zerodie/thlai

## thlai
{
 "metadata": {
  "name": "",
  "signature": "sha256:c4127bf13e205884a68f050643941af8402579dfbff070d1e9fda58d44136233"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import pandas as pd\n",
      "from pandas import DataFrame, Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Mall 48 -Conversion Rate of top 20 hot-selling items\n",
      "###Time: 2014-09-30 -- 2014-10-05 \n",
      "###x-axis: urls, obtained from i-to-i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_itemlist = pd.read_csv('../M48_HotBuy_20140930-1005.txt',sep='\\t')\n",
      "f48 = pd.read_csv('Summary_48.csv')\n",
      "\n",
      "## f48_n : M48, n-th Item ID, with attribuites: ItemID, DesURL, Nclks, Nclks_CKID, Nbuys, Nbuys_CKID, Convs, Convs_CKID\n",
      "i=1\n",
      "for itemid in f_itemlist.ID:\n",
      "    globals()['f48_'+str(i)] = f48[f48.ItemID == itemid]\n",
      "    globals()['f48_'+str(i)] = globals()['f48_'+str(i)].sort_index(by='Convs_CKID', ascending=False)\n",
      "    i+=1\n",
      "    \n",
      "## plot! \n",
      "for i in range(1,len(f_itemlist.ID)+1):\n",
      "    globals()['ax48_'+str(i)]=subplots()\n",
      "    x_range = range(len(globals()['f48_'+str(i)]))\n",
      "    plot(x_range, globals()['f48_'+str(i)].Convs_CKID,'k.')\n",
      "    title('Conversion Rate, M48, item= ' + str(f_itemlist.ID[i-1]))\n",
      "    ylim([0,1])\n",
      "    ylabel('Con. Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d9hBnU9VnYd8jczzX5en+qc+IaMoHqSvqYWAyMBpYCExpdFyF+P4IbFFV9p+k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\nODmYmVmJk4OZmZU4OZiZWYmTg5mZlfwvDjatJY6edIoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf8a9670>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kULrqe3/aT+cdUPuUzSw7HLiTOtoHScMk3UuKc2FEPEj9xP914Gxga66sXmKH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\nUAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf172a30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gJ+B/cp2zgTPy/Laqs6bmfYHr27FOUv/8/fn2UPVvpw3r3IV0AsEDpCPaTdut\nxlznhsBzwMaFee1Y59dIofog6VukhzRbpy9MMzMzoL27jMzMrA85EMzMDHAgmJlZ5kAwMzPAgWBm\nZpkDwczMAAeCmZllDgQzMwPg/wNm89jbqMqanQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf156cb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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STiI1rb6jMLoT6t+btGNxZ/4MTwJuU/q9ugHX387hsPEiOknbkDpT5re4pj5J\nEnAhsCgizi9Mmg+8O99/N/CT+mXbQUScHRGTI2IvUkfoLyPiRDqn/oeBFZL2yaOOAn4H/JQOqJ/U\neX6opO3ze+ko0okBnVI/9P5emQ+8TdI2kvYCpgI3t6C+Pin9m4HTgVkR8dfCpLavPyLuiohdI2Kv\n/BleSTq5YTWDqT8i2vZG+j8P95A6T85qdT1N1HsEqa3+DuD2fJsBTACuJp398AtgfKtrbeKxHAnM\nz/c7pn7gQOAW4E7SnveOHVb/GaRAu4vUobt1u9ZPOrp8EHiW1D/4nr5qJTV5LCOF4OvbsP6TgXtJ\nZ/nUPr9f74D6n6k9/3XTfw9MGGz9vgjOzMxK2rlZyczMWsThYGZmJQ4HMzMrcTiYmVmJw8HMzEoc\nDmZmVuJwMDOzEoeDmZmV/DdoS7JMXZVTZAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf255930>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf115850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xe131630>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xe11d0b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xddb39d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xe0fdc50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xdd45e30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xd6d2790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xd7de030>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xb743450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZWYmDg5mZlTg4mJlZyf8Hvofh+wDrqkYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x709c650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf60d890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf871bb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IdRGxkvThmjkScfalj/ihfAygxeKPiDURcXt+/jiwlHTtT1vs/37ihzbY/wAR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\ng5mZlTg5mJlZyf8HoBj/lpWR2xMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb46cdd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf7f4cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xb477330>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3Is4rJLVcfUia0jXjRNJzgDcBd9CCdRERZ0XEzIh4PvAOYHlEvJsWrAtJkyTt\nll8/FzgWuJvBqotmj7b3MRJ/PGmWymrgzGaXZwjO91LS3eSbSeMt7wUmk35zqhP4EdBWyH9WrptV\nwHHNLv8g18VRpD7lO0kXwjtIP//ecvUBHAz8V66Lu4CP5PUtVxd19fJ6ts9Warm6AJ6fPxN3Avd0\nXSMHqy58E5yZmZUM524lMzNrEgcHMzMrcXAwM7MSBwczMytxcDAzsxIHBzMzK3FwMDOzEgcHMzMr\n+V8PUBqTkBbIQwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf6f4230>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Mall 49 -Conversion Rate of top 20 hot-selling items\n",
      "###Time: 2014-09-30 -- 2014-10-05 \n",
      "###x-axis: urls, obtained from i-to-i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_itemlist = pd.read_csv('../M49_HotBuy_20140930-1005.txt',sep='\\t')\n",
      "f49 = pd.read_csv('Summary_49.csv')\n",
      "\n",
      "## f49_n : M49, n-th Item ID, with attribuites: ItemID, DesURL, Nclks, Nclks_CKID, Nbuys, Nbuys_CKID, Convs, Convs_CKID\n",
      "i=1\n",
      "for itemid in f_itemlist.ID:\n",
      "    globals()['f49_'+str(i)] = f49[f49.ItemID == itemid]\n",
      "    globals()['f49_'+str(i)] = globals()['f48_'+str(i)].sort_index(by='Convs_CKID', ascending=False)\n",
      "    i+=1\n",
      "    \n",
      "## plot! \n",
      "for i in range(1,len(f_itemlist.ID)+1):\n",
      "    globals()['ax49_'+str(i)]=subplots()\n",
      "    x_range = range(len(globals()['f49_'+str(i)]))\n",
      "    plot(x_range, globals()['f49_'+str(i)].Convs_CKID,'k.')\n",
      "    title('Conversion Rate, M49, item= ' + str(f_itemlist.ID[i-1]))\n",
      "    ylim([0,1])\n",
      "    ylabel('Con. Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iVL97pvoeUsc4LwDOqbBsPeMcD7w5vR8NLAMmN1KddhJjI9bnqPTvMOBe4PBG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\nZlbg5GBmZgVODmZmVvD/AZSPtkzJYQVMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xf5447b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sei9gSTZ9HjArV+9m4PBax1+yL/OAqfW4D8Aw4B7gNfUSP+kOvVuBtwLX19vf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\nnBzMzKzAycHMzAqcHMzMrMDJwczMCv4H0EYHcowlJVsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xff943d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xff99b30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EPK3tSDqW1LX63sLkTqh/N9KGxR35MzwBuFXp9+r6XX87h8P6k+gkbUkaTJnT\n4pp6JUnARcD8iDinMGsO8MF8/YPAZbX3bQcRcWZETIyIV5IGQn8dEe+nc+p/CFgmafc86RDgT8Dl\ndED9pMHzAyW9JL+XDiEdGNAp9UPP75U5wLslbSnplcAk4KYW1NcrpX8zcCowPSKeLcxq+/oj4q6I\nGBcRr8yf4eWkgxtWMpD6I6JtL6T/83APafDkjFbX00C9byL11d8O3JYvU4ExwLWkox+uAUa3utYG\nHsvBwJx8vWPqB/YFbgbuIG15b9dh9Z9GCrS7SAO6W7Rr/aS9yweA50njg8f1Viupy2MxKQTf3ob1\nHw/cSzrKp/r5/VYH1P9c9fmvmf9nYMxA6/dJcGZmVtLO3UpmZtYiDgczMytxOJiZWYnDwczMShwO\nZmZW4nAwM7MSh4OZmZU4HMzMrOT/A9PbzXEFpRDBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xffe2990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HKs5mTg4t1VMeKRX3FPOgbo+knYArgf8ZEU93CaRJYo2IFyPiNaSj8zdJenPV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\nsxInBzMzK3FyMDOzkv8PAxfNhGYiOXAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x120d1e30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x120f7810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12125b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1214abf0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IupD0BuDBiLhDUmtX6zRKXWTHRcRmSROBn+d7xXbak7oYyi2HTcC0wvw0do96\njeIBSZMAJE0GHszplfXTktNGDEljSYHhsoi4Oic3bH0ARMR24KfAUTRmXbwSeKOkPwJXAq+RdBmN\nWRdExOb890/AVaRuogGpi6EcHHbeRCdpL9JNdNfUuUz1cA3wzjz9TuDqQvpbJe0l6fnATGB5HcpX\nE0pNhEuBVRFxUWFRw9WHpAmdV5xI+i/Aa4E7aMC6iIjzI2JaRDwfeCtwY0S8nQasC0lNkvbN088D\nTgbuZKDqot6j7b2MxM8lXaWyDjiv3uUZhP29knQ3+dOk8ZZ3AeNJz5xaC/wMaC6sf36umzXA6+pd\n/gGui+NJfcorSAfCO0iPf2+4+gAOBf5frouVwEdzesPVRUW9nMCuq5Uari6A5+fvxArgrs5j5EDV\nhW+CMzOzkqHcrWRmZnXi4GBmZiUODmZmVuLgYGZmJQ4OZmZW4uBgZmYlDg5mZlbi4GBmZiX/HyhR\nFSVOPgyvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1217a530>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DHAhmZpY5EMzMDID/DwNn0O9G9scYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1219feb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x121d4370>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x121f9710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x12228cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1224ec90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x12282310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x122aa230>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0QrqONjn+UDN+GIbj38rJoZ6L6FpRAIsl3S7pXXne3rFlFNZaoGFXgQ+TWvHu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\nzMysxMnBzMxKnBzMzKzkvwGNPfopoY797AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x122d7890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x123007b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1232de10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x12354d30>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Mall 51 -Conversion Rate of top 20 hot-selling items\n",
      "###Time: 2014-09-30 -- 2014-10-05 \n",
      "###x-axis: urls, obtained from i-to-i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_itemlist = pd.read_csv('../M51_HotBuy_20140930-1005.txt',sep='\\t')\n",
      "f51 = pd.read_csv('Summary_51.csv')\n",
      "\n",
      "## f51_n : M49, n-th Item ID, with attribuites: ItemID, DesURL, Nclks, Nclks_CKID, Nbuys, Nbuys_CKID, Convs, Convs_CKID\n",
      "i=1\n",
      "for itemid in f_itemlist.ID:\n",
      "    globals()['f51_'+str(i)] = f51[f51.ItemID == itemid]\n",
      "    globals()['f51_'+str(i)] = globals()['f51_'+str(i)].sort_index(by='Convs_CKID', ascending=False)\n",
      "    i+=1\n",
      "    \n",
      "## plot! \n",
      "for i in range(1,len(f_itemlist.ID)+1):\n",
      "    globals()['ax51_'+str(i)]=subplots()\n",
      "    x_range = range(len(globals()['f51_'+str(i)]))\n",
      "    plot(x_range, globals()['f51_'+str(i)].Convs_CKID,'k.')\n",
      "    title('Conversion Rate, M51, item= ' + str(f_itemlist.ID[i-1]))\n",
      "    ylim([0,1])\n",
      "    ylabel('Con. Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng5mZlTgYzMysxMFgZmYl/x+KCoWWqezDqgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13455dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1349feb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x134aa4b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x134f5490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13525930>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zK3KQMDOzIgcJMzMrcpAwM7Oi/wFqH7sPN31Y8QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13549310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13577690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1359d6f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x135cc030>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZWdFIubvJzMxGICcJMzMrcpIwM7MiJwkzMytykjAzsyInCTMzK3KSMDOzIicJ\nMzMr+m943dp2TwfMEAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x135f29b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13621ef0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1364d2b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MrMhBwszMihwkzMysyEHCzMyKHCTMzKzo/wO6zKR0b9AcmAAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1367b7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DYSJaT4NO2qwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136a1830>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Da2wAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x136d0e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x136f6db0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1372c390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x137502b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1377f910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x137a68d0>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Mall 61 -Conversion Rate of top 20 hot-selling items\n",
      "###Time: 2014-09-30 -- 2014-10-05 \n",
      "###x-axis: urls, obtained from i-to-i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_itemlist = pd.read_csv('../M49_HotBuy_20140930-1005.txt',sep='\\t')\n",
      "f61 = pd.read_csv('Summary_61.csv')\n",
      "\n",
      "## f49_n : M61, n-th Item ID, with attribuites: ItemID, DesURL, Nclks, Nclks_CKID, Nbuys, Nbuys_CKID, Convs, Convs_CKID\n",
      "i=1\n",
      "for itemid in f_itemlist.ID:\n",
      "    globals()['f61_'+str(i)] = f61[f61.ItemID == itemid]\n",
      "    globals()['f61_'+str(i)] = globals()['f61_'+str(i)].sort_index(by='Convs_CKID', ascending=False)\n",
      "    i+=1\n",
      "    \n",
      "## plot! \n",
      "for i in range(1,len(f_itemlist.ID)+1):\n",
      "    globals()['ax61_'+str(i)]=subplots()\n",
      "    x_range = range(len(globals()['f61_'+str(i)]))\n",
      "    plot(x_range, globals()['f61_'+str(i)].Convs_CKID,'k.')\n",
      "    title('Conversion Rate, M61, item= ' + str(f_itemlist.ID[i-1]))\n",
      "    ylim([0,1])\n",
      "    ylabel('Con. Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf26ef70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf36e050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf357930>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf584390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfabc8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf89c650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf1f4230>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf6640d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfe3b7b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfde6df0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf752390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfc18970>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf65e7f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfa0dad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfaade70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xf956b30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfae70d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfb44af0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfb14170>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xfabf090>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Mall 64 -Conversion Rate of top 20 hot-selling items\n",
      "###Time: 2014-09-30 -- 2014-10-05 \n",
      "###x-axis: urls, obtained from i-to-i"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_itemlist = pd.read_csv('../M64_HotBuy_20140930-1005.txt',sep='\\t')\n",
      "f64 = pd.read_csv('Summary_64.csv')\n",
      "\n",
      "## f49_n : M49, n-th Item ID, with attribuites: ItemID, DesURL, Nclks, Nclks_CKID, Nbuys, Nbuys_CKID, Convs, Convs_CKID\n",
      "i=1\n",
      "for itemid in f_itemlist.ID:\n",
      "    globals()['f64_'+str(i)] = f64[f64.ItemID == itemid]\n",
      "    globals()['f64_'+str(i)] = globals()['f64_'+str(i)].sort_index(by='Convs_CKID', ascending=False)\n",
      "    i+=1\n",
      "    \n",
      "## plot! \n",
      "for i in range(1,len(f_itemlist.ID)+1):\n",
      "    globals()['ax64_'+str(i)]=subplots()\n",
      "    x_range = range(len(globals()['f64_'+str(i)]))\n",
      "    plot(x_range, globals()['f64_'+str(i)].Convs_CKID,'k.')\n",
      "    title('Conversion Rate, M64, item= ' + str(f_itemlist.ID[i-1]))\n",
      "    ylim([0,1])\n",
      "    ylabel('Con. Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nndFSJZ6WxinpBGBVRNxfUdRWcQITgTdK+pWkLkmT6h1nOyeKtuxll7QPcBXw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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xfe167d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13aa56d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13aabeb0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13af3d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13b271d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13b46b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F5UdJGidpD2BPYH4Lytc0pVvXzwEWRsQZhVVtUUdJE2tXe0jaBng1cCttUL+I\nODUipkTEHsBRwK8i4h20Qd0AJI2XtF2efgbwGuBO2qR+EbEKWC7peXnRIcDvSY8l2vL6tXq0vZeR\n+NeRrn5ZCpzS6vL0sw4Xku4Qf4o0hvJuYALpOVJLgF8CHYX0p+b6LgZe2+ryN1G/WaT+6ttIB81b\nSY9pb4s6AvsAt+T63QF8KC9vi/oVyvxyNl2t1BZ1I/XJ35Zfd9WOIe1Sv1zeFwA3A7cDPyJdrTQg\n9fNNcGZmVjKUu5XMzKxFHBzMzKzEwcHMzEocHMzMrMTBwczMShwczMysxMHBzMxKHBzMzKzk/wOO\nuC58vxolRgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13b72f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x13b97f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xAniIdIFPXeJv+E71sbOvBa7MzX8S+GSj4+ol1slsnByWATtm0zsBy7Lps4Gz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\nrOD/A19TJM9lKfaPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x13bc69d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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