x 3 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = amzn_df.copy()\n",
    "lowAdjectivesRanking = df.where(df['review_rating'] < 2).dropna()\n",
    "lowAdjectivesRanking = SingleWords_df(lowAdjectivesRanking)\n",
    "\n",
    "highAdjectivesRanking = df.where(df['review_rating'] > 4).dropna()\n",
    "highAdjectivesRanking = SingleWords_df(highAdjectivesRanking)\n",
    "    \n",
    "adjCount_temp = highAdjectivesRanking[['words','count']].groupby(['words']).sum().sort_values(by='count',ascending=False).reset_index()\n",
    "adjCount_temp2 = lowAdjectivesRanking[['words','count']].groupby(['words']).sum().sort_values(by='count',ascending=False).reset_index()\n",
    "\n",
    "intersection = adjCount_temp.merge(adjCount_temp2, left_on='words', right_on='words', how='inner')\n",
    "\n",
    "\n",
    "lowwordsDump = lowAdjectivesRanking[~lowAdjectivesRanking['words'].isin(intersection['words'].unique())].groupby('words').sum().sort_values(by='count',ascending=False).reset_index()\n",
    "highwordsDump = highAdjectivesRanking[~highAdjectivesRanking['words'].isin(intersection['words'].unique())].groupby('words').sum().sort_values(by='count',ascending=False).reset_index()\n",
    "highwordsDump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = highwordsDump[0:40]['words'].apply(lambda x: str(x)).values\n",
    "\n",
    "text = ' '.join(text)\n",
    "# txt = \"The rain in Spain\"\n",
    "# x = re.search(\"^The.*Spain$\", txt)\n",
    "\n",
    "# import re\n",
    "# for elem in text:\n",
    "#     print(re.findall(r\"\\w[\\w']+\", elem))\n",
    "    \n",
    "    \n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Generate a word cloud image\n",
    "# wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "# the matplotlib way:\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.imshow(wordcloud, interpolation='bilinear')\n",
    "# plt.axis(\"off\")\n",
    "\n",
    "# lower max_font_size\n",
    "wordcloud = WordCloud(max_font_size=40).generate(text)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "wordcloud.to_file(path.join(d, 'Top40Words.png'))\n",
    "\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>words</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>supposed</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.006278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weren</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pathetic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>email</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amd</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.004484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>oppressive</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>outlier</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>outright</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>overblown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>4th</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>317 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          words  verified_purchase     count\n",
       "0      supposed                7.0  0.006278\n",
       "1         weren                5.0  0.004484\n",
       "2      pathetic                4.0  0.004484\n",
       "3         email                5.0  0.004484\n",
       "4           amd                3.0  0.004484\n",
       "..          ...                ...       ...\n",
       "312  oppressive                1.0  0.000897\n",
       "313     outlier                1.0  0.000897\n",
       "314    outright                1.0  0.000897\n",
       "315   overblown                0.0  0.000897\n",
       "316         4th                1.0  0.000897\n",
       "\n",
       "[317 rows x 3 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowwordsDump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = lowwordsDump[0:40]['words'].apply(lambda x: str(x)).values\n",
    "\n",
    "text = ' '.join(text)\n",
    "# txt = \"The rain in Spain\"\n",
    "# x = re.search(\"^The.*Spain$\", txt)\n",
    "\n",
    "# import re\n",
    "# for elem in text:\n",
    "#     print(re.findall(r\"\\w[\\w']+\", elem))\n",
    "    \n",
    "    \n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Generate a word cloud image\n",
    "# wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "# the matplotlib way:\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.imshow(wordcloud, interpolation='bilinear')\n",
    "# plt.axis(\"off\")\n",
    "\n",
    "# lower max_font_size\n",
    "wordcloud = WordCloud(max_font_size=40).generate(text)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "wordcloud.to_file(path.join(d, 'Top40Words_lowRatings.png'))\n",
    "\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>words</th>\n",
       "      <th>count_x</th>\n",
       "      <th>count_y</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>great</td>\n",
       "      <td>1.414493</td>\n",
       "      <td>0.226009</td>\n",
       "      <td>1.640502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>good</td>\n",
       "      <td>1.003382</td>\n",
       "      <td>0.255605</td>\n",
       "      <td>1.258987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sound</td>\n",
       "      <td>0.487802</td>\n",
       "      <td>0.137220</td>\n",
       "      <td>0.625022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>other</td>\n",
       "      <td>0.401691</td>\n",
       "      <td>0.209865</td>\n",
       "      <td>0.611556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ear</td>\n",
       "      <td>0.449034</td>\n",
       "      <td>0.136323</td>\n",
       "      <td>0.585357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>te</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.001018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>alone</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.001018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>supreme</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.001018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>precious</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.001018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>know</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.001018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>851 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        words   count_x   count_y     count\n",
       "0       great  1.414493  0.226009  1.640502\n",
       "1        good  1.003382  0.255605  1.258987\n",
       "2       sound  0.487802  0.137220  0.625022\n",
       "3       other  0.401691  0.209865  0.611556\n",
       "4         ear  0.449034  0.136323  0.585357\n",
       "..        ...       ...       ...       ...\n",
       "846        te  0.000121  0.000897  0.001018\n",
       "847     alone  0.000121  0.000897  0.001018\n",
       "848   supreme  0.000121  0.000897  0.001018\n",
       "849  precious  0.000121  0.000897  0.001018\n",
       "850      know  0.000121  0.000897  0.001018\n",
       "\n",
       "[851 rows x 4 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intersection ['count'] = intersection['count_x']+intersection['count_y']\n",
    "common50Words = intersection.groupby('words').sum().sort_values(by='count',ascending=False).reset_index()#[['words']][:49]\n",
    "common50Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = common50Words[0:40]['words'].apply(lambda x: str(x)).values\n",
    "\n",
    "text = ' '.join(text)\n",
    "# txt = \"The rain in Spain\"\n",
    "# x = re.search(\"^The.*Spain$\", txt)\n",
    "\n",
    "# import re\n",
    "# for elem in text:\n",
    "#     print(re.findall(r\"\\w[\\w']+\", elem))\n",
    "    \n",
    "    \n",
    "from wordcloud import WordCloud\n",
    "\n",
    "# Generate a word cloud image\n",
    "# wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "# the matplotlib way:\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.imshow(wordcloud, interpolation='bilinear')\n",
    "# plt.axis(\"off\")\n",
    "\n",
    "# lower max_font_size\n",
    "wordcloud = WordCloud(max_font_size=40).generate(text)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "wordcloud.to_file(path.join(d, 'commonWords.png'))\n",
    "\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from os import path\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "d = path.dirname(__file__) if \"__file__\" in locals() else os.getcwd()\n",
    "\n",
    "\n",
    "wordsDump = highAdjectivesRanking[~highAdjectivesRanking['words'].isin(intersection['words'].unique())].groupby('words').sum().sort_values(by='count',ascending=False).reset_index()\n",
    "text = wordsDump['words'].apply(lambda x: str(x)).values\n",
    "\n",
    "text = ' '.join(text)\n",
    "\n",
    "# read the mask image\n",
    "# taken from\n",
    "# http://www.stencilry.org/stencils/movies/alice%20in%20wonderland/255fk.jpg\n",
    "alice_mask = np.array(Image.open(path.join(d, '1200px-Michigan_Wolverines_logo.png')))\n",
    "\n",
    "stopwords = set(STOPWORDS)\n",
    "stopwords.add(\"said\")\n",
    "\n",
    "wc = WordCloud(background_color=\"white\", max_words=2000, mask=alice_mask,\n",
    "               stopwords=stopwords, contour_width=3, contour_color='steelblue')\n",
    "# plt.figure(figsize=(20,5))\n",
    "\n",
    "\n",
    "\n",
    "# generate word cloud\n",
    "wc.generate(text)\n",
    "\n",
    "# store to file\n",
    "wc.to_file(path.join(d, 'wordcloud.png'))\n",
    "\n",
    "# show\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.figure()\n",
    "plt.imshow(alice_mask, cmap=plt.cm.gray, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .merge(df2, left_on='lkey', right_on='rkey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get top positive words for each product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The words for each product are not to be unique. We will want to also include negative words. the DF should look like:<br><br>\n",
    "    ProductName1, product name 2, product name 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word pairs entire list of reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create a data frame of all the reviews. We want to see overall what are the different words between the purchased and not purchased sets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def GetReviewPairsCounts(df):\n",
    "    data = df.copy()\n",
    "#     data[\"tempPairs\"] = data[pairsColumn]\n",
    "#     data = data[pairsColumn].apply(pd.Series)\\\n",
    "#     .merge(data, right_index = True, left_index = True) \\\n",
    "#     .drop([\"tempPairs\"], axis = 1)\\\n",
    "#     .drop([pairsColumn], axis = 1)\\\n",
    "#     .melt(id_vars =columnstoKeep, value_name = \"pairs\") \\\n",
    "#     .drop(\"variable\", axis = 1).dropna()\n",
    "\n",
    "\n",
    "    data['text_pairs_valid']=data['text_pairs'].apply(lambda x: True if len(x)>0 else False)\n",
    "    data = data.where(data['text_pairs_valid'] == True).dropna().drop(columns=['text_pairs_valid'])\n",
    "\n",
    "    data['text_pairs'] = data['text_pairs'].apply(lambda x: [tuple(elem) for elem in x ])\n",
    "    data= data[['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date', 'review_title', 'review_text','text_pairs']]\n",
    "\n",
    "    data = data.set_index(['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "       'review_date', 'review_title', 'review_text']).apply(pd.Series.explode).reset_index()\n",
    "\n",
    "    \n",
    "    \n",
    "    # count should be a ratio of the current elem/total sets. NOTE: can't really count them here...value too small.\n",
    "    totalReview= len(df['verified_purchase'])\n",
    "    \n",
    "    \n",
    "    #NOTE: WE HAVE TO DO THE RATIO FOR THE NUMBER OF REVIEWS THAT PRODUCT HAS?\n",
    "    \n",
    "    \n",
    "    \n",
    "#     data['count'] = data['verified_purchase'].apply(lambda x: 1/totalReview)\n",
    "#     data['count'] = data['verified_purchase'].apply(lambda x: 1)\n",
    "    \n",
    "    # we can't group by a set, so we need to make it a string like object, so we can hash it.\n",
    "#     data['pairs_hashable'] = data['pairs'].apply(lambda x: repr(x))\n",
    "    \n",
    "#     countsdf = data.pairs.value_counts().reset_index().rename(columns={'pairs':'total','index':'pairs'})\n",
    "    \n",
    "#     pd.merge(data,countsdf, on='pairs', )\n",
    "    \n",
    "    return data\n",
    "\n",
    "def CreateFullDFOfProductsAndWordPairs(amzn_Df):\n",
    "    ### this function will try to keep the product name field and format a dataframe that we can easily parse\n",
    "    final_df = None\n",
    "#     for elem in amzn_df['ProductName'].unique():\n",
    "        \n",
    "#         df = amzn_df.where(amzn_df['ProductName'] == elem).dropna()\n",
    "#         df = df[['review_date','verified_purchase','text_pairs']]\n",
    "#         df = GetReviewPairsCounts(df )\n",
    "#         df['ProductName'] = df['pairs'].apply(lambda x: elem)\n",
    "#         if final_df is None:\n",
    "#             final_df=df\n",
    "#         else:\n",
    "#             final_df = pd.concat([final_df,df])\n",
    "            \n",
    "#     return final_df\n",
    "\n",
    "    return GetReviewPairsCounts(amzn_Df )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# df = amzn_df.where(amzn_df['ProductName'] == 'AirPods Pro').dropna()\n",
    "# df = df[['review_date','verified_purchase','text_pairs']]\n",
    "# df = GetReviewPairsCounts(df, 'text_pairs',['review_date', 'verified_purchase'] )\n",
    "\n",
    "# df_happy_explode = df_happy.set_index(['Country', 'Happiness Score', 'Standard Error', 'lower_95',\n",
    "#                                        'upper_95', 'lower_66', 'upper_66'])\\\n",
    "#                             .apply(pd.Series.explode)\\\n",
    "#                             .reset_index()\n",
    "# print(amzn_df.columns)\n",
    "# df = amzn_df.copy()\n",
    "# df['text_pairs'] = df['text_pairs'].apply(lambda x: [tuple(elem) for elem in x ])\n",
    "# df= df[['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "#        'review_date', 'review_title', 'review_text','text_pairs']]\n",
    "\n",
    "# df.set_index(['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
    "#        'review_date', 'review_title', 'review_text']).apply(pd.Series.explode).reset_index()\n",
    "# import numpy as np\n",
    "# import pandas as pd\n",
    "# df.explode('text_pairs')\n",
    "# df['text_pairs'].apply(pd.Series.explode)\n",
    "# df.head()\n",
    "\n",
    "# df[:1]['text_pairs'].apply(lambda x: [elem for elem in x])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0.3'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# amzn_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Manufacturer', 'ProductName', 'review_rating', 'verified_purchase',\n",
      "       'review_date', 'review_title', 'review_text', 'text_pairs'],\n",
      "      dtype='object')\n",
      "Index(['index', 'AirPods Pro_best', 'AirPods Pro_worst', 'Elite 75t_best',\n",
      "       'Elite 75t_worst', 'Free_best', 'Free_worst', 'Galaxy Buds Plus_best',\n",
      "       'Galaxy Buds Plus_worst', 'NC700_best', 'NC700_worst',\n",
      "       'Soundcore Liberty 2 Pro_best', 'Soundcore Liberty 2 Pro_worst',\n",
      "       'Soundcore Liberty Air 2_best', 'Soundcore Liberty Air 2_worst',\n",
      "       'Soundcore Life Q20_best', 'Soundcore Life Q20_worst',\n",
      "       'WF-1000XM3_best', 'WF-1000XM3_worst'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# data\n",
    "fivestarsPairs_df = amzn_df.where(amzn_df['review_rating'] == 5).dropna()\n",
    "\n",
    "\n",
    "# wordPairsAllReviews_df= CreateFullDFOfProductsAndWordPairs(fivestarsPairs_df)\n",
    "# wordPairsAllReviews_df.head()\n",
    "\n",
    "df =GetReviewPairsCounts(fivestarsPairs_df)\n",
    "print(df.columns)\n",
    "df['count']=df['text_pairs'].apply(lambda x: 1)\n",
    "\n",
    "df = df.groupby(['ProductName','Manufacturer','text_pairs']).sum().reset_index().sort_values(by=['ProductName','count'], ascending=False)\n",
    "\n",
    "\n",
    "def GetTopProductPairs(df):\n",
    "    df = GetReviewPairsCounts(df)\n",
    "    df['count']=df['text_pairs'].apply(lambda x: 1)\n",
    "    \n",
    "    highReviews = df.where(df['review_rating'] > 4).dropna()\n",
    "    lowReviews = df.where(df['review_rating'] < 2).dropna()\n",
    "    \n",
    "    products = df['ProductName'].unique()\n",
    "    \n",
    "    temp = None\n",
    "    for product in products:\n",
    "        temp_df = df.where(df['ProductName'] == product).dropna()\n",
    "        high = temp_df.where(temp_df['review_rating'] > 4).dropna()[['text_pairs','count']]\n",
    "        low  = temp_df.where(temp_df['review_rating'] < 2).dropna()[['text_pairs','count']]\n",
    "        \n",
    "        intersection = high[['text_pairs']].merge(low, left_on='text_pairs', right_on='text_pairs', how='inner')\n",
    "#         temp = high\n",
    "        \n",
    "        high['text_pairs'].isin(intersection['text_pairs'].unique())\n",
    "        \n",
    "        top_adj_words_df = high[~high['text_pairs'].isin(intersection['text_pairs'].unique())].groupby('text_pairs').sum().sort_values(by='count',ascending=False).reset_index()\n",
    "        lower_adj_words_df = low[~low['text_pairs'].isin(intersection['text_pairs'].unique())].groupby('text_pairs').sum().sort_values(by='count',ascending=False).reset_index()\n",
    "        top_adj_words_df = top_adj_words_df.rename(columns={'text_pairs':product+'_best'}).reset_index().drop(columns=['count'])\n",
    "        lower_adj_words_df = lower_adj_words_df.rename(columns={'text_pairs':product+'_worst'}).reset_index().drop(columns=['count'])\n",
    "        \n",
    "        tdf = top_adj_words_df.merge(lower_adj_words_df, left_on='index', right_on='index', how='inner')\n",
    "#         tdf= tds.drop(columns=['count'])\n",
    "        if temp is None:\n",
    "            temp=tdf\n",
    "        else:\n",
    "            temp= temp.merge(tdf, left_on='index', right_on='index', how='inner')\n",
    "        \n",
    "#         high = high[['text_pairs','count']].groupby(['text_pairs']).sum().reset_index().sort_values(by=['ProductName','count'], ascending=False)\n",
    "    return temp\n",
    "\n",
    "productWordPairs = GetTopProductPairs(amzn_df)[:20]\n",
    "\n",
    "productBest = productWordPairs[['index', 'AirPods Pro_best','Elite 75t_best',\n",
    "       'Free_best', 'Galaxy Buds Plus_best',\n",
    "       'NC700_best',\n",
    "       'Soundcore Liberty 2 Pro_best', \n",
    "       'Soundcore Liberty Air 2_best', \n",
    "       'Soundcore Life Q20_best', \n",
    "       'WF-1000XM3_best']]\n",
    "\n",
    "productWorst = productWordPairs[['index', 'AirPods Pro_worst','Elite 75t_worst', 'Free_worst',\n",
    "       'Galaxy Buds Plus_worst', 'NC700_worst',\n",
    "       'Soundcore Liberty 2 Pro_worst','Soundcore Liberty Air 2_worst',\n",
    "       'Soundcore Life Q20_worst', 'WF-1000XM3_worst']]\n",
    "\n",
    "print(productWordPairs.columns)\n",
    "productWordPairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>AirPods Pro_best</th>\n",
      "      <th>Elite 75t_best</th>\n",
      "      <th>Free_best</th>\n",
      "      <th>Galaxy Buds Plus_best</th>\n",
      "      <th>NC700_best</th>\n",
      "      <th>Soundcore Liberty 2 Pro_best</th>\n",
      "      <th>Soundcore Liberty Air 2_best</th>\n",
      "      <th>Soundcore Life Q20_best</th>\n",
      "      <th>WF-1000XM3_best</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>0</th>\n",
      "      <td>(product, great)</td>\n",
      "      <td>(truli, wireless)</td>\n",
      "      <td>(so, far)</td>\n",
      "      <td>(ambient, sound)</td>\n",
      "      <td>(control, touch)</td>\n",
      "      <td>(in, ear)</td>\n",
      "      <td>(as, well)</td>\n",
      "      <td>(as, good)</td>\n",
      "      <td>(connect, app)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>1</th>\n",
      "      <td>(amaz, sound)</td>\n",
      "      <td>(so, far)</td>\n",
      "      <td>(usb, c)</td>\n",
      "      <td>(call, qualiti)</td>\n",
      "      <td>(veri, comfort)</td>\n",
      "      <td>(ear, your)</td>\n",
      "      <td>(charg, wireless)</td>\n",
      "      <td>(so, far)</td>\n",
      "      <td>(as, far)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>2</th>\n",
      "      <td>(qualiti, great)</td>\n",
      "      <td>(good, realli)</td>\n",
      "      <td>(qualiti, great)</td>\n",
      "      <td>(pro, airpod)</td>\n",
      "      <td>(bose, sound)</td>\n",
      "      <td>(earbud, wireless)</td>\n",
      "      <td>(pro, airpod)</td>\n",
      "      <td>(price, great)</td>\n",
      "      <td>(that, headphon)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>3</th>\n",
      "      <td>(great, fit)</td>\n",
      "      <td>(recommend, highli)</td>\n",
      "      <td>(as, good)</td>\n",
      "      <td>(so, far)</td>\n",
      "      <td>(out, block)</td>\n",
      "      <td>(amaz, sound)</td>\n",
      "      <td>(recommend, highli)</td>\n",
      "      <td>(last, batteri)</td>\n",
      "      <td>(product, great)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>4</th>\n",
      "      <td>(love, he)</td>\n",
      "      <td>(in, app)</td>\n",
      "      <td>(good, realli)</td>\n",
      "      <td>(earbud, wireless)</td>\n",
      "      <td>(nois, level)</td>\n",
      "      <td>(in, your)</td>\n",
      "      <td>(product, great)</td>\n",
      "      <td>(price, at)</td>\n",
      "      <td>(best, earbud)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>5</th>\n",
      "      <td>(earbud, wireless)</td>\n",
      "      <td>(qualiti, great)</td>\n",
      "      <td>(recommend, highli)</td>\n",
      "      <td>(qualiti, great)</td>\n",
      "      <td>(veri, well)</td>\n",
      "      <td>(veri, good)</td>\n",
      "      <td>(good, realli)</td>\n",
      "      <td>(bass, boost)</td>\n",
      "      <td>(easi, use)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>6</th>\n",
      "      <td>(worth, well)</td>\n",
      "      <td>(galaxi, bud)</td>\n",
      "      <td>(product, great)</td>\n",
      "      <td>(day, all)</td>\n",
      "      <td>(veri, nice)</td>\n",
      "      <td>(listen, music)</td>\n",
      "      <td>(wireless, true)</td>\n",
      "      <td>(nois, good)</td>\n",
      "      <td>(galaxi, bud)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>7</th>\n",
      "      <td>(worth, definit)</td>\n",
      "      <td>(high, mid)</td>\n",
      "      <td>(free, earfun)</td>\n",
      "      <td>(ear, your)</td>\n",
      "      <td>(veri, happi)</td>\n",
      "      <td>(case, charg)</td>\n",
      "      <td>(better, sound)</td>\n",
      "      <td>(product, great)</td>\n",
      "      <td>(so, good)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>8</th>\n",
      "      <td>(like, realli)</td>\n",
      "      <td>(through, featur)</td>\n",
      "      <td>(last, batteri)</td>\n",
      "      <td>(listen, music)</td>\n",
      "      <td>(high, end)</td>\n",
      "      <td>(best, sound)</td>\n",
      "      <td>(control, touch)</td>\n",
      "      <td>(set, headphon)</td>\n",
      "      <td>(nois, level)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>9</th>\n",
      "      <td>(worth, everi)</td>\n",
      "      <td>(secur, fit)</td>\n",
      "      <td>(good, sound)</td>\n",
      "      <td>(better, much)</td>\n",
      "      <td>(clear, crystal)</td>\n",
      "      <td>(phone, call)</td>\n",
      "      <td>(first, pair)</td>\n",
      "      <td>(valu, great)</td>\n",
      "      <td>(how, much)</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "html = productBest[:10].drop(columns=['index']).to_html()\n",
    "print(html)\n",
    "# We want ProductName, Manufacturer, pairs_hashable, sum\n",
    "# highRatings = data.where(data['review_rating'] >= 4).dropna()\n",
    "# lowRatings = data.where(data['review_rating'] <= 2).dropna()\n",
    "# lowRatings\n",
    "# lowRatings.groupby(['verified_purchase','ProductName','Manufacturer','pairs_hashable']).sum()\n",
    "\n",
    "# data[['pairs_hashable','count']].groupby('pairs_hashable').sum()\n",
    "\n",
    "\n",
    "# data[['pairs','count']].groupby('pairs').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>AirPods Pro_worst</th>\n",
      "      <th>Elite 75t_worst</th>\n",
      "      <th>Free_worst</th>\n",
      "      <th>Galaxy Buds Plus_worst</th>\n",
      "      <th>NC700_worst</th>\n",
      "      <th>Soundcore Liberty 2 Pro_worst</th>\n",
      "      <th>Soundcore Liberty Air 2_worst</th>\n",
      "      <th>Soundcore Life Q20_worst</th>\n",
      "      <th>WF-1000XM3_worst</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>0</th>\n",
      "      <td>(work, doesn)</td>\n",
      "      <td>(support, jabra)</td>\n",
      "      <td>(in, cut)</td>\n",
      "      <td>(left, side)</td>\n",
      "      <td>(disappoint, veri)</td>\n",
      "      <td>(left, bud)</td>\n",
      "      <td>(connect, stay)</td>\n",
      "      <td>(n, c)</td>\n",
      "      <td>(softwar, connect)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>1</th>\n",
      "      <td>(disappoint, extrem)</td>\n",
      "      <td>(would, earbud)</td>\n",
      "      <td>(that, state)</td>\n",
      "      <td>(money, back)</td>\n",
      "      <td>(minut, 10)</td>\n",
      "      <td>(stop, connect)</td>\n",
      "      <td>(won, pair)</td>\n",
      "      <td>(stop, charg)</td>\n",
      "      <td>(disappoint, in)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>2</th>\n",
      "      <td>(tri, everi)</td>\n",
      "      <td>(out, figur)</td>\n",
      "      <td>(stop, one)</td>\n",
      "      <td>(disappoint, so)</td>\n",
      "      <td>(support, technic)</td>\n",
      "      <td>(bud, start)</td>\n",
      "      <td>(when, wa)</td>\n",
      "      <td>(issu, wa)</td>\n",
      "      <td>(nois, no)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>3</th>\n",
      "      <td>(left, airpod)</td>\n",
      "      <td>(contact, jabra)</td>\n",
      "      <td>(would, pair)</td>\n",
      "      <td>(work, one)</td>\n",
      "      <td>(be, return)</td>\n",
      "      <td>(dont, headphon)</td>\n",
      "      <td>(5, 6)</td>\n",
      "      <td>(off, mode)</td>\n",
      "      <td>(want, realli)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>4</th>\n",
      "      <td>(box, wa)</td>\n",
      "      <td>(left, side)</td>\n",
      "      <td>(went, one)</td>\n",
      "      <td>(return, go)</td>\n",
      "      <td>(return, go)</td>\n",
      "      <td>(last, one)</td>\n",
      "      <td>(work, doesn)</td>\n",
      "      <td>(turn, time)</td>\n",
      "      <td>(disappoint, realli)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>5</th>\n",
      "      <td>(hurt, realli)</td>\n",
      "      <td>(work, doesn)</td>\n",
      "      <td>(money, wast)</td>\n",
      "      <td>(back, take)</td>\n",
      "      <td>(no, sound)</td>\n",
      "      <td>(disconnect, no)</td>\n",
      "      <td>(drop, few)</td>\n",
      "      <td>(have, wa)</td>\n",
      "      <td>(again, tri)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>6</th>\n",
      "      <td>(luck, no)</td>\n",
      "      <td>(support, tech)</td>\n",
      "      <td>(turn, tri)</td>\n",
      "      <td>(hear, cannot)</td>\n",
      "      <td>(privaci, polici)</td>\n",
      "      <td>(lack, given)</td>\n",
      "      <td>(time, 50)</td>\n",
      "      <td>(after, week)</td>\n",
      "      <td>(wast, time)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>7</th>\n",
      "      <td>(use, tri)</td>\n",
      "      <td>(simpl, as)</td>\n",
      "      <td>(week, 3)</td>\n",
      "      <td>(beep, your)</td>\n",
      "      <td>(issu, all)</td>\n",
      "      <td>(in, what)</td>\n",
      "      <td>(play, onli)</td>\n",
      "      <td>(would, caus)</td>\n",
      "      <td>(would, connect)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>8</th>\n",
      "      <td>(out, pop)</td>\n",
      "      <td>(day, 1)</td>\n",
      "      <td>(return, window)</td>\n",
      "      <td>(work, noth)</td>\n",
      "      <td>(asleep, or)</td>\n",
      "      <td>(email, support)</td>\n",
      "      <td>(told, support)</td>\n",
      "      <td>(everi, ten)</td>\n",
      "      <td>(fix, issu)</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>9</th>\n",
      "      <td>(week, two)</td>\n",
      "      <td>(reset, factori)</td>\n",
      "      <td>(work, get)</td>\n",
      "      <td>(trebl, 1)</td>\n",
      "      <td>(unit, replac)</td>\n",
      "      <td>(stop, pair)</td>\n",
      "      <td>(pair, like)</td>\n",
      "      <td>(loud, caus)</td>\n",
      "      <td>(out, fell)</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n"
     ]
    }
   ],
   "source": [
    "html = productWorst[:10].drop(columns=['index']).to_html()\n",
    "print(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: right;\">\n",
    "      <th></th>\n",
    "      <th>index</th>\n",
    "      <th>AirPods Pro_best</th>\n",
    "      <th>Elite 75t_best</th>\n",
    "      <th>Free_best</th>\n",
    "      <th>Galaxy Buds Plus_best</th>\n",
    "      <th>NC700_best</th>\n",
    "      <th>Soundcore Liberty 2 Pro_best</th>\n",
    "      <th>Soundcore Liberty Air 2_best</th>\n",
    "      <th>Soundcore Life Q20_best</th>\n",
    "      <th>WF-1000XM3_best</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <th>0</th>\n",
    "      <td>0</td>\n",
    "      <td>(product, great)</td>\n",
    "      <td>(truli, wireless)</td>\n",
    "      <td>(so, far)</td>\n",
    "      <td>(ambient, sound)</td>\n",
    "      <td>(control, touch)</td>\n",
    "      <td>(in, ear)</td>\n",
    "      <td>(as, well)</td>\n",
    "      <td>(as, good)</td>\n",
    "      <td>(connect, app)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>1</th>\n",
    "      <td>1</td>\n",
    "      <td>(amaz, sound)</td>\n",
    "      <td>(so, far)</td>\n",
    "      <td>(usb, c)</td>\n",
    "      <td>(call, qualiti)</td>\n",
    "      <td>(veri, comfort)</td>\n",
    "      <td>(ear, your)</td>\n",
    "      <td>(charg, wireless)</td>\n",
    "      <td>(so, far)</td>\n",
    "      <td>(as, far)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>2</th>\n",
    "      <td>2</td>\n",
    "      <td>(qualiti, great)</td>\n",
    "      <td>(good, realli)</td>\n",
    "      <td>(qualiti, great)</td>\n",
    "      <td>(pro, airpod)</td>\n",
    "      <td>(bose, sound)</td>\n",
    "      <td>(earbud, wireless)</td>\n",
    "      <td>(pro, airpod)</td>\n",
    "      <td>(price, great)</td>\n",
    "      <td>(that, headphon)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>3</th>\n",
    "      <td>3</td>\n",
    "      <td>(great, fit)</td>\n",
    "      <td>(recommend, highli)</td>\n",
    "      <td>(as, good)</td>\n",
    "      <td>(so, far)</td>\n",
    "      <td>(out, block)</td>\n",
    "      <td>(amaz, sound)</td>\n",
    "      <td>(recommend, highli)</td>\n",
    "      <td>(last, batteri)</td>\n",
    "      <td>(product, great)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>4</th>\n",
    "      <td>4</td>\n",
    "      <td>(love, he)</td>\n",
    "      <td>(in, app)</td>\n",
    "      <td>(good, realli)</td>\n",
    "      <td>(earbud, wireless)</td>\n",
    "      <td>(nois, level)</td>\n",
    "      <td>(in, your)</td>\n",
    "      <td>(product, great)</td>\n",
    "      <td>(price, at)</td>\n",
    "      <td>(best, earbud)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>5</th>\n",
    "      <td>5</td>\n",
    "      <td>(earbud, wireless)</td>\n",
    "      <td>(qualiti, great)</td>\n",
    "      <td>(recommend, highli)</td>\n",
    "      <td>(qualiti, great)</td>\n",
    "      <td>(veri, well)</td>\n",
    "      <td>(veri, good)</td>\n",
    "      <td>(good, realli)</td>\n",
    "      <td>(bass, boost)</td>\n",
    "      <td>(easi, use)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>6</th>\n",
    "      <td>6</td>\n",
    "      <td>(worth, well)</td>\n",
    "      <td>(galaxi, bud)</td>\n",
    "      <td>(product, great)</td>\n",
    "      <td>(day, all)</td>\n",
    "      <td>(veri, nice)</td>\n",
    "      <td>(listen, music)</td>\n",
    "      <td>(wireless, true)</td>\n",
    "      <td>(nois, good)</td>\n",
    "      <td>(galaxi, bud)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>7</th>\n",
    "      <td>7</td>\n",
    "      <td>(worth, definit)</td>\n",
    "      <td>(high, mid)</td>\n",
    "      <td>(free, earfun)</td>\n",
    "      <td>(ear, your)</td>\n",
    "      <td>(veri, happi)</td>\n",
    "      <td>(case, charg)</td>\n",
    "      <td>(better, sound)</td>\n",
    "      <td>(product, great)</td>\n",
    "      <td>(so, good)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>8</th>\n",
    "      <td>8</td>\n",
    "      <td>(like, realli)</td>\n",
    "      <td>(through, featur)</td>\n",
    "      <td>(last, batteri)</td>\n",
    "      <td>(listen, music)</td>\n",
    "      <td>(high, end)</td>\n",
    "      <td>(best, sound)</td>\n",
    "      <td>(control, touch)</td>\n",
    "      <td>(set, headphon)</td>\n",
    "      <td>(nois, level)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>9</th>\n",
    "      <td>9</td>\n",
    "      <td>(worth, everi)</td>\n",
    "      <td>(secur, fit)</td>\n",
    "      <td>(good, sound)</td>\n",
    "      <td>(better, much)</td>\n",
    "      <td>(clear, crystal)</td>\n",
    "      <td>(phone, call)</td>\n",
    "      <td>(first, pair)</td>\n",
    "      <td>(valu, great)</td>\n",
    "      <td>(how, much)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>10</th>\n",
    "      <td>10</td>\n",
    "      <td>(from, upgrad)</td>\n",
    "      <td>(out, take)</td>\n",
    "      <td>(price, great)</td>\n",
    "      <td>(amaz, sound)</td>\n",
    "      <td>(volum, control)</td>\n",
    "      <td>(recommend, highli)</td>\n",
    "      <td>(appl, airpod)</td>\n",
    "      <td>(veri, nice)</td>\n",
    "      <td>(sound, good)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>11</th>\n",
    "      <td>11</td>\n",
    "      <td>(amaz, qualiti)</td>\n",
    "      <td>(while, listen)</td>\n",
    "      <td>(ear, fit)</td>\n",
    "      <td>(work, at)</td>\n",
    "      <td>(high, qualiti)</td>\n",
    "      <td>(in, stay)</td>\n",
    "      <td>(headphon, wireless)</td>\n",
    "      <td>(in, headphon)</td>\n",
    "      <td>(qualiti, excel)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>12</th>\n",
    "      <td>12</td>\n",
    "      <td>(penni, everi)</td>\n",
    "      <td>(ever, own)</td>\n",
    "      <td>(amaz, sound)</td>\n",
    "      <td>(nois, background)</td>\n",
    "      <td>(comfort, fit)</td>\n",
    "      <td>(veri, comfort)</td>\n",
    "      <td>(price, at)</td>\n",
    "      <td>(cancel, great)</td>\n",
    "      <td>(so, sound)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>13</th>\n",
    "      <td>13</td>\n",
    "      <td>(out, nois)</td>\n",
    "      <td>(5, hour)</td>\n",
    "      <td>(easi, pair)</td>\n",
    "      <td>(abl, be)</td>\n",
    "      <td>(best, far)</td>\n",
    "      <td>(would, be)</td>\n",
    "      <td>(as, good)</td>\n",
    "      <td>(out, nois)</td>\n",
    "      <td>(ear, fit)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>14</th>\n",
    "      <td>14</td>\n",
    "      <td>(veri, nice)</td>\n",
    "      <td>(featur, great)</td>\n",
    "      <td>(work, at)</td>\n",
    "      <td>(as, good)</td>\n",
    "      <td>(cancel, amaz)</td>\n",
    "      <td>(soundcor, liberti)</td>\n",
    "      <td>(amaz, sound)</td>\n",
    "      <td>(batteri, long)</td>\n",
    "      <td>(volum, no)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>15</th>\n",
    "      <td>15</td>\n",
    "      <td>(get, use)</td>\n",
    "      <td>(in, secur)</td>\n",
    "      <td>(great, fit)</td>\n",
    "      <td>(featur, sound)</td>\n",
    "      <td>(from, expect)</td>\n",
    "      <td>(so, far)</td>\n",
    "      <td>(left, ear)</td>\n",
    "      <td>(ear, headphon)</td>\n",
    "      <td>(complaint, onli)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>16</th>\n",
    "      <td>16</td>\n",
    "      <td>(product, good)</td>\n",
    "      <td>(awesom, sound)</td>\n",
    "      <td>(easi, use)</td>\n",
    "      <td>(that, bud)</td>\n",
    "      <td>(talk, phone)</td>\n",
    "      <td>(pro, airpod)</td>\n",
    "      <td>(build, qualiti)</td>\n",
    "      <td>(batteri, great)</td>\n",
    "      <td>(cancel, as)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>17</th>\n",
    "      <td>17</td>\n",
    "      <td>(recommend, definit)</td>\n",
    "      <td>(best, earbud)</td>\n",
    "      <td>(time, long)</td>\n",
    "      <td>(bud, wireless)</td>\n",
    "      <td>(comfort, super)</td>\n",
    "      <td>(high, mid)</td>\n",
    "      <td>(ha, been)</td>\n",
    "      <td>(rang, price)</td>\n",
    "      <td>(how, well)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>18</th>\n",
    "      <td>18</td>\n",
    "      <td>(time, at)</td>\n",
    "      <td>(love, that)</td>\n",
    "      <td>(use, been)</td>\n",
    "      <td>(in, stay)</td>\n",
    "      <td>(out, filter)</td>\n",
    "      <td>(set, eq)</td>\n",
    "      <td>(issu, ani)</td>\n",
    "      <td>(hour, 40)</td>\n",
    "      <td>(in, place)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <th>19</th>\n",
    "      <td>19</td>\n",
    "      <td>(use, eas)</td>\n",
    "      <td>(that, bud)</td>\n",
    "      <td>(love, that)</td>\n",
    "      <td>(in, case)</td>\n",
    "      <td>(nois, best)</td>\n",
    "      <td>(ear, tip)</td>\n",
    "      <td>(usb, c)</td>\n",
    "      <td>(more, much)</td>\n",
    "      <td>(would, definit)</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['review_date', 'verified_purchase', 'pairs', 'count', 'pairs_hashable',\n",
      "       'ProductName', 'numPairs'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pairs_hashable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>{'quality', 'sound'}</th>\n",
       "      <td>2.355383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'noise', 'cancelling'}</th>\n",
       "      <td>1.733864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'life', 'battery'}</th>\n",
       "      <td>1.723270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'cancellation', 'noise'}</th>\n",
       "      <td>1.252164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'great', 'sound'}</th>\n",
       "      <td>0.869760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'fantastic', 'ok'}</th>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'street', 'sounds'}</th>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'fantastic', 'modes'}</th>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'fantastic', 'mind'}</th>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>{'failed', 'apple'}</th>\n",
       "      <td>0.000242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253381 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count\n",
       "pairs_hashable                     \n",
       "{'quality', 'sound'}       2.355383\n",
       "{'noise', 'cancelling'}    1.733864\n",
       "{'life', 'battery'}        1.723270\n",
       "{'cancellation', 'noise'}  1.252164\n",
       "{'great', 'sound'}         0.869760\n",
       "...                             ...\n",
       "{'fantastic', 'ok'}        0.000242\n",
       "{'street', 'sounds'}       0.000242\n",
       "{'fantastic', 'modes'}     0.000242\n",
       "{'fantastic', 'mind'}      0.000242\n",
       "{'failed', 'apple'}        0.000242\n",
       "\n",
       "[253381 rows x 1 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(wordPairsAllReviews_df.columns)\n",
    "# wordPairsAllReviews_df[['count', 'pairs_hashable','ProductName']].groupby(by=['ProductName','pairs_hashable']).sum().sort_values(by='count',ascending=False)\n",
    "\n",
    "# have to drop the single values in the pairs...not sure why there's a single value.\n",
    "wordPairsAllReviews_df['numPairs'] = wordPairsAllReviews_df['pairs'].apply(lambda x: len(x))\n",
    "wordPairsAllReviews_df = wordPairsAllReviews_df.where(wordPairsAllReviews_df['numPairs'] > 1).dropna()\n",
    "# wordPairsAllReviews_df['pairs_hashable']#.apply(lambda x: len(x))\n",
    "# wordPairsAllReviews_df['pairs'].apply(lambda x: len(x))\n",
    "wordPairsAllReviews_df[['count', 'pairs_hashable']].groupby(by=['pairs_hashable']).sum().sort_values(by='count',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "overallPopularwords= wordPairsAllReviews_df.groupby(['pairs_hashable','verified_purchase']).sum().reset_index()\n",
    "\n",
    "\n",
    "overallPopularwords_notPurchased = overallPopularwords.where(overallPopularwords['verified_purchase'] == 0).dropna()\n",
    "overallPopularwords_notPurchased = overallPopularwords_notPurchased.sort_values(by=['count'],ascending =False).reset_index().drop(columns=['index'])\n",
    "\n",
    "overallPopularwords_purchased = overallPopularwords.where(overallPopularwords['verified_purchase'] == 1).dropna()\n",
    "overallPopularwords_purchased = overallPopularwords_purchased.sort_values(by=['count'],ascending =False).reset_index().drop(columns=['index'])\n",
    "\n",
    "# now we have two datasets one with purchased the other not purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>pairs_hashable</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>count</th>\n",
       "      <th>numPairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'quality', 'sound'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495996</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'noise', 'cancelling'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.466216</td>\n",
       "      <td>1228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'life', 'battery'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362033</td>\n",
       "      <td>784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'cancellation', 'noise'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309101</td>\n",
       "      <td>802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'as', 'well'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172777</td>\n",
       "      <td>364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'great', 'sound'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142890</td>\n",
       "      <td>298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'as', 'good'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136030</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'ear', 'buds'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133262</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'case', 'charging'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131994</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'call', 'quality'}</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123919</td>\n",
       "      <td>244.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pairs_hashable  verified_purchase     count  numPairs\n",
       "0       {'quality', 'sound'}                0.0  0.495996    1150.0\n",
       "1    {'noise', 'cancelling'}                0.0  0.466216    1228.0\n",
       "2        {'life', 'battery'}                0.0  0.362033     784.0\n",
       "3  {'cancellation', 'noise'}                0.0  0.309101     802.0\n",
       "4             {'as', 'well'}                0.0  0.172777     364.0\n",
       "5         {'great', 'sound'}                0.0  0.142890     298.0\n",
       "6             {'as', 'good'}                0.0  0.136030     308.0\n",
       "7            {'ear', 'buds'}                0.0  0.133262     286.0\n",
       "8       {'case', 'charging'}                0.0  0.131994     278.0\n",
       "9        {'call', 'quality'}                0.0  0.123919     244.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overallPopularwords_notPurchased.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>pairs_hashable</th>\n",
       "      <th>verified_purchase</th>\n",
       "      <th>count</th>\n",
       "      <th>numPairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'quality', 'sound'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.859387</td>\n",
       "      <td>5212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'life', 'battery'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.361237</td>\n",
       "      <td>3404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'noise', 'cancelling'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.267649</td>\n",
       "      <td>4106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'cancellation', 'noise'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.943063</td>\n",
       "      <td>3518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'great', 'sound'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.726869</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'ear', 'buds'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.490417</td>\n",
       "      <td>1206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'in', 'ear'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.458294</td>\n",
       "      <td>1404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'as', 'well'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.449028</td>\n",
       "      <td>1198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'very', 'good'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.410845</td>\n",
       "      <td>1198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>{'as', 'good'}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408481</td>\n",
       "      <td>1238.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pairs_hashable  verified_purchase     count  numPairs\n",
       "0       {'quality', 'sound'}                1.0  1.859387    5212.0\n",
       "1        {'life', 'battery'}                1.0  1.361237    3404.0\n",
       "2    {'noise', 'cancelling'}                1.0  1.267649    4106.0\n",
       "3  {'cancellation', 'noise'}                1.0  0.943063    3518.0\n",
       "4         {'great', 'sound'}                1.0  0.726869    1988.0\n",
       "5            {'ear', 'buds'}                1.0  0.490417    1206.0\n",
       "6              {'in', 'ear'}                1.0  0.458294    1404.0\n",
       "7             {'as', 'well'}                1.0  0.449028    1198.0\n",
       "8           {'very', 'good'}                1.0  0.410845    1198.0\n",
       "9             {'as', 'good'}                1.0  0.408481    1238.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overallPopularwords_purchased.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we need to merge the two verified _purchase lists into a data frame. We don't want pairs that appear in the other list.\n",
    "# we can say from the top 20 words, which ones are unique?\n",
    "\n",
    "# we can take the intersection between two lists then remove those words found in the intersection from those of both lists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-113-bfd7bccdb051>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmostPopularWordPairs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pairs_hashable'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'verified_purchase'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmostPopularWordPairsPurchased\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmostPopularWordPairs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmostPopularWordPairs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'verified_purchase'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmostPopularWordPairsPurchased\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mascending\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'index'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "mostPopularWordPairs = data.groupby(['pairs_hashable','verified_purchase']).sum().reset_index()\n",
    "mostPopularWordPairsPurchased = mostPopularWordPairs.where(mostPopularWordPairs['verified_purchase'] == 1).dropna()\n",
    "mostPopularWordPairsPurchased.sort_values(by=['count'],ascending =False).reset_index().drop(columns=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}