Source_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Python Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import altair as alt\n",
    "from altair import datum\n",
    "from scipy.stats import linregress\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Import and Data Manipulation for Amazon Review and Google Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def review_data_import_clean(product_name, verified_purchase = True):\n",
    "    '''\n",
    "    Takes a product name and (optionally) a verified purchase flag \n",
    "    to return this product's reviews loaded in pandas DataFrame\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - product_name: one of the wireless headset names\n",
    "    - verified_purchase: whether or not the reviews should be filtered for verified purchase only\n",
    "    '''\n",
    "    df_review = pd.read_csv('data/{}.csv'.format(product_name))\n",
    "    if verified_purchase:\n",
    "        df_review = df_review[df_review['verified_purchase'] == 1]\n",
    "    df_review['review_date_key'] = pd.to_datetime(df_review['review_date'])\n",
    "    df_review_weekly = df_review.groupby(pd.Grouper(key='review_date_key', freq='W-SUN', label='left', closed='left'))['review_rating']\\\n",
    "                        .agg(['mean', 'count'])\\\n",
    "                        .reset_index()\n",
    "    df_review_weekly.rename(columns={'mean':'average_rating', 'count':'review_count'}, inplace=True)\n",
    "    \n",
    "    return df_review_weekly\n",
    "\n",
    "def google_data_import_clean(product_name):\n",
    "    '''\n",
    "    Takes a product name to return this product's \n",
    "    Google Trend Index loaded in pandas DataFrame\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - product_name: one of the wireless headset names\n",
    "    '''\n",
    "    df_gg = pd.read_excel('data/Google Trend.xlsx', sheet_name = product_name)\n",
    "    df_gg['week_key'] = pd.to_datetime(df_gg['Week'])\n",
    "    \n",
    "    df_gg.rename(columns = {df_gg.columns[1]:'trend_index'}, inplace = True)\n",
    "    df_gg_filter = df_gg[df_gg['trend_index'].cumsum() > 0]\n",
    "    \n",
    "    return df_gg_filter\n",
    "\n",
    "def data_import_clean(product_name, price, verified_purchase = 1):\n",
    "    '''\n",
    "    Takes a product name, price and (optionally) a verified purchase flag \n",
    "    to return this product's reviews and Google Trend in pandas DataFrame\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - product_name: one of the wireless headset names\n",
    "    - price: price of the wireless headset\n",
    "    - verified_purchase: whether or not the reviews should be filtered for verified purchase only\n",
    "    ''' \n",
    "    df_review_clean = review_data_import_clean(product_name, verified_purchase)\n",
    "    df_gg_clean = google_data_import_clean(product_name)\n",
    "    \n",
    "    df = df_review_clean.merge(df_gg_clean, left_on = 'review_date_key', right_on='week_key', how='right')\n",
    "    df['product_name'] = product_name\n",
    "    df['price'] = price\n",
    "    df = df.sort_values('week_key').reset_index().drop('index', axis=1)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def calculate_correlation_review_count(df):\n",
    "    '''\n",
    "    Takes a DataFrame of product reviews and Google trend \n",
    "    to return the correlation for different lag periods\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - df: DataFrame containing weekly review count and Google Trend Index\n",
    "    '''  \n",
    "    corr_lst = [(i, df['review_count'].corr(df['trend_index'].shift(i))) for i in range(len(df)//2)]\n",
    "    df_corr = pd.DataFrame(corr_lst, columns=['lag', 'corr'])\n",
    "    df_corr['product_name'] = df['product_name'][0]\n",
    "    df_corr['price'] = df['price'][0]\n",
    "    return df_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "product_list = ['AirPods Pro', 'Elite 75t', 'Free', 'Galaxy Buds Plus', 'NC700', 'Soundcore Liberty 2 Pro', 'Soundcore Liberty Air 2', 'Soundcore Life Q20', 'WF-1000XM3']\n",
    "price_list = [249.0, 179.0, 49.0, 149.0, 399.0, 149.0, 100.0, 59.0, 198.0]\n",
    "\n",
    "df_corr_review_count = pd.DataFrame(columns=['lag', 'corr', 'product_name', 'price'])\n",
    "\n",
    "for product, price in zip(*[product_list, price_list]):\n",
    "    df = data_import_clean(product, price, True)\n",
    "    df_corr_review_count = pd.concat([df_corr_review_count, calculate_correlation_review_count(df)])\n",
    "    \n",
    "df_corr_review_count['segment'] = pd.cut(df_corr_review_count['price'], bins=[0,100,150,200,1000], labels=['Below 100', '100 to 150', '150 to 200', 'Above 200'], right=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-11695d013fbf4a39ad1ad13c584e5adc\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-11695d013fbf4a39ad1ad13c584e5adc\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-11695d013fbf4a39ad1ad13c584e5adc\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-ed1f73a00db1cea159f2ff2aa4b31312\"}, \"facet\": {\"column\": {\"type\": \"nominal\", \"field\": \"segment\", \"sort\": [\"Below 100\", \"100 to 150\", \"150 to 200\", \"Above 200\"]}}, \"spec\": {\"layer\": [{\"mark\": {\"type\": \"circle\", \"size\": 50}, \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"product_name\", \"legend\": null, \"title\": \"Product Name\"}, \"x\": {\"type\": \"ordinal\", \"field\": \"lag\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"corr\"}}, \"transform\": [{\"window\": [{\"op\": \"rank\", \"field\": \"corr\", \"as\": \"rank\"}], \"groupby\": [\"segment\", \"product_name\"], \"sort\": [{\"field\": \"corr\", \"order\": \"descending\"}]}, {\"filter\": \"(datum.rank === 1)\"}]}, {\"mark\": {\"type\": \"text\", \"color\": \"red\", \"dx\": -15, \"dy\": -10}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"product_name\"}, \"x\": {\"type\": \"ordinal\", \"field\": \"lag\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"corr\"}}, \"transform\": [{\"window\": [{\"op\": \"rank\", \"field\": \"corr\", \"as\": \"rank\"}], \"groupby\": [\"segment\", \"product_name\"], \"sort\": [{\"field\": \"corr\", \"order\": \"descending\"}]}, {\"filter\": \"(datum.rank === 1)\"}]}]}, \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"title\": \"Best Correlation Found for Each Product in Different Price Segments\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-ed1f73a00db1cea159f2ff2aa4b31312\": [{\"lag\": 0, \"corr\": -0.3160132020458464, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 1, \"corr\": 0.07420299700845506, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 2, \"corr\": 0.37618952753241697, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 3, \"corr\": 0.4124188215095457, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 4, \"corr\": 0.4277608551701801, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 5, \"corr\": 0.4506785089441423, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 6, \"corr\": 0.45671222278954604, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 7, \"corr\": 0.3606195222876077, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 8, \"corr\": 0.42372974351194725, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 9, \"corr\": 0.5672660450773565, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 10, \"corr\": 0.18513404903032857, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 11, \"corr\": 0.051429668065262855, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 12, \"corr\": -0.2098075000986056, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 13, \"corr\": 0.03167311119014617, \"product_name\": \"AirPods Pro\", \"price\": 249.0, \"segment\": \"Above 200\"}, {\"lag\": 0, \"corr\": -0.18505541494780325, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 1, \"corr\": 0.01402550415214063, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 2, \"corr\": 0.1700459791558085, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 3, \"corr\": 0.32754257027731126, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 4, \"corr\": 0.3943657720295641, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 5, \"corr\": 0.3604902128218511, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 6, \"corr\": 0.48065383174433546, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 7, \"corr\": 0.5720170289364405, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 8, \"corr\": 0.4576831855317749, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 9, \"corr\": 0.629518824899061, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 10, \"corr\": 0.5403746437443708, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 11, \"corr\": 0.508821219659622, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 12, \"corr\": 0.395435140967681, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 13, \"corr\": 0.40470300129377373, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 14, \"corr\": 0.11696132515107453, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 15, \"corr\": -0.11282167268729344, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 16, \"corr\": -0.220024437386179, \"product_name\": \"Elite 75t\", \"price\": 179.0, \"segment\": \"150 to 200\"}, {\"lag\": 0, \"corr\": 0.3672467396405457, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 1, \"corr\": 0.5107557633348014, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 2, \"corr\": 0.5415246050670085, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 3, \"corr\": 0.5412981746436201, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 4, \"corr\": 0.43762133802178754, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 5, \"corr\": 0.47093726215010995, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 6, \"corr\": 0.47414011907360903, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 7, \"corr\": 0.46596820261331834, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 8, \"corr\": 0.504052765646935, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 9, \"corr\": 0.5227157671478021, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 10, \"corr\": 0.44442044088023036, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 11, \"corr\": 0.47273611290444645, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 12, \"corr\": 0.48221499176885463, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 13, \"corr\": 0.4565379002384416, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 14, \"corr\": 0.41068570258479686, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 15, \"corr\": 0.4180515286255459, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 16, \"corr\": 0.3484635404514691, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 17, \"corr\": 0.2773619762073645, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 18, \"corr\": 0.24480497423785577, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 19, \"corr\": 0.24937251428651183, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 20, \"corr\": 0.12942766142787585, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 21, \"corr\": 0.3809164260120549, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 22, \"corr\": 0.26571728008147844, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 23, \"corr\": 0.15480758333324227, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 24, \"corr\": 0.2958053640534015, \"product_name\": \"Free\", \"price\": 49.0, \"segment\": \"Below 100\"}, {\"lag\": 0, \"corr\": 0.49416403778779333, \"product_name\": \"Galaxy Buds Plus\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 1, \"corr\": -0.3706234302450831, \"product_name\": \"Galaxy Buds Plus\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 2, \"corr\": -0.07712087621714983, \"product_name\": \"Galaxy Buds Plus\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 3, \"corr\": -0.48005771670251796, \"product_name\": \"Galaxy Buds Plus\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 0, \"corr\": -0.10113458152968459, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 1, \"corr\": 0.08472048145372134, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 2, \"corr\": 0.28908032938774303, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 3, \"corr\": 0.28883210857354347, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 4, \"corr\": 0.36014684603029895, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 5, \"corr\": 0.45221485463514893, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 6, \"corr\": 0.6265611119541433, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 7, \"corr\": 0.5303682700309629, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 8, \"corr\": 0.2504636571609208, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 9, \"corr\": 0.2866647980205713, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 10, \"corr\": 0.3565574834060962, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 11, \"corr\": 0.34256825314463973, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 12, \"corr\": 0.1627806178849268, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 13, \"corr\": -0.07044317071487945, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 14, \"corr\": -0.1837368606834901, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 15, \"corr\": -0.23953687628143297, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 16, \"corr\": -0.36684783645682206, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 17, \"corr\": -0.29618683626882414, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 18, \"corr\": -0.3990820255053199, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 19, \"corr\": -0.30338157074330635, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 20, \"corr\": -0.38901259697036755, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 21, \"corr\": -0.32475913439997334, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 22, \"corr\": -0.1847875190420406, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 23, \"corr\": -0.2031154096978578, \"product_name\": \"NC700\", \"price\": 399.0, \"segment\": \"Above 200\"}, {\"lag\": 0, \"corr\": -0.08329250049175371, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 1, \"corr\": 0.0017448852481882684, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 2, \"corr\": 0.046457752848531216, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 3, \"corr\": 0.19671601992830476, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 4, \"corr\": 0.0716735575824089, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 5, \"corr\": 0.18718904379559662, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 6, \"corr\": 0.114486800497186, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 7, \"corr\": 0.06387482187809476, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 8, \"corr\": 0.13397993948028175, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 9, \"corr\": 0.17663853274782865, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 10, \"corr\": 0.13071731729249741, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 11, \"corr\": 0.2751247536877225, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 12, \"corr\": 0.2365270404461065, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 13, \"corr\": 0.2718159375621714, \"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"segment\": \"100 to 150\"}, {\"lag\": 0, \"corr\": -0.06321585963149381, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 1, \"corr\": 0.18678865968048422, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 2, \"corr\": 0.3492386443938977, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 3, \"corr\": 0.4862898863635767, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 4, \"corr\": 0.6458205919398988, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 5, \"corr\": 0.7543361688249998, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 6, \"corr\": 0.6626659425559075, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 7, \"corr\": 0.5773208889979298, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 8, \"corr\": 0.6153875080988677, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 9, \"corr\": 0.6402938519595269, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 10, \"corr\": 0.5311279154758677, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 11, \"corr\": 0.09056296153030212, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 12, \"corr\": 0.06684273680114595, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 13, \"corr\": 0.027865516431420705, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 14, \"corr\": -0.0950434980307492, \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"segment\": \"100 to 150\"}, {\"lag\": 0, \"corr\": 0.09904506312545602, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 1, \"corr\": 0.13527266468339014, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 2, \"corr\": 0.143521168818208, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 3, \"corr\": -0.0075924938440098755, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 4, \"corr\": -0.030848975075384436, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 5, \"corr\": 0.0024763150462043197, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 6, \"corr\": -0.025185740891758387, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 7, \"corr\": 0.002836405846646177, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 8, \"corr\": 0.14876926904051482, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 9, \"corr\": 0.1425660285221845, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 10, \"corr\": 0.01727009719685443, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 11, \"corr\": -0.06397057259942157, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 12, \"corr\": -0.10628585019187979, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 13, \"corr\": -0.13877713678959122, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 14, \"corr\": -0.1291492906984638, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 15, \"corr\": -0.17042577510234447, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 16, \"corr\": -0.2848093417780308, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 17, \"corr\": -0.18291617890355893, \"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"segment\": \"Below 100\"}, {\"lag\": 0, \"corr\": 0.2124400621037264, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 1, \"corr\": 0.264079699770755, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 2, \"corr\": 0.34523686274352733, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 3, \"corr\": 0.40140807882165375, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 4, \"corr\": 0.331637082006819, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 5, \"corr\": 0.3195615082482708, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 6, \"corr\": 0.4263296849773592, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 7, \"corr\": 0.41700207335293826, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 8, \"corr\": 0.47890413466942133, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 9, \"corr\": 0.6705030851490711, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 10, \"corr\": 0.5057586481473086, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 11, \"corr\": 0.3573606776896485, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 12, \"corr\": 0.27722722349669837, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 13, \"corr\": 0.23367436291277482, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 14, \"corr\": 0.08386177159628558, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 15, \"corr\": 0.07233372128644966, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 16, \"corr\": -0.16067530938623958, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 17, \"corr\": -0.17234289132080668, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 18, \"corr\": -0.03354854933109822, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 19, \"corr\": -0.14733990237267644, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 20, \"corr\": -0.42105796070218954, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}, {\"lag\": 21, \"corr\": -0.5484087584722239, \"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"segment\": \"150 to 200\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circle = alt.Chart(df_corr_review_count).mark_circle(\n",
    "    size = 50\n",
    ").transform_window(\n",
    "        sort=[alt.SortField(\"corr\", order=\"descending\")], \n",
    "        rank=\"rank(corr)\",\n",
    "        groupby=['segment','product_name']\n",
    ").transform_filter(\n",
    "    alt.datum.rank == 1\n",
    ").encode(\n",
    "        x=alt.X(\"lag:O\"), \n",
    "        y=alt.Y(\"corr:Q\"),\n",
    "        color=alt.Color('product_name:N', \n",
    "                        title='Product Name', \n",
    "                        legend=None)\n",
    ")\n",
    "\n",
    "text = alt.Chart(df_corr_review_count).mark_text(\n",
    "    dy=-10, \n",
    "    dx=-15,\n",
    "    color=\"red\"\n",
    ").transform_window(\n",
    "        sort=[alt.SortField(\"corr\", order=\"descending\")], \n",
    "        rank=\"rank(corr)\",\n",
    "        groupby=['segment','product_name']\n",
    ").transform_filter(\n",
    "    alt.datum.rank == 1\n",
    ").encode(\n",
    "        x=alt.X(\"lag:O\"), \n",
    "        y=alt.Y(\"corr:Q\"),\n",
    "        text=alt.Text(\"product_name:N\"),\n",
    ")\n",
    "\n",
    "(circle + text).facet(\n",
    "    column=alt.Column('segment',\n",
    "                      sort=['Below 100', '100 to 150', '150 to 200', 'Above 200']\n",
    "                     )\n",
    ").properties(\n",
    "    title='Best Correlation Found for Each Product in Different Price Segments',\n",
    ").resolve_scale(\n",
    "    color='independent'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_corr(product_name):\n",
    "    '''\n",
    "    Takes a product name \n",
    "    to return the time series visualization of its Amazon reiview and Google Trends\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - product_name: name of a wireless headset\n",
    "    '''   \n",
    "    df = data_import_clean(product_name, 100)\n",
    "    df_corr = calculate_correlation_review_count(df)\n",
    "    max_lag = df_corr.iloc[df_corr['corr'].argmax(), 0]\n",
    "    df['Shifted Google Trend Index'] = df['trend_index'].shift(max_lag)\n",
    "    df.dropna(subset=['review_count', 'Shifted Google Trend Index'], how='any', inplace=True)\n",
    "    df['Normalized Amazon Review Count'] = (df['review_count']/df['review_count'].max()*100).round()\n",
    "\n",
    "    corr_chart = alt.Chart(df).mark_line().transform_fold(\n",
    "        fold=['Normalized Amazon Review Count', 'Shifted Google Trend Index'], \n",
    "        as_=['variable', 'value']\n",
    "    ).encode(\n",
    "        x=alt.X('week_key', title=''),\n",
    "        y=alt.Y('value:Q'),\n",
    "        color=alt.Color('variable:N', title='', legend=alt.Legend(orient='bottom-right')),\n",
    "    ).configure_axis(\n",
    "        grid=False\n",
    "    ).properties(\n",
    "        title=[product_name, \n",
    "               'Trend of Normalized Amazon Review Count and Google Trend Index (Shifted {} Weeks)'.format(max_lag)]\n",
    "    )\n",
    "    \n",
    "    return corr_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-039f8169d40c42e388b01f3f07929891\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-039f8169d40c42e388b01f3f07929891\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-039f8169d40c42e388b01f3f07929891\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false}}, \"data\": {\"name\": \"data-8b9d065ce57747788623f8fdeee801fb\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"variable\", \"legend\": {\"orient\": \"bottom-right\"}, \"title\": \"\"}, \"x\": {\"type\": \"temporal\", \"field\": \"week_key\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"title\": [\"Soundcore Liberty Air 2\", \"Trend of Normalized Amazon Review Count and Google Trend Index (Shifted 5 Weeks)\"], \"transform\": [{\"fold\": [\"Normalized Amazon Review Count\", \"Shifted Google Trend Index\"], \"as\": [\"variable\", \"value\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-8b9d065ce57747788623f8fdeee801fb\": [{\"review_date_key\": \"2019-11-03T00:00:00\", \"average_rating\": 4.4, \"review_count\": 5.0, \"Week\": \"2019-11-03T00:00:00\", \"trend_index\": 43, \"week_key\": \"2019-11-03T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 15.0, \"Normalized Amazon Review Count\": 10.0}, {\"review_date_key\": \"2019-11-10T00:00:00\", \"average_rating\": 3.0, \"review_count\": 2.0, \"Week\": \"2019-11-10T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-11-10T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 22.0, \"Normalized Amazon Review Count\": 4.0}, {\"review_date_key\": \"2019-11-17T00:00:00\", \"average_rating\": 5.0, \"review_count\": 1.0, \"Week\": \"2019-11-17T00:00:00\", \"trend_index\": 100, \"week_key\": \"2019-11-17T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 0.0, \"Normalized Amazon Review Count\": 2.0}, {\"review_date_key\": \"2019-11-24T00:00:00\", \"average_rating\": 5.0, \"review_count\": 3.0, \"Week\": \"2019-11-24T00:00:00\", \"trend_index\": 98, \"week_key\": \"2019-11-24T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 22.0, \"Normalized Amazon Review Count\": 6.0}, {\"review_date_key\": \"2019-12-01T00:00:00\", \"average_rating\": 4.125, \"review_count\": 16.0, \"Week\": \"2019-12-01T00:00:00\", \"trend_index\": 63, \"week_key\": \"2019-12-01T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 15.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2019-12-08T00:00:00\", \"average_rating\": 3.076923076923077, \"review_count\": 13.0, \"Week\": \"2019-12-08T00:00:00\", \"trend_index\": 72, \"week_key\": \"2019-12-08T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 43.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-12-15T00:00:00\", \"average_rating\": 4.722222222222222, \"review_count\": 18.0, \"Week\": \"2019-12-15T00:00:00\", \"trend_index\": 66, \"week_key\": \"2019-12-15T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 38.0}, {\"review_date_key\": \"2019-12-22T00:00:00\", \"average_rating\": 4.285714285714286, \"review_count\": 35.0, \"Week\": \"2019-12-22T00:00:00\", \"trend_index\": 90, \"week_key\": \"2019-12-22T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 100.0, \"Normalized Amazon Review Count\": 73.0}, {\"review_date_key\": \"2019-12-29T00:00:00\", \"average_rating\": 3.774193548387097, \"review_count\": 31.0, \"Week\": \"2019-12-29T00:00:00\", \"trend_index\": 53, \"week_key\": \"2019-12-29T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 98.0, \"Normalized Amazon Review Count\": 65.0}, {\"review_date_key\": \"2020-01-05T00:00:00\", \"average_rating\": 3.84375, \"review_count\": 32.0, \"Week\": \"2020-01-05T00:00:00\", \"trend_index\": 57, \"week_key\": \"2020-01-05T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 63.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-01-12T00:00:00\", \"average_rating\": 4.214285714285714, \"review_count\": 28.0, \"Week\": \"2020-01-12T00:00:00\", \"trend_index\": 47, \"week_key\": \"2020-01-12T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 72.0, \"Normalized Amazon Review Count\": 58.0}, {\"review_date_key\": \"2020-01-19T00:00:00\", \"average_rating\": 3.975609756097561, \"review_count\": 41.0, \"Week\": \"2020-01-19T00:00:00\", \"trend_index\": 50, \"week_key\": \"2020-01-19T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 66.0, \"Normalized Amazon Review Count\": 85.0}, {\"review_date_key\": \"2020-01-26T00:00:00\", \"average_rating\": 3.9166666666666665, \"review_count\": 48.0, \"Week\": \"2020-01-26T00:00:00\", \"trend_index\": 62, \"week_key\": \"2020-01-26T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 90.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2020-02-02T00:00:00\", \"average_rating\": 4.34375, \"review_count\": 32.0, \"Week\": \"2020-02-02T00:00:00\", \"trend_index\": 62, \"week_key\": \"2020-02-02T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 53.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-02-09T00:00:00\", \"average_rating\": 4.483870967741935, \"review_count\": 31.0, \"Week\": \"2020-02-09T00:00:00\", \"trend_index\": 62, \"week_key\": \"2020-02-09T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 57.0, \"Normalized Amazon Review Count\": 65.0}, {\"review_date_key\": \"2020-02-16T00:00:00\", \"average_rating\": 4.068181818181818, \"review_count\": 44.0, \"Week\": \"2020-02-16T00:00:00\", \"trend_index\": 41, \"week_key\": \"2020-02-16T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 47.0, \"Normalized Amazon Review Count\": 92.0}, {\"review_date_key\": \"2020-02-23T00:00:00\", \"average_rating\": 4.027027027027027, \"review_count\": 37.0, \"Week\": \"2020-02-23T00:00:00\", \"trend_index\": 46, \"week_key\": \"2020-02-23T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 50.0, \"Normalized Amazon Review Count\": 77.0}, {\"review_date_key\": \"2020-03-01T00:00:00\", \"average_rating\": 4.104166666666667, \"review_count\": 48.0, \"Week\": \"2020-03-01T00:00:00\", \"trend_index\": 46, \"week_key\": \"2020-03-01T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 62.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2020-03-08T00:00:00\", \"average_rating\": 3.8275862068965516, \"review_count\": 29.0, \"Week\": \"2020-03-08T00:00:00\", \"trend_index\": 39, \"week_key\": \"2020-03-08T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 62.0, \"Normalized Amazon Review Count\": 60.0}, {\"review_date_key\": \"2020-03-15T00:00:00\", \"average_rating\": 4.25, \"review_count\": 32.0, \"Week\": \"2020-03-15T00:00:00\", \"trend_index\": 39, \"week_key\": \"2020-03-15T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 62.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-03-22T00:00:00\", \"average_rating\": 4.392857142857143, \"review_count\": 28.0, \"Week\": \"2020-03-22T00:00:00\", \"trend_index\": 32, \"week_key\": \"2020-03-22T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 41.0, \"Normalized Amazon Review Count\": 58.0}, {\"review_date_key\": \"2020-03-29T00:00:00\", \"average_rating\": 4.08, \"review_count\": 25.0, \"Week\": \"2020-03-29T00:00:00\", \"trend_index\": 43, \"week_key\": \"2020-03-29T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 46.0, \"Normalized Amazon Review Count\": 52.0}, {\"review_date_key\": \"2020-04-05T00:00:00\", \"average_rating\": 3.8333333333333335, \"review_count\": 30.0, \"Week\": \"2020-04-05T00:00:00\", \"trend_index\": 25, \"week_key\": \"2020-04-05T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 46.0, \"Normalized Amazon Review Count\": 62.0}, {\"review_date_key\": \"2020-04-12T00:00:00\", \"average_rating\": 3.5, \"review_count\": 24.0, \"Week\": \"2020-04-12T00:00:00\", \"trend_index\": 49, \"week_key\": \"2020-04-12T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 39.0, \"Normalized Amazon Review Count\": 50.0}, {\"review_date_key\": \"2020-04-19T00:00:00\", \"average_rating\": 3.7586206896551726, \"review_count\": 29.0, \"Week\": \"2020-04-19T00:00:00\", \"trend_index\": 48, \"week_key\": \"2020-04-19T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 39.0, \"Normalized Amazon Review Count\": 60.0}, {\"review_date_key\": \"2020-04-26T00:00:00\", \"average_rating\": 4.238095238095238, \"review_count\": 21.0, \"Week\": \"2020-04-26T00:00:00\", \"trend_index\": 48, \"week_key\": \"2020-04-26T00:00:00\", \"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100, \"Shifted Google Trend Index\": 32.0, \"Normalized Amazon Review Count\": 44.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_corr('Soundcore Liberty Air 2') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-4e485d8b64984fcb883e477ae3bb3306\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-4e485d8b64984fcb883e477ae3bb3306\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-4e485d8b64984fcb883e477ae3bb3306\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false}}, \"data\": {\"name\": \"data-e8a723c83f65faa08b45b039323555f3\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"variable\", \"legend\": {\"orient\": \"bottom-right\"}, \"title\": \"\"}, \"x\": {\"type\": \"temporal\", \"field\": \"week_key\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"title\": [\"WF-1000XM3\", \"Trend of Normalized Amazon Review Count and Google Trend Index (Shifted 9 Weeks)\"], \"transform\": [{\"fold\": [\"Normalized Amazon Review Count\", \"Shifted Google Trend Index\"], \"as\": [\"variable\", \"value\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-e8a723c83f65faa08b45b039323555f3\": [{\"review_date_key\": \"2019-09-01T00:00:00\", \"average_rating\": 3.235294117647059, \"review_count\": 17.0, \"Week\": \"2019-09-01T00:00:00\", \"trend_index\": 62, \"week_key\": \"2019-09-01T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 8.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-09-08T00:00:00\", \"average_rating\": 3.782608695652174, \"review_count\": 23.0, \"Week\": \"2019-09-08T00:00:00\", \"trend_index\": 63, \"week_key\": \"2019-09-08T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 37.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2019-09-15T00:00:00\", \"average_rating\": 3.9047619047619047, \"review_count\": 21.0, \"Week\": \"2019-09-15T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-09-15T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 30.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2019-09-22T00:00:00\", \"average_rating\": 3.466666666666667, \"review_count\": 15.0, \"Week\": \"2019-09-22T00:00:00\", \"trend_index\": 46, \"week_key\": \"2019-09-22T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 22.0, \"Normalized Amazon Review Count\": 23.0}, {\"review_date_key\": \"2019-09-29T00:00:00\", \"average_rating\": 3.3636363636363638, \"review_count\": 22.0, \"Week\": \"2019-09-29T00:00:00\", \"trend_index\": 54, \"week_key\": \"2019-09-29T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 71.0, \"Normalized Amazon Review Count\": 34.0}, {\"review_date_key\": \"2019-10-06T00:00:00\", \"average_rating\": 3.2, \"review_count\": 35.0, \"Week\": \"2019-10-06T00:00:00\", \"trend_index\": 74, \"week_key\": \"2019-10-06T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 82.0, \"Normalized Amazon Review Count\": 55.0}, {\"review_date_key\": \"2019-10-13T00:00:00\", \"average_rating\": 3.1739130434782608, \"review_count\": 23.0, \"Week\": \"2019-10-13T00:00:00\", \"trend_index\": 81, \"week_key\": \"2019-10-13T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 68.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2019-10-20T00:00:00\", \"average_rating\": 3.888888888888889, \"review_count\": 27.0, \"Week\": \"2019-10-20T00:00:00\", \"trend_index\": 75, \"week_key\": \"2019-10-20T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 31.0, \"Normalized Amazon Review Count\": 42.0}, {\"review_date_key\": \"2019-10-27T00:00:00\", \"average_rating\": 3.4074074074074074, \"review_count\": 27.0, \"Week\": \"2019-10-27T00:00:00\", \"trend_index\": 100, \"week_key\": \"2019-10-27T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 61.0, \"Normalized Amazon Review Count\": 42.0}, {\"review_date_key\": \"2019-11-03T00:00:00\", \"average_rating\": 3.7241379310344827, \"review_count\": 29.0, \"Week\": \"2019-11-03T00:00:00\", \"trend_index\": 97, \"week_key\": \"2019-11-03T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 62.0, \"Normalized Amazon Review Count\": 45.0}, {\"review_date_key\": \"2019-11-10T00:00:00\", \"average_rating\": 3.8181818181818183, \"review_count\": 22.0, \"Week\": \"2019-11-10T00:00:00\", \"trend_index\": 74, \"week_key\": \"2019-11-10T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 63.0, \"Normalized Amazon Review Count\": 34.0}, {\"review_date_key\": \"2019-11-17T00:00:00\", \"average_rating\": 3.869565217391304, \"review_count\": 23.0, \"Week\": \"2019-11-17T00:00:00\", \"trend_index\": 100, \"week_key\": \"2019-11-17T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2019-11-24T00:00:00\", \"average_rating\": 3.44, \"review_count\": 25.0, \"Week\": \"2019-11-24T00:00:00\", \"trend_index\": 90, \"week_key\": \"2019-11-24T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 46.0, \"Normalized Amazon Review Count\": 39.0}, {\"review_date_key\": \"2019-12-01T00:00:00\", \"average_rating\": 3.7142857142857144, \"review_count\": 21.0, \"Week\": \"2019-12-01T00:00:00\", \"trend_index\": 70, \"week_key\": \"2019-12-01T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 54.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2019-12-08T00:00:00\", \"average_rating\": 4.078125, \"review_count\": 64.0, \"Week\": \"2019-12-08T00:00:00\", \"trend_index\": 77, \"week_key\": \"2019-12-08T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 74.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2019-12-15T00:00:00\", \"average_rating\": 3.75, \"review_count\": 60.0, \"Week\": \"2019-12-15T00:00:00\", \"trend_index\": 83, \"week_key\": \"2019-12-15T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 81.0, \"Normalized Amazon Review Count\": 94.0}, {\"review_date_key\": \"2019-12-22T00:00:00\", \"average_rating\": 3.6578947368421053, \"review_count\": 38.0, \"Week\": \"2019-12-22T00:00:00\", \"trend_index\": 54, \"week_key\": \"2019-12-22T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 75.0, \"Normalized Amazon Review Count\": 59.0}, {\"review_date_key\": \"2019-12-29T00:00:00\", \"average_rating\": 3.6458333333333335, \"review_count\": 48.0, \"Week\": \"2019-12-29T00:00:00\", \"trend_index\": 48, \"week_key\": \"2019-12-29T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 100.0, \"Normalized Amazon Review Count\": 75.0}, {\"review_date_key\": \"2020-01-05T00:00:00\", \"average_rating\": 3.8444444444444446, \"review_count\": 45.0, \"Week\": \"2020-01-05T00:00:00\", \"trend_index\": 45, \"week_key\": \"2020-01-05T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 97.0, \"Normalized Amazon Review Count\": 70.0}, {\"review_date_key\": \"2020-01-12T00:00:00\", \"average_rating\": 3.717948717948718, \"review_count\": 39.0, \"Week\": \"2020-01-12T00:00:00\", \"trend_index\": 31, \"week_key\": \"2020-01-12T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 74.0, \"Normalized Amazon Review Count\": 61.0}, {\"review_date_key\": \"2020-01-19T00:00:00\", \"average_rating\": 4.111111111111111, \"review_count\": 27.0, \"Week\": \"2020-01-19T00:00:00\", \"trend_index\": 59, \"week_key\": \"2020-01-19T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 100.0, \"Normalized Amazon Review Count\": 42.0}, {\"review_date_key\": \"2020-01-26T00:00:00\", \"average_rating\": 4.09375, \"review_count\": 32.0, \"Week\": \"2020-01-26T00:00:00\", \"trend_index\": 30, \"week_key\": \"2020-01-26T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 90.0, \"Normalized Amazon Review Count\": 50.0}, {\"review_date_key\": \"2020-02-02T00:00:00\", \"average_rating\": 3.6470588235294117, \"review_count\": 34.0, \"Week\": \"2020-02-02T00:00:00\", \"trend_index\": 53, \"week_key\": \"2020-02-02T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 70.0, \"Normalized Amazon Review Count\": 53.0}, {\"review_date_key\": \"2020-02-09T00:00:00\", \"average_rating\": 3.5833333333333335, \"review_count\": 36.0, \"Week\": \"2020-02-09T00:00:00\", \"trend_index\": 44, \"week_key\": \"2020-02-09T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 77.0, \"Normalized Amazon Review Count\": 56.0}, {\"review_date_key\": \"2020-02-16T00:00:00\", \"average_rating\": 3.255813953488372, \"review_count\": 43.0, \"Week\": \"2020-02-16T00:00:00\", \"trend_index\": 62, \"week_key\": \"2020-02-16T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 83.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-02-23T00:00:00\", \"average_rating\": 3.65625, \"review_count\": 32.0, \"Week\": \"2020-02-23T00:00:00\", \"trend_index\": 29, \"week_key\": \"2020-02-23T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 54.0, \"Normalized Amazon Review Count\": 50.0}, {\"review_date_key\": \"2020-03-01T00:00:00\", \"average_rating\": 3.8333333333333335, \"review_count\": 30.0, \"Week\": \"2020-03-01T00:00:00\", \"trend_index\": 51, \"week_key\": \"2020-03-01T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 48.0, \"Normalized Amazon Review Count\": 47.0}, {\"review_date_key\": \"2020-03-08T00:00:00\", \"average_rating\": 3.6538461538461537, \"review_count\": 26.0, \"Week\": \"2020-03-08T00:00:00\", \"trend_index\": 24, \"week_key\": \"2020-03-08T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 45.0, \"Normalized Amazon Review Count\": 41.0}, {\"review_date_key\": \"2020-03-15T00:00:00\", \"average_rating\": 4.133333333333334, \"review_count\": 15.0, \"Week\": \"2020-03-15T00:00:00\", \"trend_index\": 35, \"week_key\": \"2020-03-15T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 31.0, \"Normalized Amazon Review Count\": 23.0}, {\"review_date_key\": \"2020-03-22T00:00:00\", \"average_rating\": 3.35, \"review_count\": 20.0, \"Week\": \"2020-03-22T00:00:00\", \"trend_index\": 38, \"week_key\": \"2020-03-22T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 59.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2020-03-29T00:00:00\", \"average_rating\": 4.428571428571429, \"review_count\": 7.0, \"Week\": \"2020-03-29T00:00:00\", \"trend_index\": 35, \"week_key\": \"2020-03-29T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 30.0, \"Normalized Amazon Review Count\": 11.0}, {\"review_date_key\": \"2020-04-05T00:00:00\", \"average_rating\": 3.0, \"review_count\": 8.0, \"Week\": \"2020-04-05T00:00:00\", \"trend_index\": 27, \"week_key\": \"2020-04-05T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 53.0, \"Normalized Amazon Review Count\": 12.0}, {\"review_date_key\": \"2020-04-12T00:00:00\", \"average_rating\": 3.8461538461538463, \"review_count\": 13.0, \"Week\": \"2020-04-12T00:00:00\", \"trend_index\": 40, \"week_key\": \"2020-04-12T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 44.0, \"Normalized Amazon Review Count\": 20.0}, {\"review_date_key\": \"2020-04-19T00:00:00\", \"average_rating\": 3.1904761904761907, \"review_count\": 21.0, \"Week\": \"2020-04-19T00:00:00\", \"trend_index\": 58, \"week_key\": \"2020-04-19T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 62.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2020-04-26T00:00:00\", \"average_rating\": 3.823529411764706, \"review_count\": 17.0, \"Week\": \"2020-04-26T00:00:00\", \"trend_index\": 28, \"week_key\": \"2020-04-26T00:00:00\", \"product_name\": \"WF-1000XM3\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 27.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_corr('WF-1000XM3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-8780f5fd28ce4f2ebeea59715f4bc07d\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-8780f5fd28ce4f2ebeea59715f4bc07d\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-8780f5fd28ce4f2ebeea59715f4bc07d\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false}}, \"data\": {\"name\": \"data-6de31faab700e1667b18d3afcc6223bb\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"variable\", \"legend\": {\"orient\": \"bottom-right\"}, \"title\": \"\"}, \"x\": {\"type\": \"temporal\", \"field\": \"week_key\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"title\": [\"Elite 75t\", \"Trend of Normalized Amazon Review Count and Google Trend Index (Shifted 9 Weeks)\"], \"transform\": [{\"fold\": [\"Normalized Amazon Review Count\", \"Shifted Google Trend Index\"], \"as\": [\"variable\", \"value\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-6de31faab700e1667b18d3afcc6223bb\": [{\"review_date_key\": \"2019-11-17T00:00:00\", \"average_rating\": 2.3333333333333335, \"review_count\": 3.0, \"Week\": \"2019-11-17T00:00:00\", \"trend_index\": 44, \"week_key\": \"2019-11-17T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 10.0, \"Normalized Amazon Review Count\": 8.0}, {\"review_date_key\": \"2019-11-24T00:00:00\", \"average_rating\": 3.6666666666666665, \"review_count\": 3.0, \"Week\": \"2019-11-24T00:00:00\", \"trend_index\": 79, \"week_key\": \"2019-11-24T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 0.0, \"Normalized Amazon Review Count\": 8.0}, {\"review_date_key\": \"2019-12-01T00:00:00\", \"average_rating\": 3.75, \"review_count\": 4.0, \"Week\": \"2019-12-01T00:00:00\", \"trend_index\": 63, \"week_key\": \"2019-12-01T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 7.0, \"Normalized Amazon Review Count\": 11.0}, {\"review_date_key\": \"2019-12-08T00:00:00\", \"average_rating\": 2.0, \"review_count\": 8.0, \"Week\": \"2019-12-08T00:00:00\", \"trend_index\": 31, \"week_key\": \"2019-12-08T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 24.0, \"Normalized Amazon Review Count\": 22.0}, {\"review_date_key\": \"2019-12-15T00:00:00\", \"average_rating\": 3.4166666666666665, \"review_count\": 12.0, \"Week\": \"2019-12-15T00:00:00\", \"trend_index\": 48, \"week_key\": \"2019-12-15T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 14.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2019-12-22T00:00:00\", \"average_rating\": 3.7142857142857144, \"review_count\": 21.0, \"Week\": \"2019-12-22T00:00:00\", \"trend_index\": 58, \"week_key\": \"2019-12-22T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 21.0, \"Normalized Amazon Review Count\": 58.0}, {\"review_date_key\": \"2019-12-29T00:00:00\", \"average_rating\": 3.3076923076923075, \"review_count\": 13.0, \"Week\": \"2019-12-29T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-12-29T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 17.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2020-01-05T00:00:00\", \"average_rating\": 3.6818181818181817, \"review_count\": 22.0, \"Week\": \"2020-01-05T00:00:00\", \"trend_index\": 50, \"week_key\": \"2020-01-05T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 21.0, \"Normalized Amazon Review Count\": 61.0}, {\"review_date_key\": \"2020-01-12T00:00:00\", \"average_rating\": 3.5, \"review_count\": 28.0, \"Week\": \"2020-01-12T00:00:00\", \"trend_index\": 26, \"week_key\": \"2020-01-12T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 30.0, \"Normalized Amazon Review Count\": 78.0}, {\"review_date_key\": \"2020-01-19T00:00:00\", \"average_rating\": 3.111111111111111, \"review_count\": 18.0, \"Week\": \"2020-01-19T00:00:00\", \"trend_index\": 50, \"week_key\": \"2020-01-19T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 44.0, \"Normalized Amazon Review Count\": 50.0}, {\"review_date_key\": \"2020-01-26T00:00:00\", \"average_rating\": 3.5217391304347827, \"review_count\": 23.0, \"Week\": \"2020-01-26T00:00:00\", \"trend_index\": 46, \"week_key\": \"2020-01-26T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 79.0, \"Normalized Amazon Review Count\": 64.0}, {\"review_date_key\": \"2020-02-02T00:00:00\", \"average_rating\": 3.5161290322580645, \"review_count\": 31.0, \"Week\": \"2020-02-02T00:00:00\", \"trend_index\": 42, \"week_key\": \"2020-02-02T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 63.0, \"Normalized Amazon Review Count\": 86.0}, {\"review_date_key\": \"2020-02-09T00:00:00\", \"average_rating\": 3.5652173913043477, \"review_count\": 23.0, \"Week\": \"2020-02-09T00:00:00\", \"trend_index\": 42, \"week_key\": \"2020-02-09T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 31.0, \"Normalized Amazon Review Count\": 64.0}, {\"review_date_key\": \"2020-02-16T00:00:00\", \"average_rating\": 3.814814814814815, \"review_count\": 27.0, \"Week\": \"2020-02-16T00:00:00\", \"trend_index\": 52, \"week_key\": \"2020-02-16T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 48.0, \"Normalized Amazon Review Count\": 75.0}, {\"review_date_key\": \"2020-02-23T00:00:00\", \"average_rating\": 4.083333333333333, \"review_count\": 36.0, \"Week\": \"2020-02-23T00:00:00\", \"trend_index\": 41, \"week_key\": \"2020-02-23T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 58.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2020-03-01T00:00:00\", \"average_rating\": 3.925925925925926, \"review_count\": 27.0, \"Week\": \"2020-03-01T00:00:00\", \"trend_index\": 47, \"week_key\": \"2020-03-01T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 75.0}, {\"review_date_key\": \"2020-03-08T00:00:00\", \"average_rating\": 4.357142857142857, \"review_count\": 28.0, \"Week\": \"2020-03-08T00:00:00\", \"trend_index\": 50, \"week_key\": \"2020-03-08T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 50.0, \"Normalized Amazon Review Count\": 78.0}, {\"review_date_key\": \"2020-03-15T00:00:00\", \"average_rating\": 3.869565217391304, \"review_count\": 23.0, \"Week\": \"2020-03-15T00:00:00\", \"trend_index\": 25, \"week_key\": \"2020-03-15T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 26.0, \"Normalized Amazon Review Count\": 64.0}, {\"review_date_key\": \"2020-03-22T00:00:00\", \"average_rating\": 3.32, \"review_count\": 25.0, \"Week\": \"2020-03-22T00:00:00\", \"trend_index\": 27, \"week_key\": \"2020-03-22T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 50.0, \"Normalized Amazon Review Count\": 69.0}, {\"review_date_key\": \"2020-03-29T00:00:00\", \"average_rating\": 3.142857142857143, \"review_count\": 7.0, \"Week\": \"2020-03-29T00:00:00\", \"trend_index\": 23, \"week_key\": \"2020-03-29T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 46.0, \"Normalized Amazon Review Count\": 19.0}, {\"review_date_key\": \"2020-04-05T00:00:00\", \"average_rating\": 3.1538461538461537, \"review_count\": 13.0, \"Week\": \"2020-04-05T00:00:00\", \"trend_index\": 33, \"week_key\": \"2020-04-05T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 42.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2020-04-12T00:00:00\", \"average_rating\": 3.8181818181818183, \"review_count\": 11.0, \"Week\": \"2020-04-12T00:00:00\", \"trend_index\": 69, \"week_key\": \"2020-04-12T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 42.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2020-04-19T00:00:00\", \"average_rating\": 2.9473684210526314, \"review_count\": 19.0, \"Week\": \"2020-04-19T00:00:00\", \"trend_index\": 43, \"week_key\": \"2020-04-19T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 52.0, \"Normalized Amazon Review Count\": 53.0}, {\"review_date_key\": \"2020-04-26T00:00:00\", \"average_rating\": 3.25, \"review_count\": 20.0, \"Week\": \"2020-04-26T00:00:00\", \"trend_index\": 100, \"week_key\": \"2020-04-26T00:00:00\", \"product_name\": \"Elite 75t\", \"price\": 100, \"Shifted Google Trend Index\": 41.0, \"Normalized Amazon Review Count\": 56.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_corr('Elite 75t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-d5879b946cb64e4591b3533b8b55814f\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-d5879b946cb64e4591b3533b8b55814f\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-d5879b946cb64e4591b3533b8b55814f\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false}}, \"data\": {\"name\": \"data-df6e87e48428b62a262d1c7e9d6a739f\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"variable\", \"legend\": {\"orient\": \"bottom-right\"}, \"title\": \"\"}, \"x\": {\"type\": \"temporal\", \"field\": \"week_key\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"title\": [\"NC700\", \"Trend of Normalized Amazon Review Count and Google Trend Index (Shifted 6 Weeks)\"], \"transform\": [{\"fold\": [\"Normalized Amazon Review Count\", \"Shifted Google Trend Index\"], \"as\": [\"variable\", \"value\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-df6e87e48428b62a262d1c7e9d6a739f\": [{\"review_date_key\": \"2019-07-07T00:00:00\", \"average_rating\": 3.1363636363636362, \"review_count\": 22.0, \"Week\": \"2019-07-07T00:00:00\", \"trend_index\": 67, \"week_key\": \"2019-07-07T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 40.0, \"Normalized Amazon Review Count\": 49.0}, {\"review_date_key\": \"2019-07-14T00:00:00\", \"average_rating\": 3.727272727272727, \"review_count\": 11.0, \"Week\": \"2019-07-14T00:00:00\", \"trend_index\": 39, \"week_key\": \"2019-07-14T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 38.0, \"Normalized Amazon Review Count\": 24.0}, {\"review_date_key\": \"2019-07-21T00:00:00\", \"average_rating\": 3.25, \"review_count\": 12.0, \"Week\": \"2019-07-21T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-07-21T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 23.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-07-28T00:00:00\", \"average_rating\": 3.4705882352941178, \"review_count\": 17.0, \"Week\": \"2019-07-28T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-07-28T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 16.0, \"Normalized Amazon Review Count\": 38.0}, {\"review_date_key\": \"2019-08-04T00:00:00\", \"average_rating\": 4.071428571428571, \"review_count\": 14.0, \"Week\": \"2019-08-04T00:00:00\", \"trend_index\": 32, \"week_key\": \"2019-08-04T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 45.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2019-08-11T00:00:00\", \"average_rating\": 3.8974358974358974, \"review_count\": 39.0, \"Week\": \"2019-08-11T00:00:00\", \"trend_index\": 50, \"week_key\": \"2019-08-11T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 86.0, \"Normalized Amazon Review Count\": 87.0}, {\"review_date_key\": \"2019-08-18T00:00:00\", \"average_rating\": 4.2, \"review_count\": 15.0, \"Week\": \"2019-08-18T00:00:00\", \"trend_index\": 15, \"week_key\": \"2019-08-18T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 67.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2019-08-25T00:00:00\", \"average_rating\": 3.7142857142857144, \"review_count\": 14.0, \"Week\": \"2019-08-25T00:00:00\", \"trend_index\": 37, \"week_key\": \"2019-08-25T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 39.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2019-09-01T00:00:00\", \"average_rating\": 2.1666666666666665, \"review_count\": 6.0, \"Week\": \"2019-09-01T00:00:00\", \"trend_index\": 36, \"week_key\": \"2019-09-01T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 13.0}, {\"review_date_key\": \"2019-09-08T00:00:00\", \"average_rating\": 4.3125, \"review_count\": 16.0, \"Week\": \"2019-09-08T00:00:00\", \"trend_index\": 29, \"week_key\": \"2019-09-08T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 36.0}, {\"review_date_key\": \"2019-09-15T00:00:00\", \"average_rating\": 4.3076923076923075, \"review_count\": 13.0, \"Week\": \"2019-09-15T00:00:00\", \"trend_index\": 21, \"week_key\": \"2019-09-15T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 32.0, \"Normalized Amazon Review Count\": 29.0}, {\"review_date_key\": \"2019-09-22T00:00:00\", \"average_rating\": 4.409090909090909, \"review_count\": 22.0, \"Week\": \"2019-09-22T00:00:00\", \"trend_index\": 25, \"week_key\": \"2019-09-22T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 50.0, \"Normalized Amazon Review Count\": 49.0}, {\"review_date_key\": \"2019-09-29T00:00:00\", \"average_rating\": 3.5833333333333335, \"review_count\": 12.0, \"Week\": \"2019-09-29T00:00:00\", \"trend_index\": 39, \"week_key\": \"2019-09-29T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 15.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-10-06T00:00:00\", \"average_rating\": 2.6666666666666665, \"review_count\": 6.0, \"Week\": \"2019-10-06T00:00:00\", \"trend_index\": 29, \"week_key\": \"2019-10-06T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 37.0, \"Normalized Amazon Review Count\": 13.0}, {\"review_date_key\": \"2019-10-13T00:00:00\", \"average_rating\": 3.0, \"review_count\": 13.0, \"Week\": \"2019-10-13T00:00:00\", \"trend_index\": 33, \"week_key\": \"2019-10-13T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 36.0, \"Normalized Amazon Review Count\": 29.0}, {\"review_date_key\": \"2019-10-20T00:00:00\", \"average_rating\": 4.1, \"review_count\": 10.0, \"Week\": \"2019-10-20T00:00:00\", \"trend_index\": 61, \"week_key\": \"2019-10-20T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 22.0}, {\"review_date_key\": \"2019-10-27T00:00:00\", \"average_rating\": 4.214285714285714, \"review_count\": 14.0, \"Week\": \"2019-10-27T00:00:00\", \"trend_index\": 25, \"week_key\": \"2019-10-27T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 21.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2019-11-03T00:00:00\", \"average_rating\": 4.111111111111111, \"review_count\": 9.0, \"Week\": \"2019-11-03T00:00:00\", \"trend_index\": 50, \"week_key\": \"2019-11-03T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 25.0, \"Normalized Amazon Review Count\": 20.0}, {\"review_date_key\": \"2019-11-10T00:00:00\", \"average_rating\": 4.176470588235294, \"review_count\": 17.0, \"Week\": \"2019-11-10T00:00:00\", \"trend_index\": 45, \"week_key\": \"2019-11-10T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 39.0, \"Normalized Amazon Review Count\": 38.0}, {\"review_date_key\": \"2019-11-17T00:00:00\", \"average_rating\": 4.0, \"review_count\": 18.0, \"Week\": \"2019-11-17T00:00:00\", \"trend_index\": 63, \"week_key\": \"2019-11-17T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 40.0}, {\"review_date_key\": \"2019-11-24T00:00:00\", \"average_rating\": 3.4166666666666665, \"review_count\": 12.0, \"Week\": \"2019-11-24T00:00:00\", \"trend_index\": 100, \"week_key\": \"2019-11-24T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 33.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-12-01T00:00:00\", \"average_rating\": 3.4166666666666665, \"review_count\": 12.0, \"Week\": \"2019-12-01T00:00:00\", \"trend_index\": 69, \"week_key\": \"2019-12-01T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 61.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2019-12-08T00:00:00\", \"average_rating\": 4.0, \"review_count\": 28.0, \"Week\": \"2019-12-08T00:00:00\", \"trend_index\": 89, \"week_key\": \"2019-12-08T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 25.0, \"Normalized Amazon Review Count\": 62.0}, {\"review_date_key\": \"2019-12-15T00:00:00\", \"average_rating\": 3.4705882352941178, \"review_count\": 17.0, \"Week\": \"2019-12-15T00:00:00\", \"trend_index\": 82, \"week_key\": \"2019-12-15T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 50.0, \"Normalized Amazon Review Count\": 38.0}, {\"review_date_key\": \"2019-12-22T00:00:00\", \"average_rating\": 4.185185185185185, \"review_count\": 27.0, \"Week\": \"2019-12-22T00:00:00\", \"trend_index\": 64, \"week_key\": \"2019-12-22T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 45.0, \"Normalized Amazon Review Count\": 60.0}, {\"review_date_key\": \"2019-12-29T00:00:00\", \"average_rating\": 3.9705882352941178, \"review_count\": 34.0, \"Week\": \"2019-12-29T00:00:00\", \"trend_index\": 26, \"week_key\": \"2019-12-29T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 63.0, \"Normalized Amazon Review Count\": 76.0}, {\"review_date_key\": \"2020-01-05T00:00:00\", \"average_rating\": 4.466666666666667, \"review_count\": 45.0, \"Week\": \"2020-01-05T00:00:00\", \"trend_index\": 17, \"week_key\": \"2020-01-05T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 100.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2020-01-12T00:00:00\", \"average_rating\": 4.25, \"review_count\": 32.0, \"Week\": \"2020-01-12T00:00:00\", \"trend_index\": 26, \"week_key\": \"2020-01-12T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 69.0, \"Normalized Amazon Review Count\": 71.0}, {\"review_date_key\": \"2020-01-19T00:00:00\", \"average_rating\": 4.0476190476190474, \"review_count\": 21.0, \"Week\": \"2020-01-19T00:00:00\", \"trend_index\": 56, \"week_key\": \"2020-01-19T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 89.0, \"Normalized Amazon Review Count\": 47.0}, {\"review_date_key\": \"2020-01-26T00:00:00\", \"average_rating\": 4.5, \"review_count\": 38.0, \"Week\": \"2020-01-26T00:00:00\", \"trend_index\": 51, \"week_key\": \"2020-01-26T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 82.0, \"Normalized Amazon Review Count\": 84.0}, {\"review_date_key\": \"2020-02-02T00:00:00\", \"average_rating\": 4.2682926829268295, \"review_count\": 41.0, \"Week\": \"2020-02-02T00:00:00\", \"trend_index\": 37, \"week_key\": \"2020-02-02T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 64.0, \"Normalized Amazon Review Count\": 91.0}, {\"review_date_key\": \"2020-02-09T00:00:00\", \"average_rating\": 4.333333333333333, \"review_count\": 30.0, \"Week\": \"2020-02-09T00:00:00\", \"trend_index\": 41, \"week_key\": \"2020-02-09T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 26.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-02-16T00:00:00\", \"average_rating\": 4.3478260869565215, \"review_count\": 23.0, \"Week\": \"2020-02-16T00:00:00\", \"trend_index\": 23, \"week_key\": \"2020-02-16T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 17.0, \"Normalized Amazon Review Count\": 51.0}, {\"review_date_key\": \"2020-02-23T00:00:00\", \"average_rating\": 4.24, \"review_count\": 25.0, \"Week\": \"2020-02-23T00:00:00\", \"trend_index\": 26, \"week_key\": \"2020-02-23T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 26.0, \"Normalized Amazon Review Count\": 56.0}, {\"review_date_key\": \"2020-03-01T00:00:00\", \"average_rating\": 4.142857142857143, \"review_count\": 21.0, \"Week\": \"2020-03-01T00:00:00\", \"trend_index\": 20, \"week_key\": \"2020-03-01T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 56.0, \"Normalized Amazon Review Count\": 47.0}, {\"review_date_key\": \"2020-03-08T00:00:00\", \"average_rating\": 4.470588235294118, \"review_count\": 17.0, \"Week\": \"2020-03-08T00:00:00\", \"trend_index\": 29, \"week_key\": \"2020-03-08T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 51.0, \"Normalized Amazon Review Count\": 38.0}, {\"review_date_key\": \"2020-03-15T00:00:00\", \"average_rating\": 3.7857142857142856, \"review_count\": 14.0, \"Week\": \"2020-03-15T00:00:00\", \"trend_index\": 32, \"week_key\": \"2020-03-15T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 37.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2020-03-22T00:00:00\", \"average_rating\": 2.8666666666666667, \"review_count\": 15.0, \"Week\": \"2020-03-22T00:00:00\", \"trend_index\": 19, \"week_key\": \"2020-03-22T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 41.0, \"Normalized Amazon Review Count\": 33.0}, {\"review_date_key\": \"2020-03-29T00:00:00\", \"average_rating\": 2.8333333333333335, \"review_count\": 6.0, \"Week\": \"2020-03-29T00:00:00\", \"trend_index\": 39, \"week_key\": \"2020-03-29T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 23.0, \"Normalized Amazon Review Count\": 13.0}, {\"review_date_key\": \"2020-04-05T00:00:00\", \"average_rating\": 3.35, \"review_count\": 20.0, \"Week\": \"2020-04-05T00:00:00\", \"trend_index\": 25, \"week_key\": \"2020-04-05T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 26.0, \"Normalized Amazon Review Count\": 44.0}, {\"review_date_key\": \"2020-04-12T00:00:00\", \"average_rating\": 3.25, \"review_count\": 12.0, \"Week\": \"2020-04-12T00:00:00\", \"trend_index\": 33, \"week_key\": \"2020-04-12T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 20.0, \"Normalized Amazon Review Count\": 27.0}, {\"review_date_key\": \"2020-04-19T00:00:00\", \"average_rating\": 3.0, \"review_count\": 18.0, \"Week\": \"2020-04-19T00:00:00\", \"trend_index\": 18, \"week_key\": \"2020-04-19T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 40.0}, {\"review_date_key\": \"2020-04-26T00:00:00\", \"average_rating\": 3.142857142857143, \"review_count\": 14.0, \"Week\": \"2020-04-26T00:00:00\", \"trend_index\": 27, \"week_key\": \"2020-04-26T00:00:00\", \"product_name\": \"NC700\", \"price\": 100, \"Shifted Google Trend Index\": 32.0, \"Normalized Amazon Review Count\": 31.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_corr('NC700')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-597e96640efd445bb483d330483e1a9b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-597e96640efd445bb483d330483e1a9b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-597e96640efd445bb483d330483e1a9b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false}}, \"data\": {\"name\": \"data-b48d85909c039c6f51f437e7c39232bb\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"variable\", \"legend\": {\"orient\": \"bottom-right\"}, \"title\": \"\"}, \"x\": {\"type\": \"temporal\", \"field\": \"week_key\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"value\"}}, \"title\": [\"AirPods Pro\", \"Trend of Normalized Amazon Review Count and Google Trend Index (Shifted 9 Weeks)\"], \"transform\": [{\"fold\": [\"Normalized Amazon Review Count\", \"Shifted Google Trend Index\"], \"as\": [\"variable\", \"value\"]}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-b48d85909c039c6f51f437e7c39232bb\": [{\"review_date_key\": \"2019-12-15T00:00:00\", \"average_rating\": 4.390070921985815, \"review_count\": 141.0, \"Week\": \"2019-12-15T00:00:00\", \"trend_index\": 75, \"week_key\": \"2019-12-15T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 1.0, \"Normalized Amazon Review Count\": 44.0}, {\"review_date_key\": \"2019-12-22T00:00:00\", \"average_rating\": 4.530172413793103, \"review_count\": 232.0, \"Week\": \"2019-12-22T00:00:00\", \"trend_index\": 71, \"week_key\": \"2019-12-22T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 4.0, \"Normalized Amazon Review Count\": 73.0}, {\"review_date_key\": \"2019-12-29T00:00:00\", \"average_rating\": 4.62776025236593, \"review_count\": 317.0, \"Week\": \"2019-12-29T00:00:00\", \"trend_index\": 35, \"week_key\": \"2019-12-29T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 100.0, \"Normalized Amazon Review Count\": 100.0}, {\"review_date_key\": \"2020-01-05T00:00:00\", \"average_rating\": 4.635627530364372, \"review_count\": 247.0, \"Week\": \"2020-01-05T00:00:00\", \"trend_index\": 29, \"week_key\": \"2020-01-05T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 49.0, \"Normalized Amazon Review Count\": 78.0}, {\"review_date_key\": \"2020-01-12T00:00:00\", \"average_rating\": 4.398406374501992, \"review_count\": 251.0, \"Week\": \"2020-01-12T00:00:00\", \"trend_index\": 27, \"week_key\": \"2020-01-12T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 40.0, \"Normalized Amazon Review Count\": 79.0}, {\"review_date_key\": \"2020-01-19T00:00:00\", \"average_rating\": 4.568181818181818, \"review_count\": 132.0, \"Week\": \"2020-01-19T00:00:00\", \"trend_index\": 25, \"week_key\": \"2020-01-19T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 38.0, \"Normalized Amazon Review Count\": 42.0}, {\"review_date_key\": \"2020-01-26T00:00:00\", \"average_rating\": 4.530516431924883, \"review_count\": 213.0, \"Week\": \"2020-01-26T00:00:00\", \"trend_index\": 22, \"week_key\": \"2020-01-26T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 77.0, \"Normalized Amazon Review Count\": 67.0}, {\"review_date_key\": \"2020-02-02T00:00:00\", \"average_rating\": 4.441747572815534, \"review_count\": 206.0, \"Week\": \"2020-02-02T00:00:00\", \"trend_index\": 29, \"week_key\": \"2020-02-02T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 73.0, \"Normalized Amazon Review Count\": 65.0}, {\"review_date_key\": \"2020-02-09T00:00:00\", \"average_rating\": 4.437869822485207, \"review_count\": 169.0, \"Week\": \"2020-02-09T00:00:00\", \"trend_index\": 32, \"week_key\": \"2020-02-09T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 63.0, \"Normalized Amazon Review Count\": 53.0}, {\"review_date_key\": \"2020-02-16T00:00:00\", \"average_rating\": 4.358381502890174, \"review_count\": 173.0, \"Week\": \"2020-02-16T00:00:00\", \"trend_index\": 24, \"week_key\": \"2020-02-16T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 75.0, \"Normalized Amazon Review Count\": 55.0}, {\"review_date_key\": \"2020-02-23T00:00:00\", \"average_rating\": 4.533678756476684, \"review_count\": 193.0, \"Week\": \"2020-02-23T00:00:00\", \"trend_index\": 24, \"week_key\": \"2020-02-23T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 71.0, \"Normalized Amazon Review Count\": 61.0}, {\"review_date_key\": \"2020-03-01T00:00:00\", \"average_rating\": 4.445054945054945, \"review_count\": 182.0, \"Week\": \"2020-03-01T00:00:00\", \"trend_index\": 22, \"week_key\": \"2020-03-01T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 35.0, \"Normalized Amazon Review Count\": 57.0}, {\"review_date_key\": \"2020-03-08T00:00:00\", \"average_rating\": 4.438271604938271, \"review_count\": 162.0, \"Week\": \"2020-03-08T00:00:00\", \"trend_index\": 16, \"week_key\": \"2020-03-08T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 51.0}, {\"review_date_key\": \"2020-03-15T00:00:00\", \"average_rating\": 4.11340206185567, \"review_count\": 97.0, \"Week\": \"2020-03-15T00:00:00\", \"trend_index\": 20, \"week_key\": \"2020-03-15T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 27.0, \"Normalized Amazon Review Count\": 31.0}, {\"review_date_key\": \"2020-03-22T00:00:00\", \"average_rating\": 4.2875, \"review_count\": 80.0, \"Week\": \"2020-03-22T00:00:00\", \"trend_index\": 20, \"week_key\": \"2020-03-22T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 25.0, \"Normalized Amazon Review Count\": 25.0}, {\"review_date_key\": \"2020-03-29T00:00:00\", \"average_rating\": 3.7752808988764044, \"review_count\": 89.0, \"Week\": \"2020-03-29T00:00:00\", \"trend_index\": 18, \"week_key\": \"2020-03-29T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 22.0, \"Normalized Amazon Review Count\": 28.0}, {\"review_date_key\": \"2020-04-05T00:00:00\", \"average_rating\": 3.688888888888889, \"review_count\": 90.0, \"Week\": \"2020-04-05T00:00:00\", \"trend_index\": 19, \"week_key\": \"2020-04-05T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 29.0, \"Normalized Amazon Review Count\": 28.0}, {\"review_date_key\": \"2020-04-12T00:00:00\", \"average_rating\": 3.992063492063492, \"review_count\": 126.0, \"Week\": \"2020-04-12T00:00:00\", \"trend_index\": 25, \"week_key\": \"2020-04-12T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 32.0, \"Normalized Amazon Review Count\": 40.0}, {\"review_date_key\": \"2020-04-19T00:00:00\", \"average_rating\": 4.079754601226994, \"review_count\": 163.0, \"Week\": \"2020-04-19T00:00:00\", \"trend_index\": 24, \"week_key\": \"2020-04-19T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 24.0, \"Normalized Amazon Review Count\": 51.0}, {\"review_date_key\": \"2020-04-26T00:00:00\", \"average_rating\": 4.062937062937063, \"review_count\": 143.0, \"Week\": \"2020-04-26T00:00:00\", \"trend_index\": 22, \"week_key\": \"2020-04-26T00:00:00\", \"product_name\": \"AirPods Pro\", \"price\": 100, \"Shifted Google Trend Index\": 24.0, \"Normalized Amazon Review Count\": 45.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize_corr('AirPods Pro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression of Review Count and Average Review Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculat_weekly_average(verified_purchase=True):\n",
    "    '''\n",
    "    Takes an optional parameter of verified purchase flag \n",
    "    to return a dataframe of weekly average rating and review count for all wireless headsets\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - verified_purchase: whether or not the reviews should be filtered for verified purchase only\n",
    "    '''   \n",
    "    df = pd.DataFrame(columns=['review_date_key', 'average_rating', 'review_count', 'product_name', 'price'])\n",
    "    for product, price in zip(*[product_list, price_list]):\n",
    "        df_new = review_data_import_clean(product, verified_purchase)\n",
    "        df_new['product_name'] = product\n",
    "        df_new['price'] = price\n",
    "        df = pd.concat([df, df_new])\n",
    "    \n",
    "    df['review_count'] = df['review_count'].astype(int) # pandas assigns review_count column to object type by default\n",
    "    \n",
    "    df_agg = df.groupby(['product_name', 'price']).agg({'average_rating':'mean', 'review_count':'mean'}).reset_index()\n",
    "    df_agg['review_count_log'] = np.log(df_agg['review_count']) \n",
    "    return df_agg\n",
    "\n",
    "def visualize_regression(df):\n",
    "    '''\n",
    "    Takes a dataframe of weekly average rating and review count for all wireless headsets\n",
    "    to return a scatter plot with linear regreesion line\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    - df: dataframe containing weekly average rating and review count\n",
    "    ''' \n",
    "    coef = linregress(df['review_count_log'], df['average_rating'])\n",
    "    \n",
    "    scatter = alt.Chart(df).mark_circle(color='lightgrey').encode(\n",
    "                            x = alt.X('review_count_log', \n",
    "                                      scale={\"domain\":[2.5,5]}, \n",
    "                                      title='Weekly Review Count (Log Scale)'),\n",
    "                            y = alt.Y('average_rating', \n",
    "                                      scale={\"domain\":[3,5]}, \n",
    "                                      title='Average Rating')\n",
    "                        )\n",
    "    regression = scatter.transform_regression('review_count_log',\n",
    "                                              'average_rating',\n",
    "                                              method=\"linear\"\n",
    "                        ).mark_line(color='black')\n",
    "    labels = scatter.mark_text(\n",
    "                            dy=-10, \n",
    "                            dx=-20,\n",
    "                            size=10\n",
    "                        ).encode(                \n",
    "                            text=alt.Text(\"product_name:N\")\n",
    "                        )\n",
    "    \n",
    "    combined = (scatter+regression+labels).properties(title=['Regression Between Weekly Review Count and Average Rating ', \n",
    "                                                             'R-Squared: {:.3f}'.format(coef[2]**2)])\n",
    "    \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-e16e68a396dc400e8223744f9e8aad4e\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-e16e68a396dc400e8223744f9e8aad4e\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-e16e68a396dc400e8223744f9e8aad4e\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"circle\", \"color\": \"lightgrey\"}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"review_count_log\", \"scale\": {\"domain\": [2.5, 5]}, \"title\": \"Weekly Review Count (Log Scale)\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"average_rating\", \"scale\": {\"domain\": [3, 5]}, \"title\": \"Average Rating\"}}}, {\"mark\": {\"type\": \"line\", \"color\": \"black\"}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"review_count_log\", \"scale\": {\"domain\": [2.5, 5]}, \"title\": \"Weekly Review Count (Log Scale)\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"average_rating\", \"scale\": {\"domain\": [3, 5]}, \"title\": \"Average Rating\"}}, \"transform\": [{\"on\": \"review_count_log\", \"regression\": \"average_rating\", \"method\": \"linear\"}]}, {\"mark\": {\"type\": \"text\", \"dx\": -20, \"dy\": -10, \"size\": 10}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"product_name\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"review_count_log\", \"scale\": {\"domain\": [2.5, 5]}, \"title\": \"Weekly Review Count (Log Scale)\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"average_rating\", \"scale\": {\"domain\": [3, 5]}, \"title\": \"Average Rating\"}}}], \"data\": {\"name\": \"data-a9168fa108e9880aec226c37ffa9ad4d\"}, \"title\": [\"Regression Between Weekly Review Count and Average Rating \", \"R-Squared: 0.436\"], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-a9168fa108e9880aec226c37ffa9ad4d\": [{\"product_name\": \"AirPods Pro\", \"price\": 249.0, \"average_rating\": 4.320193937363577, \"review_count\": 145.92592592592592, \"review_count_log\": 4.983099136287651}, {\"product_name\": \"Elite 75t\", \"price\": 179.0, \"average_rating\": 3.4486539675048813, \"review_count\": 18.541666666666668, \"review_count_log\": 2.920020451818295}, {\"product_name\": \"Free\", \"price\": 49.0, \"average_rating\": 4.401079289665902, \"review_count\": 41.68181818181818, \"review_count_log\": 3.730065018898149}, {\"product_name\": \"Galaxy Buds Plus\", \"price\": 149.0, \"average_rating\": 4.314176244220914, \"review_count\": 68.25, \"review_count_log\": 4.223177434065069}, {\"product_name\": \"NC700\", \"price\": 399.0, \"average_rating\": 3.773196402102148, \"review_count\": 18.772727272727273, \"review_count_log\": 2.932405139602717}, {\"product_name\": \"Soundcore Liberty 2 Pro\", \"price\": 149.0, \"average_rating\": 4.284994268613469, \"review_count\": 28.6, \"review_count_log\": 3.353406717825807}, {\"product_name\": \"Soundcore Liberty Air 2\", \"price\": 100.0, \"average_rating\": 4.097923370026839, \"review_count\": 24.5, \"review_count_log\": 3.1986731175506815}, {\"product_name\": \"Soundcore Life Q20\", \"price\": 59.0, \"average_rating\": 4.189310496482259, \"review_count\": 37.1578947368421, \"review_count_log\": 3.6151762583268017}, {\"product_name\": \"WF-1000XM3\", \"price\": 198.0, \"average_rating\": 3.6348284488208806, \"review_count\": 28.4, \"review_count_log\": 3.3463891451671604}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_review_weekly = calculat_weekly_average()\n",
    "visualize_regression(df_review_weekly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon Review Text Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Normalize(array):\n",
    "    return array / np.linalg.norm(array, ord=1)\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\\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.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",
    "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",
    "    \n",
    "amzn_df = GetMergedAmazonDataFrames()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "product_list = [ 'NC700','AirPods Pro','WF-1000XM3', 'Elite 75t',  'Galaxy Buds Plus','Soundcore Liberty 2 Pro', 'Soundcore Liberty Air 2', 'Soundcore Life Q20', 'Free']\n",
    "price_list = [ 399.0,     249.0,        198.0,            179.0,           149.0, 149.0,    100.0, 59.0, 49.0]\n",
    "\n",
    "sns_plot = sns.violinplot(x=\"ProductName\", y=\"review_rating\", hue=\"verified_purchase\",\n",
    "                    data=amzn_df, palette=\"muted\", split=True\\\n",
    "                    , order=product_list\n",
    "                    ).set_title('Distribution of reviews between purchases and not purchased')\n",
    "\n",
    "\n",
    "fig = sns_plot.get_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x257da0e21f0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 831.525x1101.6 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "\n",
    "\n",
    "\n",
    "df = GetAmazonNumericalReviewsDf().dropna()\n",
    "\n",
    "ordered_days = df.ProductName\n",
    "g = sns.FacetGrid(df, row=\"ProductName\", hue=\"verified_purchase\", height=1.7, aspect=6,)\n",
    "g.map(sns.lineplot, \"review_date\", \"review_rating_norm\").add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amazon Unique Words and Word Pair Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading averaged_perceptron_tagger: <urlopen error\n",
      "[nltk_data]     Remote end closed connection without response>\n",
      "[nltk_data] Error loading tagsets: <urlopen error Remote end closed\n",
      "[nltk_data]     connection without response>\n"
     ]
    }
   ],
   "source": [
    "\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",
    "    tokens = tokenizer.tokenize(text)\n",
    "    return tokens\n",
    "\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",
    "    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 GetWordPairs(text):\n",
    "    text = CleanText(text)\n",
    "    tokens = TokenizeText(text)\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",
    "    from nltk.stem import PorterStemmer\n",
    "    ps = PorterStemmer()\n",
    "    tokens = [ ps.stem(elem) for elem in tokens if elem not in STOPWORDS]\n",
    "    \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",
    "    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\n",
    "\n",
    "\n",
    "def AddAdjectiveNounsVerbsToDF(df, textColumn):\n",
    "    df = df.copy()\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",
    "    # create word pairs.\n",
    "    df['text_pairs'] = df[textColumn].apply(lambda row: GetWordPairs(row))\n",
    "    return df\n",
    "\n",
    "def GetDataframeOfWords(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",
    "    return df\n",
    "\n",
    "amzn_df = GetAmazonTextReviewsDf()\n",
    "amzn_df = AddAdjectiveNounsVerbsToDF(amzn_df,'review_text')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-330f052a09cb>: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",
      "  data = data['text_adjectives'].apply(lambda x: pd.Series(list(x)))\\\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "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']]\n",
    "adjCount_temp2 = lowAdjectivesRanking[['words']]\n",
    "\n",
    "intersection = adjCount_temp.merge(adjCount_temp2, left_on='words', right_on='words', how='inner')\n",
    "\n",
    "\n",
    "lowwordsDump = lowAdjectivesRanking[~lowAdjectivesRanking['words']\\\n",
    "                                    .isin(intersection['words'].unique())]\\\n",
    "                                    .groupby('words').sum()\\\n",
    "                                    .sort_values(by='count',ascending=False).reset_index()\n",
    "highwordsDump = highAdjectivesRanking[~highAdjectivesRanking['words']\\\n",
    "                                      .isin(intersection['words'].unique())]\\\n",
    "                                      .groupby('words').sum()\\\n",
    "                                      .sort_values(by='count',ascending=False).reset_index()\n",
    "highwordsDump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
    "\n",
    "wordcloud = WordCloud(max_font_size=40).generate(text)\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-330f052a09cb>: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",
      "  data = data['text_adjectives'].apply(lambda x: pd.Series(list(x)))\\\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>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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\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']]\\\n",
    "        .groupby([wordColumn])\\\n",
    "        .sum()\\\n",
    "        .sort_values(by='count',ascending=False).reset_index()\n",
    "    adjCount_temp2 = lowAdjectivesRanking[[wordColumn,'count']]\\\n",
    "        .groupby([wordColumn])\\\n",
    "        .sum()\\\n",
    "        .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",
    "    top_adj_words_df = highAdjectivesRanking[~highAdjectivesRanking[wordColumn]\\\n",
    "                                             .isin(intersection[wordColumn].unique())].groupby(wordColumn).sum()\\\n",
    "                                             .sort_values(by='count',ascending=False)\n",
    "    lower_adj_words_df = lowAdjectivesRanking[~lowAdjectivesRanking[wordColumn]\\\n",
    "                                              .isin(intersection[wordColumn].unique())].groupby(wordColumn).sum()\\\n",
    "                                              .sort_values(by='count',ascending=False)\n",
    "\n",
    "    df = lower_adj_words_df.reset_index().reset_index()[['index',wordColumn]]\n",
    "    top_adj_words_df=top_adj_words_df.reset_index().reset_index()[['index',wordColumn]]\n",
    "\n",
    "    onevfiveStarReviewWords_df = df.merge(top_adj_words_df, left_on='index', right_on='index', how='inner')\n",
    "    onevfiveStarReviewWords_df = onevfiveStarReviewWords_df\\\n",
    "                        .rename(columns={wordColumn+'_x':'oneStarWords', \n",
    "                                         wordColumn+'_y':'fiveStarWords','index':'rank'})\n",
    "    return onevfiveStarReviewWords_df\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\n",
    "\n",
    "productSingleWords = GetProductUniqueWords(amzn_df)\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",
    "bestSingleProductWords = productSingleWords[bestColumns]\n",
    "bestSingleProductWords[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
     ]
    },
    {
     "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>(product, great)</td>\n",
       "      <td>(wireless, truli)</td>\n",
       "      <td>(far, so)</td>\n",
       "      <td>(sound, ambient)</td>\n",
       "      <td>(touch, control)</td>\n",
       "      <td>(ear, in)</td>\n",
       "      <td>(well, as)</td>\n",
       "      <td>(good, as)</td>\n",
       "      <td>(connect, app)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(sound, amaz)</td>\n",
       "      <td>(far, so)</td>\n",
       "      <td>(c, usb)</td>\n",
       "      <td>(qualiti, call)</td>\n",
       "      <td>(comfort, veri)</td>\n",
       "      <td>(ear, your)</td>\n",
       "      <td>(charg, wireless)</td>\n",
       "      <td>(price, great)</td>\n",
       "      <td>(far, as)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(qualiti, great)</td>\n",
       "      <td>(recommend, highli)</td>\n",
       "      <td>(qualiti, great)</td>\n",
       "      <td>(airpod, pro)</td>\n",
       "      <td>(sound, bose)</td>\n",
       "      <td>(earbud, wireless)</td>\n",
       "      <td>(recommend, highli)</td>\n",
       "      <td>(far, so)</td>\n",
       "      <td>(that, headphon)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(great, fit)</td>\n",
       "      <td>(realli, good)</td>\n",
       "      <td>(realli, good)</td>\n",
       "      <td>(far, so)</td>\n",
       "      <td>(block, out)</td>\n",
       "      <td>(sound, amaz)</td>\n",
       "      <td>(airpod, pro)</td>\n",
       "      <td>(batteri, last)</td>\n",
       "      <td>(product, great)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(love, he)</td>\n",
       "      <td>(app, in)</td>\n",
       "      <td>(good, as)</td>\n",
       "      <td>(earbud, wireless)</td>\n",
       "      <td>(level, nois)</td>\n",
       "      <td>(in, your)</td>\n",
       "      <td>(product, great)</td>\n",
       "      <td>(price, at)</td>\n",
       "      <td>(use, easi)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(earbud, wireless)</td>\n",
       "      <td>(through, featur)</td>\n",
       "      <td>(recommend, highli)</td>\n",
       "      <td>(qualiti, great)</td>\n",
       "      <td>(veri, nice)</td>\n",
       "      <td>(veri, good)</td>\n",
       "      <td>(realli, good)</td>\n",
       "      <td>(bass, boost)</td>\n",
       "      <td>(earbud, best)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(well, worth)</td>\n",
       "      <td>(fit, secur)</td>\n",
       "      <td>(product, great)</td>\n",
       "      <td>(day, all)</td>\n",
       "      <td>(happi, veri)</td>\n",
       "      <td>(charg, case)</td>\n",
       "      <td>(sound, better)</td>\n",
       "      <td>(good, nois)</td>\n",
       "      <td>(use, in)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(definit, worth)</td>\n",
       "      <td>(mid, high)</td>\n",
       "      <td>(price, great)</td>\n",
       "      <td>(ear, your)</td>\n",
       "      <td>(veri, well)</td>\n",
       "      <td>(music, listen)</td>\n",
       "      <td>(touch, control)</td>\n",
       "      <td>(product, great)</td>\n",
       "      <td>(so, good)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(worth, everi)</td>\n",
       "      <td>(qualiti, great)</td>\n",
       "      <td>(earfun, free)</td>\n",
       "      <td>(music, listen)</td>\n",
       "      <td>(end, high)</td>\n",
       "      <td>(sound, best)</td>\n",
       "      <td>(wireless, true)</td>\n",
       "      <td>(veri, nice)</td>\n",
       "      <td>(bud, galaxi)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(from, upgrad)</td>\n",
       "      <td>(take, out)</td>\n",
       "      <td>(batteri, last)</td>\n",
       "      <td>(better, much)</td>\n",
       "      <td>(volum, control)</td>\n",
       "      <td>(recommend, highli)</td>\n",
       "      <td>(airpod, appl)</td>\n",
       "      <td>(set, headphon)</td>\n",
       "      <td>(qualiti, excel)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AirPods Pro_best       Elite 75t_best            Free_best  \\\n",
       "0    (product, great)    (wireless, truli)            (far, so)   \n",
       "1       (sound, amaz)            (far, so)             (c, usb)   \n",
       "2    (qualiti, great)  (recommend, highli)     (qualiti, great)   \n",
       "3        (great, fit)       (realli, good)       (realli, good)   \n",
       "4          (love, he)            (app, in)           (good, as)   \n",
       "5  (earbud, wireless)    (through, featur)  (recommend, highli)   \n",
       "6       (well, worth)         (fit, secur)     (product, great)   \n",
       "7    (definit, worth)          (mid, high)       (price, great)   \n",
       "8      (worth, everi)     (qualiti, great)       (earfun, free)   \n",
       "9      (from, upgrad)          (take, out)      (batteri, last)   \n",
       "\n",
       "  Galaxy Buds Plus_best        NC700_best Soundcore Liberty 2 Pro_best  \\\n",
       "0      (sound, ambient)  (touch, control)                    (ear, in)   \n",
       "1       (qualiti, call)   (comfort, veri)                  (ear, your)   \n",
       "2         (airpod, pro)     (sound, bose)           (earbud, wireless)   \n",
       "3             (far, so)      (block, out)                (sound, amaz)   \n",
       "4    (earbud, wireless)     (level, nois)                   (in, your)   \n",
       "5      (qualiti, great)      (veri, nice)                 (veri, good)   \n",
       "6            (day, all)     (happi, veri)                (charg, case)   \n",
       "7           (ear, your)      (veri, well)              (music, listen)   \n",
       "8       (music, listen)       (end, high)                (sound, best)   \n",
       "9        (better, much)  (volum, control)          (recommend, highli)   \n",
       "\n",
       "  Soundcore Liberty Air 2_best Soundcore Life Q20_best   WF-1000XM3_best  \n",
       "0                   (well, as)              (good, as)    (connect, app)  \n",
       "1            (charg, wireless)          (price, great)         (far, as)  \n",
       "2          (recommend, highli)               (far, so)  (that, headphon)  \n",
       "3                (airpod, pro)         (batteri, last)  (product, great)  \n",
       "4             (product, great)             (price, at)       (use, easi)  \n",
       "5               (realli, good)           (bass, boost)    (earbud, best)  \n",
       "6              (sound, better)            (good, nois)         (use, in)  \n",
       "7             (touch, control)        (product, great)        (so, good)  \n",
       "8             (wireless, true)            (veri, nice)     (bud, galaxi)  \n",
       "9               (airpod, appl)         (set, headphon)  (qualiti, excel)  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def GetReviewPairsCounts(df):\n",
    "    data = df.copy()\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",
    "    totalReview= len(df['verified_purchase'])\n",
    "    \n",
    "    return data\n",
    "\n",
    "\n",
    "fivestarsPairs_df = amzn_df.where(amzn_df['review_rating'] == 5).dropna()\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()\\\n",
    "        .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",
    "\n",
    "        high['text_pairs'].isin(intersection['text_pairs'].unique())\n",
    "        \n",
    "        top_adj_words_df = high[~high['text_pairs']\\\n",
    "                                .isin(intersection['text_pairs'].unique())]\\\n",
    "                                .groupby('text_pairs').sum()\\\n",
    "                                .sort_values(by='count',ascending=False).reset_index()\n",
    "        lower_adj_words_df = low[~low['text_pairs']\\\n",
    "                                 .isin(intersection['text_pairs'].unique())]\\\n",
    "                                 .groupby('text_pairs').sum()\\\n",
    "                                 .sort_values(by='count',ascending=False).reset_index()\n",
    "        top_adj_words_df = top_adj_words_df.rename(columns={'text_pairs':product+'_best'})\\\n",
    "                            .reset_index().drop(columns=['count'])\n",
    "        lower_adj_words_df = lower_adj_words_df.rename(columns={'text_pairs':product+'_worst'})\\\n",
    "                            .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",
    "\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",
    "    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",
    "productBest[:10].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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}