{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Analysis of the data from usesthis.com\n",
      "\n",
      "The Setup at usesthis.com finds out what people use to get their job done. This is an effort to try to read and analyze that data.\n",
      "\n",
      "The base dir is the location where the github url of the usesthis repo is checked out."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import os, markdown, glob, yaml\n",
      "from re import match, findall, DOTALL\n",
      "base_dir = '/Users/lpd/gh/usesthis/'\n",
      "\n",
      "interview_files = glob.glob('%s/_posts/*'%base_dir)\n",
      "\n",
      "all_interviews = []\n",
      "for interview_file_name in interview_files:\n",
      "    with open(interview_file_name,'r') as interview_file:\n",
      "        md_string = interview_file.read()\n",
      "        yml_header = match('(?P<yml>^---.*)\\n---', md_string, DOTALL).groupdict()['yml']\n",
      "        interview = yaml.load(yml_header)\n",
      "        interview['wares'] = [wares[1] for wares in findall('\\[([^\\[\\(\\)]+)\\]\\[([a-z0-9\\.\\-]+)?\\]', md_string) if wares[1]]\n",
      "        all_interviews.append(interview)\n",
      "interview_frame = pd.DataFrame(all_interviews)\n",
      "\n",
      "del interview_frame['layout']\n",
      "del interview_frame['credits']\n",
      "del interview_frame['summary']\n",
      "del interview_frame['title']\n",
      "\n",
      "each_used_ware = []\n",
      "for person_ware in interview_frame.wares:\n",
      "    each_used_ware+=person_ware\n",
      "ps_wares = pd.Series(each_used_ware)\n",
      "\n",
      "ps_wares.value_counts()\n",
      "\n",
      "ps_wares.value_counts()[:15].plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x108d3e690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+/HkcOnQIBoMBvr6+qF69uuqxtm7dGtOnT8fYsWOxbds2rFixAnl5eVbPVTYa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\nItK5/wdwvkKpTkrIDAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108d1f9d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interview_frame.ix[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 11 entries, 0 to 10\n",
        "Data columns (total 4 columns):\n",
        "categories    11  non-null values\n",
        "license       0  non-null values\n",
        "slug          11  non-null values\n",
        "wares         11  non-null values\n",
        "dtypes: object(4)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "                                categories license              slug  \\\n",
        "0                    [developer, mac, web]     NaN        alex.payne   \n",
        "1                          [mac, musician]     NaN     jona.bechtolt   \n",
        "2                    [game, suit, windows]     NaN       gabe.newell   \n",
        "3               [developer, mac, software]     NaN      steven.frank   \n",
        "4                            [mac, writer]     NaN       john.gruber   \n",
        "5                      [artist, icon, mac]     NaN      david.lanham   \n",
        "6                          [mac, musician]     NaN  jonathan.coulton   \n",
        "7           [developer, mac, web, windows]     NaN               why   \n",
        "8   [developer, mac, web, windows, writer]     NaN      gina.trapani   \n",
        "9                    [developer, mac, web]     NaN       paul.graham   \n",
        "10              [developer, mac, software]     NaN     james.thomson   \n",
        "\n",
        "                                                wares  \n",
        "0   [macbook-pro, cinema-display, readynas-nv-plus...  \n",
        "1   [audio-kontrol-1, powershot-sd1000, live, mda-...  \n",
        "2   [oa-desk, beyond-tv, windows-xp, office, visua...  \n",
        "3   [mac-pro, macbook-air, apple-tv, itunes, addre...  \n",
        "4   [macbook-pro, cinema-display, iphone-3g, gr-di...  \n",
        "5   [mac-pro, intuos, cintiq, illustrator, on-the-...  \n",
        "6   [mac-mini, digi-002, 376, pod-pro, mx200, ksm3...  \n",
        "7                                                  []  \n",
        "8   [macbook-pro, icurve, jerker, g1, windows-xp, ...  \n",
        "9   [macbook-air, cinema-display, google-calendar,...  \n",
        "10  [twitkitteh-ios, mac-pro, ipod-touch, filemake...  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interview_frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 374 entries, 0 to 373\n",
        "Data columns (total 4 columns):\n",
        "categories    374  non-null values\n",
        "license       1  non-null values\n",
        "slug          374  non-null values\n",
        "wares         374  non-null values\n",
        "dtypes: object(4)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 374 entries, 0 to 373\n",
        "Data columns (total 4 columns):\n",
        "categories    374  non-null values\n",
        "license       1  non-null values\n",
        "slug          374  non-null values\n",
        "wares         374  non-null values\n",
        "dtypes: object(4)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(ps_wares)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "5071"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "iv_cats = interview_frame.categories"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_cats = []\n",
      "for el in iv_cats:\n",
      "    all_cats+=el"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_cats[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "['developer',\n",
        " 'mac',\n",
        " 'web',\n",
        " 'mac',\n",
        " 'musician',\n",
        " 'game',\n",
        " 'suit',\n",
        " 'windows',\n",
        " 'developer',\n",
        " 'mac']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set_cats = set(all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "1260"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(set_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "72"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps_all_cats = pd.Series(all_cats)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps_all_cats.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x108d85ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xCZLFbI642MHy0XufOPPTNr3n5y9DPqlTr902eu4zJiJtM+SYjcFSJiLyC8ds\niIhIE1hsdETvfcbMT9uYn7EZcsymulvnEAU7szkC+flH1A6D6LIZcsyG19mQdnHMkWofx2yIiEgT\nWGx0xaN2AArzqB2AwjxqB6AovY9p6D0/f7HYEBGR4i5rzCY5ORlmsxnjxo0LyM4Dvb1LwTEb0jaO\n2VDtq/Uxm0DP4lJ6VpjxHjFARBScLlpspk6diqioKLhcLuzcuRMAsHfvXtx2223o0qULEhISsHPn\nTvz111+wWCy+zxUWFqJ169YoLS2tcv1z5eTkoFu3brDb7RgwYACOHTsG4PTT78aOHQun0wmr1YrN\nmzf7tj9y5EjExcUhJiYGX3zxBQAgNTUV/fv3R1JSEvr06eP3D0hbPGoHoDCP2gEozKN2AIrS+5iG\n3vPz1wWvs8nKysKSJUuwbds2nDp1CjExMYiNjcW//vUvzJkzB23btsV3332HRx55BGvWrIHD4fDd\nn+urr77CrbfeipCQEIwePRrvvPPOeesDZ85uHnjgAcyePRsulwsTJ07EpEmT8Prrr8NkMuHEiRPI\nzs5GRkYGRo4cie3bt2Pq1KlISkrCvHnzcOzYMcTFxfkeA52dnY3t27cjPDy8mszcACzlr8MBOHDm\nOTCe8q9ssx2M7cr3wKv4Bcc224FsV7z2er0IlAuO2bzxxhs4duwYkpOTAQDjxo3DVVdd5TvbqVBc\nXIwffvgBixcvxrp16/D222/j7rvvxmOPPYa4uDg0bdoUHTp0OG/9SZMmwWw2Y9SoUbBarfj5558B\nAD/99BMGDRqErKws9OrVCxMnTvT9MK6//nrk5uYiKSkJJ0+eRN26p+vl0aNHsXLlSnz33Xf473//\ni3nz5lWdMMdsSNM4ZkO1LxBjNhc8s6lqB2VlZQgPD0d2dvZ56/fr1w/PP/88jh49iq1bt+Lmm29G\nQUEBIiIiqly/OhdLquJs6NNPP0W7du0qfe+7775Dw4YNL3lfRESkvAuO2SQkJODzzz9HUVERCgoK\n8OWXX6JBgwaIjIxEWloagNOFYdu2bQCAsLAw3HjjjXjiiSfQr18/mEwmNGrU6Lz1c3NzffsQETRq\n1AgRERFYv349AGDhwoW+MxkRwZIlSwAA69evR3h4OBo1aoS+ffti5syZvu1UFDNj/9XnUTsAhXnU\nDkBhHrUDUJTexzT0np+/Lnhm43Q6MWTIENjtdjRr1gxdu3aFyWTCokWL8PDDD2PKlCk4deoU7r33\nXtjtdgAygoVTAAAIdUlEQVTAkCFDMHjw4Eo/+KrWt9lsAM6cpSxYsABjxozB8ePH0aZNG8yfP9/3\n/fr16yMmJgYlJSW+7rEJEyZg7NixsNlsKCsrww033IAvvvgCJpOJ9z4jIgoyQX9vtF69emH69OmI\niYkJyPY4ZkPaxjEbqn28NxoREWlC0Beb9PT0gJ3V6J9H7QAU5lE7AIV51A5AUXof09B7fv4K+mJD\nRETaF/RjNoHGyQOkZXx4GqlB8ets9Mpg9ZWISHXsRtMRvfcZMz9tY37GxmJDRESKM+SYjcFSJiLy\nC8dsaoiTBIiILizQk1EMeWaj3zsIeHD2rej1xwPmp2UeMD8tOXM2wzsIEBGRJvDMhoiIqsAzGyIi\n0hgWG13xqB2AwjxqB6Awj9oBKMyjdgAK86gdQFALumLj9XrRoUMHjBgxAlFRURg6dChWrVqF7t27\no3379ti8eTM2b96M+Ph4xMTEoHv37ti1axcAoLS0FE8//TSsVivsdjvefPNNlbMhIiIAgASZvLw8\nqVu3rnz//fdSVlYmsbGxMnLkSBERWbZsmdx1111SUFAgJSUlIiKyevVqGThwoIiIvPXWWzJo0CAp\nLS0VEZEjR46ct30AAggXLly4cLnggkq/N/0VlNfZREZGonPnzgCAzp07o3fv3gCA6OhoeL1eHDt2\nDMOGDcOePXtgMplQUlICAFizZg0efvhh1Klz+oQtIiKimj24AVjKX4cDcODMlEVP+Ve22WabbaO2\nT3O73QgYv8tVgOXl5Ul0dLSv7Xa7JS0trdL33G63zJo1y/eexWIREZGBAwfK6tWrL7h9QM9nNulB\nEAPzY37MTx8LKv3e9FfQjdlcjIggPz8f1157LQAgNTXV970+ffrgnXfeQWlpKQDg6NGjaoRIRETn\nCMpic+7tZM5u16lTB8888wyee+45xMTEoLS01Pf9UaNGoXXr1rDZbHA4HFi8eHGtxq2+RLUDUFii\n2gEoLFHtABSWqHYACktUO4Cgxos6iYioCryok6rlUTsAhXnUDkBhHrUDUJhH7QAU5lE7gKDGYkNE\nRIpjNxoREVUhsN1oQXmdjfL4PBsiogsxm6u7TrFmDFls9Hoy5/F4kJiYqHYYimF+2sb8jI1jNjqS\nk5OjdgiKYn7axvyMjcVGR44dO6Z2CIpiftrG/IyNxYaIiBTHYqMjXq9X7RAUxfy0jfkZm0GnPhMR\n0eXg1OfLZLDaSkQUFNiNRkREimOxISIixbHYEBGR4gxVbFauXIkOHTqgXbt2ePnll9UOx28WiwU2\nmw1OpxNdu3YFABw5cgR9+vRB+/btccstt2hq7v/IkSPRvHlzWK1W33sXyuell15Cu3bt0KFDB6xa\ntUqNkC9LVfklJyejVatWcDqdcDqdWLFihe97Wsrvl19+Qa9evdC5c2dER0dj5syZAPRz/KrLTy/H\nr6ioCHFxcXA4HOjUqROee+45AAE+fn4/61MjSkpKpE2bNpKXlyfFxcVit9tlx44daoflF4vFIocP\nH6703jPPPCMvv/yyiIhMmzZNxo8fr0ZoNbJu3TrZunVrpceCV5fPDz/8IHa7XYqLiyUvL0/atGkj\npaWlqsR9qarKLzk5WaZPn37eulrL7+DBg5KdnS0iIgUFBdK+fXvZsWOHbo5fdfnp5fiJiBQWFoqI\nyKlTpyQuLk4yMjICevwMc2aTmZmJtm3bwmKxIDQ0FPfccw+WLVumdlh+k3Nm133xxRcYPnw4AGD4\n8OH4/PPP1QirRlwuFyIiKt/8r7p8li1bhnvvvRehoaGwWCxo27YtMjMzaz3my1FVfkDVMyS1lt81\n11wDh8MBAAgLC0PHjh2xf/9+3Ry/6vID9HH8AKBBgwYAgOLiYpSWliIiIiKgx88wxWb//v247rrr\nfO1WrVr5/rFolclkQu/evdGlSxe8++67AIDffvsNzZs3BwA0b94cv/32m5oh+q26fA4cOIBWrVr5\n1tPy8Zw1axbsdjsefPBBXzeFlvPzer3Izs5GXFycLo9fRX7dunUDoJ/jV1ZWBofDgebNm/u6DAN5\n/AxTbPR4MeeGDRuQnZ2NFStWYPbs2cjIyKj0fZPJpKu8L5aPFnN9+OGHkZeXh5ycHLRo0QLjxo2r\ndl0t5Pf3339j4MCBmDFjBsxmc6Xv6eH4/f333/jnP/+JGTNmICwsTFfHr06dOsjJycGvv/6KdevW\nIT09vdL3/T1+hik2LVu2xC+//OJr//LLL5Uqsxa1aNECANC0aVPcfffdyMzMRPPmzXHo0CEAwMGD\nB9GsWTM1Q/Rbdfmcezx//fVXtGzZUpUY/dGsWTPff+JRo0b5uiK0mN+pU6cwcOBADBs2DHfddRcA\nfR2/ivzuv/9+X356On4VGjdujNtvvx1ZWVkBPX6GKTZdunTB7t274fV6UVxcjCVLlqB///5qh1Vj\nx48fR0FBAQCgsLAQq1atgtVqRf/+/bFgwQIAwIIFC3z/KbSqunz69++P//znPyguLkZeXh52797t\nm5GnJQcPHvS9/uyzz3wz1bSWn4jgwQcfRKdOnTB27Fjf+3o5ftXlp5fj9+eff/q6AE+cOIHVq1fD\n6XQG9vgpNrUhCH399dfSvn17adOmjbz44otqh+OXn376Sex2u9jtduncubMvn8OHD0tSUpK0a9dO\n+vTpI0ePHlU50kt3zz33SIsWLSQ0NFRatWol8+bNu2A+U6dOlTZt2khUVJSsXLlSxcgvzbn5vf/+\n+zJs2DCxWq1is9nkzjvvlEOHDvnW11J+GRkZYjKZxG63i8PhEIfDIStWrNDN8asqv6+//lo3xy83\nN1ecTqfY7XaxWq3yyiuviMiFf59cbn6GuxEnERHVPsN0oxERkXpYbIiISHEsNkREpDgWGyIiUhyL\nDRERKY7FhoiIFPf/UGjKcr4tG+AAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106b5add0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps_all_cats.value_counts()[-25:].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x108da9390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rU6dOFRGRhQsXSsuWLeXcuXNy48YN+f3vfy8XL14sNZbbAQSbCxeVKSvNsOZP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\nlQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108dc89d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "set_wares = set(ps_wares)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(set_wares)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "2236"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wares = glob.glob('%s/wares/hardware/*'%base_dir)\n",
      "hardware = []\n",
      "for el in wares:\n",
      "    desc = yaml.load(open(el))\n",
      "    desc['title'] = os.path.splitext(os.path.basename(el))[0]\n",
      "    hardware.append(desc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wares = glob.glob('%s/wares/software/*'%base_dir)\n",
      "software = []\n",
      "for el in wares:\n",
      "    desc = yaml.load(open(el))\n",
      "    desc['title'] = os.path.splitext(os.path.basename(el))[0]\n",
      "    software.append(desc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ps_wares"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0          macbook-pro\n",
        "1       cinema-display\n",
        "2     readynas-nv-plus\n",
        "3         time-machine\n",
        "4      airport-express\n",
        "5       safari-adblock\n",
        "6      google-calendar\n",
        "7            istat-pro\n",
        "8          google-apps\n",
        "9         review-board\n",
        "10           bloom-ios\n",
        "11       google-reader\n",
        "12              plan-9\n",
        "13     audio-kontrol-1\n",
        "14    powershot-sd1000\n",
        "...\n",
        "5056    expansion-portable\n",
        "5057              mac-os-x\n",
        "5058        thinkpad-t410s\n",
        "5059         thinkpad-t410\n",
        "5060              iphone-5\n",
        "5061         vmware-fusion\n",
        "5062                chrome\n",
        "5063            subversion\n",
        "5064           macbook-pro\n",
        "5065           google-docs\n",
        "5066          tweetbot-ios\n",
        "5067                  live\n",
        "5068         final-cut-pro\n",
        "5069        creative-suite\n",
        "5070           macbook-pro\n",
        "Length: 5071, dtype: object"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf_used_ware = pd.DataFrame(ps_wares)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf_used_ware"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5071 entries, 0 to 5070\n",
        "Data columns (total 1 columns):\n",
        "0    5071  non-null values\n",
        "dtypes: object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5071 entries, 0 to 5070\n",
        "Data columns (total 1 columns):\n",
        "0    5071  non-null values\n",
        "dtypes: object(1)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf_used_ware.columns = ['title']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pf_used_ware"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5071 entries, 0 to 5070\n",
        "Data columns (total 1 columns):\n",
        "title    5071  non-null values\n",
        "dtypes: object(1)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5071 entries, 0 to 5070\n",
        "Data columns (total 1 columns):\n",
        "title    5071  non-null values\n",
        "dtypes: object(1)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "software[:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "[{'description': 'An audio plugin that emulates the 1176 limiter.',\n",
        "  'name': '1176 (plugin)',\n",
        "  'title': '1176',\n",
        "  'url': 'http://www.uaudio.com/store/compressors-limiters/1176-collection.html'},\n",
        " {'description': 'A web service for uploading videos from mobile phones or webcams.',\n",
        "  'name': '12seconds',\n",
        "  'title': '12seconds',\n",
        "  'url': 'http://12seconds.tv/'}]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for el in software:\n",
      "    el['type'] = 'software'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for el in hardware:\n",
      "    el['type'] = 'hardware'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ware = hardware+software"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ware[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "[{'description': 'A very powerful scanner.',\n",
        "  'name': '10000XL',\n",
        "  'title': '10000xl',\n",
        "  'type': 'hardware',\n",
        "  'url': 'http://www.epson.com/cgi-bin/Store/jsp/Product.do?sku=E10000XL-PH'},\n",
        " {'description': 'Old speakers.',\n",
        "  'name': '104aB',\n",
        "  'title': '104ab',\n",
        "  'type': 'hardware',\n",
        "  'url': 'http://www.kef.com/html/us/explore/about_kef/museum/1970s/104aBKIT_CantataKIT/index.html'},\n",
        " {'description': 'A subwoofer.',\n",
        "  'name': '10S',\n",
        "  'title': '10s',\n",
        "  'type': 'hardware',\n",
        "  'url': 'http://www.shopping.com/KRK-KRK-10S-Subwoofer/info?sb=1'}]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(ware)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "3326"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(hardware)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "1373"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(software)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "1953"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1953+1373"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "3326"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extracted_ware = pd.DataFrame(ware)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares = pf_used_ware.merge(extracted_ware,on='title',how='inner')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares[:20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 20 entries, 0 to 19\n",
        "Data columns (total 5 columns):\n",
        "title          20  non-null values\n",
        "description    20  non-null values\n",
        "name           20  non-null values\n",
        "type           20  non-null values\n",
        "url            20  non-null values\n",
        "dtypes: object(5)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "          title                          description         name      type  \\\n",
        "0   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "1   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "2   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "3   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "4   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "5   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "6   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "7   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "8   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "9   macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "10  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "11  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "12  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "13  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "14  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "15  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "16  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "17  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "18  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "19  macbook-pro  The popular Intel-based Mac laptop.  MacBook Pro  hardware   \n",
        "\n",
        "                                 url  \n",
        "0   http://www.apple.com/macbookpro/  \n",
        "1   http://www.apple.com/macbookpro/  \n",
        "2   http://www.apple.com/macbookpro/  \n",
        "3   http://www.apple.com/macbookpro/  \n",
        "4   http://www.apple.com/macbookpro/  \n",
        "5   http://www.apple.com/macbookpro/  \n",
        "6   http://www.apple.com/macbookpro/  \n",
        "7   http://www.apple.com/macbookpro/  \n",
        "8   http://www.apple.com/macbookpro/  \n",
        "9   http://www.apple.com/macbookpro/  \n",
        "10  http://www.apple.com/macbookpro/  \n",
        "11  http://www.apple.com/macbookpro/  \n",
        "12  http://www.apple.com/macbookpro/  \n",
        "13  http://www.apple.com/macbookpro/  \n",
        "14  http://www.apple.com/macbookpro/  \n",
        "15  http://www.apple.com/macbookpro/  \n",
        "16  http://www.apple.com/macbookpro/  \n",
        "17  http://www.apple.com/macbookpro/  \n",
        "18  http://www.apple.com/macbookpro/  \n",
        "19  http://www.apple.com/macbookpro/  "
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.type=='hardware'].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109cf5e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bh+eee06Zetp896MVQEbtshQNjzbsbF9RG5d53vPvX/8vcrvd3q7bvvZ5vNmO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109a31850>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.type=='software'].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109d3f450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SgqlTpzpMnqravD0ff/uV3dRarVbDKrbqWZcuXUpz584V29OmTaN58+ZRixYt\nxHnnzp2jgIAAysnJqXR+dnY2tWzZUpx/6tQp8vPzI6Lye26BgYGUnp5eIUNkZCTpdDrq168fEd3d\nc0tOTiYiotTUVAoJCak0u1qtptTUVCIiio2NpcjISCIiatCgARUXFxMRUU5OjrjnNn36dFq9enWF\n5WRkZIh5KwMoZc/N5AAZnCFjdeVElb9j1jKZTDZfZnXgnLallJzW/M7brObWpUsXxMfHo6ioCPn5\n+dizZw/q1KmD+vXr49ChQwCATZs2wWg0ol69epXO9/T0hIeHB5KSkgAAW7durXRdffr0wfLly8W2\n2WwGAKxfvx5ms7nc3t7d7QJ4e3vj6tWrOHr0KADgzp07OHPmDAAgPz8fTZo0wZ07d7B58+Zyn6ks\nQ1xcXLn1r1+/HgUFBQCAixcv4urVq9ZuOgdklDuABEa5A0hklDuAJEqpvXBO21JKTmvYbFgyKCgI\n/fv3h1arRePGjaHRaODl5YUNGzbg9ddfx82bN9GmTRvExMQAQJXz161bh9deew0uLi4IDQ2Fp6en\nuI6yoxVnz56NqVOnQqvVorS0FK1bt67yoJKy97i7u2P79u14++23kZOTg+LiYrzzzjvw9fXFggUL\nEBwcjIYNGyI4OBj5+fkAgGXLlmHUqFH4+OOPMWDAAHFZvXr1wtmzZxESEgIA8PDwwObNm8XLIzHG\nGJOZLXcd8/PziYiooKCAgoKCyGw2W70MIqKFCxeKB5o4A4CHJWtWxurKCZv/bipleIpz2pZSclrz\nO2/TO3FPnDgRZ86cQWFhISIiIqDT6R55GXv37sXChQtRXFwMtVotFhUZY4wxqfjaknbE15ZktsHX\nlmQ1C19bkjHGGAN3bjJQ8cTTY00eHvVha0q5xiDntC2l5LQGd252RkQOP5lMJtkzOEPG6sqZm3tD\n7l9jxhwe19zsyJpxY8YYq+m45sYYY4yBOzdWCSWMwyshI8A5bY1z2pZSclqDOzfGGGNOh2tudsSX\n5mI1iYdHfT74hdmENTU37tzsiE/iZjULH0DFbIMPKGE2IsgdQAJB7gASCXIHkEiQO4AkSqkRcU75\ncefGGGPM6ShmWFKtVuPkyZN4+umnJb3+p59+gru7u3hbmkcRGxuL5ORkrFix4pHf+yA8LMlqFh6W\nZLbh1MOSj/rhTCYTDh8+bPW6GGOMKZfDdW4WiwU+Pj545ZVX4Ovri2HDhuHWrVsAgBUrViAwMBBa\nrRa//PL1lSZHAAAZeklEQVQLAODGjRsYOHAg/P39ERISgrS0NFgsFqxevRqffvop9Ho9EhMTYbFY\n0L17d/j7+6Nnz57IzMwEAGzbtg0ajQY6nU68Ky0R4dKlS3jppZfQrl07vPfee2K+LVu2QKvVQqPR\n4P333xfn161bF9OmTYOfnx969uyJa9eu2WmLVQdB7gASCHIHkEiQO4BEgtwBJFFKjYhzOgByMBkZ\nGaRSqejw4cNERDR+/HhavHgxqdVq+uyzz4iIaNWqVTRhwgQiIpo8eTLNnz+fiIgOHDhAOp2OiIii\noqJoyZIl4nLDwsJo48aNRES0fv16GjhwIBERaTQaunTpEhER5eTkEBFRTEwMtW7dmnJzc6mwsJBa\ntmxJv//+O128eJFatGhB165do+LiYurevTvt2rWLiIhUKhV98cUXREQ0f/58mjx5coXPBvDNSmtW\nxpqeEzb8n+Eupdxck3PaljW/Sw635wYAzZs3F2tlr7zyCg4dOgQAGDx4MAAgICAAFosFAJCYmIgx\nY8YAAF588UVcv34deXl5AIC72+Suo0ePYtSoURWW2aVLF4wbNw5r165FcXExgLvDkj169ICHhwdq\n164NX19fWCwWHD9+HEajEQ0aNICrqytGjx6NgwcPAgBcXFwwfPjwCsuvKAJA1L1pKcr/xSw4SNvo\nYHkqaxsdLE9VbTzkeUdpG6th+eX3DARBeOz2X5dt6+Xbqm00Gh0qT1Xt+zlCnrK2IAiIiIhAREQE\noqKiYBXb97GPJyMjg1q2bCm2f/zxRxo0aBCp1Wq6fv06EREdP36cjEYjERHp9Xr67bffxNc3b96c\ncnNzKSoqiqKjo8X5zzzzDN25c4eIiG7fvk3PPPOM+NyxY8dozpw54jpiYmLK7XmFhYWRIAj09ddf\n09ixY8X5a9eupenTpxMRkaurK5WUlBAR0fnz50mv11f4bIBS9tx44skWE6z+f4Cx+1nzu+SQe24X\nLlzA0aNHAQBffPEFunbtWuVrDQYD4uLiANzt7Rs2bAgPDw94eHiIe3AA0LlzZ2zduhUAEBcXh27d\nugEAzp8/j06dOmHevHlo2LAhMjMzKz2gRKVSoVOnTvjpp59w/fp1lJSUYOvWrQgNDQUAlJaWYtu2\nbWJmg8Fggy0hF0HuABIIcgeQSJA7gESC3AEk+evehqPinPJzyM7N29sbK1euhK+vL3JycvDGG2+U\ne16lUokdUFRUFJKTk+Hv748PPvgAGzZsAACEh4dj586d4gElK1asQExMDPz9/REXF4dly5YBAP7f\n//t/4gEiXbp0gb+/v7iOv2rSpAkWLVqEF198ETqdDkFBQQgPDwcA1KlTB0lJSdBoNBAEAXPmzKm2\n7cMYY+zBHO48N4vFgvDwcKSlpckd5ZH8dU+xMnyeG6tZ+Dw3ZhtOc56bEs8zU2JmxhhzVg7XuanV\naqSmpsod45Hl5ubKHcGGBLkDSCDIHUAiQe4AEglyB5BEKTUizik/h+vcGGOMscflcDU3Z8ZDl6wm\n4fu5MVuxpuZWq5qysCrw3xKMMVb9eFiSVaCEcXglZAQ4p61xTttSSk5rcOfGGGPM6XDNzY6sGTdm\njLGajmtuCsAHlTBWNT4IhdkKD0vaHSlgMjlABmfIyDkfdcrLy8KDKKVGxDnlx50bY4wxp8M1Nzvi\na0sy9jBcl2YVOc21JRljjLHHwZ0bq4QgdwAJBLkDSCTIHUAiQe4AkiilRsQ55aeozq1fv36PdIFi\ni8UCjUZTjYnu+uKLL6DX68XJ1dVVkRd/ZowxZ+HUNTc57g33888/Y9CgQfj1118rPMc1N8Yehmtu\nrCLF19wWL16MFStWAADeeecd9OjRAwBw4MABjB49Gq1atcKNGzdgsVjQvn17TJw4EX5+fujTpw8K\nCwsBQLwrt06nw6pVq8RlFxYWIjIyElqtFgEBAeLueFhYmNj56fV6LFiwAAAwZ84crF27FpcvX0a3\nbt2g1+uh0Whw6NChB36GL774AiNGjLDpdmGMMfZoHKpz69atGxISEgAAJ06cQEFBAYqLi3Ho0CGE\nhoaWe+25c+cwefJk/Pzzz/Dy8sKOHTsAAJGRkVi5ciVSUlLKvX7lypXicOGWLVswbtw4FBUVwWAw\nICEhAbm5uXBzc8Phw4cBQFznli1b0LdvX5jNZqSmpkKn0z3wM3z11VcYOXLkA14RASDq3rQU5Wsd\ngoO0hYc87wjtv2aVO09V7aUOlqeqtvCQ5+3Vvq8lCOVqQoIgYOnSpQ983lHaZY8dJU9VbUfdnoIg\nICIiAhEREYiKioJVyIHcvn2bWrduTbm5udSzZ0+aOnUqHTlyhHr27ElnzpwhtVpN169fp4yMDGrb\ntq34vo8//pj+8Y9/UHZ2NrVo0UKcn5qaSn5+fkRENGjQIDKZTOJzBoOBUlNTKTExkYYPH07ffPMN\nRUVFUdeuXenmzZukVquJiOjgwYP0/PPPU1RUFKWkpDww/9GjR0mj0VT5PAACSAGTyQEyOENGzvno\nEx74Hbv/O+zIOKdtWdNVOdSem5ubG1q1aoXY2Fh07twZXbt2xYEDB3D+/Hm0b9++3Gtr164tPnZ1\ndUVxcXGF5d3dJlW3VSoVOnbsiBMnTiAhIQHdunWDTqfD559/jqCgIAAQ9+yee+45REREYNOmTdi1\na5d48EhycrK4vK1bt2LUqFGPvR3kZ5Q7gARGuQNIZJQ7gERGuQNIYjQa5Y4gCeeUn0N1bsDdziQ6\nOhqhoaEwGAz417/+Bb1eL+m9np6e8PLyQmJiIgAgLi6u3HLL2unp6bhw4QK8vb3h5uaGZs2aYdu2\nbejcubO4/m7dugEALly4gIYNG2LChAmYMGECzGYzBg4cCLPZDLPZjMDAQABAaWkptm3bxvU2xhhz\nAA7ZuV25cgUhISFo1KgRnnzySRgMhgqv++sFiMvaMTExePPNN8UOsWz+pEmTUFpaCq1WixEjRmDD\nhg1wc3MDcLfW17hxY9SuXRtdu3bFpUuXxHUKggCdToeAgAB89dVXmDJlSqW5Dx48iBYtWkCtVttk\nO8hLkDuABILcASQS5A4gkSB3AEnur9E4Ms4pP6c+FcDRKOdUAAGOP0wlwPEzApzzUT34kG9BEBQx\nlMY5bcuaUwG4c7Mj5XRujMmFz3NjFSn+PDfGGGPMFrhzszsVTzzxVMXk4VEfD6KUGhHnlB93bnZG\nRA4/mUwm2TM4Q0bO+egT34Wb2QrX3OzImnFjxhir6bjmxhhjjIE7N1YJJYzDKyEjwDltjXPallJy\nWqOW3AFqmr+efM4Yc34eHvW5nmhnXHOzIz7PjbGaiuvtj4Nrbowxxhi4c2OVEuQOIIEgdwCJBLkD\nSCTIHUAiQe4AEglyB5DEmWtu3LkxxhhzOlxzsyOuuTFWU3HN7XFwzY0xxhiDwjo3i8UCHx8fREZG\nwtvbG6NHj8a+ffvQpUsXtGvXDsePH8fx48fRuXNnBAQEoEuXLkhPTwcAlJSU4N1334VGo4G/vz8+\n++yzStfxySefQKPRQKPRYNmyZQCAgoIC9OvXDzqdDhqNBl999VWF9+3cuRM9e/YEAFy+fBne3t74\n888/q2lLVDdB7gASCHIHkEiQO4BEgtwBJBLkDiCRIHcASZy55qa489zOnz+PHTt2wNfXFx07dsSX\nX36JxMRE7N69Gx999BE2bdqEhIQEuLq6Yv/+/fjggw+wfft2fP7557hw4QJOnToFFxcXZGVlVVh2\ncnIyYmNjkZSUhNLSUgQHByM0NBTnz5/Hc889h7179wIAcnNzK7x30KBB+Pe//43PPvsM33//PebP\nn49GjRpV8gkiAKjvPfYCoMP/7qMl3PspdxsPeZ7b0tspDpZH6W3lbs+yjqTs/mmO0E5JSXGoPGVt\nQRAQGxsLANbfAJoUJCMjg9q2bSu2x44dS1988QUREZ0/f550Oh1lZmbSwIEDyc/PjzQaDbVv356I\niIYMGUL79+9/4PKXLl1Kc+fOFduzZ8+m5cuXU3p6OqnVanrvvfcoISGhyvdnZWVR06ZNaejQoZU+\nD4AA4oknnmrcBKn/zbFKWLP9FDUsCQC1a9cWH7u4uMDd3V18XFxcjNmzZ6NHjx5IS0vD7t27cevW\nLfH1d7fR/yQlJUGv10Ov1yM+Pr5C0ZKIoFKp0LZtW5jNZmg0GsyaNQsLFiwo9949e/YAADIzM+Hq\n6oo//vijwroYY4zZj+I6twchIuTm5qJp06YAIO7WAkCvXr2wevVqlJSUAACysrLQqVMnmM1mmM1m\nhIeHw2AwYNeuXbh16xYKCgqwa9cuGAwGXL58GU888QRGjx6Nd999FydPniz33rCwMBQXF+PVV1/F\n1q1b4ePjg08++USOTWAjgtwBJBDkDiCRIHcAiQS5A0gkyB1AIkHuAJI4c81NcZ3bX6/NeH/bxcUF\nM2bMwMyZMxEQEICSkhLx+QkTJqBFixbQarXQ6XTYsmVLhWXr9XpERESgU6dOeOGFF/Daa6/B398f\naWlpCA4Ohl6vx4IFCzB79uwK7124cCG6deuGzp0745NPPsHatWvxyy+/2PjTM8YYk4LPc7MjPs+N\nsZqKz3N7HHyeG2OMMQbu3FilBLkDSCDIHUAiQe4AEglyB5BIkDuARILcASThmhtjjDGmIFxzsyO+\nUSljNRPfrPTxWFNzU9wVSpSO/5ZgjLHqx8OSrAIljMMrISPAOW2Nc9qWUnJagzs3xhhjTodrbnZk\nzbgxY4zVdFxzUwA+qIQx5iwc+UAZHpa0O1LAZHKADM6QkXNyTkefHi9nXl7FW4c5Cu7cGGOMOR2u\nudkRX1uSMeZc7HMcAV9bEncPbQ0PD7f6/RaLBRqNxoaJGGOM2ZvTdW7MFgS5A0ggyB1AIkHuABIJ\ncgeQSJA7gESC3AEkEuQOUG1k6dwWLFgAHx8fGAwGjBo1CkuWLEFKSgpeeOEF+Pv7Y/DgwcjOzgaA\nKucfP34cWq0Wer0eM2bMqHRvq6CgAOPHj0dwcDACAgKwe/fuSvMkJyfD398fOp0Oq1atEucXFhYi\nMjISWq0WAQEB4gmPJSUlePfdd6HRaODv74+VK1cCAN5//3106NAB/v7+mDFjhi03GWOMsUdBdpaU\nlEQ6nY6KioooLy+P2rZtS9HR0aTVaungwYNERDRnzhyaOnUqERFpNJpK53fo0IGOHj1KRETvv/8+\naTQaIiIymUwUFhZGREQzZ86kzZs3ExFRVlYWtWvXjgoKCipk0mg0lJCQQEREM2bMID8/PyIiio6O\npldffZWIiP7zn/9QixYtqLCwkFatWkXDhg2jkpISIiK6ceMGXbt2jby9vcVl5uTkVFgPAAKIJ554\n4slJJljdFzwKa9Zj9/PcEhMTMXDgQLi7u8Pd3R3h4eEoKChAdnY2DAYDAGDcuHEYNmwYcnNzkZOT\nU2F+Tk4O8vPzERwcDAAYNWoU9uzZU2Fd+/btQ3x8PKKjowEARUVFyMzMhLe3t/ia7Oxs5OTkoGvX\nrgCAMWPG4NtvvxWzvv322wAAb29vtGzZEunp6fjxxx/xxhtvwMXl7o5v/fr1UVJSgieeeAKvvvoq\nwsLCEBYWVsUWiACgvvfYC4AOgPFeW7j3k9vc5ja3ldK+17o3smU0Gh+7LQgCYmNjAQBqtRpWqYZO\n9oGWLl1Kc+fOFdvTpk2jefPmUYsWLcR5586do4CAAMrJyal0fnZ2NrVs2VKcf+rUKXFv6/49t8DA\nQEpPT6+QITIyknQ6HfXr14+ys7PLreP+ZQ0aNIgOHDggPmcwGCg1NZWGDBlCP/zwQ4XlFhUV0Tff\nfEPjx4+n7t27V3geUMqem8kBMjhDRs7JOR19etyceNh/+TZhzXrsXnPr0qUL4uPjUVRUhPz8fOzZ\nswd16tRB/fr1cejQIQDApk2bYDQaUa9evUrne3p6wsPDA0lJSQCArVu3VrquPn36YPny5WLbbDYD\nANavXw+z2Yw9e/bA09MTXl5eSExMBADExcWJrzcYDGI7PT0dFy5cgI+PD3r16oXVq1ejpKQEAJCV\nlSXufb700kv45JNPcOrUKVtuNsYYY4/A7sOSQUFB6N+/P7RaLRo3bgyNRgMvLy9s2LABr7/+Om7e\nvIk2bdogJiYGAKqcv27dOrz22mtwcXFBaGgoPD09xXWUXeJq9uzZmDp1KrRaLUpLS9G6detKDyqJ\niYnB+PHjoVKp0Lt3b/H9kyZNwhtvvAGtVotatWphw4YNcHNzw4QJE5Ceng6tVgs3NzdMnDgRgwcP\nxoABA1BYWAgiwqefflrdm7IaGeUOIIFR7gASGeUOIJFR7gASGeUOIJFR7gASGeUOUG1kOYm7oKAA\nderUwc2bNxEaGoo1a9ZAp9NZtQwAWLRoEf744w+H71D4JG7GmHPhk7jLmThxIvR6PQIDAzF06NBH\n7tgAYO/evdDr9dBoNEhMTMSsWbOqIWlNJcgdQAJB7gASCXIHkEiQO4BEgtwBJBLkDiCRIHeAaiPL\nXQHur2tZ6+WXX8bLL79sgzSMMcacDV9b0o54WJIx5lwcd1iS7+dmd3w/N8aYc/DwqC93hCrxtSXt\njIgcfjKZTLJncIaMnJNzOvr0uDkd9UalAHdurBIpKSlyR3goJWQEOKetcU7bUkpOa3Dnxioouzi1\nI1NCRoBz2hrntC2l5LQGd26MMcacDndurAKLxSJ3hIdSQkaAc9oa57QtpeS0Bp8KYEdll/VijDH2\naB61q+JTAeyI/45gjDH74GFJxhhjToc7N8YYY06HOzfGGGNOhzs3O/nuu+/g4+ODtm3b4uOPP5Y7\njmj8+PHiffXK3LhxA7169UK7du3Qu3dv2c+FyczMxIsvvogOHTrAz89PvAGto+UsLCxEcHAwdDod\nfH19MXPmTIfMWaakpAR6vR7h4eEAHDOnWq2GVquFXq9Hp06dADhmzuzsbAwdOhTt27eHr68vjh07\n5nA5f/nlF+j1enHy9PTE8uXLHS7nwoUL0aFDB2g0GowaNQpFRUVWZeTOzQ5KSkowefJkfPfddzhz\n5gy2bNmCs2fPyh0LABAZGYnvvvuu3LxFixahV69eSE9PR48ePbBo0SKZ0t3l5uaGTz/9FKdPn8bR\no0excuVKnD171uFyPvHEEzCZTEhJSUFqaipMJhMOHTrkcDnLLFu2DL6+vuJRvI6YU6VSQRAEmM1m\nJCUlAXDMnFOmTMH//d//4ezZs0hNTYWPj4/D5fT29obZbIbZbEZycjKeeuopDBo0yKFyWiwWrFmz\nBidPnkRaWhpKSkqwdetW6zISq3aHDx+mPn36iO2FCxfSwoULZUxUXkZGBvn5+Yltb29vunLlChER\nXb58mby9veWKVqkBAwbQDz/84NA5CwoKKCgoiH7++WeHzJmZmUk9evSgAwcOUFhYGBE55r+7Wq2m\na9eulZvnaDmzs7OpVatWFeY7Ws77ff/999S1a1cicqyc169fp3bt2tGNGzfozp07FBYWRvv27bMq\nI++52cHFixfRvHlzsd2sWTNcvHhRxkQP9scff6Bx48YAgMaNG+OPP/6QOdH/WCwWmM1mBAcHO2TO\n0tJS6HQ6NG7cWBxKdcSc77zzDhYvXgwXl//9F+CIOVUqFXr27ImgoCCsWbMGgOPlzMjIQMOGDREZ\nGYmAgAC89tprKCgocLic99u6dStGjhwJwLG259NPP43p06ejRYsWaNq0Kby8vNCrVy+rMnLnZgdK\nPnlbpVI5TP78/HwMGTIEy5Ytg4eHR7nnHCWni4sLUlJS8Pvvv+PgwYMwmUzlnneEnHv27EGjRo2g\n1+urPPfSEXICQGJiIsxmM7799lusXLkSCQkJ5Z53hJzFxcU4efIkJk2ahJMnT6JOnToVhs0cIWeZ\n27dvIz4+HsOGDavwnNw5z58/j6VLl8JiseDSpUvIz8/H5s2by71Gakbu3OzgueeeQ2ZmptjOzMxE\ns2bNZEz0YI0bN8aVK1cAAJcvX0ajRo1kTgTcuXMHQ4YMwZgxYzBw4EAAjpmzjKenJ/r164fk5GSH\ny3n48GHs3r0brVq1wsiRI3HgwAGMGTPG4XICwLPPPgsAaNiwIQYNGoSkpCSHy9msWTM0a9YMHTt2\nBAAMHToUJ0+eRJMmTRwqZ5lvv/0WgYGBaNiwIQDH+h6dOHECnTt3RoMGDVCrVi0MHjwYR44csWpb\ncudmB0FBQfj1119hsVhw+/ZtfPnll+jfv7/csarUv39/bNiwAQCwYcMGsTORCxHh1Vdfha+vL6ZO\nnSrOd7Sc165dE4/iunXrFn744Qfo9XqHy/nRRx8hMzMTGRkZ2Lp1K7p3745NmzY5XM6bN28iLy8P\nAFBQUIB9+/ZBo9E4XM4mTZqgefPmSE9PBwDs378fHTp0QHh4uEPlLLNlyxZxSBJwrO+Rj48Pjh49\nilu3boGIsH//fvj6+lq3Lau1OshE33zzDbVr147atGlDH330kdxxRCNGjKBnn32W3NzcqFmzZrR+\n/Xq6fv069ejRg9q2bUu9evWirKwsWTMmJCSQSqUif39/0ul0pNPp6Ntvv3W4nKmpqaTX68nf3580\nGg3985//JCJyuJz3EwSBwsPDicjxcv7222/k7+9P/v7+1KFDB/F742g5iYhSUlIoKCiItFotDRo0\niLKzsx0yZ35+PjVo0IByc3PFeY6W8+OPPyZfX1/y8/OjsWPH0u3bt63KyBdOZowx5nR4WJIxxpjT\n4c6NMcaY0+HOjTHGmNPhzo0xxpjT4c6NMcaY0+HOjTHGmNP5/xxeQao6We33AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d62110>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('headphone')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109f37650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rVy9UVFQAqO1aVKlUMBqNCA0NxcSJEzFmzBgkJCTccFvZ2dnw9fWFm5sbdu3a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\nEEKI0KiQEUIIERoVMkIIIUL7/8kdkjGWtjexAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109f37110>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('keyboard')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109fdd0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eXh6DBw/Gw8ODsLAw8vLy/rAF+dlnn9GhQwcyMjLYsGEDHTp0wNfXl/DwcHJz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\nIiKKGBcQREREFDEuIIiIiChi/we6g50iG+p2EgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109fe5090>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('display')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a2394d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AE6yeDuAkq6cDuBULGRERKY1Ti27AqUUiouZwapGIiKgJFjKvZvV0ACdZPR3A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\nsBphEepBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a224650>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('phone')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a2d38d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iKCgIGRkZ+OijjzB06FDluZMnT2Lv3r1ISUnBzJkzUVBQgOPHj2PDhg04dOgQ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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a2dca90>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('text')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a485290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Uo+uYgY65JJNzJJPzdM3lqkp3bswff/yRzZs3k5CQQNWqVZk0aRIffvghOTk5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\n+Q1TAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a21c750>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('backup')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a8011d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IDAzEtGnTUF5errz/jTfeQGBgIEJCQvDdd98BABwOB4YMGQKDwYChQ4fizJkz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\nERGRy2OyIyIil/d/FTFDduMi9g4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7f04d0>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('disk')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a818690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wIACgYcOGiImJAQDs3bsXP//8M+655x4AgNPpLDEE3b9/fwBAaGgo0tPT1ZxF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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a832110>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('storage')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ac8d8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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smAs75sKOuag9dnBEROSUWINTES/fRdXx8PBCQcEZtcMgclg1rcGxg1MRT/Sm\n6vHgI6Ib4UEmdJuxqB0AOSDWneyYi9pjB0dERE7pturgkpOTkZCQoHYYMJvNyM7OVjsMJ2NWOwBy\nQDz53465qL167eBExClqCxqNhgeIEBE5uDrv4PLy8qDVahEdHQ2dTocZM2YgKCgIer0eiYmJynoD\nBgxAYGAg/Pz8sGjRIuX5pKQkaLVaBAcHY+vWrQCAixcvokOHDigrKwNQcZdtW9tsNmPKlCkIDg6G\nVqvFli1blDhCQ0MREBCAgIAA/PDDDwCA/Px8hIaGwmg0QqfTKetfraioCEOGDIGvry8iIyNRVFSk\nvObubr9uYGpqKmJjY5X99ezZE3q9Hk888QSOHz9+izLqrCxqB0AOiHUnO+ai9uplBHfkyBH885//\nxOzZs3Hy5Ens2LEDVqsV2dnZyMzMBAAsXrwYu3btws6dOzFv3jycPXsW+fn5SExMxNatW7Flyxbs\n378fGo0G7u7uMJvNWLduHQBgxYoVGDhwIBo1agSNRoPy8nJs374dc+bMwfTp0wEArVu3xnfffYfs\n7GysWLECzz//PADgk08+Qe/evWG1WrF3795q7we3YMECuLu7Y//+/Zg+fXqV6cmrR3JXP05ISEBs\nbCz27NmD4cOHK/sjIqL6US8XW37wwQcRFBSESZMm4dtvv4XRaARQcTftI0eOICQkBHPnzkVaWhoA\n4MSJE8jNzUV+fj7MZjPuueceAEBUVBRyc3MBAPHx8Xj77bfRv39/JCcn46OPPlL2FxkZCQAwmUzI\ny8sDAJSUlGDcuHHYs2cPGjZsiMOHDwMAgoKCEBcXh9LSUkRERECv118Tf2ZmJsaPHw8A0Ol08Pf3\nv+ln3rZtm/J5nnnmGbz00kvXWTMGgHflY08ABtjrUZbKn67QNjtYPGq2K1uVf7Hbai+u2rZxlHjU\natuec5R46rNtsViQnJwMAPD29kaNSR07duyY+Pn5iYjIiy++KAsXLrxmnYyMDOnevbsUFRWJiIjZ\nbBaLxSJpaWkycuRIZb25c+fKuHHjlLZer5eMjAwJCgpSnjObzZKdnS0iIqdOnRJvb28REZk2bZpM\nnjxZRETKysqkUaNGynvy8/Nl0aJFYjAYZOnSpfL555+LwWAQo9Eou3btkoiICNm0aZOyvslkUvbh\n4eGhPJ+SkiIxMTEiItKiRQspLS0VEZGSkhJp0aLFNZ8bgADChcufFtz8F4vIhdX0d6ReDzLp1asX\nFi9ejMLd+k+LAAATH0lEQVTCQgDAyZMncerUKRQUFMDLywtubm44ePAgtm3bBo1Gg+DgYHz//fc4\nc+YMSktLsXr16irbGzlyJIYPH464uLib7rugoABt2rQBACxduhTl5eUAgF9//RUtW7ZEfHw84uPj\nYbVaERERAavVit27dyMgIAChoaH45JNPAAA//vgj9u7dq2y3devWOHjwIK5cuYLPP/9cmabs2rUr\nVqxYAQBYvnw5QkND/2b2nJ1F7QDIAbHuZMdc1F69TFHavvSffPJJHDhwAF26dAEAeHh4YNmyZejd\nuzc++OAD+Pr6QqvVKq+3adMGiYmJ6NKlCzw9PWE0GqvUuYYNG4apU6di6NChN9332LFjMXDgQCxd\nuhS9e/dWDg7JyMjAzJkzcccdd8DDwwNLly69ZhvPPfccYmNj4evri0cffRSBgYHKa2+99Rb69u2L\nli1bIjAwUOm833vvPcTGxuKdd95Bq1atkJSU9HdSSEREtXRbX6orNTUV6enpWLJkidqh/CW8VBdV\nj5fqIrqRml6q67a9o3dCQgK++eYbrF+/Xu1QiIjIAd3WI7jbHUdwV7OAVzOx4QjO5uqjBl0dc2HH\niy0TEZFL4whORbzcF1WH94MjujGnr8E5C/59QURUNzhFSQ6B5/jYMRd2zIUdc1F77OCIiMgpsQan\noprOIxMRkR1rcLcJHmhCdH084Ib+Dk5Rqk64QABkOEAMjrIwF7blwoWzoAqswdUeOzgiInJKrMGp\niFcyIboZ1qnpWrySCRERuTR2cOQgLGoH4EAsagdADog1uNpjB1dHRo0aBYPBAH9/fwwYMADnz59X\nOyQiIpfCGlwduXDhAjw8PAAAL774Iry8vDB16tQq67AGR3QzrMHRtViDq0eFhYXo06cPDAYDdDod\nVq1apXRuIoKioiK0aNFC5SiJiFwLT/S+Bb7++mu0bdsW69atAwAUFBQAAGJjY/HVV1/Bx8cH8+bN\nu867YwB4Vz72BGCA/b5olsqfrtC2PXaUeNRs255zlHjUble2KmtQtnuiuVp7zpw5MBgMDhNPfbYt\nFguSk5MBAN7e3qgpTlHeAocPH0Z4eDiioqLQt29fdO/eXXntypUrGDduHFq3bo1p06ZVeR+nKK9m\nAW94amMBc2HDKUob3vDUjlOU9ejhhx+G1WqFTqfD1KlTMWPGDOW1Bg0aYMiQIdi5c6eKEd4OzGoH\n4EDMagdADoidW+1xivIWyM/Ph5eXF4YPH47mzZvjo48+wtGjR/HQQw9BRLB27VoYjUa1wyQicins\n4G6Bffv2YfLkyWjQoAEaN26M//znP4iOjlZqcYGBgZg/f77KUTo6CzhysbGAuaA/4xRl7bGDuwXC\nw8MRHh5e5bktW7aoFA0REQE8yERVPMiE6GZ4kAldiweZEBGRS+MUpep4w1Oi67nzTne1Q3AYrMHV\nHjs4lXH6pQJ/ee2YCzteYJj+DtbgVFTTeWQiIrJjDY6IiFwaOzhyCJyKsmMu7JgLO+ai9liDU1nF\nqQJEVJ0773THpUsX1A6DblOswamI58ER3Qzr1HQt1uCIiMilsYMjB2FROwAHYlE7AHJArMHVHjs4\nIiJySvXWwSUnJyMhIaG+dlfnnO3zqM+sdgAOxKx2AOSAePJ/7dW4gxMRFnuvwqMfiYgc2w07uLy8\nPGi1WkRHR0On02HGjBkICgqCXq9HYmKist6AAQMQGBgIPz8/LFq0SHk+KSkJWq0WwcHB2Lp1KwDg\n4sWL6NChA8rKygAABQUFSttsNmPKlCkIDg6GVqtVbjmTl5eH0NBQBAQEICAgAD/88AOAihuNhoaG\nwmg0QqfTVXuLmuTkZERGRuKpp57CI488gpdffhkA8MEHH+Cll16qsl51IzKz2YwJEyYo+7Ddmfvq\nzj4mJgZr1qxR2u7u7jWOj2wsagfgQCxqB0AOiDW42rvpeXBHjhxBSkoKzp8/j9TUVOzYsQNXrlxB\n//79kZmZiZCQECxevBheXl4oKipCUFAQBg0ahOLiYiQmJmL37t246667EBYWBpPJBHd3d5jNZqxb\ntw79+/fHihUrMHDgQDRq1AgajQbl5eXYvn07vvrqK0yfPh3fffcdWrduje+++w5NmjTB4cOHMWzY\nMOzcuROffPIJevfujVdffRUigsLCwmo/w549e5CTk4PGjRtDq9Xi+eefx6BBg9ClSxe8/fbbAIBV\nq1Zh6tSp17xXo9GgqKgIVqsVmZmZiIuLw759+65Zp7p2zeKLAeBd+dgTgAH2KSpL5U+2XauNm7zu\nau3KVuUXvG2qztXaOTk5DhVPfbYtFguSk5MBAN7e3qgxuYFjx45J+/btRUTkxRdfFG9vbzEYDGIw\nGOThhx+WxYsXi4jItGnTRK/Xi16vF09PT9m2bZt8/vnnMnLkSGVb8+bNk3HjxomISFZWlvTv319E\nRLp06SI//fSTiIiYzWbZunWriIj8/vvv4uPjIyIi586dk2eeeUZ0Op0YDAZp1qyZiIhs3rxZfHx8\nJDExUXJycqr9DMnJyfLss88q7aeeekqysrJERCQ8PFy2bdsmp0+fVj7nn5nNZsnIyFDa7dq1k3Pn\nzklSUpLyeWJiYiQ1NVVZx93dvUbxARBAuHDhct0F1f5ekmur6f+Lm9bgmjVrpjx+5ZVXYLVaYbVa\nkZubi9jYWFgsFmzcuBHbtm1DTk4ODAYDiouLrxnVVMRUoWvXrsjLy4PFYkF5eTl8fX2V15o0aQIA\naNiwoTKNOXv2bNx7773Yu3cvdu3ahcuXLwMAQkJCkJmZibZt2yImJgYpKSlIS0uD0WiEyWRCdnZ2\nlW3+ebtDhgzBqlWr8NlnnyEyMhIAEBsbC6PRiL59+143Jw0aVE1bo0aNcOXKFQDAlStXUFJSct34\niIioftT4IJNevXph8eLFyjTbyZMncerUKRQUFMDLywtubm44ePAgtm3bBo1Gg+DgYHz//fc4c+YM\nSktLsXr16irbGzlyJIYPH464uLib7rugoABt2rQBACxduhTl5eUAgF9//RUtW7ZEfHw84uPjYbVa\nERERAavVit27dyMgIKBKx2pje27AgAFIS0vDp59+iiFDhgCoqBtarVZ8+eWXyvorV64EAGzZsgWe\nnp7w8PCosj1vb2+lM127di1KS0uvGx9dj0XtAByIRe0AyAGxBld7N63B2UZiTz75JA4cOIAuXboA\nADw8PLBs2TL07t0bH3zwAXx9faHVapXX27Rpg8TERHTp0gWenp4wGo1VRnXDhg3D1KlTMXTo0Jvu\ne+zYsRg4cCCWLl2K3r17KwdxZGRkYObMmbjjjjvg4eGBpUuXVruN69XIPD094evriwMHDiAwMPC6\ncbi5ucFkMqGsrAyLFy++ZrvPPvss+vfvD4PBUOv4iIiobqh2LcrU1FSkp6djyZIlauy+xsLCwjBr\n1iyYTKZbvm1ei5LoZngtSrpWTa9FqcrdBBISEvDNN99g/fr1auyeiIhcAO8moCKO4K5mAa/gYWMB\nc2HDEZyNxWLh1Uwq8W4CRETk0jiCUxEv90V0Yx4eXigoOKN2GORgHLoGR3b8+4KIqG5wipIcAs/x\nsWMu7JgLO+ai9tjBERGRU2INTkU1nUcmIiI71uBuEzzQhIiobrCDUx1HcBUs4LlfNhYwFzYWMBc2\nFjAXNjUbGLAGR0RETok1OBXxSiZERH+Fi1/JxGKxoF+/fn/5/Xl5edDpdNW+9ttvv2Hw4MF/edtE\nRFT3nLaDqytlZWW47777rrm/Hf1dFrUDcCAWtQNwIBa1A3AgFrUDuO2o2sHNmDEDHTt2REhICIYN\nG4ZZs2YhJycHjz32GPR6PSIjI3Hu3DkAuO7zO3fuhL+/P4xGIyZPnlztqKuwsBBxcXEIDg6GyWTC\n2rVrq40nOzsber0eBoMB77//vvJ8cnIynn76aTz++ON48skn8csvvyj7eeyxx7B//35lXbPZjN27\nd9d4n0REVEdEJTt27BCDwSCXL1+WCxcuyMMPPywzZ84Uf39/2bx5s4iIvP766zJhwgQREdHpdNU+\n36lTJ9m2bZuIiEyZMkV0Op2IiGRkZEjfvn1FROSVV16RZcuWiYjI2bNn5ZFHHpHCwsJrYtLpdJKZ\nmSkiIpMnTxY/Pz8REUlKSpL7779fzp49KyIix44dU16bPXu2TJs2TUREfvvtN9FqtTXeJwABhAsX\nLly41GpBjfoZ1UZwWVlZiIiIQOPGjeHu7o5+/fqhsLAQ586dQ0hICAAgOjoamzdvRkFBAc6fP3/N\n8+fPn8fFixcRHBwMoOIu4RX9RlXffvst3nrrLRiNRoSFheHy5cs4fvx4lXXOnTuH8+fPo3v37gCA\nESNGVHk9PDwcnp6e12x78ODBSE1NBQCsWrVKqc3VZJ8VYgAkVi5zUHUawsI222yzzTYsqPiujEHF\nd2UN1agbrANz5sxRRj4iIi+88IJMnz5d2rVrpzx35MgRMZlMcv78+WqfP3funDz44IPK83v27FFG\nVleP4AICAiQ3N/eaGGJjY8VgMEifPn3k3LlzVfZx9baSkpJk3LhxymtXj+BEREJDQ2Xv3r3StWtX\n2bdv3w33eTWAIzj7kuEAMTjKwlwwF8zFjRfc8LvVRrURXLdu3ZCeno7Lly/j4sWL+PLLL9GsWTN4\neXlhy5YtAICUlBSYzWbcdddd1T7fvHlzeHh4YMeOHQCAFStWVLuvXr16Yd68eUrbarUCABYvXgyr\n1Yovv/wSzZs3h6enJ7KysgAAy5cvr/FniYqKwr///W8UFBTAz8/vhvskIqL6odqVTAIDA/H000/D\n398frVu3hk6ng6enJ5YsWYIxY8bg0qVLeOihh5CUlAQA133+448/xrPPPosGDRqgR48eaN68ubIP\n22WwXnvtNUyYMAH+/v64cuUKOnToUO1BH0lJSYiLi4NGo0F4eLjyfo1Gc80lta5uDxo0COPHj8fr\nr7+uPFfTfZKNWe0AHIhZ7QAciFntAByIWe0AbjuqnuhdWFiIZs2a4dKlS+jRowcWLVoEg8Hwl7YB\nAG+99Rb++OMPzJ49uy7CveV4ojcR0V9xG5zoPXr0aBiNRgQEBGDQoEG17twAYN26dTAajdDpdMjK\nysLUqVPrIFKqexa1A3AgFrUDcCAWtQNwIBa1A7jt8FJdKuII7moWcArGxgLmwsYC5sLGAubCpmYj\nOHZwKmIHR0T0V/B+cLcJ3g+OiKgusINTGQfQFSwWC8xms9phOATmwo65sGMu7Gp6o2hebJkcQk5O\njtohOAzmwo65sGMuao8dHDkE28Wzibm4GnNhx1zUHjs4IiJySuzgyCHk5eWpHYLDYC7smAs75qL2\neJqAimpaKCUioqp4moCD498WRER1h1OURETklNjBERGRU2IHR0RETokdnEq+/vprdOzYEQ8//DD+\n/e9/qx2OauLi4pT7Abq648ePIywsDJ06dYKfn1+VG+a6muLiYgQHB8NgMMDX1xevvPKK2iGprry8\nHEajEf369VM7FFV5e3vD398fRqMRQUFBN1yXR1GqoLy8HFqtFhs2bEDbtm3RuXNnfPrpp3j00UfV\nDq3eZWZmwt3dHSNHjsS+ffvUDkdVv//+O37//XcYDAZcvHgRAQEBSEtLc8n/FwBw6dIlNG3aFGVl\nZejevTtmzpyJ7t27qx2Wat59911kZ2fjwoULLn3z5Pbt2yM7Oxt33333TdflCE4FO3bsgI+PD7y9\nvXHHHXdgyJAh+OKLL9QOSxUhISHw8vJSOwyH0KZNG+WeiO7u7nj00Ufx22+/qRyVepo2bQoAKCkp\nQXl5eY2+0JzViRMnsH79esTHx/Poa9T8CHR2cCo4efIkHnjgAaV9//334+TJkypGRI4mLy8PVqsV\nwcHBaoeimitXrsBgMKB169YICwuDr6+v2iGpZuLEiXjnnXfQoAG/sjUaDZ544gkEBgZi0aJFN1yX\n2VIBT/CmG7l48SIGDRqEuXPnwt3dXe1wVNOgQQPk5OTgxIkT2Lx5MywWi9ohqeLLL79Eq1atYDQa\nOXoDkJWVBavViq+++grz589HZmbmdddlB6eCtm3b4vjx40r7+PHjuP/++1WMiBxFaWkpBg4ciGee\neQYRERFqh+MQmjdvjj59+mDXrl1qh6KKrVu3Yu3atWjfvj2GDh2KTZs2YeTIkWqHpZp7770XANCy\nZUsMGDAAO3bsuO667OBUEBgYiMOHDyMvLw8lJSVYuXIlnn76abXDIpWJCEaNGgVfX19MmDBB7XBU\ndfr0aeXq+UVFRfjuu+9gNBpVjkod//d//4fjx4/j2LFjWLFiBXr27ImlS5eqHZYqLl26hAsXLgAA\nCgsL8e23397wCGx2cCpo1KgR/vOf/6BXr17w9fVFVFSUyx4pN3ToUHTt2hW5ubl44IEHkJSUpHZI\nqsnKysKyZcuQkZEBo9EIo9GIr7/+Wu2wVJGfn4+ePXvCYDAgODgY/fr1w+OPP652WA7BlUscf/zx\nB0JCQpT/F3379kV4ePh11+dpAkRE5JQ4giMiIqfEDo6IiJwSOzgiInJK7OCIiMgpsYMjIiKnxA6O\niIic0v8HtemQ1MS8xNcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac7f1d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('server')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10aca4310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n5wdUJNNz109PT68Uq1JKKddz6hjdNddcw+TJkwkODqZly5Z06tTJ/l7Dhg2x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\nqk77f0GzXORA98qNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a842a10>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('chair')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ace0f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bU6dOhZ+fHwYPHoyysjLNcqTGp3rPg/k5NtXzs5bTF7mjR49i/vz5SEtLQ1ZW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b00f5d0>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('tablet')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b0286d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DDzyAsLAwAECfPn3wzTffoHPnzg3s3QXAevn3tgBsAJyXy9rln9dbvly6fPpe88RMlllm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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b0b7250>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('console')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b37e950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wq7j//vvFyZMnvf+2dOlSYbFYRFRUlHjxxRe9j+/atUtERUWJ3r17i2eeeebK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b37f650>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('terminal')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b394e50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ffml0WV4TGBiIZ555Bvv27UNJSQmee+45pfIBQEFBAeLi4pQ8gvWhhx7CiBEj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\nkTL+H0u5S84kgpeWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a2e5e50>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('pen')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b3c9e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wr0WLsu2hxYIC296rkojoYXCOzEJ4HBkRUf1xjoyIiBwOCxlZHMf/FcyFgrlQMBfmYSEj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\nsmssZEREZNdYyIiIyK6xkBERkV37f4hJYCI4bWbmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b3cd990>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('printer')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b8183d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Btra2lJ+fT0REJSUlRER06dIlUqvVVFdXRxqNhvbs2dNkXQ0aNIjKy8uJiCg/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\nIh6m5ioiIuJhaq4iIiIepuYqIiLiYf8ABtun8VfU2ZkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b023090>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares.ix[user_wares.description.str.contains('scanner')].title.value_counts()[:10].plot(kind='barh')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b8be390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KhWnXrguobfeEUkqpX2jXrlJKKXUFaEWqGj2zj9OYOT4zxwYan6vQilQppZS6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\n4fF4yM3NJSUlhdTUVLZt22Z0pJC5evUqs2fPxm6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       "text": [
        "<matplotlib.figure.Figure at 0x10b8055d0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "whos"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Variable              Type         Data/Info\n",
        "--------------------------------------------\n",
        "DOTALL                int          16\n",
        "all_cats              list         n=1260\n",
        "all_interviews        list         n=374\n",
        "base_dir              str          /Users/lpd/gh/usesthis/\n",
        "desc                  dict         n=5\n",
        "each_used_ware        list         n=5071\n",
        "el                    dict         n=5\n",
        "extracted_ware        DataFrame    <class 'pandas.core.frame<...>values\\ndtypes: object(5)\n",
        "findall               function     <function findall at 0x100467f50>\n",
        "glob                  module       <module 'glob' from '/Use<...>/lib/python2.7/glob.pyc'>\n",
        "hardware              list         n=1373\n",
        "interview             dict         n=6\n",
        "interview_file        file         <closed file '/Users/lpd/<...> mode 'r' at 0x1087ffdb0>\n",
        "interview_file_name   str          /Users/lpd/gh/usesthis//_<...>9-jason.forrest.interview\n",
        "interview_files       list         n=374\n",
        "interview_frame       DataFrame    <class 'pandas.core.frame<...>values\\ndtypes: object(4)\n",
        "iv_cats               Series       0                      [d<...>ength: 374, dtype: object\n",
        "markdown              module       <module 'markdown' from '<...>s/markdown/__init__.pyc'>\n",
        "match                 function     <function match at 0x100467cf8>\n",
        "md_string             str          ---\\nlayout: interview\\ns<...>ed in generational terms.\n",
        "os                    module       <module 'os' from '/Users<...>da/lib/python2.7/os.pyc'>\n",
        "pd                    module       <module 'pandas' from '/U<...>ges/pandas/__init__.pyc'>\n",
        "person_ware           list         n=4\n",
        "pf_used_ware          DataFrame    <class 'pandas.core.frame<...>values\\ndtypes: object(1)\n",
        "ps_all_cats           Series       0     developer\\n1       <...>ngth: 1260, dtype: object\n",
        "ps_wares              Series       0          macbook-pro\\n1<...>ngth: 5071, dtype: object\n",
        "set_cats              set          set(['crafter', 'biker', <...>', 'baker', 'usability'])\n",
        "set_wares             set          set(['apex-model-edge-pro<...>-quadra', 'path-finder'])\n",
        "software              list         n=1953\n",
        "user_wares            DataFrame    <class 'pandas.core.frame<...>values\\ndtypes: object(5)\n",
        "ware                  list         n=3326\n",
        "wares                 list         n=1953\n",
        "yaml                  module       <module 'yaml' from '/Use<...>kages/yaml/__init__.pyc'>\n",
        "yml_header            str          ---\\nlayout: interview\\ns<...>ncategories: [mac, music]\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user_wares"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 5070 entries, 0 to 5069\n",
        "Data columns (total 5 columns):\n",
        "title          5070  non-null values\n",
        "description    5070  non-null values\n",
        "name           5070  non-null values\n",
        "type           5070  non-null values\n",
        "url            5070  non-null values\n",
        "dtypes: object(5)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 5070 entries, 0 to 5069\n",
        "Data columns (total 5 columns):\n",
        "title          5070  non-null values\n",
        "description    5070  non-null values\n",
        "name           5070  non-null values\n",
        "type           5070  non-null values\n",
        "url            5070  non-null values\n",
        "dtypes: object(5)"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}