02a_Exploratory-Full Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory analysis\n",
    "\n",
    "1. This workbook covers some basic exploratory analyses to understand the data better\n",
    "2. Both Review data and Product (meta data) have been analyzed here\n",
    "3. Image data has not been analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import gzip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Deepthi/Documents/DSE/Capstone/Data/From Julius\n"
     ]
    }
   ],
   "source": [
    "%cd /Users/Deepthi/Documents/DSE/Capstone/Data/From Julius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# !ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Women's metadata\n",
    "df_meta = pd.read_hdf('cleaned.hd5', key='womens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_review = pd.read_csv('reviews_Women_ALL_scraped.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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sXvk8oL+WjFlUKV9qW3n+mnn5ett6i4g4PyKGRcSwdddZuwuv0szMmtGjSShfOX0ycEC+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\nMzMrzf8HJEKPw22XM2IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f62bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Number of reviews by each customer\n",
    "## This histogram shows that most customers write 5 reviews\n",
    "\n",
    "# df_review.groupby(['reviewerID']).asin.nunique().reset_index()['asin'].hist(bins=range(5,25,1))\n",
    "\n",
    "ax = df_review.groupby(['reviewerId']).asin.nunique().reset_index()['asin'].plot(kind = 'hist',bins =range(1,18,1),\\\n",
    "                                                                                 title = 'Distribution of #reviews by each customer (Full dataset)'\\\n",
    "                                                                                ,facecolor='#80cbc4',edgecolor='black')\n",
    "ax.set_xlabel('# number of reviews')\n",
    "ax.set_ylabel('# number of customers')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_review2 = df_review.groupby(['reviewerId']).asin.nunique().reset_index().groupby('asin').reviewerId.nunique().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_review2['pct_reviewers']=df_review2['reviewerId']*100/df_review2['reviewerId'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Customers with 0-4 reviews form about 95% of the data. So 5 core happens to be 5% of the data.\n",
    "# df_review2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## To plot the same distribution as %\n",
    "df = df_review.groupby(['reviewerId']).asin.nunique().reset_index()\n",
    "df2= df.groupby(['asin']).reviewerId.nunique().reset_index().sort_values('reviewerId',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def flag(row):\n",
    "        if row['asin'] == 0 :\n",
    "            return '00'\n",
    "        if row['asin'] == 1 :\n",
    "            return '01'\n",
    "        if row['asin'] == 2 :\n",
    "            return '02'\n",
    "        if row['asin'] == 3 :\n",
    "            return '03'\n",
    "        if row['asin'] == 4 :\n",
    "            return '04'\n",
    "        if row['asin'] == 5 :\n",
    "            return '05'\n",
    "        if row['asin'] == 6 :\n",
    "            return '06'\n",
    "        if row['asin'] == 7 :\n",
    "            return '07'\n",
    "        if row['asin'] == 8 :\n",
    "            return '08'\n",
    "        if row['asin'] == 9 :\n",
    "            return '09'\n",
    "        if row['asin'] == 10 :\n",
    "            return '10'\n",
    "        if row['asin'] > 10 :\n",
    "            return '>10'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2['flag']=df2.apply(lambda row: flag(row),axis=1)\n",
    "df3 = df2.groupby(['flag']).reviewerId.sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3['pct'] = df3['reviewerId']/(df3['reviewerId'].sum())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LLSaOy39PL0wLYOs1XxwzM6u3dhNBRGxVj4KYmVlj1HJB2QaSviFpXB4fKenD5RfNzMzq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\nT2P3w9Z2WK0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126660a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df3.sort_values('flag',ascending=True).plot(kind='bar',x = 'flag',y='pct', color ='#80cbc4',\\\n",
    "                                     title='%customers by number of reviews'\\\n",
    "                                     ,legend = False,edgecolor='black')\n",
    "ax.set_xlabel('# reviews')\n",
    "ax.set_ylabel('% customers')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "plt.show()\n",
    "\n",
    "## This graph says that 40% of the customers write 5 reviews \n",
    "## 75% of the reviews are written by customers writing upto 7 reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reviews by time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_review['unixReviewTime2']=pd.to_datetime(df_review['unixReviewTime'],unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reviews_by_time = df_review.groupby(['unixReviewTime2']).reviewerId.count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reviews_by_time['year'] = pd.DatetimeIndex(reviews_by_time['unixReviewTime2']).year\n",
    "reviews_by_time['month'] = pd.DatetimeIndex(reviews_by_time['unixReviewTime2']).month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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24JLPfT7ucLKeEpWISAf79iMPs3ZjPX+c9FU9DLED6BMUEelAUysrefCdt7lm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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124b6f650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = reviews_by_time.groupby('year').reviewerId.sum().reset_index().plot(x='year',y='reviewerId', color='#00695c',\\\n",
    "                                                                         legend = False, title = '#Reviews over time')\n",
    "\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('# Ratings')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "\n",
    "## Huge increase in reviews since 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reviews_by_time1= reviews_by_time[reviews_by_time['year']>2011]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cuQhFRNqfFRs2EF+zhm988ri0t6E71kVE2qmSeAyAY0ak1x4CSiIiIu1WcSxG17w8xg4b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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115aea050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = reviews_by_time1[(reviews_by_time1['year']<>2014)].groupby(['month']).reviewerId.mean().reset_index().\\\n",
    "plot(x='month',y='reviewerId', legend = False, title = 'Avg. number of Ratings by month-(2011 to 2013)',color='#00695c')\n",
    "\n",
    "\n",
    "ax.set_xlabel('Month')\n",
    "ax.set_ylabel('Avg. number of Ratings')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "## Highest number of reviews during holiday season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze products\n",
    "\n",
    "#### Rerun this with the full metadata once Julius finishes scrapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12725"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta['asin'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BRAND                  12609\n",
       "FEATURE_BULLETS        12725\n",
       "NAME                   12716\n",
       "ORIGINAL_PRICE          7807\n",
       "PRODUCT_DESCRIPTION    12725\n",
       "PRODUCT_INFORMATION    12725\n",
       "SALE_PRICE              7807\n",
       "URL                    12725\n",
       "asin                   12725\n",
       "dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_meta['ORIGINAL_PRICE'].replace('None', '0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_meta['ORIGINAL_PRICE']=df_meta['ORIGINAL_PRICE'].fillna('$0')\n",
    "df_meta['SALE_PRICE']=df_meta['SALE_PRICE'].fillna('$0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "def is_number(s):\n",
    "    try:\n",
    "        complex(s) # for int, long, float and complex\n",
    "    except ValueError:\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "getMoney = lambda text : [ ''.join(x[0]) for x in re.findall(r'(\\$?\\d+([,\\.]\\d+)?k?)', text.replace(',', '').replace('$',''))]\n",
    "\n",
    "def getAvg(x):\n",
    "    s = average([(float(y) if is_number(y) else -1) for y in getMoney(x.SALE_PRICE)])\n",
    "    o = average([(float(y) if is_number(y) else -1) for y in getMoney(x.ORIGINAL_PRICE)])\n",
    "    return average([s, o])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_meta['price2']=df_meta.apply(lambda x: getAvg(x), axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6RVLf1L5bmm9Ny0eXreuC1P68pCld/3HNzGxnVXM57k3AAmAosD/ZuY6bq/jce8AxEfEZ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\nmJlZLg4OMzPLxcFhZma5ODjMzCyX/w/JG9Yd4pOGNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c61c610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_meta['price2']\\\n",
    "      .plot(kind = 'hist',bins=range(0,400,10),\\\n",
    "       title = 'Price distribution'\\\n",
    "       ,facecolor='#80cbc4',edgecolor='black')\n",
    "    \n",
    "    \n",
    "ax.set_xlabel('Price $')\n",
    "ax.set_ylabel('# of products')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kPZViuFlS71Q+XNLrue14SYPiq7g/JZ2Vtt/Tkg5tUHzX5WJbLGluKu/07Vcz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\nM7NCnDjMzKwQJw4zMyvEicPMzAr5HzcdRvDC1AE6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1218b5c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_meta[['price2']]\\\n",
    "      .plot(kind = 'hist',bins=range(0,200,10),\\\n",
    "       title = 'Price distribution (Category - Women)'\\\n",
    "       ,facecolor='#80cbc4', legend = False, edgecolor='black')\n",
    "    \n",
    "    \n",
    "ax.set_xlabel('Price $')\n",
    "ax.set_ylabel('# of products')\n",
    "ax.set_facecolor('#fffbf0')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}