02_Twitter_data.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import bz2\n",
      "\n",
      "data = bz2.open('./data/test.20140209-140126.json.bz2', 'rt').readlines()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json.loads(data[101])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "{'entities': {'symbols': [],\n",
        "  'hashtags': [{'text': 'NowListening', 'indices': [0, 13]}],\n",
        "  'user_mentions': [],\n",
        "  'urls': []},\n",
        " 'retweet_count': 0,\n",
        " 'in_reply_to_status_id': None,\n",
        " 'geo': None,\n",
        " 'coordinates': None,\n",
        " 'text': '#NowListening Ylvis - The Fox (What Does The Fox Say)',\n",
        " 'contributors': None,\n",
        " 'in_reply_to_user_id_str': None,\n",
        " 'lang': 'en',\n",
        " 'created_at': 'Sun Feb 09 14:34:35 +0000 2014',\n",
        " 'favorited': False,\n",
        " 'filter_level': 'medium',\n",
        " 'in_reply_to_user_id': None,\n",
        " 'truncated': False,\n",
        " 'place': None,\n",
        " 'id_str': '432522924503740416',\n",
        " 'in_reply_to_status_id_str': None,\n",
        " 'user': {'profile_use_background_image': True,\n",
        "  'protected': False,\n",
        "  'default_profile': True,\n",
        "  'name': 'Ismail Iskandar',\n",
        "  'statuses_count': 7213,\n",
        "  'profile_text_color': '333333',\n",
        "  'url': None,\n",
        "  'follow_request_sent': None,\n",
        "  'favourites_count': 46,\n",
        "  'is_translator': False,\n",
        "  'screen_name': 'RusaKurus',\n",
        "  'is_translation_enabled': False,\n",
        "  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
        "  'profile_link_color': '0084B4',\n",
        "  'followers_count': 271,\n",
        "  'id_str': '1479875263',\n",
        "  'contributors_enabled': False,\n",
        "  'utc_offset': None,\n",
        "  'time_zone': None,\n",
        "  'profile_sidebar_fill_color': 'DDEEF6',\n",
        "  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/414770965331988480/bZvuU09G_normal.jpeg',\n",
        "  'created_at': 'Mon Jun 03 14:37:20 +0000 2013',\n",
        "  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/1479875263/1387873160',\n",
        "  'location': 'EAST BORNEO - INDONESIA',\n",
        "  'listed_count': 0,\n",
        "  'verified': False,\n",
        "  'notifications': None,\n",
        "  'profile_image_url': 'http://pbs.twimg.com/profile_images/414770965331988480/bZvuU09G_normal.jpeg',\n",
        "  'following': None,\n",
        "  'description': 'Your Friend',\n",
        "  'geo_enabled': True,\n",
        "  'friends_count': 159,\n",
        "  'default_profile_image': False,\n",
        "  'profile_sidebar_border_color': 'C0DEED',\n",
        "  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
        "  'lang': 'en',\n",
        "  'profile_background_color': 'C0DEED',\n",
        "  'profile_background_tile': False,\n",
        "  'id': 1479875263},\n",
        " 'source': '<a href=\"http://blackberry.com/twitter\" rel=\"nofollow\">Twitter for BlackBerry\u00ae</a>',\n",
        " 'id': 432522924503740416,\n",
        " 'favorite_count': 0,\n",
        " 'retweeted': False,\n",
        " 'in_reply_to_screen_name': None}"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tw_data = open('./data/twitter.csv', 'w')\n",
      "\n",
      "for tw in data:\n",
      "    dt = json.loads(tw)\n",
      "    hs = dt['entities']['hashtags']\n",
      "    hashtags = []\n",
      "    for item in hs:\n",
      "        hashtags.append(item['text'])\n",
      "        \n",
      "    tw_data.write('%s, %s, %s\\n' % (dt['id'], dt['created_at'], dt['user']['time_zone']))\n",
      "    tw_data.flush()\n",
      "tw_data.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import matplotlib.dates as dates\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "\n",
      "# Set some Pandas options\n",
      "pd.set_option('display.notebook_repr_html', False)\n",
      "pd.set_option('display.max_columns', 20)\n",
      "pd.set_option('display.max_rows', 25)\n",
      "\n",
      "mpl.rc('figure', figsize=(10, 8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('./data/twitter.csv', parse_dates=[1], index_col=[1], names=['id', 'date', 'time-zone'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                                     id                    time-zone\n",
        "date                                                                \n",
        "2014-02-09 14:02:01  432514725994176512                         None\n",
        "2014-02-09 14:02:22  432514817526480896                       Alaska\n",
        "2014-02-09 14:02:29  432514846022590465                 Kuala Lumpur\n",
        "2014-02-09 14:02:56  432514959080050688                         None\n",
        "2014-02-09 14:03:46  432515170112266240   Pacific Time (US & Canada)\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly = df.groupby(pd.TimeGrouper('h'))['id'].count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "hourly"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "2014-02-09 14:00:00    156\n",
        "2014-02-09 15:00:00    180\n",
        "2014-02-09 16:00:00    105\n",
        "2014-02-09 17:00:00     99\n",
        "2014-02-09 18:00:00     70\n",
        "2014-02-09 19:00:00     62\n",
        "2014-02-09 20:00:00     70\n",
        "2014-02-09 21:00:00     51\n",
        "2014-02-09 22:00:00     69\n",
        "2014-02-09 23:00:00    107\n",
        "...\n",
        "2014-02-11 12:00:00     98\n",
        "2014-02-11 13:00:00    195\n",
        "2014-02-11 14:00:00    210\n",
        "2014-02-11 15:00:00    169\n",
        "2014-02-11 16:00:00    131\n",
        "2014-02-11 17:00:00     78\n",
        "2014-02-11 18:00:00     63\n",
        "2014-02-11 19:00:00     82\n",
        "2014-02-11 20:00:00     51\n",
        "2014-02-11 21:00:00     48\n",
        "2014-02-11 22:00:00     18\n",
        "Length: 57"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Note: All times are in GMT\n",
      "\n",
      "hourly.ix['2014-02-09':'2014-02-10']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "2014-02-09 14:00:00    156\n",
        "2014-02-09 15:00:00    180\n",
        "2014-02-09 16:00:00    105\n",
        "2014-02-09 17:00:00     99\n",
        "2014-02-09 18:00:00     70\n",
        "2014-02-09 19:00:00     62\n",
        "2014-02-09 20:00:00     70\n",
        "2014-02-09 21:00:00     51\n",
        "2014-02-09 22:00:00     69\n",
        "2014-02-09 23:00:00    107\n",
        "...\n",
        "2014-02-10 13:00:00    145\n",
        "2014-02-10 14:00:00    147\n",
        "2014-02-10 15:00:00    204\n",
        "2014-02-10 16:00:00    235\n",
        "2014-02-10 17:00:00    151\n",
        "2014-02-10 18:00:00    113\n",
        "2014-02-10 19:00:00     94\n",
        "2014-02-10 20:00:00     75\n",
        "2014-02-10 21:00:00     69\n",
        "2014-02-10 22:00:00     69\n",
        "2014-02-10 23:00:00     85\n",
        "Length: 34"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1069fbb90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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UPmosUCqvg1THjtJnn0k7dnh3zEpF/40vr2qk9thDatWKv7elCGWQmjpVamqSVq4MuiUA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\nAAAAAAAAAAAAAAAAAJH3/wHvmaaEufSulgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1069b55d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly.ix['2014-02-11':'2014-02-11'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x106f7ac10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tWBDd6mYFv6SxNtavXwwpf/ZZ0pWoVBR1kIIIOmPGxLBAQ4fWpk2LoZaePaNT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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10729bc90>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hourly.ix['2014-02-10':'2014-02-10'].plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x106fa3210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x71RbW3vvtavOA2mPKRf9GtReXLvUQgoAYEjMSAFMqeo8kLafO8zGjBQAQA9KLaQq7p1q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\n/x+H+MzpqVfywwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106fd3910>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = df.groupby(pd.TimeGrouper('h')).apply(lambda x: x.groupby('time-zone')['id'].count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                     time-zone                  \n",
        "2014-02-09 14:00:00   Alaska                         4\n",
        "                      Amsterdam                      1\n",
        "                      Arizona                        3\n",
        "                      Athens                         1\n",
        "                      Bangkok                        6\n",
        "                      Beijing                       12\n",
        "                      Buenos Aires                   1\n",
        "                      Casablanca                     1\n",
        "                      Central Time (US & Canada)     2\n",
        "                      Eastern Time (US & Canada)     2\n",
        "...\n",
        "2014-02-11 22:00:00   Atlantic Time (Canada)        1\n",
        "                      Brasilia                      1\n",
        "                      Central Time (US & Canada)    1\n",
        "                      Eastern Time (US & Canada)    2\n",
        "                      Hawaii                        2\n",
        "                      Istanbul                      1\n",
        "                      Kuala Lumpur                  1\n",
        "                      None                          3\n",
        "                      Quito                         3\n",
        "                      Singapore                     1\n",
        "                      Tokyo                         2\n",
        "Length: 1364, dtype: int64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.ix['2014-02-10 16:00:00'].sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "235"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.ix['2014-02-10 16:00:00'].plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1070a6f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JkiRlZc2SJPXMmiWpf9YsSZIkzUmKzlKGedEsMTLkMKQYGXLIEiNDDkOKkaWWYihtYYxc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\nEQft99X8maO6KM7u0r2Izt/1xFIGdyJ2sGkK+ad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       "text": [
        "<matplotlib.figure.Figure at 0x1070a9510>"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}