Analysis UK Amazon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_reviews = pd.read_csv('uk_reviewsactual.csv', header = None, names = ['date', 'star_rating', 'user_review_headers', 'user_review_texts','user_name','user_type', 'helpful'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>user_review_headers</th>\n",
       "      <th>user_review_texts</th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_type</th>\n",
       "      <th>helpful</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>on 19 May 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>delivered in time and actually Apple so very h...</td>\n",
       "      <td>I was initially a bit wary of buying it throug...</td>\n",
       "      <td>Cliente Amazon</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>30 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>on 21 February 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Cheaper than that available from apple - quick...</td>\n",
       "      <td>mr n kitchen</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>15 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>on 24 February 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great brand new iPhone !</td>\n",
       "      <td>Really good iPhone ! Arrived way earlier than ...</td>\n",
       "      <td>Karima Mohamed</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>13 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>on 1 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Genuine iPhone x</td>\n",
       "      <td>I received a genuine iPhone X, I was initially...</td>\n",
       "      <td>Ritlady</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>7 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>on 10 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>iPhoneX, at good price - love it.</td>\n",
       "      <td>It's an iPhoneX, at good price - love it.</td>\n",
       "      <td>Helicotrema</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>4 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>on 12 August 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great iPhone</td>\n",
       "      <td>Received my new iPhone 8 Plus yesterday, and I...</td>\n",
       "      <td>T. Gauld</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>on 18 September 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great phone at a decent price.</td>\n",
       "      <td>Purchased for my daughter and she absolutely l...</td>\n",
       "      <td>Wasim</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>on 30 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Excellent phone</td>\n",
       "      <td>Great phone as predicted. Simple upgrade from ...</td>\n",
       "      <td>Rob Sherwood</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>on 28 April 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>absolute bargain for the iphone 10  also arriv...</td>\n",
       "      <td>Matt</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>on 26 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Excellent customer service</td>\n",
       "      <td>supplier is amazing. Received this phone befor...</td>\n",
       "      <td>Rustam</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>on 27 September 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>It’s a legit unopened I Phone X.</td>\n",
       "      <td>When I ordered this I was a bit skeptical due ...</td>\n",
       "      <td>George</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>on 30 March 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Hope with it</td>\n",
       "      <td>Timothy Wootton</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>on 10 March 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Great product and fast shipping</td>\n",
       "      <td>Karist Ng</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>on 2 April 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>lovely phone</td>\n",
       "      <td>Buyer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>on 26 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Amazing</td>\n",
       "      <td>Wonderful product and service, I highly recomm...</td>\n",
       "      <td>Cliente Amazon</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>on 6 October 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Excellent preowned mobile</td>\n",
       "      <td>Excellent preowned mobile will recommend to al...</td>\n",
       "      <td>EDG</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>on 17 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Not much to say, it’s Apple..</td>\n",
       "      <td>Latest and greatest.</td>\n",
       "      <td>Mo</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>on 21 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Good product</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>on 28 May 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>very good</td>\n",
       "      <td>Brian D.</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>on 3 August 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Love iPhone, bit too pricy</td>\n",
       "      <td>Great iPhone, not learned all features yet, sh...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>on 29 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Great phone and cheapest reliable place to buy...</td>\n",
       "      <td>Kit Langran-Wheeler</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>on 9 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Very pleased... thanks</td>\n",
       "      <td>George</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>on 6 October 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Came pretty quickly and in good condition. Did...</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>on 4 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Very happy with the product.</td>\n",
       "      <td>David</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>on 30 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>All good</td>\n",
       "      <td>Everything working as I expected</td>\n",
       "      <td>Dhruba</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>on 30 July 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Good</td>\n",
       "      <td>It was a good buy as it still had warranty and...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>on 5 October 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Loved the product</td>\n",
       "      <td>The phone was in very good condition. I really...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>on 2 May 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>all good</td>\n",
       "      <td>mike</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>on 11 July 2018</td>\n",
       "      <td>2.0 out of 5 stars</td>\n",
       "      <td>Over heating</td>\n",
       "      <td>The phone is overheating after a little use. N...</td>\n",
       "      <td>Muj</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>on 14 June 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>... used to using the phone but so far very pl...</td>\n",
       "      <td>Just getting used to using the phone but so fa...</td>\n",
       "      <td>M.C. Boik</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>on 3 January 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Pros screen quality volume camera (look up the...</td>\n",
       "      <td>I upgraded from the IPhone 6S to the 8 plus.Pr...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>9 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>on 22 November 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>... delivery phone new and as it should be ver...</td>\n",
       "      <td>fast delivery phone new and as it should be ve...</td>\n",
       "      <td>ron jagger</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>10 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>on 23 February 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great service and product</td>\n",
       "      <td>Brilliant!!</td>\n",
       "      <td>Mike</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>on 16 January 2018</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>Amazing phone, but...</td>\n",
       "      <td>As much as I love the phone, the reason I drop...</td>\n",
       "      <td>Luke</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>14 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>on 14 April 2018</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>iPhone 8</td>\n",
       "      <td>Good</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>on 24 January 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>What are you waiting for?</td>\n",
       "      <td>Its just like your old iPhone but faster and i...</td>\n",
       "      <td>Mountain Nolan</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>on 4 April 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>battery life is not so well 👎</td>\n",
       "      <td>intems ok!</td>\n",
       "      <td>Ghinda Alexandru</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>on 10 December 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>SUPERB PHONE.......NO LONGER JUST A PHONE BUT ...</td>\n",
       "      <td>Having used this phone now for almost 2 months...</td>\n",
       "      <td>Mr. M. Platt</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>12 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>on 14 September 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Brilliant</td>\n",
       "      <td>Janet B.</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>on 9 December 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Quick delivery. The produt is as described.</td>\n",
       "      <td>Quick delivery.  The produt is as described.</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>on 22 January 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Excelent</td>\n",
       "      <td>i love my new iphone</td>\n",
       "      <td>Mr Kaka Neery</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>on 26 May 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Testing</td>\n",
       "      <td>Hi there,Would u allow me to test an iPhone X ...</td>\n",
       "      <td>Ebony</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>on 21 November 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>It's an early Christmas present to replace a s...</td>\n",
       "      <td>Bought this for my daughter and she absolutely...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>on 14 December 2017</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>The best iphone out there, but is it worth the...</td>\n",
       "      <td>Screen -This 1125 x 2436 pixel screen is very ...</td>\n",
       "      <td>PTZ</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>12 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>on 29 January 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Sleeker than ever</td>\n",
       "      <td>If you have owned I iPhone before you know exa...</td>\n",
       "      <td>Andy Mc</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>on 31 December 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>on 20 January 2018</td>\n",
       "      <td>3.0 out of 5 stars</td>\n",
       "      <td>Disappointed</td>\n",
       "      <td>The previous Griffin cases I had were a rubber...</td>\n",
       "      <td>Nutter</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>6 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>on 16 March 2018</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>Its an iPhone.</td>\n",
       "      <td>MR A J OLVER</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>on 18 June 2018</td>\n",
       "      <td>3.0 out of 5 stars</td>\n",
       "      <td>Lovely looking phone that burns its screen</td>\n",
       "      <td>Good phone that Apple needed to release! It's ...</td>\n",
       "      <td>Ryan UK</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>on 24 December 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Recommendable</td>\n",
       "      <td>About the phone, I would say that changing fro...</td>\n",
       "      <td>Ana Jimenez</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>10 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>on 26 February 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Great expensive phone!</td>\n",
       "      <td>You think the price is high but what you get i...</td>\n",
       "      <td>Romeo P.</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>on 8 July 2018</td>\n",
       "      <td>1.0 out of 5 stars</td>\n",
       "      <td>SCREEN DAMGED AND CAMERA</td>\n",
       "      <td>I got and it was rubbish the screen was broke ...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>2 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>on 28 November 2017</td>\n",
       "      <td>3.0 out of 5 stars</td>\n",
       "      <td>It's a quality product, but I wouldn't recomme...</td>\n",
       "      <td>Here's a few of my feelings after using this p...</td>\n",
       "      <td>Mystified Mister</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>16 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>on 2 December 2017</td>\n",
       "      <td>3.0 out of 5 stars</td>\n",
       "      <td>Harrasment</td>\n",
       "      <td>This i'dis the 3rd request in 20 minutes no no...</td>\n",
       "      <td>walt21st</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>4 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>on 31 January 2018</td>\n",
       "      <td>1.0 out of 5 stars</td>\n",
       "      <td>wifi chip is broken in most of these.</td>\n",
       "      <td>awful phone as nevers get on my selected wifi ...</td>\n",
       "      <td>Antonio</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>8 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>on 15 November 2017</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>hey i would like an iphon my insa is lough</td>\n",
       "      <td>irma</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>on 20 February 2018</td>\n",
       "      <td>1.0 out of 5 stars</td>\n",
       "      <td>One Star</td>\n",
       "      <td>Bad phone</td>\n",
       "      <td>ZK</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>7 people found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>on 9 October 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>good seller！</td>\n",
       "      <td>fast dispatch，nice product，everything is good</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>on 13 October 2018</td>\n",
       "      <td>1.0 out of 5 stars</td>\n",
       "      <td>did not arrive</td>\n",
       "      <td>scam</td>\n",
       "      <td>Rachel</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>on 10 October 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>Superior.</td>\n",
       "      <td>Very good phone. Can't fault it.</td>\n",
       "      <td>Dr. Fade Ibitoye</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>One person found this helpful</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date         star_rating  \\\n",
       "0          on 19 May 2018  5.0 out of 5 stars   \n",
       "1     on 21 February 2018  5.0 out of 5 stars   \n",
       "2     on 24 February 2018  5.0 out of 5 stars   \n",
       "3          on 1 June 2018  5.0 out of 5 stars   \n",
       "4         on 10 July 2018  5.0 out of 5 stars   \n",
       "5       on 12 August 2018  5.0 out of 5 stars   \n",
       "6    on 18 September 2018  5.0 out of 5 stars   \n",
       "7         on 30 July 2018  5.0 out of 5 stars   \n",
       "8        on 28 April 2018  5.0 out of 5 stars   \n",
       "9         on 26 June 2018  5.0 out of 5 stars   \n",
       "10   on 27 September 2018  5.0 out of 5 stars   \n",
       "11       on 30 March 2018  5.0 out of 5 stars   \n",
       "12       on 10 March 2018  5.0 out of 5 stars   \n",
       "13        on 2 April 2018  5.0 out of 5 stars   \n",
       "14        on 26 July 2018  5.0 out of 5 stars   \n",
       "15      on 6 October 2018  5.0 out of 5 stars   \n",
       "16        on 17 June 2018  5.0 out of 5 stars   \n",
       "17        on 21 June 2018  5.0 out of 5 stars   \n",
       "18         on 28 May 2018  5.0 out of 5 stars   \n",
       "19       on 3 August 2018  5.0 out of 5 stars   \n",
       "20        on 29 June 2018  5.0 out of 5 stars   \n",
       "21         on 9 June 2018  5.0 out of 5 stars   \n",
       "22      on 6 October 2018  5.0 out of 5 stars   \n",
       "23         on 4 July 2018  5.0 out of 5 stars   \n",
       "24        on 30 July 2018  5.0 out of 5 stars   \n",
       "25        on 30 July 2018  5.0 out of 5 stars   \n",
       "26      on 5 October 2018  5.0 out of 5 stars   \n",
       "27          on 2 May 2018  5.0 out of 5 stars   \n",
       "28        on 11 July 2018  2.0 out of 5 stars   \n",
       "29        on 14 June 2018  5.0 out of 5 stars   \n",
       "..                    ...                 ...   \n",
       "156     on 3 January 2018  5.0 out of 5 stars   \n",
       "157   on 22 November 2017  5.0 out of 5 stars   \n",
       "158   on 23 February 2018  5.0 out of 5 stars   \n",
       "159    on 16 January 2018  4.0 out of 5 stars   \n",
       "160      on 14 April 2018  4.0 out of 5 stars   \n",
       "161    on 24 January 2018  5.0 out of 5 stars   \n",
       "162       on 4 April 2018  5.0 out of 5 stars   \n",
       "163   on 10 December 2017  5.0 out of 5 stars   \n",
       "164  on 14 September 2018  5.0 out of 5 stars   \n",
       "165    on 9 December 2017  5.0 out of 5 stars   \n",
       "166    on 22 January 2018  5.0 out of 5 stars   \n",
       "167        on 26 May 2018  5.0 out of 5 stars   \n",
       "168   on 21 November 2017  5.0 out of 5 stars   \n",
       "169   on 14 December 2017  4.0 out of 5 stars   \n",
       "170    on 29 January 2018  5.0 out of 5 stars   \n",
       "171   on 31 December 2017  5.0 out of 5 stars   \n",
       "172    on 20 January 2018  3.0 out of 5 stars   \n",
       "173      on 16 March 2018  4.0 out of 5 stars   \n",
       "174       on 18 June 2018  3.0 out of 5 stars   \n",
       "175   on 24 December 2017  5.0 out of 5 stars   \n",
       "176   on 26 February 2018  5.0 out of 5 stars   \n",
       "177        on 8 July 2018  1.0 out of 5 stars   \n",
       "178   on 28 November 2017  3.0 out of 5 stars   \n",
       "179    on 2 December 2017  3.0 out of 5 stars   \n",
       "180    on 31 January 2018  1.0 out of 5 stars   \n",
       "181   on 15 November 2017  5.0 out of 5 stars   \n",
       "182   on 20 February 2018  1.0 out of 5 stars   \n",
       "183     on 9 October 2018  5.0 out of 5 stars   \n",
       "184    on 13 October 2018  1.0 out of 5 stars   \n",
       "185    on 10 October 2018  5.0 out of 5 stars   \n",
       "\n",
       "                                   user_review_headers  \\\n",
       "0    delivered in time and actually Apple so very h...   \n",
       "1                                           Five Stars   \n",
       "2                             Great brand new iPhone !   \n",
       "3                                     Genuine iPhone x   \n",
       "4                    iPhoneX, at good price - love it.   \n",
       "5                                         Great iPhone   \n",
       "6                       Great phone at a decent price.   \n",
       "7                                      Excellent phone   \n",
       "8                                           Five Stars   \n",
       "9                           Excellent customer service   \n",
       "10                    It’s a legit unopened I Phone X.   \n",
       "11                                          Five Stars   \n",
       "12                                         Great phone   \n",
       "13                                          Five Stars   \n",
       "14                                             Amazing   \n",
       "15                           Excellent preowned mobile   \n",
       "16                       Not much to say, it’s Apple..   \n",
       "17                                          Five Stars   \n",
       "18                                          Five Stars   \n",
       "19                          Love iPhone, bit too pricy   \n",
       "20                                          Five Stars   \n",
       "21                                          Five Stars   \n",
       "22                                         Great phone   \n",
       "23                                          Five Stars   \n",
       "24                                            All good   \n",
       "25                                                Good   \n",
       "26                                   Loved the product   \n",
       "27                                          Five Stars   \n",
       "28                                        Over heating   \n",
       "29   ... used to using the phone but so far very pl...   \n",
       "..                                                 ...   \n",
       "156  Pros screen quality volume camera (look up the...   \n",
       "157  ... delivery phone new and as it should be ver...   \n",
       "158                          Great service and product   \n",
       "159                              Amazing phone, but...   \n",
       "160                                           iPhone 8   \n",
       "161                          What are you waiting for?   \n",
       "162                      battery life is not so well 👎   \n",
       "163  SUPERB PHONE.......NO LONGER JUST A PHONE BUT ...   \n",
       "164                                         Five Stars   \n",
       "165        Quick delivery. The produt is as described.   \n",
       "166                                           Excelent   \n",
       "167                                            Testing   \n",
       "168  It's an early Christmas present to replace a s...   \n",
       "169  The best iphone out there, but is it worth the...   \n",
       "170                                  Sleeker than ever   \n",
       "171                                         Five Stars   \n",
       "172                                       Disappointed   \n",
       "173                                         Four Stars   \n",
       "174         Lovely looking phone that burns its screen   \n",
       "175                                      Recommendable   \n",
       "176                             Great expensive phone!   \n",
       "177                           SCREEN DAMGED AND CAMERA   \n",
       "178  It's a quality product, but I wouldn't recomme...   \n",
       "179                                         Harrasment   \n",
       "180              wifi chip is broken in most of these.   \n",
       "181                                         Five Stars   \n",
       "182                                           One Star   \n",
       "183                                       good seller！   \n",
       "184                                     did not arrive   \n",
       "185                                          Superior.   \n",
       "\n",
       "                                     user_review_texts            user_name  \\\n",
       "0    I was initially a bit wary of buying it throug...       Cliente Amazon   \n",
       "1    Cheaper than that available from apple - quick...         mr n kitchen   \n",
       "2    Really good iPhone ! Arrived way earlier than ...       Karima Mohamed   \n",
       "3    I received a genuine iPhone X, I was initially...              Ritlady   \n",
       "4            It's an iPhoneX, at good price - love it.          Helicotrema   \n",
       "5    Received my new iPhone 8 Plus yesterday, and I...             T. Gauld   \n",
       "6    Purchased for my daughter and she absolutely l...                Wasim   \n",
       "7    Great phone as predicted. Simple upgrade from ...         Rob Sherwood   \n",
       "8    absolute bargain for the iphone 10  also arriv...                 Matt   \n",
       "9    supplier is amazing. Received this phone befor...               Rustam   \n",
       "10   When I ordered this I was a bit skeptical due ...               George   \n",
       "11                                        Hope with it      Timothy Wootton   \n",
       "12                     Great product and fast shipping            Karist Ng   \n",
       "13                                        lovely phone                Buyer   \n",
       "14   Wonderful product and service, I highly recomm...       Cliente Amazon   \n",
       "15   Excellent preowned mobile will recommend to al...                  EDG   \n",
       "16                                Latest and greatest.                   Mo   \n",
       "17                                        Good product      Amazon Customer   \n",
       "18                                           very good             Brian D.   \n",
       "19   Great iPhone, not learned all features yet, sh...      Amazon Customer   \n",
       "20   Great phone and cheapest reliable place to buy...  Kit Langran-Wheeler   \n",
       "21                              Very pleased... thanks               George   \n",
       "22   Came pretty quickly and in good condition. Did...                Kelly   \n",
       "23                        Very happy with the product.                David   \n",
       "24                    Everything working as I expected               Dhruba   \n",
       "25   It was a good buy as it still had warranty and...      Amazon Customer   \n",
       "26   The phone was in very good condition. I really...      Amazon Customer   \n",
       "27                                            all good                 mike   \n",
       "28   The phone is overheating after a little use. N...                 Muj    \n",
       "29   Just getting used to using the phone but so fa...            M.C. Boik   \n",
       "..                                                 ...                  ...   \n",
       "156  I upgraded from the IPhone 6S to the 8 plus.Pr...      Amazon Customer   \n",
       "157  fast delivery phone new and as it should be ve...           ron jagger   \n",
       "158                                        Brilliant!!                 Mike   \n",
       "159  As much as I love the phone, the reason I drop...                 Luke   \n",
       "160                                               Good      Amazon Customer   \n",
       "161  Its just like your old iPhone but faster and i...       Mountain Nolan   \n",
       "162                                         intems ok!     Ghinda Alexandru   \n",
       "163  Having used this phone now for almost 2 months...         Mr. M. Platt   \n",
       "164                                          Brilliant             Janet B.   \n",
       "165       Quick delivery.  The produt is as described.                Diana   \n",
       "166                               i love my new iphone        Mr Kaka Neery   \n",
       "167  Hi there,Would u allow me to test an iPhone X ...                Ebony   \n",
       "168  Bought this for my daughter and she absolutely...      Amazon Customer   \n",
       "169  Screen -This 1125 x 2436 pixel screen is very ...                  PTZ   \n",
       "170  If you have owned I iPhone before you know exa...              Andy Mc   \n",
       "171                                          Excellent      Amazon Customer   \n",
       "172  The previous Griffin cases I had were a rubber...               Nutter   \n",
       "173                                     Its an iPhone.         MR A J OLVER   \n",
       "174  Good phone that Apple needed to release! It's ...              Ryan UK   \n",
       "175  About the phone, I would say that changing fro...          Ana Jimenez   \n",
       "176  You think the price is high but what you get i...             Romeo P.   \n",
       "177  I got and it was rubbish the screen was broke ...      Amazon Customer   \n",
       "178  Here's a few of my feelings after using this p...     Mystified Mister   \n",
       "179  This i'dis the 3rd request in 20 minutes no no...             walt21st   \n",
       "180  awful phone as nevers get on my selected wifi ...              Antonio   \n",
       "181         hey i would like an iphon my insa is lough                 irma   \n",
       "182                                          Bad phone                   ZK   \n",
       "183      fast dispatch，nice product，everything is good      Amazon Customer   \n",
       "184                                               scam               Rachel   \n",
       "185                   Very good phone. Can't fault it.     Dr. Fade Ibitoye   \n",
       "\n",
       "                 user_type                        helpful  \n",
       "0        Verified Purchase   30 people found this helpful  \n",
       "1        Verified Purchase   15 people found this helpful  \n",
       "2        Verified Purchase   13 people found this helpful  \n",
       "3        Verified Purchase    7 people found this helpful  \n",
       "4        Verified Purchase    4 people found this helpful  \n",
       "5        Verified Purchase  One person found this helpful  \n",
       "6        Verified Purchase  One person found this helpful  \n",
       "7        Verified Purchase    2 people found this helpful  \n",
       "8        Verified Purchase    2 people found this helpful  \n",
       "9        Verified Purchase  One person found this helpful  \n",
       "10       Verified Purchase    2 people found this helpful  \n",
       "11       Verified Purchase    3 people found this helpful  \n",
       "12       Verified Purchase    3 people found this helpful  \n",
       "13       Verified Purchase  One person found this helpful  \n",
       "14       Verified Purchase  One person found this helpful  \n",
       "15       Verified Purchase  One person found this helpful  \n",
       "16       Verified Purchase  One person found this helpful  \n",
       "17       Verified Purchase  One person found this helpful  \n",
       "18       Verified Purchase  One person found this helpful  \n",
       "19       Verified Purchase  One person found this helpful  \n",
       "20       Verified Purchase  One person found this helpful  \n",
       "21       Verified Purchase  One person found this helpful  \n",
       "22       Verified Purchase  One person found this helpful  \n",
       "23       Verified Purchase  One person found this helpful  \n",
       "24       Verified Purchase  One person found this helpful  \n",
       "25       Verified Purchase  One person found this helpful  \n",
       "26       Verified Purchase  One person found this helpful  \n",
       "27       Verified Purchase  One person found this helpful  \n",
       "28       Verified Purchase  One person found this helpful  \n",
       "29       Verified Purchase  One person found this helpful  \n",
       "..                     ...                            ...  \n",
       "156      Verified Purchase    9 people found this helpful  \n",
       "157      Verified Purchase   10 people found this helpful  \n",
       "158      Verified Purchase  One person found this helpful  \n",
       "159      Verified Purchase   14 people found this helpful  \n",
       "160      Verified Purchase  One person found this helpful  \n",
       "161      Verified Purchase    8 people found this helpful  \n",
       "162      Verified Purchase  One person found this helpful  \n",
       "163      Verified Purchase   12 people found this helpful  \n",
       "164  Not Verified Purchase  One person found this helpful  \n",
       "165      Verified Purchase    3 people found this helpful  \n",
       "166      Verified Purchase    3 people found this helpful  \n",
       "167  Not Verified Purchase  One person found this helpful  \n",
       "168      Verified Purchase    8 people found this helpful  \n",
       "169  Not Verified Purchase   12 people found this helpful  \n",
       "170  Not Verified Purchase    2 people found this helpful  \n",
       "171      Verified Purchase  One person found this helpful  \n",
       "172      Verified Purchase    6 people found this helpful  \n",
       "173      Verified Purchase    3 people found this helpful  \n",
       "174  Not Verified Purchase    2 people found this helpful  \n",
       "175  Not Verified Purchase   10 people found this helpful  \n",
       "176  Not Verified Purchase  One person found this helpful  \n",
       "177  Not Verified Purchase    2 people found this helpful  \n",
       "178  Not Verified Purchase   16 people found this helpful  \n",
       "179      Verified Purchase    4 people found this helpful  \n",
       "180  Not Verified Purchase    8 people found this helpful  \n",
       "181  Not Verified Purchase  One person found this helpful  \n",
       "182  Not Verified Purchase    7 people found this helpful  \n",
       "183      Verified Purchase  One person found this helpful  \n",
       "184      Verified Purchase  One person found this helpful  \n",
       "185      Verified Purchase  One person found this helpful  \n",
       "\n",
       "[186 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_users = pd.read_csv('user_values_uk.csv', header = None, names = ['user_name1', 'fake_review_index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk = pd.concat([uk_reviews, uk_users], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk.drop('user_name1', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk.drop(uk.index[169], inplace = True)\n",
    "uk.drop(uk.index[156], inplace = True)\n",
    "\n",
    "uk['date'] = uk['date'].apply(lambda x: x.strip('on '))\n",
    "uk['date'] = pd.to_datetime(uk['date'])\n",
    "uk['star_rating'] = uk['star_rating'].apply(lambda x: int(x[0]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk['helpful'] = uk['helpful'].apply(lambda x: re.sub('\\n', '', x))\n",
    "uk['helpful'] = uk['helpful'].apply(lambda x: re.sub('One', '1', x))\n",
    "uk['helpful'] = uk['helpful'].apply(lambda x: re.sub('[a-zA-Z]', '', x))\n",
    "uk['helpful'] = uk['helpful'].apply(lambda x: float(x))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk.index = uk.date\n",
    "uk = uk.drop('date', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk = uk.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>user_review_headers</th>\n",
       "      <th>user_review_texts</th>\n",
       "      <th>user_name</th>\n",
       "      <th>user_type</th>\n",
       "      <th>helpful</th>\n",
       "      <th>fake_review_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-08</th>\n",
       "      <td>4</td>\n",
       "      <td>In-depth review of iPhone 8</td>\n",
       "      <td>Another year has passed and a new iPhone (well...</td>\n",
       "      <td>Kyriakos Zaritis</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>5</td>\n",
       "      <td>Amazing!</td>\n",
       "      <td>• BLACKS: blacks look properly black. Unlike L...</td>\n",
       "      <td>Tactless (no tact)</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-15</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>hey i would like an iphon my insa is lough</td>\n",
       "      <td>irma</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-17</th>\n",
       "      <td>5</td>\n",
       "      <td>WebOS reborn - the most beautiful, elegant, in...</td>\n",
       "      <td>This is WebOS reborn and good riddance Home Bu...</td>\n",
       "      <td>Ibraar</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-19</th>\n",
       "      <td>4</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>Good but crashing within 2 days after installi...</td>\n",
       "      <td>M. K. Sharma</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21</th>\n",
       "      <td>5</td>\n",
       "      <td>It's an early Christmas present to replace a s...</td>\n",
       "      <td>Bought this for my daughter and she absolutely...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22</th>\n",
       "      <td>5</td>\n",
       "      <td>... delivery phone new and as it should be ver...</td>\n",
       "      <td>fast delivery phone new and as it should be ve...</td>\n",
       "      <td>ron jagger</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>10.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28</th>\n",
       "      <td>3</td>\n",
       "      <td>It's a quality product, but I wouldn't recomme...</td>\n",
       "      <td>Here's a few of my feelings after using this p...</td>\n",
       "      <td>Mystified Mister</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>3</td>\n",
       "      <td>Harrasment</td>\n",
       "      <td>This i'dis the 3rd request in 20 minutes no no...</td>\n",
       "      <td>walt21st</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-04</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>One of the best purchases I've made in my life...</td>\n",
       "      <td>HD Distribution Ltd.</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-06</th>\n",
       "      <td>5</td>\n",
       "      <td>Fast shipment All good Would buy front the seller</td>\n",
       "      <td>Fast shipmentAll goodWould buy front the selle...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-09</th>\n",
       "      <td>5</td>\n",
       "      <td>Quick delivery. The produt is as described.</td>\n",
       "      <td>Quick delivery.  The produt is as described.</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>5</td>\n",
       "      <td>SUPERB PHONE.......NO LONGER JUST A PHONE BUT ...</td>\n",
       "      <td>Having used this phone now for almost 2 months...</td>\n",
       "      <td>Mr. M. Platt</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>5</td>\n",
       "      <td>Recommendable</td>\n",
       "      <td>About the phone, I would say that changing fro...</td>\n",
       "      <td>Ana Jimenez</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>10.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Very happy thank you</td>\n",
       "      <td>Gary M.</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Great product</td>\n",
       "      <td>Emily A Jazayeri</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>This phone is exactly what I want in a mobile ...</td>\n",
       "      <td>Daphne J. Tisbury</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>5</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Fast service. Excellent product</td>\n",
       "      <td>Professor Ernest Choy</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>5</td>\n",
       "      <td>5* seller</td>\n",
       "      <td>Quality product, iPhones are overpriced for wh...</td>\n",
       "      <td>William Jenkinson</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>4</td>\n",
       "      <td>Amazing phone, but...</td>\n",
       "      <td>As much as I love the phone, the reason I drop...</td>\n",
       "      <td>Luke</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Susan Buckley</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>6.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>5</td>\n",
       "      <td>Very impressed</td>\n",
       "      <td>Very impressed with the screen size and its cl...</td>\n",
       "      <td>Jollybusman</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>20.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-20</th>\n",
       "      <td>3</td>\n",
       "      <td>Disappointed</td>\n",
       "      <td>The previous Griffin cases I had were a rubber...</td>\n",
       "      <td>Nutter</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-20</th>\n",
       "      <td>1</td>\n",
       "      <td>The fone never worked properly from the start,...</td>\n",
       "      <td>The fone never worked properly from the start,...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-20</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Everything was as expected</td>\n",
       "      <td>potari</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-21</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Excellent phone!</td>\n",
       "      <td>Kamilla Andressa Almeida Maia</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>3</td>\n",
       "      <td>Good but no headphone jack??? What were you th...</td>\n",
       "      <td>Just upgraded from my iPhone 6 and to be hones...</td>\n",
       "      <td>Neil D''Monte</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>5</td>\n",
       "      <td>Excelent</td>\n",
       "      <td>i love my new iphone</td>\n",
       "      <td>Mr Kaka Neery</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>5</td>\n",
       "      <td>What are you waiting for?</td>\n",
       "      <td>Its just like your old iPhone but faster and i...</td>\n",
       "      <td>Mountain Nolan</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>8.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-05</th>\n",
       "      <td>5</td>\n",
       "      <td>iPhone X</td>\n",
       "      <td>Good.</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-12</th>\n",
       "      <td>5</td>\n",
       "      <td>Great iPhone</td>\n",
       "      <td>Received my new iPhone 8 Plus yesterday, and I...</td>\n",
       "      <td>T. Gauld</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-15</th>\n",
       "      <td>5</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Brand new phone with all the accessories must buy</td>\n",
       "      <td>mc japheth calumia</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-15</th>\n",
       "      <td>5</td>\n",
       "      <td>Perfect gadget</td>\n",
       "      <td>Met my expectations</td>\n",
       "      <td>Vonai</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-15</th>\n",
       "      <td>1</td>\n",
       "      <td>Don't own the phone but I troubleshoot tech fo...</td>\n",
       "      <td>So I have only used the phone around work and ...</td>\n",
       "      <td>Dan</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-18</th>\n",
       "      <td>5</td>\n",
       "      <td>Brilliant!</td>\n",
       "      <td>I love my new iPhone x</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-23</th>\n",
       "      <td>5</td>\n",
       "      <td>Great deal, but be sure to check from where it...</td>\n",
       "      <td>IMPORTANT! SELLER WAS: KOOKcommunicationSeller...</td>\n",
       "      <td>thibo hernot</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-24</th>\n",
       "      <td>1</td>\n",
       "      <td>Not unlocked</td>\n",
       "      <td>This was supposed to be an unlimited cord phon...</td>\n",
       "      <td>Maria57</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26</th>\n",
       "      <td>5</td>\n",
       "      <td>Great customer service</td>\n",
       "      <td>I’m very pleased with my phone. I have the ros...</td>\n",
       "      <td>Elena</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-29</th>\n",
       "      <td>5</td>\n",
       "      <td>👍</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>binu varughese</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-30</th>\n",
       "      <td>4</td>\n",
       "      <td>Great alternative</td>\n",
       "      <td>Beautifully made, quality screen. I replaced t...</td>\n",
       "      <td>Vdubmikey</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-02</th>\n",
       "      <td>5</td>\n",
       "      <td>An honest review.</td>\n",
       "      <td>The most powerful phone I’ve ever had. I was l...</td>\n",
       "      <td>Andreas Patsalos</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-07</th>\n",
       "      <td>3</td>\n",
       "      <td>Wrong colour delivered</td>\n",
       "      <td>The add said iPhone 8 Gold, I opened the box a...</td>\n",
       "      <td>Colin John Coates</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-09</th>\n",
       "      <td>5</td>\n",
       "      <td>Very good phone</td>\n",
       "      <td>Really good iPhone, swapped from a 6s so it’s ...</td>\n",
       "      <td>MrsJHS</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-14</th>\n",
       "      <td>5</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Brilliant</td>\n",
       "      <td>Janet B.</td>\n",
       "      <td>Not Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-15</th>\n",
       "      <td>5</td>\n",
       "      <td>Great</td>\n",
       "      <td>Great phone no complaints</td>\n",
       "      <td>Ciarán McGee</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-18</th>\n",
       "      <td>5</td>\n",
       "      <td>Great phone at a decent price.</td>\n",
       "      <td>Purchased for my daughter and she absolutely l...</td>\n",
       "      <td>Wasim</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-19</th>\n",
       "      <td>5</td>\n",
       "      <td>Great screen clarity and quick processor</td>\n",
       "      <td>Why pay Apple direct price when u get same pho...</td>\n",
       "      <td>D G SMITH</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-20</th>\n",
       "      <td>5</td>\n",
       "      <td>I phone 8 plus</td>\n",
       "      <td>Loving my new I phone 8 plus!</td>\n",
       "      <td>HELEN O'NEILL</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-26</th>\n",
       "      <td>5</td>\n",
       "      <td>The screen is amazing</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Shaun Campbell</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-27</th>\n",
       "      <td>5</td>\n",
       "      <td>It’s a legit unopened I Phone X.</td>\n",
       "      <td>When I ordered this I was a bit skeptical due ...</td>\n",
       "      <td>George</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>5</td>\n",
       "      <td>Good service</td>\n",
       "      <td>Decent. No scratches or anything. Just like ne...</td>\n",
       "      <td>B</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-05</th>\n",
       "      <td>5</td>\n",
       "      <td>Loved the product</td>\n",
       "      <td>The phone was in very good condition. I really...</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-06</th>\n",
       "      <td>5</td>\n",
       "      <td>Excellent preowned mobile</td>\n",
       "      <td>Excellent preowned mobile will recommend to al...</td>\n",
       "      <td>EDG</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-06</th>\n",
       "      <td>5</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Came pretty quickly and in good condition. Did...</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-07</th>\n",
       "      <td>5</td>\n",
       "      <td>Great phone</td>\n",
       "      <td>Quick delivery and great phone.</td>\n",
       "      <td>Mrs MM</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-09</th>\n",
       "      <td>5</td>\n",
       "      <td>good seller！</td>\n",
       "      <td>fast dispatch，nice product，everything is good</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-10</th>\n",
       "      <td>5</td>\n",
       "      <td>Reliable company</td>\n",
       "      <td>Good service great phone</td>\n",
       "      <td>Phoebe</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-10</th>\n",
       "      <td>5</td>\n",
       "      <td>Superior.</td>\n",
       "      <td>Very good phone. Can't fault it.</td>\n",
       "      <td>Dr. Fade Ibitoye</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-13</th>\n",
       "      <td>1</td>\n",
       "      <td>did not arrive</td>\n",
       "      <td>scam</td>\n",
       "      <td>Rachel</td>\n",
       "      <td>Verified Purchase</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            star_rating                                user_review_headers  \\\n",
       "date                                                                         \n",
       "2017-11-08            4                        In-depth review of iPhone 8   \n",
       "2017-11-12            5                                           Amazing!   \n",
       "2017-11-15            5                                         Five Stars   \n",
       "2017-11-17            5  WebOS reborn - the most beautiful, elegant, in...   \n",
       "2017-11-19            4                                         Four Stars   \n",
       "2017-11-21            5  It's an early Christmas present to replace a s...   \n",
       "2017-11-22            5  ... delivery phone new and as it should be ver...   \n",
       "2017-11-28            3  It's a quality product, but I wouldn't recomme...   \n",
       "2017-12-02            3                                         Harrasment   \n",
       "2017-12-04            5                                         Five Stars   \n",
       "2017-12-06            5  Fast shipment All good Would buy front the seller   \n",
       "2017-12-09            5        Quick delivery. The produt is as described.   \n",
       "2017-12-10            5  SUPERB PHONE.......NO LONGER JUST A PHONE BUT ...   \n",
       "2017-12-24            5                                      Recommendable   \n",
       "2017-12-27            5                                         Five Stars   \n",
       "2017-12-31            5                                         Five Stars   \n",
       "2018-01-01            5                                         Five Stars   \n",
       "2018-01-06            5                                         Five Stars   \n",
       "2018-01-10            5                                          Excellent   \n",
       "2018-01-11            5                                          5* seller   \n",
       "2018-01-16            4                              Amazing phone, but...   \n",
       "2018-01-16            5                                         Five Stars   \n",
       "2018-01-19            5                                     Very impressed   \n",
       "2018-01-20            3                                       Disappointed   \n",
       "2018-01-20            1  The fone never worked properly from the start,...   \n",
       "2018-01-20            5                                         Five Stars   \n",
       "2018-01-21            5                                         Five Stars   \n",
       "2018-01-22            3  Good but no headphone jack??? What were you th...   \n",
       "2018-01-22            5                                           Excelent   \n",
       "2018-01-24            5                          What are you waiting for?   \n",
       "...                 ...                                                ...   \n",
       "2018-08-05            5                                           iPhone X   \n",
       "2018-08-12            5                                       Great iPhone   \n",
       "2018-08-15            5                                        Great phone   \n",
       "2018-08-15            5                                     Perfect gadget   \n",
       "2018-08-15            1  Don't own the phone but I troubleshoot tech fo...   \n",
       "2018-08-18            5                                         Brilliant!   \n",
       "2018-08-23            5  Great deal, but be sure to check from where it...   \n",
       "2018-08-24            1                                       Not unlocked   \n",
       "2018-08-26            5                             Great customer service   \n",
       "2018-08-29            5                                                  👍   \n",
       "2018-08-30            4                                  Great alternative   \n",
       "2018-09-02            5                                  An honest review.   \n",
       "2018-09-07            3                             Wrong colour delivered   \n",
       "2018-09-09            5                                    Very good phone   \n",
       "2018-09-14            5                                         Five Stars   \n",
       "2018-09-15            5                                              Great   \n",
       "2018-09-18            5                     Great phone at a decent price.   \n",
       "2018-09-19            5           Great screen clarity and quick processor   \n",
       "2018-09-20            5                                     I phone 8 plus   \n",
       "2018-09-26            5                              The screen is amazing   \n",
       "2018-09-27            5                   It’s a legit unopened I Phone X.   \n",
       "2018-10-01            5                                       Good service   \n",
       "2018-10-05            5                                  Loved the product   \n",
       "2018-10-06            5                          Excellent preowned mobile   \n",
       "2018-10-06            5                                        Great phone   \n",
       "2018-10-07            5                                        Great phone   \n",
       "2018-10-09            5                                       good seller！   \n",
       "2018-10-10            5                                   Reliable company   \n",
       "2018-10-10            5                                          Superior.   \n",
       "2018-10-13            1                                     did not arrive   \n",
       "\n",
       "                                            user_review_texts  \\\n",
       "date                                                            \n",
       "2017-11-08  Another year has passed and a new iPhone (well...   \n",
       "2017-11-12  • BLACKS: blacks look properly black. Unlike L...   \n",
       "2017-11-15         hey i would like an iphon my insa is lough   \n",
       "2017-11-17  This is WebOS reborn and good riddance Home Bu...   \n",
       "2017-11-19  Good but crashing within 2 days after installi...   \n",
       "2017-11-21  Bought this for my daughter and she absolutely...   \n",
       "2017-11-22  fast delivery phone new and as it should be ve...   \n",
       "2017-11-28  Here's a few of my feelings after using this p...   \n",
       "2017-12-02  This i'dis the 3rd request in 20 minutes no no...   \n",
       "2017-12-04  One of the best purchases I've made in my life...   \n",
       "2017-12-06  Fast shipmentAll goodWould buy front the selle...   \n",
       "2017-12-09       Quick delivery.  The produt is as described.   \n",
       "2017-12-10  Having used this phone now for almost 2 months...   \n",
       "2017-12-24  About the phone, I would say that changing fro...   \n",
       "2017-12-27                               Very happy thank you   \n",
       "2017-12-31                                          Excellent   \n",
       "2018-01-01                                      Great product   \n",
       "2018-01-06  This phone is exactly what I want in a mobile ...   \n",
       "2018-01-10                    Fast service. Excellent product   \n",
       "2018-01-11  Quality product, iPhones are overpriced for wh...   \n",
       "2018-01-16  As much as I love the phone, the reason I drop...   \n",
       "2018-01-16                                          Excellent   \n",
       "2018-01-19  Very impressed with the screen size and its cl...   \n",
       "2018-01-20  The previous Griffin cases I had were a rubber...   \n",
       "2018-01-20  The fone never worked properly from the start,...   \n",
       "2018-01-20                         Everything was as expected   \n",
       "2018-01-21                                   Excellent phone!   \n",
       "2018-01-22  Just upgraded from my iPhone 6 and to be hones...   \n",
       "2018-01-22                               i love my new iphone   \n",
       "2018-01-24  Its just like your old iPhone but faster and i...   \n",
       "...                                                       ...   \n",
       "2018-08-05                                              Good.   \n",
       "2018-08-12  Received my new iPhone 8 Plus yesterday, and I...   \n",
       "2018-08-15  Brand new phone with all the accessories must buy   \n",
       "2018-08-15                                Met my expectations   \n",
       "2018-08-15  So I have only used the phone around work and ...   \n",
       "2018-08-18                             I love my new iPhone x   \n",
       "2018-08-23  IMPORTANT! SELLER WAS: KOOKcommunicationSeller...   \n",
       "2018-08-24  This was supposed to be an unlimited cord phon...   \n",
       "2018-08-26  I’m very pleased with my phone. I have the ros...   \n",
       "2018-08-29                                        Great phone   \n",
       "2018-08-30  Beautifully made, quality screen. I replaced t...   \n",
       "2018-09-02  The most powerful phone I’ve ever had. I was l...   \n",
       "2018-09-07  The add said iPhone 8 Gold, I opened the box a...   \n",
       "2018-09-09  Really good iPhone, swapped from a 6s so it’s ...   \n",
       "2018-09-14                                          Brilliant   \n",
       "2018-09-15                          Great phone no complaints   \n",
       "2018-09-18  Purchased for my daughter and she absolutely l...   \n",
       "2018-09-19  Why pay Apple direct price when u get same pho...   \n",
       "2018-09-20                      Loving my new I phone 8 plus!   \n",
       "2018-09-26                                        Great phone   \n",
       "2018-09-27  When I ordered this I was a bit skeptical due ...   \n",
       "2018-10-01  Decent. No scratches or anything. Just like ne...   \n",
       "2018-10-05  The phone was in very good condition. I really...   \n",
       "2018-10-06  Excellent preowned mobile will recommend to al...   \n",
       "2018-10-06  Came pretty quickly and in good condition. Did...   \n",
       "2018-10-07                    Quick delivery and great phone.   \n",
       "2018-10-09      fast dispatch，nice product，everything is good   \n",
       "2018-10-10                           Good service great phone   \n",
       "2018-10-10                   Very good phone. Can't fault it.   \n",
       "2018-10-13                                               scam   \n",
       "\n",
       "                                user_name              user_type  helpful  \\\n",
       "date                                                                        \n",
       "2017-11-08               Kyriakos Zaritis  Not Verified Purchase     33.0   \n",
       "2017-11-12             Tactless (no tact)  Not Verified Purchase     21.0   \n",
       "2017-11-15                           irma  Not Verified Purchase      1.0   \n",
       "2017-11-17                         Ibraar  Not Verified Purchase      4.0   \n",
       "2017-11-19                   M. K. Sharma      Verified Purchase      1.0   \n",
       "2017-11-21                Amazon Customer      Verified Purchase      8.0   \n",
       "2017-11-22                     ron jagger      Verified Purchase     10.0   \n",
       "2017-11-28               Mystified Mister  Not Verified Purchase     16.0   \n",
       "2017-12-02                       walt21st      Verified Purchase      4.0   \n",
       "2017-12-04           HD Distribution Ltd.      Verified Purchase      6.0   \n",
       "2017-12-06                Amazon Customer      Verified Purchase      8.0   \n",
       "2017-12-09                          Diana      Verified Purchase      3.0   \n",
       "2017-12-10                   Mr. M. Platt      Verified Purchase     12.0   \n",
       "2017-12-24                    Ana Jimenez  Not Verified Purchase     10.0   \n",
       "2017-12-27                        Gary M.      Verified Purchase      6.0   \n",
       "2017-12-31                Amazon Customer      Verified Purchase      1.0   \n",
       "2018-01-01               Emily A Jazayeri      Verified Purchase      1.0   \n",
       "2018-01-06              Daphne J. Tisbury      Verified Purchase      1.0   \n",
       "2018-01-10          Professor Ernest Choy      Verified Purchase      1.0   \n",
       "2018-01-11              William Jenkinson      Verified Purchase      1.0   \n",
       "2018-01-16                           Luke      Verified Purchase     14.0   \n",
       "2018-01-16                  Susan Buckley      Verified Purchase      6.0   \n",
       "2018-01-19                    Jollybusman      Verified Purchase     20.0   \n",
       "2018-01-20                         Nutter      Verified Purchase      6.0   \n",
       "2018-01-20                Amazon Customer      Verified Purchase     10.0   \n",
       "2018-01-20                         potari      Verified Purchase      3.0   \n",
       "2018-01-21  Kamilla Andressa Almeida Maia      Verified Purchase      8.0   \n",
       "2018-01-22                  Neil D''Monte  Not Verified Purchase      3.0   \n",
       "2018-01-22                  Mr Kaka Neery      Verified Purchase      3.0   \n",
       "2018-01-24                 Mountain Nolan      Verified Purchase      8.0   \n",
       "...                                   ...                    ...      ...   \n",
       "2018-08-05                Amazon Customer      Verified Purchase      1.0   \n",
       "2018-08-12                       T. Gauld      Verified Purchase      1.0   \n",
       "2018-08-15             mc japheth calumia      Verified Purchase      1.0   \n",
       "2018-08-15                          Vonai      Verified Purchase      1.0   \n",
       "2018-08-15                            Dan  Not Verified Purchase      2.0   \n",
       "2018-08-18                Amazon Customer      Verified Purchase      1.0   \n",
       "2018-08-23                   thibo hernot      Verified Purchase      9.0   \n",
       "2018-08-24                        Maria57      Verified Purchase      1.0   \n",
       "2018-08-26                          Elena      Verified Purchase      2.0   \n",
       "2018-08-29                 binu varughese      Verified Purchase      1.0   \n",
       "2018-08-30                      Vdubmikey  Not Verified Purchase      1.0   \n",
       "2018-09-02               Andreas Patsalos  Not Verified Purchase      1.0   \n",
       "2018-09-07              Colin John Coates      Verified Purchase      1.0   \n",
       "2018-09-09                         MrsJHS      Verified Purchase      1.0   \n",
       "2018-09-14                       Janet B.  Not Verified Purchase      1.0   \n",
       "2018-09-15                   Ciarán McGee      Verified Purchase      1.0   \n",
       "2018-09-18                          Wasim      Verified Purchase      1.0   \n",
       "2018-09-19                      D G SMITH      Verified Purchase      1.0   \n",
       "2018-09-20                  HELEN O'NEILL      Verified Purchase      1.0   \n",
       "2018-09-26                 Shaun Campbell      Verified Purchase      1.0   \n",
       "2018-09-27                         George      Verified Purchase      2.0   \n",
       "2018-10-01                              B      Verified Purchase      1.0   \n",
       "2018-10-05                Amazon Customer      Verified Purchase      1.0   \n",
       "2018-10-06                            EDG      Verified Purchase      1.0   \n",
       "2018-10-06                          Kelly      Verified Purchase      1.0   \n",
       "2018-10-07                         Mrs MM      Verified Purchase      1.0   \n",
       "2018-10-09                Amazon Customer      Verified Purchase      1.0   \n",
       "2018-10-10                         Phoebe      Verified Purchase      1.0   \n",
       "2018-10-10               Dr. Fade Ibitoye      Verified Purchase      1.0   \n",
       "2018-10-13                         Rachel      Verified Purchase      1.0   \n",
       "\n",
       "            fake_review_index  \n",
       "date                           \n",
       "2017-11-08               31.0  \n",
       "2017-11-12               33.0  \n",
       "2017-11-15               30.5  \n",
       "2017-11-17               28.0  \n",
       "2017-11-19               40.5  \n",
       "2017-11-21               32.0  \n",
       "2017-11-22               27.0  \n",
       "2017-11-28               33.0  \n",
       "2017-12-02               33.0  \n",
       "2017-12-04               62.0  \n",
       "2017-12-06               13.0  \n",
       "2017-12-09               33.0  \n",
       "2017-12-10               40.0  \n",
       "2017-12-24               47.0  \n",
       "2017-12-27               13.0  \n",
       "2017-12-31               23.0  \n",
       "2018-01-01               36.5  \n",
       "2018-01-06               23.0  \n",
       "2018-01-10               13.0  \n",
       "2018-01-11               30.5  \n",
       "2018-01-16               40.5  \n",
       "2018-01-16               31.0  \n",
       "2018-01-19               41.0  \n",
       "2018-01-20               23.0  \n",
       "2018-01-20               35.0  \n",
       "2018-01-20               28.0  \n",
       "2018-01-21               27.0  \n",
       "2018-01-22               13.0  \n",
       "2018-01-22               37.0  \n",
       "2018-01-24               40.5  \n",
       "...                       ...  \n",
       "2018-08-05               30.5  \n",
       "2018-08-12               31.0  \n",
       "2018-08-15               41.0  \n",
       "2018-08-15               40.5  \n",
       "2018-08-15               23.0  \n",
       "2018-08-18               41.0  \n",
       "2018-08-23               27.0  \n",
       "2018-08-24               13.0  \n",
       "2018-08-26               37.0  \n",
       "2018-08-29               23.0  \n",
       "2018-08-30               13.0  \n",
       "2018-09-02               13.0  \n",
       "2018-09-07               37.0  \n",
       "2018-09-09               54.5  \n",
       "2018-09-14               33.0  \n",
       "2018-09-15               41.0  \n",
       "2018-09-18               36.5  \n",
       "2018-09-19               42.0  \n",
       "2018-09-20               51.0  \n",
       "2018-09-26               41.0  \n",
       "2018-09-27               41.5  \n",
       "2018-10-01               54.5  \n",
       "2018-10-05               65.5  \n",
       "2018-10-06               58.0  \n",
       "2018-10-06               40.5  \n",
       "2018-10-07               40.5  \n",
       "2018-10-09               30.5  \n",
       "2018-10-10               41.0  \n",
       "2018-10-10               30.5  \n",
       "2018-10-13               30.5  \n",
       "\n",
       "[184 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in total: 4.43956043956044\n",
      "number of reviews in total: 182\n",
      "number of verified reviews in total: 146\n",
      "average rating of verified reviews in total: 4.609589041095891\n",
      "number of helpful votes for verified reviews in total: 549\n",
      "number of not verified reviews in total: 36\n",
      "average rating of not verified reviews in total: 3.75\n",
      "number of helpful votes for not verified reviews in total: 231\n"
     ]
    }
   ],
   "source": [
    "total_rating = uk['star_rating'].mean()\n",
    "total_count = uk['star_rating'].count()\n",
    "total_verified = np.sum(uk['user_type'] == 'Verified Purchase')\n",
    "total_verified_rating = uk[uk['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "total_verified_helpful = uk[uk['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "total_not_verified = np.sum(uk['user_type'] == 'Not Verified Purchase')\n",
    "total_not_verified_rating = uk[uk['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "total_not_verified_helpful = uk[uk['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in total: '+ str(total_rating))\n",
    "print('number of reviews in total: '+ str(total_count))\n",
    "print('number of verified reviews in total: '+ str(total_verified))\n",
    "print('average rating of verified reviews in total: '+ str(total_verified_rating))\n",
    "print('number of helpful votes for verified reviews in total: '+ str(total_verified_helpful))\n",
    "print('number of not verified reviews in total: '+ str(total_not_verified))\n",
    "print('average rating of not verified reviews in total: '+ str(total_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in total: '+ str(total_not_verified_helpful))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in november: 4.5\n",
      "number of reviews in november: 8\n",
      "number of verified reviews in november: 3\n",
      "average rating of verified reviews in november : 4.666666666666667\n",
      "number of helpful votes for verified reviews in november: 19\n",
      "number of not verified reviews in november: 5\n",
      "average rating of not verified reviews in november: 4.4\n",
      "number of helpful votes for not verified reviews in november: 75\n"
     ]
    }
   ],
   "source": [
    "nov17 = uk['2017-11']\n",
    "nov17_rating = nov17['star_rating'].mean()\n",
    "nov17_count = nov17['star_rating'].count()\n",
    "nov17_verified = np.sum(nov17['user_type'] == 'Verified Purchase')\n",
    "nov17_verified_rating = nov17[nov17['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "nov17_verified_helpful = nov17[nov17['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "nov17_not_verified = np.sum(nov17['user_type'] == 'Not Verified Purchase')\n",
    "nov17_not_verified_rating = nov17[nov17['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "nov17_not_verified_helpful = nov17[nov17['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in november: '+ str(nov17_rating))\n",
    "print('number of reviews in november: '+ str(nov17_count))\n",
    "print('number of verified reviews in november: '+ str(nov17_verified))\n",
    "print('average rating of verified reviews in november : '+ str(nov17_verified_rating))\n",
    "print('number of helpful votes for verified reviews in november: '+ str(nov17_verified_helpful))\n",
    "print('number of not verified reviews in november: '+ str(nov17_not_verified))\n",
    "print('average rating of not verified reviews in november: '+ str(nov17_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in november: '+ str(nov17_not_verified_helpful))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in december: 4.666666666666667\n",
      "number of reviews in december: 9\n",
      "number of verified reviews in december: 7\n",
      "average rating of verified reviews in december : 4.714285714285714\n",
      "number of helpful votes for verified reviews in december: 40\n",
      "number of not verified reviews in december: 2\n",
      "average rating of not verified reviews in december: 4.5\n",
      "number of helpful votes for not verified reviews in december: 22\n"
     ]
    }
   ],
   "source": [
    "dec17 = uk['2017-12']\n",
    "dec17_rating = dec17['star_rating'].mean()\n",
    "dec17_count = dec17['star_rating'].count()\n",
    "dec17_verified = np.sum(dec17['user_type'] == 'Verified Purchase')\n",
    "dec17_verified_rating = dec17[dec17['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "dec17_verified_helpful = dec17[dec17['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "dec17_not_verified = np.sum(dec17['user_type'] == 'Not Verified Purchase')\n",
    "dec17_not_verified_rating = dec17[dec17['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "dec17_not_verified_helpful = dec17[dec17['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in december: '+ str(dec17_rating))\n",
    "print('number of reviews in december: '+ str(dec17_count))\n",
    "print('number of verified reviews in december: '+ str(dec17_verified))\n",
    "print('average rating of verified reviews in december : '+ str(dec17_verified_rating))\n",
    "print('number of helpful votes for verified reviews in december: '+ str(dec17_verified_helpful))\n",
    "print('number of not verified reviews in december: '+ str(dec17_not_verified))\n",
    "print('average rating of not verified reviews in december: '+ str(dec17_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in december: '+ str(dec17_not_verified_helpful))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in january: 4.2105263157894735\n",
      "number of reviews in january: 19\n",
      "number of verified reviews in january: 15\n",
      "average rating of verified reviews in january : 4.533333333333333\n",
      "number of helpful votes for verified reviews in january: 92\n",
      "number of not verified reviews in january: 4\n",
      "average rating of not verified reviews in january: 3.0\n",
      "number of helpful votes for not verified reviews in january: 69\n"
     ]
    }
   ],
   "source": [
    "jan18 = uk['2018-01']\n",
    "jan18_rating = jan18['star_rating'].mean()\n",
    "jan18_count = jan18['star_rating'].count()\n",
    "jan18_verified = np.sum(jan18['user_type'] == 'Verified Purchase')\n",
    "jan18_verified_rating = jan18[jan18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "jan18_verified_helpful = jan18[jan18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "jan18_not_verified = np.sum(jan18['user_type'] == 'Not Verified Purchase')\n",
    "jan18_not_verified_rating = jan18[jan18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "jan18_not_verified_helpful = jan18[jan18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in january: '+ str(jan18_rating))\n",
    "print('number of reviews in january: '+ str(jan18_count))\n",
    "print('number of verified reviews in january: '+ str(jan18_verified))\n",
    "print('average rating of verified reviews in january : '+ str(jan18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in january: '+ str(jan18_verified_helpful))\n",
    "print('number of not verified reviews in january: '+ str(jan18_not_verified))\n",
    "print('average rating of not verified reviews in january: '+ str(jan18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in january: '+ str(jan18_not_verified_helpful))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in february: 4.294117647058823\n",
      "number of reviews in february: 17\n",
      "number of verified reviews in february: 13\n",
      "average rating of verified reviews in february : 4.6923076923076925\n",
      "number of helpful votes for verified reviews in february: 63\n",
      "number of not verified reviews in february: 4\n",
      "average rating of not verified reviews in february: 3.0\n",
      "number of helpful votes for not verified reviews in february: 21\n"
     ]
    }
   ],
   "source": [
    "feb18 = uk['2018-02']\n",
    "feb18_rating = feb18['star_rating'].mean()\n",
    "feb18_count = feb18['star_rating'].count()\n",
    "feb18_verified = np.sum(feb18['user_type'] == 'Verified Purchase')\n",
    "feb18_verified_rating = feb18[feb18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "feb18_verified_helpful = feb18[feb18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "feb18_not_verified = np.sum(feb18['user_type'] == 'Not Verified Purchase')\n",
    "feb18_not_verified_rating = feb18[feb18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "feb18_not_verified_helpful = feb18[feb18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in february: '+ str(feb18_rating))\n",
    "print('number of reviews in february: '+ str(feb18_count))\n",
    "print('number of verified reviews in february: '+ str(feb18_verified))\n",
    "print('average rating of verified reviews in february : '+ str(feb18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in february: '+ str(feb18_verified_helpful))\n",
    "print('number of not verified reviews in february: '+ str(feb18_not_verified))\n",
    "print('average rating of not verified reviews in february: '+ str(feb18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in february: '+ str(feb18_not_verified_helpful))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in march: 4.708333333333333\n",
      "number of reviews in march: 24\n",
      "number of verified reviews in march: 22\n",
      "average rating of verified reviews in march : 4.681818181818182\n",
      "number of helpful votes for verified reviews in march: 123\n",
      "number of not verified reviews in march: 2\n",
      "average rating of not verified reviews in march: 5.0\n",
      "number of helpful votes for not verified reviews in march: 5\n"
     ]
    }
   ],
   "source": [
    "march18 = uk['2018-03']\n",
    "march18_rating = march18['star_rating'].mean()\n",
    "march18_count = march18['star_rating'].count()\n",
    "march18_verified = np.sum(march18['user_type'] == 'Verified Purchase')\n",
    "march18_verified_rating = march18[march18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "march18_verified_helpful = march18[march18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "march18_not_verified = np.sum(march18['user_type'] == 'Not Verified Purchase')\n",
    "march18_not_verified_rating = march18[march18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "march18_not_verified_helpful = march18[march18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in march: '+ str(march18_rating))\n",
    "print('number of reviews in march: '+ str(march18_count))\n",
    "print('number of verified reviews in march: '+ str(march18_verified))\n",
    "print('average rating of verified reviews in march : '+ str(march18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in march: '+ str(march18_verified_helpful))\n",
    "print('number of not verified reviews in march: '+ str(march18_not_verified))\n",
    "print('average rating of not verified reviews in march: '+ str(march18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in march: '+ str(march18_not_verified_helpful))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in april: 4.625\n",
      "number of reviews in april: 24\n",
      "number of verified reviews in april: 21\n",
      "average rating of verified reviews in april : 4.619047619047619\n",
      "number of helpful votes for verified reviews in april: 50\n",
      "number of not verified reviews in april: 3\n",
      "average rating of not verified reviews in april: 4.666666666666667\n",
      "number of helpful votes for not verified reviews in april: 4\n"
     ]
    }
   ],
   "source": [
    "apr18 = uk['2018-04']\n",
    "apr18_rating = apr18['star_rating'].mean()\n",
    "apr18_count = apr18['star_rating'].count()\n",
    "apr18_verified = np.sum(apr18['user_type'] == 'Verified Purchase')\n",
    "apr18_verified_rating = apr18[apr18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "apr18_verified_helpful = apr18[apr18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "apr18_not_verified = np.sum(apr18['user_type'] == 'Not Verified Purchase')\n",
    "apr18_not_verified_rating = apr18[apr18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "apr18_not_verified_helpful = apr18[apr18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in april: '+ str(apr18_rating))\n",
    "print('number of reviews in april: '+ str(apr18_count))\n",
    "print('number of verified reviews in april: '+ str(apr18_verified))\n",
    "print('average rating of verified reviews in april : '+ str(apr18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in april: '+ str(apr18_verified_helpful))\n",
    "print('number of not verified reviews in april: '+ str(apr18_not_verified))\n",
    "print('average rating of not verified reviews in april: '+ str(apr18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in april: '+ str(apr18_not_verified_helpful))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in may: 4.083333333333333\n",
      "number of reviews in may: 12\n",
      "number of verified reviews in may: 8\n",
      "average rating of verified reviews in may : 4.625\n",
      "number of helpful votes for verified reviews in may: 58\n",
      "number of not verified reviews in may: 4\n",
      "average rating of not verified reviews in may: 3.0\n",
      "number of helpful votes for not verified reviews in may: 16\n"
     ]
    }
   ],
   "source": [
    "may18 = uk['2018-05']\n",
    "may18_rating = may18['star_rating'].mean()\n",
    "may18_count = may18['star_rating'].count()\n",
    "may18_verified = np.sum(may18['user_type'] == 'Verified Purchase')\n",
    "may18_verified_rating = may18[may18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "may18_verified_helpful = may18[may18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "may18_not_verified = np.sum(may18['user_type'] == 'Not Verified Purchase')\n",
    "may18_not_verified_rating = may18[may18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "may18_not_verified_helpful = may18[may18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in may: '+ str(may18_rating))\n",
    "print('number of reviews in may: '+ str(may18_count))\n",
    "print('number of verified reviews in may: '+ str(may18_verified))\n",
    "print('average rating of verified reviews in may : '+ str(may18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in may: '+ str(may18_verified_helpful))\n",
    "print('number of not verified reviews in may: '+ str(may18_not_verified))\n",
    "print('average rating of not verified reviews in may: '+ str(may18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in may: '+ str(may18_not_verified_helpful))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in june: 4.375\n",
      "number of reviews in june: 16\n",
      "number of verified reviews in june: 12\n",
      "average rating of verified reviews in june : 4.666666666666667\n",
      "number of helpful votes for verified reviews in june: 32\n",
      "number of not verified reviews in june: 4\n",
      "average rating of not verified reviews in june: 3.5\n",
      "number of helpful votes for not verified reviews in june: 8\n"
     ]
    }
   ],
   "source": [
    "june18 = uk['2018-06']\n",
    "june18_rating = june18['star_rating'].mean()\n",
    "june18_count = june18['star_rating'].count()\n",
    "june18_verified = np.sum(june18['user_type'] == 'Verified Purchase')\n",
    "june18_verified_rating = june18[june18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "june18_verified_helpful = june18[june18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "june18_not_verified = np.sum(june18['user_type'] == 'Not Verified Purchase')\n",
    "june18_not_verified_rating = june18[june18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "june18_not_verified_helpful = june18[june18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in june: '+ str(june18_rating))\n",
    "print('number of reviews in june: '+ str(june18_count))\n",
    "print('number of verified reviews in june: '+ str(june18_verified))\n",
    "print('average rating of verified reviews in june : '+ str(june18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in june: '+ str(june18_verified_helpful))\n",
    "print('number of not verified reviews in june: '+ str(june18_not_verified))\n",
    "print('average rating of not verified reviews in june: '+ str(june18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in june: '+ str(june18_not_verified_helpful))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in july: 4.190476190476191\n",
      "number of reviews in july: 21\n",
      "number of verified reviews in july: 19\n",
      "average rating of verified reviews in july : 4.315789473684211\n",
      "number of helpful votes for verified reviews in july: 36\n",
      "number of not verified reviews in july: 2\n",
      "average rating of not verified reviews in july: 3.0\n",
      "number of helpful votes for not verified reviews in july: 3\n"
     ]
    }
   ],
   "source": [
    "july18 = uk['2018-07']\n",
    "july18_rating = july18['star_rating'].mean()\n",
    "july18_count = july18['star_rating'].count()\n",
    "july18_verified = np.sum(july18['user_type'] == 'Verified Purchase')\n",
    "july18_verified_rating = july18[july18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "july18_verified_helpful = july18[july18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "july18_not_verified = np.sum(july18['user_type'] == 'Not Verified Purchase')\n",
    "july18_not_verified_rating = july18[july18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "july18_not_verified_helpful = july18[july18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in july: '+ str(july18_rating))\n",
    "print('number of reviews in july: '+ str(july18_count))\n",
    "print('number of verified reviews in july: '+ str(july18_verified))\n",
    "print('average rating of verified reviews in july : '+ str(july18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in july: '+ str(july18_verified_helpful))\n",
    "print('number of not verified reviews in july: '+ str(july18_not_verified))\n",
    "print('average rating of not verified reviews in july: '+ str(july18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in july: '+ str(july18_not_verified_helpful))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in august: 4.294117647058823\n",
      "number of reviews in august: 17\n",
      "number of verified reviews in august: 13\n",
      "average rating of verified reviews in august : 4.538461538461538\n",
      "number of helpful votes for verified reviews in august: 22\n",
      "number of not verified reviews in august: 4\n",
      "average rating of not verified reviews in august: 3.5\n",
      "number of helpful votes for not verified reviews in v: 6\n"
     ]
    }
   ],
   "source": [
    "august18 = uk['2018-08']\n",
    "august18_rating = august18['star_rating'].mean()\n",
    "august18_count = august18['star_rating'].count()\n",
    "august18_verified = np.sum(august18['user_type'] == 'Verified Purchase')\n",
    "august18_verified_rating = august18[august18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "august18_verified_helpful = august18[august18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "august18_not_verified = np.sum(august18['user_type'] == 'Not Verified Purchase')\n",
    "august18_not_verified_rating = august18[august18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "august18_not_verified_helpful = august18[august18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in august: '+ str(august18_rating))\n",
    "print('number of reviews in august: '+ str(august18_count))\n",
    "print('number of verified reviews in august: '+ str(august18_verified))\n",
    "print('average rating of verified reviews in august : '+ str(august18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in august: '+ str(august18_verified_helpful))\n",
    "print('number of not verified reviews in august: '+ str(august18_not_verified))\n",
    "print('average rating of not verified reviews in august: '+ str(august18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in v: '+ str(august18_not_verified_helpful))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average rating in september: 4.8\n",
      "number of reviews in september: 10\n",
      "number of verified reviews in september: 8\n",
      "average rating of verified reviews in september : 4.75\n",
      "number of helpful votes for verified reviews in september: 9\n",
      "number of not verified reviews in september: 2\n",
      "average rating of not verified reviews in september: 5.0\n",
      "number of helpful votes for not verified reviews in september: 2\n"
     ]
    }
   ],
   "source": [
    "sep18 = uk['2018-09']\n",
    "sep18_rating = sep18['star_rating'].mean()\n",
    "sep18_count = sep18['star_rating'].count()\n",
    "sep18_verified = np.sum(sep18['user_type'] == 'Verified Purchase')\n",
    "sep18_verified_rating = sep18[sep18['user_type'] == 'Verified Purchase']['star_rating'].mean()\n",
    "sep18_verified_helpful = sep18[sep18['user_type'] == 'Verified Purchase']['helpful'].sum()\n",
    "sep18_not_verified = np.sum(sep18['user_type'] == 'Not Verified Purchase')\n",
    "sep18_not_verified_rating = sep18[sep18['user_type'] == 'Not Verified Purchase']['star_rating'].mean()\n",
    "sep18_not_verified_helpful = sep18[sep18['user_type'] == 'Not Verified Purchase']['helpful'].sum()\n",
    "\n",
    "print('average rating in september: '+ str(sep18_rating))\n",
    "print('number of reviews in september: '+ str(sep18_count))\n",
    "print('number of verified reviews in september: '+ str(sep18_verified))\n",
    "print('average rating of verified reviews in september : '+ str(sep18_verified_rating))\n",
    "print('number of helpful votes for verified reviews in september: '+ str(sep18_verified_helpful))\n",
    "print('number of not verified reviews in september: '+ str(sep18_not_verified))\n",
    "print('average rating of not verified reviews in september: '+ str(sep18_not_verified_rating))\n",
    "print('number of helpful votes for not verified reviews in september: '+ str(sep18_not_verified_helpful))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_review = pd.DataFrame([[nov17_rating, nov17_count, nov17_verified, nov17_not_verified, nov17_verified_rating, nov17_not_verified_rating, nov17_verified_helpful, nov17_not_verified_helpful],\n",
    "                          [dec17_rating, dec17_count, dec17_verified, dec17_not_verified, dec17_verified_rating, dec17_not_verified_rating, dec17_verified_helpful, dec17_not_verified_helpful],\n",
    "                          [jan18_rating, jan18_count, jan18_verified, jan18_not_verified, jan18_verified_rating, jan18_not_verified_rating, jan18_verified_helpful, jan18_not_verified_helpful],\n",
    "                          [feb18_rating, feb18_count, feb18_verified, feb18_not_verified, feb18_verified_rating, feb18_not_verified_rating, feb18_verified_helpful, feb18_not_verified_helpful],\n",
    "                          [march18_rating, march18_count, march18_verified, march18_not_verified, march18_verified_rating, march18_not_verified_rating, march18_verified_helpful, march18_not_verified_helpful],\n",
    "                          [apr18_rating, apr18_count, apr18_verified, apr18_not_verified, apr18_verified_rating, apr18_not_verified_rating, apr18_verified_helpful, apr18_not_verified_helpful],\n",
    "                          [may18_rating, may18_count, may18_verified, may18_not_verified, may18_verified_rating, may18_not_verified_rating, may18_verified_helpful, may18_not_verified_helpful],\n",
    "                          [june18_rating, june18_count, june18_verified, june18_not_verified, june18_verified_rating, june18_not_verified_rating, june18_verified_helpful, june18_not_verified_helpful],\n",
    "                          [july18_rating, july18_count, july18_verified, july18_not_verified, july18_verified_rating, july18_not_verified_rating, july18_verified_helpful, july18_not_verified_helpful],\n",
    "                          [august18_rating, august18_count, august18_verified, august18_not_verified, august18_verified_rating, august18_not_verified_rating, august18_verified_helpful, august18_not_verified_helpful],\n",
    "                          [sep18_rating, sep18_count, sep18_verified, sep18_not_verified, sep18_verified_rating, sep18_not_verified_rating, sep18_verified_helpful, sep18_not_verified_helpful]],\n",
    "                         columns = ['Average Rating', 'Number of Reviews', 'Number of Verified Purchase Reviews', 'Number of NOT Verified Purchase Reviews', 'Average Rating of Verified Purchase Reviews', 'Average Rating of NOT Verified Purchase Reviews', 'Number of Helpful Votes Verified Reviews', 'Number of Helpful Votes NOT Verified Reviews'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Rating</th>\n",
       "      <th>Number of Reviews</th>\n",
       "      <th>Number of Verified Purchase Reviews</th>\n",
       "      <th>Number of NOT Verified Purchase Reviews</th>\n",
       "      <th>Average Rating of Verified Purchase Reviews</th>\n",
       "      <th>Average Rating of NOT Verified Purchase Reviews</th>\n",
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       "      <td>3.000000</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>63</td>\n",
       "      <td>21</td>\n",
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       "      <td>4.708333</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>4.681818</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>123</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.625000</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>4.619048</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.083333</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4.625000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>58</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.375000</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.190476</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>4.315789</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.294118</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4.538462</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9</td>\n",
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      "text/plain": [
       "    Average Rating  Number of Reviews  Number of Verified Purchase Reviews  \\\n",
       "0         4.500000                  8                                    3   \n",
       "1         4.666667                  9                                    7   \n",
       "2         4.210526                 19                                   15   \n",
       "3         4.294118                 17                                   13   \n",
       "4         4.708333                 24                                   22   \n",
       "5         4.625000                 24                                   21   \n",
       "6         4.083333                 12                                    8   \n",
       "7         4.375000                 16                                   12   \n",
       "8         4.190476                 21                                   19   \n",
       "9         4.294118                 17                                   13   \n",
       "10        4.800000                 10                                    8   \n",
       "\n",
       "    Number of NOT Verified Purchase Reviews  \\\n",
       "0                                         5   \n",
       "1                                         2   \n",
       "2                                         4   \n",
       "3                                         4   \n",
       "4                                         2   \n",
       "5                                         3   \n",
       "6                                         4   \n",
       "7                                         4   \n",
       "8                                         2   \n",
       "9                                         4   \n",
       "10                                        2   \n",
       "\n",
       "    Average Rating of Verified Purchase Reviews  \\\n",
       "0                                      4.666667   \n",
       "1                                      4.714286   \n",
       "2                                      4.533333   \n",
       "3                                      4.692308   \n",
       "4                                      4.681818   \n",
       "5                                      4.619048   \n",
       "6                                      4.625000   \n",
       "7                                      4.666667   \n",
       "8                                      4.315789   \n",
       "9                                      4.538462   \n",
       "10                                     4.750000   \n",
       "\n",
       "    Average Rating of NOT Verified Purchase Reviews  \\\n",
       "0                                          4.400000   \n",
       "1                                          4.500000   \n",
       "2                                          3.000000   \n",
       "3                                          3.000000   \n",
       "4                                          5.000000   \n",
       "5                                          4.666667   \n",
       "6                                          3.000000   \n",
       "7                                          3.500000   \n",
       "8                                          3.000000   \n",
       "9                                          3.500000   \n",
       "10                                         5.000000   \n",
       "\n",
       "    Number of Helpful Votes Verified Reviews  \\\n",
       "0                                         19   \n",
       "1                                         40   \n",
       "2                                         92   \n",
       "3                                         63   \n",
       "4                                        123   \n",
       "5                                         50   \n",
       "6                                         58   \n",
       "7                                         32   \n",
       "8                                         36   \n",
       "9                                         22   \n",
       "10                                         9   \n",
       "\n",
       "    Number of Helpful Votes NOT Verified Reviews  \n",
       "0                                             75  \n",
       "1                                             22  \n",
       "2                                             69  \n",
       "3                                             21  \n",
       "4                                              5  \n",
       "5                                              4  \n",
       "6                                             16  \n",
       "7                                              8  \n",
       "8                                              3  \n",
       "9                                              6  \n",
       "10                                             2  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_review.index = index = ['November 2017', 'December 2017', 'January 2018', 'February 2018', 'March 2018', 'April 2018', 'May 2018', 'June 2018', 'July 2018', 'August 2018', 'September 2018']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Rating</th>\n",
       "      <th>Number of Reviews</th>\n",
       "      <th>Number of Verified Purchase Reviews</th>\n",
       "      <th>Number of NOT Verified Purchase Reviews</th>\n",
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       "      <th>Number of Helpful Votes NOT Verified Reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>November 2017</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>19</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December 2017</th>\n",
       "      <td>4.666667</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>January 2018</th>\n",
       "      <td>4.210526</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4.533333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>92</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February 2018</th>\n",
       "      <td>4.294118</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4.692308</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>63</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March 2018</th>\n",
       "      <td>4.708333</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>4.681818</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>123</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April 2018</th>\n",
       "      <td>4.625000</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>4.619048</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May 2018</th>\n",
       "      <td>4.083333</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4.625000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>58</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June 2018</th>\n",
       "      <td>4.375000</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July 2018</th>\n",
       "      <td>4.190476</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>4.315789</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August 2018</th>\n",
       "      <td>4.294118</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4.538462</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September 2018</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Average Rating  Number of Reviews  \\\n",
       "November 2017         4.500000                  8   \n",
       "December 2017         4.666667                  9   \n",
       "January 2018          4.210526                 19   \n",
       "February 2018         4.294118                 17   \n",
       "March 2018            4.708333                 24   \n",
       "April 2018            4.625000                 24   \n",
       "May 2018              4.083333                 12   \n",
       "June 2018             4.375000                 16   \n",
       "July 2018             4.190476                 21   \n",
       "August 2018           4.294118                 17   \n",
       "September 2018        4.800000                 10   \n",
       "\n",
       "                Number of Verified Purchase Reviews  \\\n",
       "November 2017                                     3   \n",
       "December 2017                                     7   \n",
       "January 2018                                     15   \n",
       "February 2018                                    13   \n",
       "March 2018                                       22   \n",
       "April 2018                                       21   \n",
       "May 2018                                          8   \n",
       "June 2018                                        12   \n",
       "July 2018                                        19   \n",
       "August 2018                                      13   \n",
       "September 2018                                    8   \n",
       "\n",
       "                Number of NOT Verified Purchase Reviews  \\\n",
       "November 2017                                         5   \n",
       "December 2017                                         2   \n",
       "January 2018                                          4   \n",
       "February 2018                                         4   \n",
       "March 2018                                            2   \n",
       "April 2018                                            3   \n",
       "May 2018                                              4   \n",
       "June 2018                                             4   \n",
       "July 2018                                             2   \n",
       "August 2018                                           4   \n",
       "September 2018                                        2   \n",
       "\n",
       "                Average Rating of Verified Purchase Reviews  \\\n",
       "November 2017                                      4.666667   \n",
       "December 2017                                      4.714286   \n",
       "January 2018                                       4.533333   \n",
       "February 2018                                      4.692308   \n",
       "March 2018                                         4.681818   \n",
       "April 2018                                         4.619048   \n",
       "May 2018                                           4.625000   \n",
       "June 2018                                          4.666667   \n",
       "July 2018                                          4.315789   \n",
       "August 2018                                        4.538462   \n",
       "September 2018                                     4.750000   \n",
       "\n",
       "                Average Rating of NOT Verified Purchase Reviews  \\\n",
       "November 2017                                          4.400000   \n",
       "December 2017                                          4.500000   \n",
       "January 2018                                           3.000000   \n",
       "February 2018                                          3.000000   \n",
       "March 2018                                             5.000000   \n",
       "April 2018                                             4.666667   \n",
       "May 2018                                               3.000000   \n",
       "June 2018                                              3.500000   \n",
       "July 2018                                              3.000000   \n",
       "August 2018                                            3.500000   \n",
       "September 2018                                         5.000000   \n",
       "\n",
       "                Number of Helpful Votes Verified Reviews  \\\n",
       "November 2017                                         19   \n",
       "December 2017                                         40   \n",
       "January 2018                                          92   \n",
       "February 2018                                         63   \n",
       "March 2018                                           123   \n",
       "April 2018                                            50   \n",
       "May 2018                                              58   \n",
       "June 2018                                             32   \n",
       "July 2018                                             36   \n",
       "August 2018                                           22   \n",
       "September 2018                                         9   \n",
       "\n",
       "                Number of Helpful Votes NOT Verified Reviews  \n",
       "November 2017                                             75  \n",
       "December 2017                                             22  \n",
       "January 2018                                              69  \n",
       "February 2018                                             21  \n",
       "March 2018                                                 5  \n",
       "April 2018                                                 4  \n",
       "May 2018                                                  16  \n",
       "June 2018                                                  8  \n",
       "July 2018                                                  3  \n",
       "August 2018                                                6  \n",
       "September 2018                                             2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Reviews per Month')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,6))\n",
    "plt.plot(uk_review.index, uk_review['Number of Reviews'])\n",
    "\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.xlabel('Month')\n",
    "plt.title('Reviews per Month', fontsize = 20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Verified vs. Non-Verified Reviews')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,6))\n",
    "\n",
    "ver = plt.plot(uk_review.index, uk_review['Number of Verified Purchase Reviews'], label = 'Verified')\n",
    "not_ver = plt.plot(uk_review.index, uk_review['Number of NOT Verified Purchase Reviews'], label = 'Not Verified')\n",
    "plt.legend(prop={'size': 20})\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.xlabel('Month')\n",
    "plt.title('Verified vs. Non-Verified Reviews', fontsize = 20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,6))\n",
    "\n",
    "ver = plt.plot(uk_review.index, uk_review['Number of Verified Purchase Reviews'], label = 'Verified')\n",
    "not_ver = plt.plot(uk_review.index, uk_review['Number of NOT Verified Purchase Reviews'], label = 'Not Verified')\n",
    "plt.legend(prop={'size': 20})\n",
    "plt.ylabel('Number of Reviews')\n",
    "plt.xlabel('Month')\n",
    "plt.title('Verified vs. Non-Verified Reviews', fontsize = 20)\n",
    "\n",
    "\n",
    "hey = uk.groupby('user_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Boxplot Average Rating Verified vs. Not Verified')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1 = uk_review.iloc[[1 ,2 ,3 ,4 ,5,6 ,7, 8, 9], 4]\n",
    "plot_2 = uk_review.iloc[:, 5]\n",
    "\n",
    "\n",
    "\n",
    "f, axes = plt.subplots(1, 2, sharey=True)\n",
    "\n",
    "\n",
    "\n",
    "sns.boxplot(y=plot_1, ax=axes[0], showfliers=False).set(\n",
    "                                    xlabel='Average Rating Verified', \n",
    "                                    ylabel='Rating'\n",
    "                                    )\n",
    "\n",
    "sns.boxplot(y=plot_2, ax=axes[1], showfliers=False).set(\n",
    "                                    xlabel='Average Rating Not Verified', \n",
    "                                    ylabel= ''\n",
    "                                    )\n",
    "\n",
    "f.suptitle('Boxplot Average Rating Verified vs. Not Verified')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Density Curve Nov 17 - Sep 18')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uk_sorted = uk.sort_index()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "y = uk_sorted['star_rating'].plot.kde()\n",
    "plt.xlabel('Star rating')\n",
    "plt.title('Density Curve Nov 17 - Sep 18')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Density Curve Nov 17')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uk_early= uk['2017-11']\n",
    "uk_early['star_rating'].plot.kde()\n",
    "plt.xlabel('Star rating')\n",
    "plt.title('Density Curve Nov 17')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "verified = uk_early[uk_early['user_type']=='Verified Purchase']\n",
    "\n",
    "v_1 = verified[verified['star_rating'] == 1]['star_rating'].count()\n",
    "v_2 = verified[verified['star_rating'] == 2]['star_rating'].count()\n",
    "v_3 = verified[verified['star_rating'] == 3]['star_rating'].count()\n",
    "v_4 = verified[verified['star_rating'] == 4]['star_rating'].count()\n",
    "v_5 = verified[verified['star_rating'] == 5]['star_rating'].count()\n",
    "\n",
    "v_sum = pd.Series([v_1, v_2, v_3, v_4, v_5], index = ['1 Star','2 Stars','3 Stars','4 Stars','5 Stars'])\n",
    "v_ = v_sum.to_frame()\n",
    "v_ = v_.reset_index()\n",
    "v_.columns = ['Rating', 'Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Count Verified Ratings November')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Rating\", y=\"Count\", data=v_).set_title('Count Verified Ratings November')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_verified = uk_early[uk_early['user_type']=='Not Verified Purchase']\n",
    "\n",
    "nv_1 = not_verified[not_verified['star_rating'] == 1]['star_rating'].count()\n",
    "nv_2 = not_verified[not_verified['star_rating'] == 2]['star_rating'].count()\n",
    "nv_3 = not_verified[not_verified['star_rating'] == 3]['star_rating'].count()\n",
    "nv_4 = not_verified[not_verified['star_rating'] == 4]['star_rating'].count()\n",
    "nv_5 = not_verified[not_verified['star_rating'] == 5]['star_rating'].count()\n",
    "\n",
    "nv_sum = pd.Series([nv_1, nv_2, nv_3, nv_4, nv_5], index = ['1 Star','2 Stars','3 Stars','4 Stars','5 Stars'])\n",
    "nv_ = nv_sum.to_frame()\n",
    "nv_ = nv_.reset_index()\n",
    "nv_.columns = ['Rating', 'Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Count Not Verified Ratings November')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Rating\", y=\"Count\", data=nv_).set_title('Count Not Verified Ratings November')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ver_interaction_index = uk_review['Number of Helpful Votes Verified Reviews']/uk_review['Number of Verified Purchase Reviews']\n",
    "\n",
    "\n",
    "nver_interaction_index = uk_review['Number of Helpful Votes NOT Verified Reviews']/uk_review['Number of NOT Verified Purchase Reviews']\n",
    "\n",
    "ver_interaction_index = ver_interaction_index.to_frame().interpolate(method=\"linear\")\n",
    "nver_interaction_index = nver_interaction_index.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Interaction Index: Upvotes divided by Number of Reviews')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,6))\n",
    "\n",
    "ver = plt.plot(ver_interaction_index, label = 'Verified')\n",
    "not_ver = plt.plot(nver_interaction_index, label = 'Not Verified')\n",
    "plt.legend(prop={'size': 20})\n",
    "plt.ylabel('Interaction Index ')\n",
    "plt.xlabel('Month')\n",
    "plt.title('Interaction Index: Upvotes divided by Number of Reviews', fontsize = 20)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path\n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Good but crashing within 2 days after installing sim...don’t know what to do? Bought this for my daughter and she absolutely loves it! It's an early Christmas present to replace a smashed iPhone 6 and the iPhone 8 has some useful new features. The gold colour looks pretty good too! fast delivery phone new and as it should be very happy thanks This i'dis the 3rd request in 20 minutes no no more harrasment One of the best purchases I've made in my life. So happy I did not go for the X Fast shipmentAll goodWould buy front the seller again Quick delivery.  The produt is as described. Having used this phone now for almost 2 months I can say it's superb.  The only downside appears to be the speed at which the battery loses its power.  Also if purchasing the excellent Caseology iphone case, be sure not to accidentally push down the mute button on the side.  Basically, this is no longer a phone, it is a miniature computer/ipad.  The 'Touch ID' facility is amazing and allows me to access my Nat West account within seconds and move money around.  It is without doubt an amazing piece of kit.  Buy the Caseology case for it to show it off at its best. Very happy thank you Excellent\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_mask = np.array(Image.open(\"apple_logo.png\"))\n",
    "ver_reviewtext = uk.loc[uk['user_type'] == 'Verified Purchase']['2017']['review_text'].str.cat(sep=\" \")\n",
    "apple_mask\n",
    "ver_reviewtext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'WordCloud' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-6e2f70580348>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'iPhone'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'IPhone'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Phone'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'phone'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'apple'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Apple'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'new'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'release'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'within'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'days'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mwc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstopwords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackground_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"white\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapple_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontour_width\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontour_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'firebrick'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Generate a wordcloud\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'WordCloud' is not defined"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "\n",
    "stop.extend(['iPhone', 'IPhone', 'Phone', 'phone', 'apple', 'Apple', 'new', 'release', 'within', 'days'])\n",
    "\n",
    "wc = WordCloud(stopwords = stop, background_color=\"white\", max_words=1000, mask=apple_mask, contour_width=3, contour_color='firebrick')\n",
    "\n",
    "# Generate a wordcloud\n",
    "wc.generate(ver_reviewtext)\n",
    "\n",
    "# store to file\n",
    "wc.to_file(\"apple_vuk.png\")\n",
    "\n",
    "# show\n",
    "plt.figure(figsize=[20,10])\n",
    "plt.imshow(wc, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
 ],
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