96chan/TED_video_Stats

## TED_video_Stats
{
 "metadata": {
  "name": "Datamining_TEDstats"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from pandas import Series, DataFrame\n",
      "import json\n",
      "\n",
      "TEDX_JSON_FILE = \"../data/final_datamining_ted.json\"\n",
      "ted_video_list = []\n",
      "with open(TEDX_JSON_FILE, \"r\") as ted_json_file:\n",
      "    for line in ted_json_file:\n",
      "        ted_video_list.append(json.loads(line))\n",
      "    ted_df = DataFrame(ted_video_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from operator import itemgetter, attrgetter\n",
      "\n",
      "INTERESTS_VALUE = ['viewCount', 'FB_like_count', 'FB_share_count', 'FB_click_count', 'twitter_count','FB_comment_count', 'commentCount', 'ratingCount', 'duration', 'favoriteCount', 'likeCount']\n",
      "COUNTS_VALUE = []\n",
      "\n",
      "for interested in INTERESTS_VALUE:\n",
      "    df = pd.DataFrame(ted_df, columns  = [interested])\n",
      "    \n",
      "    filter_values = []\n",
      "    SIZE_BUCKET = 100 if interested == 'viewCount' or interested == 'duration' else 10\n",
      "    \n",
      "    for i in range(1000):\n",
      "        filter_values.append(i*SIZE_BUCKET)\n",
      "        \n",
      "    out = pd.cut(df[interested], bins = filter_values)\n",
      "    counts = pd.value_counts(out)\n",
      "    \n",
      "    #Counts\n",
      "    tmp_counts = []\n",
      "    for i in range(0, len(counts)):\n",
      "        index = counts.index[i]\n",
      "        tmp_counts.append((int(index[1:index.find(',')]), counts[i]))\n",
      "        \n",
      "    \n",
      "    tmp_counts = sorted(tmp_counts, key=itemgetter(0))\n",
      "    counts_index = list(x[0] for x in tmp_counts)\n",
      "    counts_value = list(x[1] for x in tmp_counts)\n",
      "    counts = Series(counts_value, index=counts_index)\n",
      "    COUNTS_VALUE.append(counts)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "viewCount"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i=0\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "print COUNTS_VALUE[i].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "viewCount\n",
        "count     28952\n",
        "unique     7359\n",
        "top         301\n",
        "freq         58\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "count     776.000000\n",
        "mean       36.701031\n",
        "std       224.899463\n",
        "min         1.000000\n",
        "25%         1.000000\n",
        "50%         3.000000\n",
        "75%         6.000000\n",
        "max      3079.000000\n",
        "dtype: float64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x310ca210>"
       ]
      },
      {
       "output_type": "display_data",
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3kjVi06ZNeuihh9TV1aW7775bd999t7q6uvTQ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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3102e630>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Facebook_like_count\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 1\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FB_like_count\n",
        "count     28952.000000\n",
        "mean        141.208103\n",
        "std        1440.845974\n",
        "min           0.000000\n",
        "25%           1.000000\n",
        "50%           8.000000\n",
        "75%          41.000000\n",
        "max      131411.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2d6bbf30>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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n2vU+Pj2OPT5v9qB07DlD8xNDy5Yt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\nDHOX8v8Ab/SvjdDQZmgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2d74eed0>"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Facebook_share_count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 2\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FB_share_count\n",
        "count     28952.000000\n",
        "mean        180.353827\n",
        "std        1815.654584\n",
        "min           0.000000\n",
        "25%           2.000000\n",
        "50%           9.000000\n",
        "75%          36.000000\n",
        "max      159002.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2ea9a410>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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/YPyBto2NjTPuiln7/7vb3NzM/WX9\ndpA7+33U1dVNqY49ulLpQ8z90+v13C2H7e3tKC0tRVdXl01dqfRhby5geMiyuclOcqfvil17MpwA\ngBEEQRAEQRAEQRAEQRDE/+E82w0QBEEQBEEQBEEQBEEQ0oImjAiCIAiCIAiCIAiCIAgeNGFEEARB\nEARBEARBEARB8KAJI4IgCIIgCIIgCIIgCIIHTRgRBEEQBEEQBEEQBEEQPP4H748vAWTV7FAAAAAA\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2e67fab0>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Facebook_click_count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 3\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FB_click_count\n",
        "count    28952.000000\n",
        "mean         0.598473\n",
        "std         20.642633\n",
        "min          0.000000\n",
        "25%          0.000000\n",
        "50%          0.000000\n",
        "75%          0.000000\n",
        "max       2390.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2ef92a50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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lpSPRMmIHEAAAAABLYQcQAAAAwIqzAwgAAABgw63JAGg2dcBCy5nC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\nDHAyASSEEEIIIYQQQggxwP0PLVgzgHE0CXAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ef6e950>"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Twitter_count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 4\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "twitter_count\n",
        "count    28952.000000\n",
        "mean        29.174634\n",
        "std        203.433854\n",
        "min          0.000000\n",
        "25%          1.000000\n",
        "50%          3.000000\n",
        "75%         11.000000\n",
        "max      13180.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2fb94090>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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sXr0avr6+iu8VwMO7ok6fPm33sxsM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\nGIZhZPwPVlV4aAv+AKcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2fb947d0>"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Facebook_comment_count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 5\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FB_comment_count\n",
        "count    28952.000000\n",
        "mean        84.724199\n",
        "std        986.418517\n",
        "min          0.000000\n",
        "25%          0.000000\n",
        "50%          3.000000\n",
        "75%         18.000000\n",
        "max      78565.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x3007abd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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f1dVXX62/+7u/0yWXXNLw966R7x/v3S7R/u05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\nWwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ffb77b0>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Youtube Comment Count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 6\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "commentCount\n",
        "count     28952\n",
        "unique      712\n",
        "top           0\n",
        "freq      11932\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x30638410>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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FgptpV11dDT8/PwDAsWPHBs2VSh5S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\nhykAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x300e4490>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Rating Count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 7\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ratingCount\n",
        "count     28952\n",
        "unique     1325\n",
        "top           2\n",
        "freq       2408\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x2c233050>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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LAKtXr37kbsO8lr+vtHRtW8hV1x4X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\n4XA4HBH/B7OW/fUnJlxBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x30b76cf0>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Duration"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = 8\n",
      "print INTERESTS_VALUE[i]\n",
      "print ted_df[INTERESTS_VALUE[i]].describe()\n",
      "COUNTS_VALUE[i][:100].plot(kind=\"bar\",use_index=True,  figsize=(20,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "duration\n",
        "count    28952.000000\n",
        "mean       856.081963\n",
        "std        338.747340\n",
        "min          0.000000\n",
        "25%        621.000000\n",
        "50%        890.000000\n",
        "75%       1086.000000\n",
        "max       4521.000000\n",
        "dtype: float64\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x31011170>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x3101f4b0>"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}