triPlot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline  \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#plt.style.use('ggplot')\n",
    "from pylab import plot, show\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.colors as mcolors\n",
    "import csv\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rating_distribution(path):\n",
    "    f=open(path,'r')\n",
    "    reader = csv.reader(f)\n",
    "    fieldnames = next(reader)\n",
    "    # print(fieldnames)\n",
    "    csv_reader = csv.DictReader(f,fieldnames=fieldnames)\n",
    "    ratingd={}\n",
    "    trcnt={}\n",
    "    pcnt={}\n",
    "    for i in range(1,6):\n",
    "        ratingd[int(i)]=0\n",
    "        trcnt[i]=[]\n",
    "        pcnt[i]=[]\n",
    "    for row in csv_reader:\n",
    "        d={}\n",
    "        for k,v in row.items():\n",
    "            d[k]=v\n",
    "        t=d['text_reviews_count']\n",
    "        r=d['rating']\n",
    "        p=d['num_pages']\n",
    "        try:\n",
    "            if t!= '' and int(math.ceil(float(r)))!=0:\n",
    "                trcnt[int(math.ceil(float(r)))].append(int(t))\n",
    "            if int(math.ceil(float(r)))!=0:\n",
    "                ratingd[int(math.ceil(float(r)))]+=1\n",
    "            #print(p)\n",
    "            if p!='' and int(math.ceil(float(r)))!=0:\n",
    "                pcnt[int(math.ceil(float(r)))].append(int(p))\n",
    "        except:\n",
    "            continue\n",
    "    #print(pcnt)\n",
    "    #print(trcnt)\n",
    "    x=[]\n",
    "    rd=[]\n",
    "    trd=[]\n",
    "    pd=[]\n",
    "    from statistics import median \n",
    "    #median and average\n",
    "    for i in ratingd:\n",
    "        x.append(\"%s - %s\"%(int(i-1),int(i)))\n",
    "        rd.append(ratingd[i])\n",
    "        trd.append(sum(trcnt[i])/len(trcnt[i]))\n",
    "        pd.append(sum(pcnt[i])/len(pcnt[i]))\n",
    "    print(x)\n",
    "    print(rd)\n",
    "    print(trd)\n",
    "    print(pd)\n",
    "    rdt=[]\n",
    "    trdt=[]\n",
    "    pdt=[]\n",
    "    \n",
    "    #normalize\n",
    "    for i in range(0,5):\n",
    "        rdt.append(rd[i]/sum(rd))\n",
    "        trdt.append(trd[i]/sum(trd))\n",
    "        pdt.append(pd[i]/sum(pd))\n",
    "    print(x)\n",
    "    print(rdt)\n",
    "    print(trdt)\n",
    "    print(pdt)\n",
    "    '''\n",
    "    ls=sns.barplot(x,rdt,color='darkred')\n",
    "    ls.set_xlabel(\"Book Ratings\",fontsize=30)\n",
    "    ls.set_ylabel(\"Ratings Count\",fontsize=30)\n",
    "    ls.set_title(\"Book Ratings Distribution\",fontsize=40)\n",
    "    '''\n",
    "    import numpy as np\n",
    "    fig=plt.figure(figsize=(40,15))\n",
    "    xx = np.arange(len(x))  \n",
    "    width = 0.2   \n",
    "    fig,ax = plt.subplots(figsize=(20,15))\n",
    "    #plt.rcParams[\"figure.figsize\"]=(50,50)\n",
    "    ax.bar(xx-width,rdt,width,color='maroon',label='Ratings Count')\n",
    "    ax.bar(xx,trdt,width,color= 'Firebrick',label='Text Reviews Count')\n",
    "    ax.bar(xx+width,pdt,width,color= 'IndianRed',label='Pages Count')\n",
    "    ax.set_xticks(xx-3*width)\n",
    "    plt.xticks(xx,['0 - 1', '1 - 2', '2 - 3', '3 - 4', '4 - 5'],fontsize=20)\n",
    "    plt.xlabel('Rating Intervals',fontweight='bold',fontsize=20)\n",
    "    plt.ylabel('Normalized Count',fontweight='bold',fontsize=20)\n",
    "    plt.tick_params(labelsize=20)\n",
    "    plt.title('')\n",
    "    ax.legend(loc=2,fontsize=20)\n",
    "    plt.show()\n",
    "    fig.savefig('triRatings.png',transparent=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0 - 1', '1 - 2', '2 - 3', '3 - 4', '4 - 5']\n",
      "[66, 210, 1058, 13475, 9110]\n",
      "[0.15151515151515152, 0.17142857142857143, 5.612476370510397, 275.1987384044527, 405.2968166849616]\n",
      "[245.3, 214.56666666666666, 222.4452887537994, 295.4828087581252, 357.98184110971]\n",
      "['0 - 1', '1 - 2', '2 - 3', '3 - 4', '4 - 5']\n",
      "[0.002759312680295999, 0.00877963125548727, 0.04423261842050253, 0.5633596722270998, 0.3808687654166144]\n",
      "[0.0002207288962663539, 0.00024973897977564614, 0.008176315716252762, 0.4009124709605921, 0.590440745447113]\n",
      "[0.1836384909189638, 0.1606306517251488, 0.16652881018663218, 0.22120675537235587, 0.2679952917968994]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path='datav4.csv'\n",
    "rating_distribution(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}