analyse_swiss_reviews.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing Switzerland to the world on Amazon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found these statistics about the dataset on amazon: http://minimaxir.com/2017/01/amazon-spark/, credits: Max Woolf. Based on his analysis we made a comparison computing the same analysis but with only swiss reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from ggplot import *\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.style.use('seaborn-notebook')\n",
    "#['grayscale', 'fivethirtyeight', 'seaborn-deep', 'bmh', 'seaborn-poster', 'seaborn-ticks', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-whitegrid', 'seaborn-white', 'seaborn-bright', 'seaborn-paper', 'seaborn-pastel', 'seaborn-colorblind', 'seaborn-notebook', 'seaborn-dark-palette', 'dark_background', 'ggplot', 'seaborn-muted', 'seaborn-talk', 'classic']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Explore products dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by looking at the product data without the reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Load the product data in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>categories</th>\n",
       "      <th>description</th>\n",
       "      <th>price</th>\n",
       "      <th>related</th>\n",
       "      <th>salesrank</th>\n",
       "      <th>imurl</th>\n",
       "      <th>asin</th>\n",
       "      <th>brand</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>victorinox, makers of the original swiss army ...</td>\n",
       "      <td>2.15</td>\n",
       "      <td>{'also_viewed': ['b000t3ov1c', 'b009t6og6k', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 49337}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31cx835u...</td>\n",
       "      <td>b000931dtk</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox bone dust scraper, square, white poly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494</th>\n",
       "      <td>[[sports &amp; outdoors, hunting &amp; fishing, fishin...</td>\n",
       "      <td>victorinox small scissor spring 6 pieces. fits...</td>\n",
       "      <td>3.21</td>\n",
       "      <td>{'also_viewed': ['b0013hbj8q', 'b001czdgto', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 22137}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31l8oo18...</td>\n",
       "      <td>b0031phofk</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 6 pc small swiss army knife scissor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4554</th>\n",
       "      <td>[[sports &amp; outdoors, outdoor gear, camping &amp; h...</td>\n",
       "      <td>a 6 pack of small replacement scissor springs ...</td>\n",
       "      <td>3.21</td>\n",
       "      <td>{'also_viewed': ['b0031phofk', 'b0035n33xk', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 18128}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31o8v7l6...</td>\n",
       "      <td>b0035mx7by</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 6 pc large swiss army knife scissor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>[[tools &amp; home improvement, power &amp; hand tools...</td>\n",
       "      <td>a 6 pack of small replacement scissor springs ...</td>\n",
       "      <td>3.21</td>\n",
       "      <td>{'also_viewed': ['b0031phofk', 'b0035mx7by', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 88423}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31eqs0nr...</td>\n",
       "      <td>b0035n33xk</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss army 38409 replacement scisso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>[[office products, office &amp; school supplies, f...</td>\n",
       "      <td>victorinox small keyring. set of six.</td>\n",
       "      <td>3.53</td>\n",
       "      <td>{'also_viewed': ['b000ovi0ac', 'b000rb77n0', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 36866}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/412ommxn...</td>\n",
       "      <td>b001h3qvpm</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>replacement key ring, small, 6 pack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5158</th>\n",
       "      <td>[[sports &amp; outdoors, outdoor gear, camping &amp; h...</td>\n",
       "      <td>victorinox large toothpick. set of six.</td>\n",
       "      <td>3.53</td>\n",
       "      <td>{'also_viewed': ['b001czdgto', 'b0016ctazc', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 6188}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21roy21o...</td>\n",
       "      <td>b003r042bi</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 6 piece large swiss army knife toot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>[[sports &amp; outdoors, hunting &amp; fishing, huntin...</td>\n",
       "      <td>signal mirror for sos kit, for swiss champ sos.</td>\n",
       "      <td>3.95</td>\n",
       "      <td>{'also_viewed': ['b007bf50o8', 'b008aynvfy', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 705730}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31jrzck4...</td>\n",
       "      <td>b0007d805w</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox - replacement signal mirror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2217</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen ut...</td>\n",
       "      <td>victorinox small toothpick. set of six.</td>\n",
       "      <td>4.00</td>\n",
       "      <td>{'also_viewed': ['b003r042bi', 'b0013hbj8q', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 2641}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31ajjppb...</td>\n",
       "      <td>b001czdgto</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>replacement toothpick, small, 6 pack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>[[office products, office &amp; school supplies, w...</td>\n",
       "      <td>victorinox retractable ink pen refill.</td>\n",
       "      <td>4.16</td>\n",
       "      <td>{'bought_together': ['b001czdgto'], 'buy_after...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/11%2bsee...</td>\n",
       "      <td>b0017uxpoa</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss army knife retractable ink pe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>[[sports &amp; outdoors, outdoor gear, camping &amp; h...</td>\n",
       "      <td>large replacement ball point pen for the swiss...</td>\n",
       "      <td>4.16</td>\n",
       "      <td>{'bought_together': ['b003r042bi', 'b0035n8bwi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/11%2b0fa...</td>\n",
       "      <td>b001czfkbq</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox replacement ball point pen, large, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>open clams or hull strawberries with this pari...</td>\n",
       "      <td>4.37</td>\n",
       "      <td>{'also_viewed': ['b001dzsy4u', 'b005lryeju', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 12680}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/3192xaih...</td>\n",
       "      <td>b00062af34</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox sheep's foot serrated stainless ste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>the r h forschner by victorinox paring knife f...</td>\n",
       "      <td>4.50</td>\n",
       "      <td>{'also_viewed': ['b008m5u1ue', 'b0019wxpqy', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 55090}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31pg44zi...</td>\n",
       "      <td>b0019wzjok</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox cutlery 3.24-inch wavy paring knife...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>the r.h. forschner collection has grown in siz...</td>\n",
       "      <td>4.95</td>\n",
       "      <td>{'also_viewed': ['b008m5u1ue', 'b005lrye36', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 6983}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31kyhdbz...</td>\n",
       "      <td>b000eyudze</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox paring knife, 3 1/4 in straight, sm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4555</th>\n",
       "      <td>[[health &amp; personal care, personal care, shavi...</td>\n",
       "      <td>victorinox large tweezer. set of six.</td>\n",
       "      <td>5.00</td>\n",
       "      <td>{'also_viewed': ['b0016ctazc', 'b001czdgto', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 7792}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31kafuj2...</td>\n",
       "      <td>b0035n8bwi</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 6 piece large swiss army knife twee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen ut...</td>\n",
       "      <td>from victorinox a durable, professional-grade ...</td>\n",
       "      <td>5.10</td>\n",
       "      <td>{'also_viewed': ['b0002hphou', 'b000amxsd4', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 137578}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21kj7j2v...</td>\n",
       "      <td>b000931e94</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 2-1/4-inch double edge peeler, blac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9342</th>\n",
       "      <td>[[tools &amp; home improvement, power &amp; hand tools...</td>\n",
       "      <td>swiss army knives - victorinox access screwdri...</td>\n",
       "      <td>5.11</td>\n",
       "      <td>{'also_viewed': ['b00165vgxs', 'b00dm0vshy', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 28469}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21indoqb...</td>\n",
       "      <td>b00csvcnw2</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>swiss army knives 30411 victorinox access scre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>victorinox 40509 bulk pack paring knife 3.25in...</td>\n",
       "      <td>5.26</td>\n",
       "      <td>{'also_viewed': ['b005lrye36', 'b0001v3tqu', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 193696}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31rlwnum...</td>\n",
       "      <td>b000ixfaue</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox bulk pack paring knife 3.25in blade...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6866</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>trusted by professionals for generations. now ...</td>\n",
       "      <td>5.41</td>\n",
       "      <td>{'also_viewed': ['b00a26mrsa', 'b005lryfoe', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 138915}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21couuvc...</td>\n",
       "      <td>b005lryf4e</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss classic 3-1/4-inch paring kni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen ut...</td>\n",
       "      <td>from victorinox a durable, professional-grade ...</td>\n",
       "      <td>5.51</td>\n",
       "      <td>{'also_viewed': ['b0006j2bhk', 'b0002hphou', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 13994}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/41x5xupd...</td>\n",
       "      <td>b0001v3tlu</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss peeler, black polypropylene h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>40553 handle color: blue features: -nylon hand...</td>\n",
       "      <td>5.74</td>\n",
       "      <td>{'also_viewed': ['b000iazbyc', 'b0001v3tr4', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 243745}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/11vro52o...</td>\n",
       "      <td>b0017t0hqu</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>4.5&amp;quot; steak knife handle color: blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>[[office products, office &amp; school supplies, f...</td>\n",
       "      <td>package of 6 large key rings.</td>\n",
       "      <td>5.88</td>\n",
       "      <td>{'also_viewed': ['b007fuw7y0', 'b0026kjrkq', '...</td>\n",
       "      <td>{'sports &amp;amp; outdoors': 238013}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31w7wecd...</td>\n",
       "      <td>b000q9c5qw</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>replacement key ring, large, 6 pack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6863</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>trusted by professionals for generations. now ...</td>\n",
       "      <td>5.90</td>\n",
       "      <td>{'also_viewed': ['b005lryfoe', 'b005lrye36', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 177171}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21oggrbe...</td>\n",
       "      <td>b005lryevi</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss classic 3-1/4-inch paring kni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>the r h forschner by victorinox tomato knife f...</td>\n",
       "      <td>5.95</td>\n",
       "      <td>{'also_viewed': ['b000h284ls', 'b000q9gjxm', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 67725}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/31drm7uo...</td>\n",
       "      <td>b0001v3tjm</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox cutlery 4-1/4inch fork-tipped tomat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7101</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>trusted by professionals for generations and n...</td>\n",
       "      <td>5.95</td>\n",
       "      <td>{'also_viewed': ['b005lrye36', 'b008m5u1ue', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 57975}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21zjlyal...</td>\n",
       "      <td>b0061swlkw</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss classic 3 1/4&amp;quot; paring kn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7136</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>trusted by professionals for generations and n...</td>\n",
       "      <td>5.95</td>\n",
       "      <td>{'also_viewed': ['b005lryeju', 'b0067vlymy', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 158560}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21mkfd09...</td>\n",
       "      <td>b0067vlxzw</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox swiss classic 3 1/4&amp;quot; paring kn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>* round tip blade * white nylon handle * blade...</td>\n",
       "      <td>6.27</td>\n",
       "      <td>{'also_viewed': ['b000x3h6aq', 'b000iazbyc', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 184331}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/21ei-1nx...</td>\n",
       "      <td>b000iazchs</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 42502 4.25&amp;quot; serated white wavy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining]]</td>\n",
       "      <td>victorinox wavy edge paring knife with black n...</td>\n",
       "      <td>6.29</td>\n",
       "      <td>{'also_viewed': ['b001dzsy4u', 'b0019wzjok', '...</td>\n",
       "      <td>{'kitchen &amp; dining': 14897}</td>\n",
       "      <td>http://ecx.images-amazon.com/images/i/318cuisb...</td>\n",
       "      <td>b000q9bb26</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox 40602 wavy edge paring knife with b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>[[home &amp; kitchen, kitchen &amp; dining, kitchen kn...</td>\n",
       "      <td>3.25 in. plain edged blade made of high carbon...</td>\n",
       "      <td>6.44</td>\n",
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       "      <td>http://ecx.images-amazon.com/images/i/41bghs1f...</td>\n",
       "      <td>b00hueq4ea</td>\n",
       "      <td>victorinox</td>\n",
       "      <td>victorinox airboss white dial brown leather me...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1056 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             categories  \\\n",
       "417   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "4494  [[sports & outdoors, hunting & fishing, fishin...   \n",
       "4554  [[sports & outdoors, outdoor gear, camping & h...   \n",
       "4556  [[tools & home improvement, power & hand tools...   \n",
       "2360  [[office products, office & school supplies, f...   \n",
       "5158  [[sports & outdoors, outdoor gear, camping & h...   \n",
       "319   [[sports & outdoors, hunting & fishing, huntin...   \n",
       "2217  [[home & kitchen, kitchen & dining, kitchen ut...   \n",
       "1906  [[office products, office & school supplies, w...   \n",
       "2218  [[sports & outdoors, outdoor gear, camping & h...   \n",
       "256   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "1992  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "661   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "4555  [[health & personal care, personal care, shavi...   \n",
       "416   [[home & kitchen, kitchen & dining, kitchen ut...   \n",
       "9342  [[tools & home improvement, power & hand tools...   \n",
       "982   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "6866  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "161   [[home & kitchen, kitchen & dining, kitchen ut...   \n",
       "1898  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "1284  [[office products, office & school supplies, f...   \n",
       "6863  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "155   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "7101  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "7136  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "910   [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "1282               [[home & kitchen, kitchen & dining]]   \n",
       "1340  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "1987  [[home & kitchen, kitchen & dining, kitchen kn...   \n",
       "199   [[home & kitchen, kitchen & dining, kitchen ut...   \n",
       "...                                                 ...   \n",
       "6471  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "3989  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "7208  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "7069  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7153  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7154  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "5270  [[sports & outdoors, accessories, sport watche...   \n",
       "6822  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "9099  [[clothing, shoes & jewelry, women, watches, w...   \n",
       "7191  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "8165  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7068  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "5741  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "6779  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "2320  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7195  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "8164  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "2321  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "5262  [[sports & outdoors, accessories, sport watche...   \n",
       "3705  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "2104  [[sports & outdoors, accessories, sport watche...   \n",
       "2323  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "8694  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "796   [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "3219  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "5980  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "7205  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7206  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "7207  [[clothing, shoes & jewelry, new arrivals], [c...   \n",
       "9901  [[clothing, shoes & jewelry, men, watches, wri...   \n",
       "\n",
       "                                            description   price  \\\n",
       "417   victorinox, makers of the original swiss army ...    2.15   \n",
       "4494  victorinox small scissor spring 6 pieces. fits...    3.21   \n",
       "4554  a 6 pack of small replacement scissor springs ...    3.21   \n",
       "4556  a 6 pack of small replacement scissor springs ...    3.21   \n",
       "2360              victorinox small keyring. set of six.    3.53   \n",
       "5158            victorinox large toothpick. set of six.    3.53   \n",
       "319     signal mirror for sos kit, for swiss champ sos.    3.95   \n",
       "2217            victorinox small toothpick. set of six.    4.00   \n",
       "1906             victorinox retractable ink pen refill.    4.16   \n",
       "2218  large replacement ball point pen for the swiss...    4.16   \n",
       "256   open clams or hull strawberries with this pari...    4.37   \n",
       "1992  the r h forschner by victorinox paring knife f...    4.50   \n",
       "661   the r.h. forschner collection has grown in siz...    4.95   \n",
       "4555              victorinox large tweezer. set of six.    5.00   \n",
       "416   from victorinox a durable, professional-grade ...    5.10   \n",
       "9342  swiss army knives - victorinox access screwdri...    5.11   \n",
       "982   victorinox 40509 bulk pack paring knife 3.25in...    5.26   \n",
       "6866  trusted by professionals for generations. now ...    5.41   \n",
       "161   from victorinox a durable, professional-grade ...    5.51   \n",
       "1898  40553 handle color: blue features: -nylon hand...    5.74   \n",
       "1284                      package of 6 large key rings.    5.88   \n",
       "6863  trusted by professionals for generations. now ...    5.90   \n",
       "155   the r h forschner by victorinox tomato knife f...    5.95   \n",
       "7101  trusted by professionals for generations and n...    5.95   \n",
       "7136  trusted by professionals for generations and n...    5.95   \n",
       "910   * round tip blade * white nylon handle * blade...    6.27   \n",
       "1282  victorinox wavy edge paring knife with black n...    6.29   \n",
       "1340  3.25 in. plain edged blade made of high carbon...    6.44   \n",
       "1987  the r h forschner by victorinox paring knife f...    6.89   \n",
       "199   from victorinox a durable, professional-grade ...    6.95   \n",
       "...                                                 ...     ...   \n",
       "6471                                                NaN  564.41   \n",
       "3989                                                NaN  567.00   \n",
       "7208                                                NaN  570.00   \n",
       "7069                                                NaN  574.99   \n",
       "7153                                                NaN  577.61   \n",
       "7154                                                NaN  580.06   \n",
       "5270                                                NaN  587.95   \n",
       "6822                                                NaN  589.99   \n",
       "9099                                                NaN  596.25   \n",
       "7191                                                NaN  599.95   \n",
       "8165                                                NaN  618.74   \n",
       "7068                                                NaN  625.09   \n",
       "5741                                                NaN  655.97   \n",
       "6779                                                NaN  667.09   \n",
       "2320                                                NaN  671.63   \n",
       "7195                                                NaN  689.00   \n",
       "8164                                                NaN  749.95   \n",
       "2321                                                NaN  775.00   \n",
       "5262                                                NaN  776.09   \n",
       "3705                                                NaN  799.95   \n",
       "2104                                                NaN  828.06   \n",
       "2323                                                NaN  937.50   \n",
       "8694                                                NaN  990.09   \n",
       "796                                                 NaN     NaN   \n",
       "3219                                                NaN     NaN   \n",
       "5980                                                NaN     NaN   \n",
       "7205                                                NaN     NaN   \n",
       "7206                                                NaN     NaN   \n",
       "7207                                                NaN     NaN   \n",
       "9901                                                NaN     NaN   \n",
       "\n",
       "                                                related  \\\n",
       "417   {'also_viewed': ['b000t3ov1c', 'b009t6og6k', '...   \n",
       "4494  {'also_viewed': ['b0013hbj8q', 'b001czdgto', '...   \n",
       "4554  {'also_viewed': ['b0031phofk', 'b0035n33xk', '...   \n",
       "4556  {'also_viewed': ['b0031phofk', 'b0035mx7by', '...   \n",
       "2360  {'also_viewed': ['b000ovi0ac', 'b000rb77n0', '...   \n",
       "5158  {'also_viewed': ['b001czdgto', 'b0016ctazc', '...   \n",
       "319   {'also_viewed': ['b007bf50o8', 'b008aynvfy', '...   \n",
       "2217  {'also_viewed': ['b003r042bi', 'b0013hbj8q', '...   \n",
       "1906  {'bought_together': ['b001czdgto'], 'buy_after...   \n",
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       "1992  {'also_viewed': ['b008m5u1ue', 'b0019wxpqy', '...   \n",
       "661   {'also_viewed': ['b008m5u1ue', 'b005lrye36', '...   \n",
       "4555  {'also_viewed': ['b0016ctazc', 'b001czdgto', '...   \n",
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       "9342  {'also_viewed': ['b00165vgxs', 'b00dm0vshy', '...   \n",
       "982   {'also_viewed': ['b005lrye36', 'b0001v3tqu', '...   \n",
       "6866  {'also_viewed': ['b00a26mrsa', 'b005lryfoe', '...   \n",
       "161   {'also_viewed': ['b0006j2bhk', 'b0002hphou', '...   \n",
       "1898  {'also_viewed': ['b000iazbyc', 'b0001v3tr4', '...   \n",
       "1284  {'also_viewed': ['b007fuw7y0', 'b0026kjrkq', '...   \n",
       "6863  {'also_viewed': ['b005lryfoe', 'b005lrye36', '...   \n",
       "155   {'also_viewed': ['b000h284ls', 'b000q9gjxm', '...   \n",
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       "910   {'also_viewed': ['b000x3h6aq', 'b000iazbyc', '...   \n",
       "1282  {'also_viewed': ['b001dzsy4u', 'b0019wzjok', '...   \n",
       "1340  {'also_viewed': ['b000ixa838', 'b008m5u1ue', '...   \n",
       "1987  {'also_viewed': ['b005lrye36', 'b008m5u1ue', '...   \n",
       "199   {'also_viewed': ['b001czbgm8', 'b000931e94', '...   \n",
       "...                                                 ...   \n",
       "6471  {'also_viewed': ['b002jt08ji', 'b005f5f942', '...   \n",
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       "7153  {'also_viewed': ['b005xnmgba', 'b005f5ftsi', '...   \n",
       "7154  {'also_viewed': ['b006hn89ck', 'b005xnmgba', '...   \n",
       "5270  {'also_viewed': ['b003sfmize', 'b002dr18dq', '...   \n",
       "6822  {'also_viewed': ['b0052z2808', 'b003iwbs8a', '...   \n",
       "9099  {'also_viewed': ['b008k5rdne', 'b008k5rfby', '...   \n",
       "7191  {'also_viewed': ['b005xnmexu', 'b006hn89ck', '...   \n",
       "8165  {'also_viewed': ['b009gbyxca', 'b009gbyxbg', '...   \n",
       "7068  {'also_viewed': ['b00eonl57g', 'b0069wd7c6', '...   \n",
       "5741  {'also_viewed': ['b0044rbdhe', 'b00ffgwr5k', '...   \n",
       "6779  {'also_viewed': ['b00513gja6', 'b005f5f942', '...   \n",
       "2320  {'also_viewed': ['b002mnavj8', 'b001fymwo2', '...   \n",
       "7195  {'also_viewed': ['b0069wd7c6', 'b005xnmgba', '...   \n",
       "8164  {'also_viewed': ['b002dr18dq', 'b003sfmize', '...   \n",
       "2321  {'also_viewed': ['b001fykrhg', 'b001rjgr32', '...   \n",
       "5262  {'also_viewed': ['b003sfmize', 'b002dr18dq', '...   \n",
       "3705  {'also_viewed': ['b008k5rmqw', 'b003sfmize', '...   \n",
       "2104  {'also_viewed': ['b003drnccu', 'b004jueytk', '...   \n",
       "2323  {'also_viewed': ['b003sfmife', 'b0044rbdhe', '...   \n",
       "8694  {'also_viewed': ['b005xnmexu', 'b0069wd7lm', '...   \n",
       "796   {'also_viewed': ['b003sfmize', 'b0019gkqfs', '...   \n",
       "3219  {'also_viewed': ['b001fykrhg', 'b001fyoxy4', '...   \n",
       "5980  {'also_viewed': ['b004jueytk', 'b004e4f8u0', '...   \n",
       "7205  {'also_viewed': ['b006hirtj4', 'b00a72uhfe', '...   \n",
       "7206  {'also_viewed': ['b00a72uhfe', 'b006hirto4', '...   \n",
       "7207  {'also_viewed': ['b006mx051s', 'b003sfkhwk', '...   \n",
       "9901  {'also_viewed': ['b00gxjzx22', 'b00afxibui', '...   \n",
       "\n",
       "                              salesrank  \\\n",
       "417         {'kitchen & dining': 49337}   \n",
       "4494   {'sports &amp; outdoors': 22137}   \n",
       "4554   {'sports &amp; outdoors': 18128}   \n",
       "4556   {'sports &amp; outdoors': 88423}   \n",
       "2360   {'sports &amp; outdoors': 36866}   \n",
       "5158    {'sports &amp; outdoors': 6188}   \n",
       "319   {'sports &amp; outdoors': 705730}   \n",
       "2217    {'sports &amp; outdoors': 2641}   \n",
       "1906                                NaN   \n",
       "2218                                NaN   \n",
       "256         {'kitchen & dining': 12680}   \n",
       "1992        {'kitchen & dining': 55090}   \n",
       "661          {'kitchen & dining': 6983}   \n",
       "4555    {'sports &amp; outdoors': 7792}   \n",
       "416        {'kitchen & dining': 137578}   \n",
       "9342   {'sports &amp; outdoors': 28469}   \n",
       "982        {'kitchen & dining': 193696}   \n",
       "6866       {'kitchen & dining': 138915}   \n",
       "161         {'kitchen & dining': 13994}   \n",
       "1898       {'kitchen & dining': 243745}   \n",
       "1284  {'sports &amp; outdoors': 238013}   \n",
       "6863       {'kitchen & dining': 177171}   \n",
       "155         {'kitchen & dining': 67725}   \n",
       "7101        {'kitchen & dining': 57975}   \n",
       "7136       {'kitchen & dining': 158560}   \n",
       "910        {'kitchen & dining': 184331}   \n",
       "1282        {'kitchen & dining': 14897}   \n",
       "1340       {'kitchen & dining': 128543}   \n",
       "1987         {'kitchen & dining': 6482}   \n",
       "199         {'kitchen & dining': 50486}   \n",
       "...                                 ...   \n",
       "6471                 {'watches': 44467}   \n",
       "3989                 {'watches': 20711}   \n",
       "7208                 {'watches': 11495}   \n",
       "7069                 {'watches': 23863}   \n",
       "7153                 {'watches': 24512}   \n",
       "7154                 {'watches': 27909}   \n",
       "5270                 {'watches': 19671}   \n",
       "6822                 {'watches': 32738}   \n",
       "9099                {'watches': 138226}   \n",
       "7191                 {'watches': 19620}   \n",
       "8165                 {'watches': 48829}   \n",
       "7068                 {'watches': 24275}   \n",
       "5741                 {'watches': 52808}   \n",
       "6779                 {'watches': 50471}   \n",
       "2320                 {'watches': 72771}   \n",
       "7195                 {'watches': 73738}   \n",
       "8164                 {'watches': 28913}   \n",
       "2321                 {'watches': 87721}   \n",
       "5262                 {'watches': 26565}   \n",
       "3705                 {'watches': 15261}   \n",
       "2104                 {'watches': 73451}   \n",
       "2323                 {'watches': 32808}   \n",
       "8694                 {'watches': 43826}   \n",
       "796                  {'watches': 13235}   \n",
       "3219                 {'watches': 56238}   \n",
       "5980                 {'watches': 69975}   \n",
       "7205                 {'watches': 63236}   \n",
       "7206                 {'watches': 35080}   \n",
       "7207                 {'watches': 57565}   \n",
       "9901                {'watches': 152976}   \n",
       "\n",
       "                                                  imurl        asin  \\\n",
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       "1992  http://ecx.images-amazon.com/images/i/31pg44zi...  b0019wzjok   \n",
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       "...                                                 ...         ...   \n",
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       "8164  http://ecx.images-amazon.com/images/i/51gzheof...  b008k5rmqw   \n",
       "2321  http://ecx.images-amazon.com/images/i/41-hdnmg...  b001fyoxy4   \n",
       "5262  http://ecx.images-amazon.com/images/i/516msqpx...  b003sfkih4   \n",
       "3705  http://ecx.images-amazon.com/images/i/41rcwa81...  b002dr18dq   \n",
       "2104  http://ecx.images-amazon.com/images/i/51hrbz%2...  b001b8fxos   \n",
       "2323  http://ecx.images-amazon.com/images/i/41btid9d...  b001fykrhg   \n",
       "8694  http://ecx.images-amazon.com/images/i/51rzjoe5...  b00afxibui   \n",
       "796   http://ecx.images-amazon.com/images/i/314vrfok...  b000ggq9iu   \n",
       "3219  http://ecx.images-amazon.com/images/i/51ick%2b...  b001rjgr32   \n",
       "5980  http://ecx.images-amazon.com/images/i/51a05289...  b004julrqi   \n",
       "7205  http://ecx.images-amazon.com/images/i/41goomwk...  b006hirto4   \n",
       "7206  http://ecx.images-amazon.com/images/i/51avea0f...  b006hirtj4   \n",
       "7207  http://ecx.images-amazon.com/images/i/51jph5pv...  b006hirtd0   \n",
       "9901  http://ecx.images-amazon.com/images/i/41bghs1f...  b00hueq4ea   \n",
       "\n",
       "           brand                                              title  \n",
       "417   victorinox   victorinox bone dust scraper, square, white poly  \n",
       "4494  victorinox  victorinox 6 pc small swiss army knife scissor...  \n",
       "4554  victorinox  victorinox 6 pc large swiss army knife scissor...  \n",
       "4556  victorinox  victorinox swiss army 38409 replacement scisso...  \n",
       "2360  victorinox                replacement key ring, small, 6 pack  \n",
       "5158  victorinox  victorinox 6 piece large swiss army knife toot...  \n",
       "319   victorinox             victorinox - replacement signal mirror  \n",
       "2217  victorinox               replacement toothpick, small, 6 pack  \n",
       "1906  victorinox  victorinox swiss army knife retractable ink pe...  \n",
       "2218  victorinox  victorinox replacement ball point pen, large, ...  \n",
       "256   victorinox  victorinox sheep's foot serrated stainless ste...  \n",
       "1992  victorinox  victorinox cutlery 3.24-inch wavy paring knife...  \n",
       "661   victorinox  victorinox paring knife, 3 1/4 in straight, sm...  \n",
       "4555  victorinox  victorinox 6 piece large swiss army knife twee...  \n",
       "416   victorinox  victorinox 2-1/4-inch double edge peeler, blac...  \n",
       "9342  victorinox  swiss army knives 30411 victorinox access scre...  \n",
       "982   victorinox  victorinox bulk pack paring knife 3.25in blade...  \n",
       "6866  victorinox  victorinox swiss classic 3-1/4-inch paring kni...  \n",
       "161   victorinox  victorinox swiss peeler, black polypropylene h...  \n",
       "1898  victorinox           4.5&quot; steak knife handle color: blue  \n",
       "1284  victorinox                replacement key ring, large, 6 pack  \n",
       "6863  victorinox  victorinox swiss classic 3-1/4-inch paring kni...  \n",
       "155   victorinox  victorinox cutlery 4-1/4inch fork-tipped tomat...  \n",
       "7101  victorinox  victorinox swiss classic 3 1/4&quot; paring kn...  \n",
       "7136  victorinox  victorinox swiss classic 3 1/4&quot; paring kn...  \n",
       "910   victorinox     victorinox 42502 4.25&quot; serated white wavy  \n",
       "1282  victorinox  victorinox 40602 wavy edge paring knife with b...  \n",
       "1340  victorinox           victorinox 46661 3.25 paring knife white  \n",
       "1987  victorinox  victorinox cutlery 3.25-inch paring knife, sma...  \n",
       "199   victorinox  victorinox 2-1/4-inch double edge peeler, blac...  \n",
       "...          ...                                                ...  \n",
       "6471  victorinox  victorinox men's 241481 alliance analog displa...  \n",
       "3989  victorinox  victorinox swiss army men's 241289 infantry vi...  \n",
       "7208  victorinox  victorinox swiss army men's 241507 air boss bl...  \n",
       "7069  victorinox  victorinox swiss army airboss automatic cream ...  \n",
       "7153  victorinox  victorinox swiss army airboss automatic cream ...  \n",
       "7154  victorinox  victorinox swiss army airboss automatic black ...  \n",
       "5270  victorinox  victorinox swiss army men's 241426 dive master...  \n",
       "6822  victorinox  victorinox swiss army men's 241478 alliance da...  \n",
       "9099  victorinox  victorinox swiss army dive master 500 midsize ...  \n",
       "7191  victorinox  victorinox swiss army men's 241505 air boss si...  \n",
       "8165  victorinox  victorinox swiss army officer's day/date mecha...  \n",
       "7068  victorinox  victorinox swiss army airboss automatic black ...  \n",
       "5741  victorinox  victorinox swiss army silver stainless band bl...  \n",
       "6779  victorinox  victorinox swiss army men's 241481 alliance tw...  \n",
       "2320  victorinox  victorinox swiss army men's 241289 infantry vi...  \n",
       "7195  victorinox  victorinox swiss army men's 241506 air boss si...  \n",
       "8164  victorinox  victorinox swiss army dive master 500 mechanic...  \n",
       "2321  victorinox  victorinox swiss army men's 241301 classic col...  \n",
       "5262  victorinox  victorinox swiss army men's 241425 dive master...  \n",
       "3705  victorinox  victorinox swiss army men's 241355 dive master...  \n",
       "2104  victorinox  victorinox swiss army men's 241193 ambassador ...  \n",
       "2323  victorinox  victorinox swiss army men's 241300 chrono clas...  \n",
       "8694  victorinox  victorinox swiss army airboss automatic power ...  \n",
       "796   victorinox  victorinox swiss army men's 24785.1000 airboss...  \n",
       "3219  victorinox  victorinox swiss army men's 241301 classic sil...  \n",
       "5980  victorinox  victorinox swiss army men's 241447 airboss mac...  \n",
       "7205  victorinox  victorinox swiss army alpnach chronograph warm...  \n",
       "7206  victorinox  victorinox swiss army alpnach chronograph blac...  \n",
       "7207  victorinox  victorinox men's 241526 infantry analog displa...  \n",
       "9901  victorinox  victorinox airboss white dial brown leather me...  \n",
       "\n",
       "[1056 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def parse(path):\n",
    "  f = open(path, 'r')\n",
    "  for l in f:\n",
    "    yield eval(l)\n",
    "  f.close()\n",
    "def getDF(path):\n",
    "  i = 0\n",
    "  df = {}\n",
    "  for d in parse(path):\n",
    "    df[i] = d\n",
    "    i += 1\n",
    "  return pd.DataFrame.from_dict(df, orient='index')\n",
    "\n",
    "df_products = getDF('data/swiss_products.json')\n",
    "df_products = df_products.sort_values(['brand', 'price'])\n",
    "df_products[df_products['brand'] == 'victorinox']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data contains false positives. We found no way of filtering them out besides a quick manual check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sib              1199\n",
       "victorinox       1056\n",
       "bsi               720\n",
       "logitech          595\n",
       "bell              451\n",
       "pfister           429\n",
       "psg               417\n",
       "bulova            399\n",
       "samsonite         366\n",
       "unique            316\n",
       "tissot            311\n",
       "zodiac            302\n",
       "mazda             269\n",
       "kuhn rikon        255\n",
       "tag heuer         211\n",
       "kraft             202\n",
       "wenger            199\n",
       "lacoste           198\n",
       "pelikan           156\n",
       "weleda            139\n",
       "davidoff           92\n",
       "raymond weil       88\n",
       "scott              88\n",
       "la prairie         79\n",
       "primo              78\n",
       "maggi              73\n",
       "ricola             67\n",
       "arctic             66\n",
       "cyma               62\n",
       "atb                59\n",
       "                 ... \n",
       "balmain             2\n",
       "assos               2\n",
       "vuarnet             2\n",
       "insight             2\n",
       "backyard            2\n",
       "sf                  2\n",
       "3a                  2\n",
       "oh!                 2\n",
       "emmi                2\n",
       "blick               2\n",
       "rsr                 2\n",
       "revue thommen       2\n",
       "lugano              2\n",
       "perskindol          1\n",
       "st. moritz          1\n",
       "seco                1\n",
       "solaris             1\n",
       "sugus               1\n",
       "as                  1\n",
       "rds                 1\n",
       "sgs                 1\n",
       "cash                1\n",
       "iec                 1\n",
       "sad                 1\n",
       "etoile              1\n",
       "rmb                 1\n",
       "naish               1\n",
       "ulysse nardin       1\n",
       "tsr                 1\n",
       "credit suisse       1\n",
       "Name: brand, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products.brand.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to remove the obvious ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "false_positives = ['samsonite', 'mazda', 'lacoste', 'pelikan', 'bell']\n",
    "df_products = df_products[~df_products.brand.isin(false_positives)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a took at the data. We start with detecting NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA values for categories: 0\n",
      "NA values for description: 1916\n",
      "NA values for price: 276\n",
      "NA values for related: 554\n",
      "NA values for salesrank: 1689\n",
      "NA values for imurl: 0\n",
      "NA values for asin: 0\n",
      "NA values for brand: 0\n",
      "NA values for title: 0\n"
     ]
    }
   ],
   "source": [
    "for column_name in df_products.columns:\n",
    "    print(\"NA values for \" + column_name + \": \" + str(df_products[column_name].isnull().sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill na values as follows: empty for salesrank, related and description, and average price for price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.26206010137464"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products['salesrank'].fillna(value='{}', inplace=True)\n",
    "df_products['related'].fillna(value='{}', inplace=True)\n",
    "df_products['description'].fillna(value='', inplace=True)\n",
    "av_price = df_products['price'].mean()\n",
    "df_products['price'].fillna(value=av_price, inplace=True)\n",
    "av_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average price of the swiss products is **$79.73**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the most expansive product on amazon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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5qxqJGEzQAruY7BHnjD8A8DW+Hg0bUvHGtGGxWDTZFRLa0isTbsYFS7Eqqqop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\nZfmyc8VieJVZ9MneKJZpoQJ4kxnLod6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7yebCrDAGSSCOeKy/gxbrb/HbXoGAxJ4atXAz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\niQPxBz+NDJhJp3Z6L+05EJvg94mQjOdLuDj6Rsa+QvhB+zx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GMAgEfkfer174A09Y9tr4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\noooEtjUtO1XloooGhaKKKBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products[df_products.price == max(df_products['price'])].iloc[0].asin\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename='data/expensivewatch.jpg') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some statistics about the products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8562.000000\n",
       "mean       81.262060\n",
       "std       138.515708\n",
       "min         0.010000\n",
       "25%        10.210000\n",
       "50%        22.745000\n",
       "75%        81.262060\n",
       "max       999.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products.price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which brand occurs the most in our dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5331f92b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fVtOIiIjoPceQ9RbR0dFo0CAWgFUB39G0gOOxiIoCbG1tJbWMiIiI3ncMWX/JCgDDEhER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a52db70828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_products.brand.value_counts()[:20].plot(kind='bar', figsize=(10,5), title=\"Number of occurences of brands\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which category occurs the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Which category occurs the most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5337b34a8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Uqve05z23JatFixY4fvw4unbtCiEExo8fj6pVq2Ls2LEwmUxwcnJCmzZtoNFo4O/vD19f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5335f35f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flatten = lambda l: [item for sublist in l for item in sublist]\n",
    "categories_list = []\n",
    "for categories in df_products.categories:\n",
    "    for categorie in flatten(categories):\n",
    "        categories_list.append(categorie)\n",
    "\n",
    "pd.Series(categories_list).value_counts()[:20].plot(kind='bar', figsize=(10,5), title=\"Number of occurences of categories\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Explore review dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transform data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A00027081JC8NE8X6TD5</td>\n",
       "      <td>B003U8ENTI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1367625600</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A00090122PEQW7864EC8L</td>\n",
       "      <td>B004JW1B8A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1354492800</td>\n",
       "      <td>[[u'toys &amp; games', u'party supplies', u'party ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0009686KROLKEH2EHF4</td>\n",
       "      <td>B000I7GST2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1372723200</td>\n",
       "      <td>[[u'home &amp; kitchen', u'kitchen &amp; dining', u'ki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0023922YYT81KP1CWU4</td>\n",
       "      <td>B000R9AAJA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1353542400</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A00277003AGRC9WJJ555O</td>\n",
       "      <td>B002FU5QM0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1353196800</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A00311601FOXCO2AIRNIF</td>\n",
       "      <td>B003LCV4RM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1382054400</td>\n",
       "      <td>[[u'patio, lawn &amp; garden', u'pest control', u'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A00317262XRVTNR5YJQYQ</td>\n",
       "      <td>B001MQ8BDE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1362096000</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A0031944XK4KVC58YJ6I</td>\n",
       "      <td>B003U8GCJC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1355702400</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A00343841Z6YRUBZ6K0W3</td>\n",
       "      <td>B0015C30J0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1386374400</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A0047322388NOTO4N8SKD</td>\n",
       "      <td>B002FU5QLQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1398038400</td>\n",
       "      <td>[[u'electronics', u'computers &amp; accessories', ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 user_id     item_id  rating   timestamp  \\\n",
       "0   A00027081JC8NE8X6TD5  B003U8ENTI     5.0  1367625600   \n",
       "1  A00090122PEQW7864EC8L  B004JW1B8A     1.0  1354492800   \n",
       "2   A0009686KROLKEH2EHF4  B000I7GST2     5.0  1372723200   \n",
       "3   A0023922YYT81KP1CWU4  B000R9AAJA     5.0  1353542400   \n",
       "4  A00277003AGRC9WJJ555O  B002FU5QM0     5.0  1353196800   \n",
       "5  A00311601FOXCO2AIRNIF  B003LCV4RM     5.0  1382054400   \n",
       "6  A00317262XRVTNR5YJQYQ  B001MQ8BDE     5.0  1362096000   \n",
       "7   A0031944XK4KVC58YJ6I  B003U8GCJC     5.0  1355702400   \n",
       "8  A00343841Z6YRUBZ6K0W3  B0015C30J0     5.0  1386374400   \n",
       "9  A0047322388NOTO4N8SKD  B002FU5QLQ     4.0  1398038400   \n",
       "\n",
       "                                            category  \n",
       "0  [[u'electronics', u'computers & accessories', ...  \n",
       "1  [[u'toys & games', u'party supplies', u'party ...  \n",
       "2  [[u'home & kitchen', u'kitchen & dining', u'ki...  \n",
       "3  [[u'electronics', u'computers & accessories', ...  \n",
       "4  [[u'electronics', u'computers & accessories', ...  \n",
       "5  [[u'patio, lawn & garden', u'pest control', u'...  \n",
       "6  [[u'electronics', u'computers & accessories', ...  \n",
       "7  [[u'electronics', u'computers & accessories', ...  \n",
       "8  [[u'electronics', u'computers & accessories', ...  \n",
       "9  [[u'electronics', u'computers & accessories', ...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/amazon_ratings.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform categories into list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A00027081JC8NE8X6TD5</td>\n",
       "      <td>B003U8ENTI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1367625600</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A00090122PEQW7864EC8L</td>\n",
       "      <td>B004JW1B8A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1354492800</td>\n",
       "      <td>toys &amp; games</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0009686KROLKEH2EHF4</td>\n",
       "      <td>B000I7GST2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1372723200</td>\n",
       "      <td>home &amp; kitchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0023922YYT81KP1CWU4</td>\n",
       "      <td>B000R9AAJA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1353542400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A00277003AGRC9WJJ555O</td>\n",
       "      <td>B002FU5QM0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1353196800</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A00311601FOXCO2AIRNIF</td>\n",
       "      <td>B003LCV4RM</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1382054400</td>\n",
       "      <td>patio, lawn &amp; garden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A00317262XRVTNR5YJQYQ</td>\n",
       "      <td>B001MQ8BDE</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1362096000</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A0031944XK4KVC58YJ6I</td>\n",
       "      <td>B003U8GCJC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1355702400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A00343841Z6YRUBZ6K0W3</td>\n",
       "      <td>B0015C30J0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1386374400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A0047322388NOTO4N8SKD</td>\n",
       "      <td>B002FU5QLQ</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1398038400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 user_id     item_id  rating   timestamp              category\n",
       "0   A00027081JC8NE8X6TD5  B003U8ENTI     5.0  1367625600           electronics\n",
       "1  A00090122PEQW7864EC8L  B004JW1B8A     1.0  1354492800          toys & games\n",
       "2   A0009686KROLKEH2EHF4  B000I7GST2     5.0  1372723200        home & kitchen\n",
       "3   A0023922YYT81KP1CWU4  B000R9AAJA     5.0  1353542400           electronics\n",
       "4  A00277003AGRC9WJJ555O  B002FU5QM0     5.0  1353196800           electronics\n",
       "5  A00311601FOXCO2AIRNIF  B003LCV4RM     5.0  1382054400  patio, lawn & garden\n",
       "6  A00317262XRVTNR5YJQYQ  B001MQ8BDE     5.0  1362096000           electronics\n",
       "7   A0031944XK4KVC58YJ6I  B003U8GCJC     5.0  1355702400           electronics\n",
       "8  A00343841Z6YRUBZ6K0W3  B0015C30J0     5.0  1386374400           electronics\n",
       "9  A0047322388NOTO4N8SKD  B002FU5QLQ     4.0  1398038400           electronics"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_category(x): \n",
    "    while 'u\\'' in x:\n",
    "        x = x.replace('u\\'','\\'')\n",
    "    for y in eval(x):\n",
    "        for first in y:\n",
    "            return first\n",
    "df['category'] = df['category'].apply(get_category)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give number to each review according to n-th review of a user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A00473363TJ8YSZ3YAGG9</td>\n",
       "      <td>B000CO256G</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1386374400</td>\n",
       "      <td>home &amp; kitchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A00473363TJ8YSZ3YAGG9</td>\n",
       "      <td>B003NR57BY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1402790400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A00522763IKV0D5Y37O73</td>\n",
       "      <td>B0077JNN4M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1380326400</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEFAT6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEIV92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEKR4Y</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A00592422DB3X8BEM3SM0</td>\n",
       "      <td>B005DSPLDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1370044800</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A006680338J29DP17XALU</td>\n",
       "      <td>B000BXHPEU</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1378080000</td>\n",
       "      <td>baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A00748713B8XQ8AQVH81N</td>\n",
       "      <td>B005DKZTNK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1391817600</td>\n",
       "      <td>electronics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A007743726L8HR1FUGX5E</td>\n",
       "      <td>B0088BMX3U</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1363305600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id     item_id  rating   timestamp  \\\n",
       "10  A00473363TJ8YSZ3YAGG9  B000CO256G     4.0  1386374400   \n",
       "11  A00473363TJ8YSZ3YAGG9  B003NR57BY     4.0  1402790400   \n",
       "12  A00522763IKV0D5Y37O73  B0077JNN4M     1.0  1380326400   \n",
       "13  A00563582R9P6LHW7UQPE  B000VEFAT6     5.0  1395273600   \n",
       "14  A00563582R9P6LHW7UQPE  B000VEIV92     5.0  1395273600   \n",
       "15  A00563582R9P6LHW7UQPE  B000VEKR4Y     5.0  1395273600   \n",
       "16  A00592422DB3X8BEM3SM0  B005DSPLDA     1.0  1370044800   \n",
       "17  A006680338J29DP17XALU  B000BXHPEU     5.0  1378080000   \n",
       "18  A00748713B8XQ8AQVH81N  B005DKZTNK     5.0  1391817600   \n",
       "20  A007743726L8HR1FUGX5E  B0088BMX3U     5.0  1363305600   \n",
       "\n",
       "                     category  \n",
       "10             home & kitchen  \n",
       "11                electronics  \n",
       "12                electronics  \n",
       "13  clothing, shoes & jewelry  \n",
       "14  clothing, shoes & jewelry  \n",
       "15  clothing, shoes & jewelry  \n",
       "16                electronics  \n",
       "17                       baby  \n",
       "18                electronics  \n",
       "20  clothing, shoes & jewelry  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(['user_id', 'timestamp'])\n",
    "df[10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A00473363TJ8YSZ3YAGG9</td>\n",
       "      <td>B000CO256G</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1386374400</td>\n",
       "      <td>home &amp; kitchen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A00473363TJ8YSZ3YAGG9</td>\n",
       "      <td>B003NR57BY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1402790400</td>\n",
       "      <td>electronics</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A00522763IKV0D5Y37O73</td>\n",
       "      <td>B0077JNN4M</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1380326400</td>\n",
       "      <td>electronics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEFAT6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEIV92</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A00563582R9P6LHW7UQPE</td>\n",
       "      <td>B000VEKR4Y</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395273600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A00592422DB3X8BEM3SM0</td>\n",
       "      <td>B005DSPLDA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1370044800</td>\n",
       "      <td>electronics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A006680338J29DP17XALU</td>\n",
       "      <td>B000BXHPEU</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1378080000</td>\n",
       "      <td>baby</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A00748713B8XQ8AQVH81N</td>\n",
       "      <td>B005DKZTNK</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1391817600</td>\n",
       "      <td>electronics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A007743726L8HR1FUGX5E</td>\n",
       "      <td>B0088BMX3U</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1363305600</td>\n",
       "      <td>clothing, shoes &amp; jewelry</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id     item_id  rating   timestamp  \\\n",
       "10  A00473363TJ8YSZ3YAGG9  B000CO256G     4.0  1386374400   \n",
       "11  A00473363TJ8YSZ3YAGG9  B003NR57BY     4.0  1402790400   \n",
       "12  A00522763IKV0D5Y37O73  B0077JNN4M     1.0  1380326400   \n",
       "13  A00563582R9P6LHW7UQPE  B000VEFAT6     5.0  1395273600   \n",
       "14  A00563582R9P6LHW7UQPE  B000VEIV92     5.0  1395273600   \n",
       "15  A00563582R9P6LHW7UQPE  B000VEKR4Y     5.0  1395273600   \n",
       "16  A00592422DB3X8BEM3SM0  B005DSPLDA     1.0  1370044800   \n",
       "17  A006680338J29DP17XALU  B000BXHPEU     5.0  1378080000   \n",
       "18  A00748713B8XQ8AQVH81N  B005DKZTNK     5.0  1391817600   \n",
       "20  A007743726L8HR1FUGX5E  B0088BMX3U     5.0  1363305600   \n",
       "\n",
       "                     category  nth_user  \n",
       "10             home & kitchen         1  \n",
       "11                electronics         2  \n",
       "12                electronics         1  \n",
       "13  clothing, shoes & jewelry         1  \n",
       "14  clothing, shoes & jewelry         2  \n",
       "15  clothing, shoes & jewelry         3  \n",
       "16                electronics         1  \n",
       "17                       baby         1  \n",
       "18                electronics         1  \n",
       "20  clothing, shoes & jewelry         2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['nth_user'] = 1\n",
    "user_id = ''\n",
    "counter = 1\n",
    "for i in range(0,df.shape[0]):\n",
    "    if df.iloc[i].user_id != user_id:\n",
    "        counter = 1\n",
    "        user_id = df.iloc[i].user_id\n",
    "    else:\n",
    "        counter += 1\n",
    "        df.set_value(i, 'nth_user', counter)\n",
    "df[10:20]        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Give number to each review according to n-th review of product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFJFXN42RZ3G2</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>946944000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3BSS2M2DPPV4T</td>\n",
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       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AETS5ZMP2EM1U</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>952560000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2DTEU2045AVZ3</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>954115200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3R2YB0WTTB0IJ</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>955324800</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     item_id  rating  timestamp     category  nth_user\n",
       "0   AFJFXN42RZ3G2  0767821556     5.0  946944000  movies & tv         1\n",
       "1  A3BSS2M2DPPV4T  0767821556     4.0  948153600  movies & tv         1\n",
       "2   AETS5ZMP2EM1U  0767821556     3.0  952560000  movies & tv         1\n",
       "3  A2DTEU2045AVZ3  0767821556     4.0  954115200  movies & tv         1\n",
       "4  A3R2YB0WTTB0IJ  0767821556     3.0  955324800  movies & tv         1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values(['item_id', 'timestamp']).reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_user</th>\n",
       "      <th>nth_product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFJFXN42RZ3G2</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>946944000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3BSS2M2DPPV4T</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>948153600</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AETS5ZMP2EM1U</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>952560000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2DTEU2045AVZ3</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>954115200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3R2YB0WTTB0IJ</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>955324800</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     item_id  rating  timestamp     category  nth_user  \\\n",
       "0   AFJFXN42RZ3G2  0767821556     5.0  946944000  movies & tv         1   \n",
       "1  A3BSS2M2DPPV4T  0767821556     4.0  948153600  movies & tv         1   \n",
       "2   AETS5ZMP2EM1U  0767821556     3.0  952560000  movies & tv         1   \n",
       "3  A2DTEU2045AVZ3  0767821556     4.0  954115200  movies & tv         1   \n",
       "4  A3R2YB0WTTB0IJ  0767821556     3.0  955324800  movies & tv         1   \n",
       "\n",
       "   nth_product  \n",
       "0            1  \n",
       "1            2  \n",
       "2            3  \n",
       "3            4  \n",
       "4            5  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['nth_product'] = 1\n",
    "item_id = ''\n",
    "counter = 1\n",
    "for i in range(0,df.shape[0]):\n",
    "    if df.iloc[i].item_id != item_id:\n",
    "        counter = 1\n",
    "        item_id = df.iloc[i].item_id\n",
    "    else:\n",
    "        counter += 1\n",
    "        df.set_value(i, 'nth_product', counter)\n",
    "df.head()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transform date into readable date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_user</th>\n",
       "      <th>nth_product</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFJFXN42RZ3G2</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>946944000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2000-01-04 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A3BSS2M2DPPV4T</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>948153600</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2000-01-18 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AETS5ZMP2EM1U</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>952560000</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-03-09 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2DTEU2045AVZ3</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>954115200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-03-27 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3R2YB0WTTB0IJ</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>3.0</td>\n",
       "      <td>955324800</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-10 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AGFBVUSNM1I89</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>958003200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2000-05-11 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A1G56KHOUOFWDW</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>958089600</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2000-05-12 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A3F77BE1V4MW4Y</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>4.0</td>\n",
       "      <td>961027200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2000-06-15 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A1HO9J4DCQDGP9</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964483200</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2000-07-25 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A2OO9DS1RY5KXU</td>\n",
       "      <td>0767821556</td>\n",
       "      <td>5.0</td>\n",
       "      <td>993686400</td>\n",
       "      <td>movies &amp; tv</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2001-06-28 02:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id     item_id  rating  timestamp     category  nth_user  \\\n",
       "0   AFJFXN42RZ3G2  0767821556     5.0  946944000  movies & tv         1   \n",
       "1  A3BSS2M2DPPV4T  0767821556     4.0  948153600  movies & tv         1   \n",
       "2   AETS5ZMP2EM1U  0767821556     3.0  952560000  movies & tv         1   \n",
       "3  A2DTEU2045AVZ3  0767821556     4.0  954115200  movies & tv         1   \n",
       "4  A3R2YB0WTTB0IJ  0767821556     3.0  955324800  movies & tv         1   \n",
       "5   AGFBVUSNM1I89  0767821556     4.0  958003200  movies & tv         1   \n",
       "6  A1G56KHOUOFWDW  0767821556     4.0  958089600  movies & tv         1   \n",
       "7  A3F77BE1V4MW4Y  0767821556     4.0  961027200  movies & tv         1   \n",
       "8  A1HO9J4DCQDGP9  0767821556     5.0  964483200  movies & tv         1   \n",
       "9  A2OO9DS1RY5KXU  0767821556     5.0  993686400  movies & tv         1   \n",
       "\n",
       "   nth_product                 time  \n",
       "0            1  2000-01-04 01:00:00  \n",
       "1            2  2000-01-18 01:00:00  \n",
       "2            3  2000-03-09 01:00:00  \n",
       "3            4  2000-03-27 02:00:00  \n",
       "4            5  2000-04-10 02:00:00  \n",
       "5            6  2000-05-11 02:00:00  \n",
       "6            7  2000-05-12 02:00:00  \n",
       "7            8  2000-06-15 02:00:00  \n",
       "8            9  2000-07-25 02:00:00  \n",
       "9           10  2001-06-28 02:00:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_time(x): \n",
    "    return datetime.datetime.fromtimestamp(\n",
    "        int(x)\n",
    "    ).strftime('%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "df['time'] = df['timestamp'].apply(get_time)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many times each rating is given?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating\n",
       "1.0     23962\n",
       "2.0     15501\n",
       "3.0     22168\n",
       "4.0     56033\n",
       "5.0    187075\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = df.groupby(df.rating).count().ix[:,0]\n",
    "df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a533abce48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AACOIOgAARhB1AACMIOoAABhB1AEAMIKoAwBgBFEHAMAIog4AgBFEHQAAI4g6AABGuKI9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\nAIARRB0AACOIOgAARhB1AACM+D8vFQj4Iu9k3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a533ad8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ratings.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do ratings evolve over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>rating</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>0.01087</td>\n",
       "      <td>0.01087</td>\n",
       "      <td>0.076087</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.836957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "rating      1.0      2.0       3.0       4.0       5.0\n",
       "1999    0.01087  0.01087  0.076087  0.065217  0.836957"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def percentage_rating(year): \n",
    "    df_year = df[df.time.str.contains(year)]\n",
    "    counts_year = df_year.groupby(['rating']).count().ix[:,0]\n",
    "    print(df_year.shape)\n",
    "    counts_year = counts_year/sum(counts_year)\n",
    "    ret = pd.DataFrame(counts_year)\n",
    "    ret.columns = [year]\n",
    "    return ret.transpose()\n",
    "percentage_rating('1999')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 8)\n",
      "(77151, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a5337bacf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2gPFblV96aUD8zHiV9VQoFMXW/PLlS3z66aewtrZGUFAQHj9+jMDAQNSvXx8B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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a533ad8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.concat([percentage_rating('1999'), percentage_rating('2014')]).transpose().plot.bar()\n",
    "ax.set_title(\"Percentage of ratings given per year\", fontsize=18)\n",
    "ax.set_xlabel(\"Rating given\", fontsize=16)\n",
    "ax.set_ylabel(\"Percentage of total reviews\", fontsize=16)\n",
    "ax.legend(loc=2,prop={'size':14})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Comparison to the world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now start with making plots to compare the swiss products to the analysis of Max Woolf, as mentioned earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     196810\n",
       "2      20626\n",
       "3       9887\n",
       "4       2974\n",
       "5       1551\n",
       "6       1461\n",
       "7        392\n",
       "8        176\n",
       "9         84\n",
       "10       134\n",
       "11        41\n",
       "12        20\n",
       "13        19\n",
       "14        12\n",
       "15         8\n",
       "16         9\n",
       "17         6\n",
       "18         3\n",
       "19         5\n",
       "20         5\n",
       "21         3\n",
       "22         2\n",
       "23         2\n",
       "24         6\n",
       "25         1\n",
       "26         1\n",
       "28         1\n",
       "29         1\n",
       "33         1\n",
       "41         1\n",
       "43         2\n",
       "65         1\n",
       "Name: item_id, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_review_counts = df.groupby(df.user_id)\n",
    "counts = df_user_review_counts.count().ix[:,0].value_counts().sort_index()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.840189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.928242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.970450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.983146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.989767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_reviews      prop\n",
       "0            1  0.840189\n",
       "1            2  0.928242\n",
       "2            3  0.970450\n",
       "3            4  0.983146\n",
       "4            5  0.989767"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_reviews = []\n",
    "prop = []\n",
    "\n",
    "s = 0\n",
    "for i in range(0,counts.shape[0]):\n",
    "    num_reviews.append(counts.index[i])\n",
    "    s += counts.iloc[i]/sum(counts)\n",
    "    prop.append(s)\n",
    "\n",
    "df_counts = pd.DataFrame(\n",
    "    {'num_reviews': num_reviews,\n",
    "     'prop': prop\n",
    "    })\n",
    "df_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the accumalative distribution of reviews a user gives on average on swiss products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 50)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7YvPmzRg3bhycnZ0RExODQ4cOFdpt6+joCF1dXXz99dcYNGgQ9PT0EB4ejsjI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\nvAEAALiQ/wfzZtCSKcf50QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a533ad8dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_counts.plot(x='num_reviews', y='prop', figsize=(10,7),\n",
    "                    color=\"#2980b9\", legend=None)\n",
    "ax.set_title(\"Cumulative Proportion of # Amazon Reviews Given by User\", fontsize=18)\n",
    "ax.set_xlabel(\"# Reviews Given By User\", fontsize=14)\n",
    "ax.set_ylabel(\"Cumulative Proportion of All Amazon Reviewers\", fontsize=14)\n",
    "ax.set_ylim((0,1.1))\n",
    "ax.yaxis.set_ticks(np.arange(0, 1.1, 0.25))\n",
    "ax.set_xlim((0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       2503\n",
       "2       1472\n",
       "3        889\n",
       "4        642\n",
       "5        457\n",
       "6        340\n",
       "7        307\n",
       "8        229\n",
       "9        207\n",
       "10       171\n",
       "11       140\n",
       "12       155\n",
       "13       141\n",
       "14       132\n",
       "15        89\n",
       "16        85\n",
       "17        84\n",
       "18        70\n",
       "19        75\n",
       "20        62\n",
       "21        62\n",
       "22        52\n",
       "23        55\n",
       "24        49\n",
       "25        64\n",
       "26        41\n",
       "27        32\n",
       "28        49\n",
       "29        41\n",
       "30        32\n",
       "        ... \n",
       "1228       1\n",
       "1256       1\n",
       "1270       1\n",
       "1307       1\n",
       "1319       1\n",
       "1416       1\n",
       "1495       1\n",
       "1525       1\n",
       "1535       1\n",
       "1542       1\n",
       "1543       1\n",
       "1546       1\n",
       "1549       1\n",
       "1572       1\n",
       "1575       1\n",
       "1578       1\n",
       "1643       1\n",
       "1691       1\n",
       "1770       1\n",
       "1791       1\n",
       "1877       1\n",
       "1910       1\n",
       "2015       1\n",
       "2180       1\n",
       "2344       1\n",
       "2425       1\n",
       "2582       2\n",
       "2729       1\n",
       "3394       1\n",
       "4185       1\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_review_counts = df.groupby(df.item_id)\n",
    "counts = df_user_review_counts.count().ix[:,0].value_counts().sort_index()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.250250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.397421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.486303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.550490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.596181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_reviews      prop\n",
       "0            1  0.250250\n",
       "1            2  0.397421\n",
       "2            3  0.486303\n",
       "3            4  0.550490\n",
       "4            5  0.596181"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_reviews = []\n",
    "prop = []\n",
    "\n",
    "s = 0\n",
    "for i in range(0,counts.shape[0]):\n",
    "    num_reviews.append(counts.index[i])\n",
    "    s += counts.iloc[i]/sum(counts)\n",
    "    prop.append(s)\n",
    "\n",
    "df_counts = pd.DataFrame(\n",
    "    {'num_reviews': num_reviews,\n",
    "     'prop': prop\n",
    "    })\n",
    "df_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the accumalative distribution of reviews a swiss product receives on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 50)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s2LEDy5cvx8GDB5GRkQFHR0dMnToVQ4YMed3D89r7eHEctJJERESo9ayNiIhQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a538b2eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_counts.plot(x='num_reviews', y='prop', figsize=(10,7),\n",
    "                    color=\"#27ae60\", legend=None)\n",
    "ax.set_title(\"Cumulative Proportion of # Reviews Given For Product\", fontsize=18)\n",
    "ax.set_xlabel(\"# Reviews Given For Product\", fontsize=14)\n",
    "ax.set_ylabel(\"Cumulative Proportion of All Amazon Products\", fontsize=14)\n",
    "ax.set_ylim((0,1.1))\n",
    "ax.yaxis.set_ticks(np.arange(0, 1.1, 0.25))\n",
    "ax.set_xlim((0,50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>all beauty</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all electronics</th>\n",
       "      <td>4.185185</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amazon fashion</th>\n",
       "      <td>4.376923</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>appliances</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arts, crafts &amp; sewing</th>\n",
       "      <td>4.321429</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>automotive</th>\n",
       "      <td>4.020660</td>\n",
       "      <td>4453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baby</th>\n",
       "      <td>4.305796</td>\n",
       "      <td>2312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baby products</th>\n",
       "      <td>4.623529</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beauty</th>\n",
       "      <td>4.281728</td>\n",
       "      <td>5878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cds &amp; vinyl</th>\n",
       "      <td>4.301692</td>\n",
       "      <td>1064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell phones &amp; accessories</th>\n",
       "      <td>4.238456</td>\n",
       "      <td>5544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clothing, shoes &amp; jewelry</th>\n",
       "      <td>4.239511</td>\n",
       "      <td>25907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collectibles &amp; fine art</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>computers</th>\n",
       "      <td>4.468391</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>electronics</th>\n",
       "      <td>4.147323</td>\n",
       "      <td>167550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grocery &amp; gourmet food</th>\n",
       "      <td>4.172119</td>\n",
       "      <td>4096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health &amp; personal care</th>\n",
       "      <td>4.114413</td>\n",
       "      <td>5821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home &amp; kitchen</th>\n",
       "      <td>4.463471</td>\n",
       "      <td>24118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home improvement</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industrial &amp; scientific</th>\n",
       "      <td>4.389685</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kitchen &amp; dining</th>\n",
       "      <td>4.544153</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movies &amp; tv</th>\n",
       "      <td>4.035569</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>musical instruments</th>\n",
       "      <td>4.219858</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>office &amp; school supplies</th>\n",
       "      <td>3.111111</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>office products</th>\n",
       "      <td>4.380833</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patio, lawn &amp; garden</th>\n",
       "      <td>4.111005</td>\n",
       "      <td>4171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pet supplies</th>\n",
       "      <td>3.907041</td>\n",
       "      <td>1818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sports &amp; outdoors</th>\n",
       "      <td>4.365588</td>\n",
       "      <td>27955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tools &amp; home improvement</th>\n",
       "      <td>4.165504</td>\n",
       "      <td>5589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toys &amp; games</th>\n",
       "      <td>3.909011</td>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video games</th>\n",
       "      <td>4.070205</td>\n",
       "      <td>10569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           avg_rating   count\n",
       "category                                     \n",
       "all beauty                   4.600000      10\n",
       "all electronics              4.185185     270\n",
       "amazon fashion               4.376923     130\n",
       "appliances                   4.600000       5\n",
       "arts, crafts & sewing        4.321429     476\n",
       "automotive                   4.020660    4453\n",
       "baby                         4.305796    2312\n",
       "baby products                4.623529     510\n",
       "beauty                       4.281728    5878\n",
       "cds & vinyl                  4.301692    1064\n",
       "cell phones & accessories    4.238456    5544\n",
       "clothing, shoes & jewelry    4.239511   25907\n",
       "collectibles & fine art      4.500000       2\n",
       "computers                    4.468391    1044\n",
       "electronics                  4.147323  167550\n",
       "grocery & gourmet food       4.172119    4096\n",
       "health & personal care       4.114413    5821\n",
       "home & kitchen               4.463471   24118\n",
       "home improvement             4.500000       2\n",
       "industrial & scientific      4.389685     349\n",
       "kitchen & dining             4.544153     419\n",
       "movies & tv                  4.035569     984\n",
       "musical instruments          4.219858     141\n",
       "office & school supplies     3.111111       9\n",
       "office products              4.380833    2400\n",
       "patio, lawn & garden         4.111005    4171\n",
       "pet supplies                 3.907041    1818\n",
       "sports & outdoors            4.365588   27955\n",
       "tools & home improvement     4.165504    5589\n",
       "toys & games                 3.909011    1143\n",
       "video games                  4.070205   10569"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_review_counts = df.groupby(df.category)\n",
    "counts = df_user_review_counts.count().ix[:,0]\n",
    "\n",
    "categories = pd.DataFrame({'avg_rating': df_user_review_counts.agg({'rating': 'mean'})['rating'], \n",
    "                            'count': counts})\n",
    "categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the average rating by product category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvfceOp0uzwKv1+t59913OXz4MK+88grr1q3j0KFDbNu2jREjRnDgwAFGjRoF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xL8KMbwyNu2cw2LBFUhY9+HXPhnO1I3jwPAWKs1GoM7AO7NYkBVyfzPCpIWPsmVz18EVcGlRmP0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5392833c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [((categories.index == \"baby products\") | (categories.index == \"office & school supplies\"))]\n",
    "cats = categories['avg_rating'].sort_values(ascending=True)\n",
    "ax = cats.plot.barh(width=1.0, color = \"#e67e22\", alpha=0.9, figsize=(6, 16))\n",
    "ax.set_title(\"Average Rating Score Given For Amazon Reviews, by Product Category\", fontsize = 16)\n",
    "ax.set_xlabel(\"Avg. Rating For Reviews Given in Category\", fontsize = 14)\n",
    "ax.set_ylabel(\"Category\", fontsize = 14)\n",
    "for i in range(0,cats.shape[0]):\n",
    "    height = cats[i]\n",
    "    ax.text(height-1/3, i-1/4,\n",
    "            '%.2f' % height,\n",
    "            ha='center', va='bottom', color = 'white', fontweight='bold', size=16)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>count_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A02444252I8NHTG9BGNBX</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1000I81ZDTJVE</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10097311BAFYJXQ2ZA7F</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A100UD67AHFODS</th>\n",
       "      <td>4.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1029LADQF0L2O</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       avg_rating  count_reviews\n",
       "user_id                                         \n",
       "A02444252I8NHTG9BGNBX         3.0              6\n",
       "A1000I81ZDTJVE                5.0              6\n",
       "A10097311BAFYJXQ2ZA7F         5.0              8\n",
       "A100UD67AHFODS                4.3              7\n",
       "A1029LADQF0L2O                5.0              6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df.groupby(df.user_id).count().ix[:,0]\n",
    "avg_rating = df.groupby(df.user_id).agg({'rating': 'mean'})['rating']\n",
    "\n",
    "users = pd.DataFrame({'avg_rating': avg_rating, \n",
    "                            'count_reviews': counts})\n",
    "users = users[users.count_reviews > 4]\n",
    "users['avg_rating'] = round(users['avg_rating'],1)\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  avg_rating\n",
       "0    1.0         110\n",
       "1    1.2           2\n",
       "2    1.3           2\n",
       "3    1.4           3\n",
       "4    1.7           2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = users['avg_rating'].value_counts()\n",
    "counts = pd.DataFrame(counts).sort_index()\n",
    "counts2 = counts.reset_index()\n",
    "counts2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.4306737588652476"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "av = sum(counts2['index']*counts2['avg_rating'])/ sum(counts2['avg_rating'])\n",
    "av"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph illustrates the average score a user gives (provided that he at least reviewd 5 products in our dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1a53923c7b8>,\n",
       " <matplotlib.text.Text at 0x1a53928b9e8>,\n",
       " <matplotlib.text.Text at 0x1a539209240>,\n",
       " <matplotlib.text.Text at 0x1a53a2f2438>,\n",
       " <matplotlib.text.Text at 0x1a53a2afb00>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3t0dMTAxUKhVycnKk5/1KpRIAsGfPHungVdb5FyX3an7btm1IS0vDxYsXpee+d+7cwc2b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\nvRNuaeEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5392464a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = counts.plot.bar(figsize=(8,5), color ='#2980b9', legend=None)\n",
    "ax.axvline(counts2[counts2['index']==round(av,1)].index, color='k', linestyle='--')\n",
    "ax.set_title(\"Distribution of Average Ratings by User, for Amazon Products\")\n",
    "ax.set_xlabel(\"Average Rating for Amazon Products Given By User (5 Ratings Minimum)\")\n",
    "ax.set_ylabel(\"Count of Users\")\n",
    "ax.set_xticks([0,7,16,26,36])\n",
    "ax.set_xticklabels(['1','2','3','4','5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>count_reviews</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0767821556</th>\n",
       "      <td>4.6</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0800121260</th>\n",
       "      <td>4.4</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0978571630</th>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B000002HK4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00000JXTC</th>\n",
       "      <td>4.7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            avg_rating  count_reviews\n",
       "item_id                              \n",
       "0767821556         4.6            190\n",
       "0800121260         4.4             49\n",
       "0978571630         4.2              5\n",
       "B000002HK4         4.6             98\n",
       "B00000JXTC         4.7              7"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df.groupby(df.item_id).count().ix[:,0]\n",
    "avg_rating = df.groupby(df.item_id).agg({'rating': 'mean'})['rating']\n",
    "\n",
    "products = pd.DataFrame({'avg_rating': avg_rating, \n",
    "                            'count_reviews': counts})\n",
    "products = products[products.count_reviews > 4]\n",
    "products['avg_rating'] = round(products['avg_rating'],1)\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  avg_rating\n",
       "0    1.1           1\n",
       "1    1.2           1\n",
       "2    1.3           3\n",
       "3    1.4           2\n",
       "4    1.5           5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = products['avg_rating'].value_counts()\n",
    "counts = pd.DataFrame(counts).sort_index()\n",
    "counts2 = counts.reset_index()\n",
    "counts2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1539145907473314"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "av = sum(counts2['index']*counts2['avg_rating'])/ sum(counts2['avg_rating'])\n",
    "av"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph illustrates the average score a product has (provided that it has at least 5 reviews)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x1a53a2e11d0>,\n",
       " <matplotlib.text.Text at 0x1a53a2e7710>,\n",
       " <matplotlib.text.Text at 0x1a53929d6a0>,\n",
       " <matplotlib.text.Text at 0x1a5392949b0>,\n",
       " <matplotlib.text.Text at 0x1a53922e240>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Y48fPx67du0SJxGUxNXV9bV1ZDIZhgwZIt4WAF4M9BYEASdPnnz9iyilolttI0eOVCp3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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a53a31dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = counts.plot.bar(figsize=(10,6), color ='#27ae60', legend=None, width = 1)\n",
    "ax.axvline(counts2[counts2['index']==round(av,1)].index, color='k', linestyle='--')\n",
    "ax.set_title(\"Distribution of Overall Ratings on Amazon Products\", fontsize = 18)\n",
    "ax.set_xlabel(\"Overall Amazon Product Ratings (5 Ratings Minimum)\", fontsize = 14)\n",
    "ax.set_ylabel(\"Count of Products\", fontsize = 14)\n",
    "ax.set_xticks([0,9,19,29,39])\n",
    "ax.set_xticklabels(['1.1','2','3','4','5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_product</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nth_user</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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20424</td>\n",
       "      <td>20424</td>\n",
       "      <td>20424</td>\n",
       "      <td>20424</td>\n",
       "      <td>20424</td>\n",
       "      <td>20424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12364</td>\n",
       "      <td>12364</td>\n",
       "      <td>12364</td>\n",
       "      <td>12364</td>\n",
       "      <td>12364</td>\n",
       "      <td>12364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>17243</td>\n",
       "      <td>17243</td>\n",
       "      <td>17243</td>\n",
       "      <td>17243</td>\n",
       "      <td>17243</td>\n",
       "      <td>17243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>42676</td>\n",
       "      <td>42676</td>\n",
       "      <td>42676</td>\n",
       "      <td>42676</td>\n",
       "      <td>42676</td>\n",
       "      <td>42676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>141538</td>\n",
       "      <td>141538</td>\n",
       "      <td>141538</td>\n",
       "      <td>141538</td>\n",
       "      <td>141538</td>\n",
       "      <td>141538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          rating  user_id  item_id  timestamp  category  nth_product    time\n",
       "nth_user                                                                    \n",
       "1            1.0    20424    20424      20424     20424        20424   20424\n",
       "1            2.0    12364    12364      12364     12364        12364   12364\n",
       "1            3.0    17243    17243      17243     17243        17243   17243\n",
       "1            4.0    42676    42676      42676     42676        42676   42676\n",
       "1            5.0   141538   141538     141538    141538       141538  141538"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_breakdown_users =df[df.nth_user <= 50].groupby(['nth_user', 'rating']).count()\n",
    "df_breakdown_users = df_breakdown_users.reset_index(level=1)\n",
    "df_breakdown_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_5</th>\n",
       "      <th>rating_4</th>\n",
       "      <th>rating_3</th>\n",
       "      <th>rating_2</th>\n",
       "      <th>rating_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.604231</td>\n",
       "      <td>0.182185</td>\n",
       "      <td>0.073611</td>\n",
       "      <td>0.052782</td>\n",
       "      <td>0.087191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.634166</td>\n",
       "      <td>0.186296</td>\n",
       "      <td>0.072766</td>\n",
       "      <td>0.048297</td>\n",
       "      <td>0.058475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.654590</td>\n",
       "      <td>0.187757</td>\n",
       "      <td>0.065025</td>\n",
       "      <td>0.045511</td>\n",
       "      <td>0.047118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670615</td>\n",
       "      <td>0.186507</td>\n",
       "      <td>0.068477</td>\n",
       "      <td>0.037995</td>\n",
       "      <td>0.036406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.675532</td>\n",
       "      <td>0.186930</td>\n",
       "      <td>0.066363</td>\n",
       "      <td>0.031915</td>\n",
       "      <td>0.039260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating_5  rating_4  rating_3  rating_2  rating_1\n",
       "1  0.604231  0.182185  0.073611  0.052782  0.087191\n",
       "2  0.634166  0.186296  0.072766  0.048297  0.058475\n",
       "3  0.654590  0.187757  0.065025  0.045511  0.047118\n",
       "4  0.670615  0.186507  0.068477  0.037995  0.036406\n",
       "5  0.675532  0.186930  0.066363  0.031915  0.039260"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "df2 = pd.DataFrame({'rating_5':[], 'rating_4':[], 'rating_3':[], 'rating_2':[], 'rating_1':[]})\n",
    "for i in range(1,51):\n",
    "    s = sum(df_breakdown_users[df_breakdown_users.index == i]['user_id'])\n",
    "    df2 = df2.append(pd.DataFrame({'rating_5':[0],\n",
    "                                       'rating_4':[0],\n",
    "                                       'rating_3':[0],\n",
    "                                       'rating_2':[0],\n",
    "                                       'rating_1':[0]},\n",
    "                    index = [i]))\n",
    "    for j in range(1,6):\n",
    "        result = df_breakdown_users[(df_breakdown_users.index == i) & (df_breakdown_users.rating == j*1.0)]\n",
    "        if not result.empty:\n",
    "            df2.set_value(i,'rating_'+str(j), result.user_id/s)\n",
    "\n",
    "df2 = df2[[\"rating_5\",\"rating_4\",\"rating_3\",\"rating_2\",\"rating_1\"]]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows how the ratings a person gives evolve over time on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DMWHCBOzZswcuLi5G6zLGUH/b2dkVqEOt/mec6zvvvIO+ffsiLi4OMTExWLNmTaEw8PX1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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a539283a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2.plot.bar(stacked=True, color = ['#003a09', '#049104', '#d36743', '#dd310f', '#870202'], width = 0.9)\n",
    "ax.set_xticks([10,20,30,40,50])\n",
    "ax.set_xticklabels(['10','20','30','40','50'])\n",
    "ax.set_title(\"Breakdown of Amazon Ratings Given by Users, by n-th Rating Given\\n\\n\")\n",
    "ax.set_xlabel(\"n-th Rating Given by User\")\n",
    "ax.set_ylabel(\"Proportion\")\n",
    "ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "           ncol=5, mode=\"expand\", borderaxespad=0.)\n",
    "ax.set_ylim((0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>category</th>\n",
       "      <th>nth_user</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nth_product</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>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>789</td>\n",
       "      <td>789</td>\n",
       "      <td>789</td>\n",
       "      <td>789</td>\n",
       "      <td>789</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>464</td>\n",
       "      <td>464</td>\n",
       "      <td>464</td>\n",
       "      <td>464</td>\n",
       "      <td>464</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1727</td>\n",
       "      <td>1727</td>\n",
       "      <td>1727</td>\n",
       "      <td>1727</td>\n",
       "      <td>1727</td>\n",
       "      <td>1727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6321</td>\n",
       "      <td>6321</td>\n",
       "      <td>6321</td>\n",
       "      <td>6321</td>\n",
       "      <td>6321</td>\n",
       "      <td>6321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             rating  user_id  item_id  timestamp  category  nth_user  time\n",
       "nth_product                                                               \n",
       "1               1.0      789      789        789       789       789   789\n",
       "1               2.0      464      464        464       464       464   464\n",
       "1               3.0      701      701        701       701       701   701\n",
       "1               4.0     1727     1727       1727      1727      1727  1727\n",
       "1               5.0     6321     6321       6321      6321      6321  6321"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_breakdown_users =df[df.nth_product <= 50].groupby(['nth_product', 'rating']).count()\n",
    "df_breakdown_users = df_breakdown_users.reset_index(level=1)\n",
    "df_breakdown_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_5</th>\n",
       "      <th>rating_4</th>\n",
       "      <th>rating_3</th>\n",
       "      <th>rating_2</th>\n",
       "      <th>rating_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.631974</td>\n",
       "      <td>0.172665</td>\n",
       "      <td>0.070086</td>\n",
       "      <td>0.046391</td>\n",
       "      <td>0.078884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.624350</td>\n",
       "      <td>0.163488</td>\n",
       "      <td>0.073610</td>\n",
       "      <td>0.045873</td>\n",
       "      <td>0.092679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.620043</td>\n",
       "      <td>0.159615</td>\n",
       "      <td>0.078314</td>\n",
       "      <td>0.054920</td>\n",
       "      <td>0.087108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.610354</td>\n",
       "      <td>0.179447</td>\n",
       "      <td>0.073180</td>\n",
       "      <td>0.047489</td>\n",
       "      <td>0.089529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.616103</td>\n",
       "      <td>0.181050</td>\n",
       "      <td>0.068060</td>\n",
       "      <td>0.043817</td>\n",
       "      <td>0.090970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating_5  rating_4  rating_3  rating_2  rating_1\n",
       "1  0.631974  0.172665  0.070086  0.046391  0.078884\n",
       "2  0.624350  0.163488  0.073610  0.045873  0.092679\n",
       "3  0.620043  0.159615  0.078314  0.054920  0.087108\n",
       "4  0.610354  0.179447  0.073180  0.047489  0.089529\n",
       "5  0.616103  0.181050  0.068060  0.043817  0.090970"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 0\n",
    "df2 = pd.DataFrame({'rating_5':[], 'rating_4':[], 'rating_3':[], 'rating_2':[], 'rating_1':[]})\n",
    "for i in range(1,51):\n",
    "    s = sum(df_breakdown_users[df_breakdown_users.index == i]['item_id'])\n",
    "    df2 = df2.append(pd.DataFrame({'rating_5':[0],\n",
    "                                       'rating_4':[0],\n",
    "                                       'rating_3':[0],\n",
    "                                       'rating_2':[0],\n",
    "                                       'rating_1':[0]},\n",
    "                    index = [i]))\n",
    "    for j in range(1,6):\n",
    "        result = df_breakdown_users[(df_breakdown_users.index == i) & (df_breakdown_users.rating == j*1.0)]\n",
    "        if not result.empty:\n",
    "            df2.set_value(i,'rating_'+str(j), result.user_id/s)\n",
    "\n",
    "df2 = df2[[\"rating_5\",\"rating_4\",\"rating_3\",\"rating_2\",\"rating_1\"]]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows how the ratings a product receives evolve over time on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cjlHmXMaNBRMAnIJ5Qt1cOyHWtszIYSnzZHWhrtQev6V8AUrWosSpKJGViztp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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a53a20bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df2.plot.bar(stacked=True, color = ['#003a09', '#049104', '#d36743', '#dd310f', '#870202'], width = 0.9)\n",
    "ax.set_xticks([10,20,30,40,50])\n",
    "ax.set_xticklabels(['10','20','30','40','50'])\n",
    "ax.set_title(\"Breakdown of Ratings for Amazon Products by n-th Item Rating Given\\n\\n\")\n",
    "ax.set_xlabel(\"n-th Review for Item Given\")\n",
    "ax.set_ylabel(\"Proportion\")\n",
    "ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n",
    "           ncol=5, mode=\"expand\", borderaxespad=0.)\n",
    "ax.set_ylim((0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing the Swiiw reviews with the entire dataset, we can observe the following things:\n",
    "- We have less users that reviewed a high amount of products because there are only 10000 products compared to 8 million\n",
    "- We have a lot of users that gave a least 5 5 stars ratings to swiss products, we also have relatively more users that gave at least 5 1 start ratings\n",
    "- because we have less preducts and thus less users that gave a lot of ratings to those products, no real conclusion can be drawn from teh comparison of ratings be the sae user over time\n",
    "- The Swiis products we endentified n our dataset have on average 30 reviews campared to 10 for the whole dataset\n",
    "- The distribution of average rating for a product with minimum 5 reviews is very similar between the two datasets, the average rating is around 4.2 for both cases, but the entire datasets contains more products that have an average rating of 5\n",
    "- When comparing the evolution of ratings of a product we see no real diffrence between the 2 datasets"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}