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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Text-Mining the DLD14 Conference"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the IPython Notebook behind <a href=\"http://beautifuldata.net/2014/01/text-mining-the-dld-conference-2014/\">this analysis of Twitter buzz to the DLD Conference 2014</a>."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reading the data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Tweets for this analysis have been collected with the TAGS Google Drive script and the search query \"#DLD13 OR #DLD\". The data (\"Archive\" tab) have then been exported as CSV file. Because the TAGS document tends to reach the Google Documents size limits quite fast, I have split the data gathering in multiple TAGS files.\n",
      "\n",
      "The first step of the analysis is to read the CSV files and combine them into a single file. This step also requires to delete duplicate Tweets."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "# 1st file\n",
      "data = pd.read_csv('TAGS - DLD14 - Archive.csv', \n",
      "                   parse_dates={'Timestamp': ['created_at']})\n",
      "\n",
      "# 2nd file\n",
      "data = data.append(pd.read_csv('TAGS DLD14.2 - Archive.csv', \n",
      "                    parse_dates={'Timestamp': ['created_at']}))\n",
      "\n",
      "# 3rd file etc.\n",
      "data = data.append(pd.read_csv('TAGS DLD14.3 - Archive.csv', \n",
      "                    parse_dates={'Timestamp': ['created_at']}))\n",
      "data = data.append(pd.read_csv('TAGS DLD14.4 - Archive.csv', \n",
      "                    parse_dates={'Timestamp': ['created_at']}))\n",
      "data = data.append(pd.read_csv('TAGS DLD14.5 - Archive.csv', \n",
      "                    parse_dates={'Timestamp': ['created_at']}))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_old = pd.read_csv(\"dld13.csv\", sep=\",\",\n",
      "                       parse_dates={'Timestamp': ['created_at']})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following is an example for duplicate content. We have to clean the data set to get rid of duplicate tweets."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter\n",
      "\n",
      "c = Counter(data.id_str)\n",
      "data[data.id_str == c.most_common()[1][0]][:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Timestamp</th>\n",
        "      <th>id_str</th>\n",
        "      <th>from_user</th>\n",
        "      <th>text</th>\n",
        "      <th>time</th>\n",
        "      <th>geo_coordinates</th>\n",
        "      <th>user_lang</th>\n",
        "      <th>in_reply_to_user_id_str</th>\n",
        "      <th>in_reply_to_screen_name</th>\n",
        "      <th>from_user_id_str</th>\n",
        "      <th>in_reply_to_status_id_str</th>\n",
        "      <th>source</th>\n",
        "      <th>profile_image_url</th>\n",
        "      <th>user_followers_count</th>\n",
        "      <th>user_friends_count</th>\n",
        "      <th>user_utc_offset</th>\n",
        "      <th>status_url</th>\n",
        "      <th>entities_str</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6904 </th>\n",
        "      <td>2014-01-19 12:01:09</td>\n",
        "      <td> 4.248742e+17</td>\n",
        "      <td> ardakutsal</td>\n",
        "      <td> RT @atillayurtseven: @webrazzi #DLD14 medya sp...</td>\n",
        "      <td> 19/01/2014 12:01:09</td>\n",
        "      <td> NaN</td>\n",
        "      <td> en</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 43854330</td>\n",
        "      <td>NaN</td>\n",
        "      <td> &lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
        "      <td> http://pbs.twimg.com/profile_images/3027796712...</td>\n",
        "      <td> 18151</td>\n",
        "      <td> 676</td>\n",
        "      <td> 7200</td>\n",
        "      <td> http://twitter.com/ardakutsal/statuses/4248741...</td>\n",
        "      <td> {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9072 </th>\n",
        "      <td>2014-01-19 12:01:09</td>\n",
        "      <td> 4.248742e+17</td>\n",
        "      <td> ardakutsal</td>\n",
        "      <td> RT @atillayurtseven: @webrazzi #DLD14 medya sp...</td>\n",
        "      <td> 19/01/2014 12:01:09</td>\n",
        "      <td> NaN</td>\n",
        "      <td> en</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 43854330</td>\n",
        "      <td>NaN</td>\n",
        "      <td> &lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
        "      <td> http://pbs.twimg.com/profile_images/3027796712...</td>\n",
        "      <td> 18149</td>\n",
        "      <td> 676</td>\n",
        "      <td> 7200</td>\n",
        "      <td> http://twitter.com/ardakutsal/statuses/4248741...</td>\n",
        "      <td> {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11010</th>\n",
        "      <td>2014-01-19 12:01:09</td>\n",
        "      <td> 4.248742e+17</td>\n",
        "      <td> ardakutsal</td>\n",
        "      <td> RT @atillayurtseven: @webrazzi #DLD14 medya sp...</td>\n",
        "      <td> 19/01/2014 12:01:09</td>\n",
        "      <td> NaN</td>\n",
        "      <td> en</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 43854330</td>\n",
        "      <td>NaN</td>\n",
        "      <td> &lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
        "      <td> http://pbs.twimg.com/profile_images/3027796712...</td>\n",
        "      <td> 18148</td>\n",
        "      <td> 676</td>\n",
        "      <td> 7200</td>\n",
        "      <td> http://twitter.com/ardakutsal/statuses/4248741...</td>\n",
        "      <td> {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                Timestamp        id_str   from_user  \\\n",
        "6904  2014-01-19 12:01:09  4.248742e+17  ardakutsal   \n",
        "9072  2014-01-19 12:01:09  4.248742e+17  ardakutsal   \n",
        "11010 2014-01-19 12:01:09  4.248742e+17  ardakutsal   \n",
        "\n",
        "                                                    text                 time  \\\n",
        "6904   RT @atillayurtseven: @webrazzi #DLD14 medya sp...  19/01/2014 12:01:09   \n",
        "9072   RT @atillayurtseven: @webrazzi #DLD14 medya sp...  19/01/2014 12:01:09   \n",
        "11010  RT @atillayurtseven: @webrazzi #DLD14 medya sp...  19/01/2014 12:01:09   \n",
        "\n",
        "      geo_coordinates user_lang  in_reply_to_user_id_str  \\\n",
        "6904              NaN        en                      NaN   \n",
        "9072              NaN        en                      NaN   \n",
        "11010             NaN        en                      NaN   \n",
        "\n",
        "      in_reply_to_screen_name  from_user_id_str  in_reply_to_status_id_str  \\\n",
        "6904                      NaN          43854330                        NaN   \n",
        "9072                      NaN          43854330                        NaN   \n",
        "11010                     NaN          43854330                        NaN   \n",
        "\n",
        "                                                  source  \\\n",
        "6904   <a href=\"http://twitter.com/download/iphone\" r...   \n",
        "9072   <a href=\"http://twitter.com/download/iphone\" r...   \n",
        "11010  <a href=\"http://twitter.com/download/iphone\" r...   \n",
        "\n",
        "                                       profile_image_url  \\\n",
        "6904   http://pbs.twimg.com/profile_images/3027796712...   \n",
        "9072   http://pbs.twimg.com/profile_images/3027796712...   \n",
        "11010  http://pbs.twimg.com/profile_images/3027796712...   \n",
        "\n",
        "       user_followers_count  user_friends_count  user_utc_offset  \\\n",
        "6904                  18151                 676             7200   \n",
        "9072                  18149                 676             7200   \n",
        "11010                 18148                 676             7200   \n",
        "\n",
        "                                              status_url  \\\n",
        "6904   http://twitter.com/ardakutsal/statuses/4248741...   \n",
        "9072   http://twitter.com/ardakutsal/statuses/4248741...   \n",
        "11010  http://twitter.com/ardakutsal/statuses/4248741...   \n",
        "\n",
        "                                            entities_str  \n",
        "6904   {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...  \n",
        "9072   {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...  \n",
        "11010  {\"symbols\":[],\"urls\":[],\"hashtags\":[{\"text\":\"D...  "
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Data Cleaning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remove entries that have a broken date."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = data[data['Timestamp'] >= '2014-01-10']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 387
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To de-duplicate the data, I used a rather unsophisticated hack: I removed all the columns that were responsible for differences in otherwise duplicate content. E.g. people were gaining followers between two sweeps of Twitter searches and their entries no longer were identified duplicate. Alternatively, the de-duplication could be done with the id_str column."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets = {}\n",
      "\n",
      "# Reduce data frame\n",
      "tweets['2014'] = data[['Timestamp', 'id_str', 'from_user', 'source', 'text']]\n",
      "tweets['2013'] = data_old[['Timestamp', 'id_str', 'from_user', 'source', 'text']]\n",
      "\n",
      "for year in ['2013', '2014']:\n",
      "    # De-dup\n",
      "    tweets[year] = tweets[year].drop_duplicates()\n",
      "    # Set Timestamp as index for the DataFrame\n",
      "    tweets[year] = tweets[year].set_index('Timestamp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 388
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Because the Twitter API and also the TAGS script changed between 2013 and 2014, the collected data must be further cleaned and homogenized in order to allow for comparisons. E.g. To compare the data-sets for 2013 and 2014 I reversed the 2014 entry for \"web\" as a Twitter client to the old value of '<a href=\"http://twitter.com\">web</a>'. This makes the extraction of the Twitter clients much easier."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets['2014']['source'][tweets['2014']['source'] == 'web'] = '<a href=\"http://twitter.com\">web</a>'\n",
      "tweets['2013']['source'] = [x.replace('&lt;', '<').replace('&gt;', '>').replace('&quot;', '\"') \n",
      "                            for x in tweets['2013']['source']]\n",
      "\n",
      "for year in ['2013', '2014']:\n",
      "    tweets[year]['source'] = [x.replace('  ', ' ') for x in tweets[year]['source']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 389
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is one of the most important parts. Now our dataframe gets translated into a real timeline. I'm aggregating all the Tweets by 15 minute intervals. But you can play around with those values to find other solutions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks = {}\n",
      "\n",
      "for year in ['2013', '2014']:\n",
      "    tweets[year]['Tweets'] = 1\n",
      "    ticks[year] = tweets[year].ix[:, ['Tweets']]\n",
      "    ticks[year] = ticks[year].Tweets.resample('15min', how='count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 390
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Data Analysis"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's do a first visualization of the buzz for the conference:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline \n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "#plt.figure(figsize=(10,6))\n",
      "plt.title('DLD14 Conference Buzz')\n",
      "plt.ylabel('Number of Tweets')\n",
      "ticks[\"2014\"].ix['2014-01-18':'2014-01-22'].plot()\n",
      "plt.savefig('DLD14_Buzz.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vb9OJYcNwUMPEsCc9D8E1aFIEPvvsM7MnWhKB3bt3Y926dejfvz8GDhwIgJuA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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19b7df98>"
       ]
      }
     ],
     "prompt_number": 490
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Which year had the highest peak? See answer below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print max(ticks[\"2013\"])\n",
      "print max(ticks[\"2014\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "247\n",
        "346\n"
       ]
      }
     ],
     "prompt_number": 392
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "DLD 2013 had been one year before DLD 2014. To overlay both buzz timelines, these value have to be time-shifted."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticks[\"2014o\"] = ticks[\"2014\"] * 1.0\n",
      "ticks[\"2013o\"] = ticks[\"2013\"].tshift(364, freq=\"d\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 484
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's a direct comparison of 2013 and 2014."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "ticks[\"2013o\"].ix['2014-01-18':'2014-01-23'].plot(color=\"red\", label=\"DLD 2013\")\n",
      "ticks[\"2014o\"].ix['2014-01-18':'2014-01-23'].plot(label=\"DLD 2014\")\n",
      "legend = ax.legend(loc='upper left', shadow=True)\n",
      "plt.xlabel('Date')\n",
      "plt.title('#DLD14 Conference Buzz')\n",
      "plt.ylabel('Number of Tweets')\n",
      "#plt.show()\n",
      "plt.savefig('DLD14_Buzz_Comp_Comparison.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aOH4cbDbpBCShJ6AIWK1WvcKvqqrS1ycnJ/POO++EpXCS6GLCBMjKatoJ1Ne3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\nc7hELl5Mh1Zdpfz8Sk6wfn31/fdqpYdrhQ4OJjtlnajInyrYvdsgmvb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       "text": [
        "<matplotlib.figure.Figure at 0x1f69d4e0>"
       ]
      }
     ],
     "prompt_number": 491
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's take a look at the clients people were using in 2014 and compare them to 2013. Which Twitter devices have gained importance?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from prettytable import PrettyTable\n",
      "import re\n",
      "import prettytable\n",
      "devices = {}\n",
      "\n",
      "for y in tweets:\n",
      "    devices[y] = tweets[y].groupby(\"source\", as_index=False)[\"source\", \"Tweets\"].sum()\n",
      "  \n",
      "dev_names = []\n",
      "dev_count = []\n",
      "    \n",
      "for x in devices[\"2014\"][\"source\"]:\n",
      "    m = re.match(\"<.*>(.*)</a>\", x)\n",
      "    try:\n",
      "        dev_names.append(m.group(1))\n",
      "        dev_count.extend(devices[\"2014\"][\"Tweets\"][devices[\"2014\"][\"source\"] == x])\n",
      "    except AttributeError:\n",
      "        pass\n",
      "        \n",
      "       \n",
      "d = pd.DataFrame({\"Device\": dev_names, \"Tweets_2014\": dev_count})\n",
      "d[\"Rel_2014\"] = 100.0 * d[\"Tweets_2014\"] / sum(d[\"Tweets_2014\"])\n",
      "\n",
      "dev_names = []\n",
      "dev_count = []\n",
      "\n",
      "for x in devices[\"2013\"][\"source\"]:\n",
      "    m = re.match(\"<.*>(.*)</a>\", x)\n",
      "    try:\n",
      "        dev_names.append(m.group(1))\n",
      "        dev_count.extend(devices[\"2013\"][\"Tweets\"][devices[\"2013\"][\"source\"] == x])\n",
      "    except AttributeError:\n",
      "        pass\n",
      "\n",
      "e = pd.DataFrame({\"Device\": dev_names, \"Tweets_2013\": dev_count})\n",
      "e[\"Rel_2013\"] = 100.0 * e[\"Tweets_2013\"] / sum(e[\"Tweets_2013\"])\n",
      "\n",
      "f = pd.merge(d, e, how=\"outer\", on=\"Device\")\n",
      "f[\"Growth\"] = f.Rel_2014 / f.Rel_2013\n",
      "\n",
      "tb = f[(f['Rel_2014'] >= 0.05) & (f['Growth'] >= 0)].sort(columns=\"Growth\", ascending=False)[[\"Device\", \"Rel_2013\", \"Rel_2014\", \"Growth\"]]\n",
      "tb.columns = [\"Device\", \"% 2013\", \"% 2014\", \"Index\"]\n",
      "tb[\"% 2014\"] = tb[\"% 2014\"].round(2)\n",
      "tb[\"% 2013\"] = tb[\"% 2013\"].round(2)\n",
      "tb[\"Index\"] = (100*tb[\"Index\"]).round(1)\n",
      "\n",
      "pt = PrettyTable(field_names=[\"Twitter Clients #DLD14\", \"Percent 2013\", \"Percent 2014\", \"Index\"]) \n",
      "[pt.add_row(a[1]) for a in tb.iterrows()]\n",
      "pt.align[pt.align['Twitter Clients #DLD14'], pt.align['Percent 2013'], pt.align['Percent 2014'], pt.align['Index']] = 'l', 'r', 'r', 'r'\n",
      "print pt\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+---------------------------+--------------+--------------+-------+\n",
        "|   Twitter Clients #DLD14  | Percent 2013 | Percent 2014 | Index |\n",
        "+---------------------------+--------------+--------------+-------+\n",
        "|  TweetCaster for Android  |     0.12     |     0.5      | 426.2 |\n",
        "|           IFTTT           |     0.16     |     0.7      | 424.8 |\n",
        "|          Janetter         |     0.02     |     0.09     | 398.1 |\n",
        "|          dlvr.it          |     0.74     |     2.49     | 337.5 |\n",
        "|            iOS            |     0.21     |     0.68     | 322.6 |\n",
        "|          Storify          |     0.04     |     0.08     | 218.5 |\n",
        "|          Facebook         |     0.16     |     0.35     | 210.7 |\n",
        "|         TweetList!        |     0.75     |     1.3      | 173.4 |\n",
        "|     Plume\u00a0for\u00a0Android     |     0.11     |     0.18     | 171.7 |\n",
        "|    Twitter for Android    |     4.67     |     7.95     | 170.1 |\n",
        "|         Flipboard         |     0.19     |     0.32     | 169.8 |\n",
        "|       Sprout Social       |     0.07     |     0.12     | 163.9 |\n",
        "|        Twitterrific       |     0.61     |     0.99     | 162.1 |\n",
        "|        twitterfeed        |     2.96     |     4.61     | 155.5 |\n",
        "|      Twitter for iPad     |     3.76     |     5.0      | 132.9 |\n",
        "|      Mobile Web (M5)      |     0.56     |     0.74     | 130.7 |\n",
        "|           Buffer          |     0.35     |     0.45     | 128.0 |\n",
        "|     Twitter for iPhone    |    17.48     |    22.21     | 127.1 |\n",
        "|    TweetCaster for iOS    |     0.06     |     0.07     | 121.8 |\n",
        "|      Tweetbot for iOS     |     2.4      |     2.9      | 120.6 |\n",
        "|         TweetDeck         |     6.16     |     6.94     | 112.6 |\n",
        "|        Tweet Button       |     2.59     |     2.9      | 112.1 |\n",
        "| Twitter for Windows Phone |     0.26     |     0.29     | 110.7 |\n",
        "|      Tweetbot for Mac     |     0.46     |     0.5      | 109.3 |\n",
        "|      Twitter for Mac      |     1.57     |     1.67     | 106.6 |\n",
        "|          Echofon          |     0.87     |     0.86     |  99.4 |\n",
        "|         HootSuite         |     4.29     |     4.15     |  96.9 |\n",
        "|      Mobile Web (M2)      |     0.28     |     0.25     |  87.8 |\n",
        "|         Instagram         |     1.3      |     1.08     |  82.7 |\n",
        "|         MetroTwit         |     0.08     |     0.07     |  80.3 |\n",
        "|           Botize          |     0.13     |     0.09     |  72.4 |\n",
        "|          SharedBy         |     0.19     |     0.13     |  67.3 |\n",
        "|            OS X           |     0.08     |     0.05     |  66.9 |\n",
        "|         foursquare        |     0.61     |     0.4      |  65.7 |\n",
        "|          LinkedIn         |     0.13     |     0.08     |  63.9 |\n",
        "|            web            |    39.28     |    22.37     |  57.0 |\n",
        "|  Twitter for BlackBerry\u00ae  |     2.1      |     0.61     |  29.3 |\n",
        "|        Twittelator        |     0.35     |     0.07     |  20.3 |\n",
        "|    Twitter for Android    |     4.67     |     0.24     |  5.2  |\n",
        "+---------------------------+--------------+--------------+-------+\n"
       ]
      }
     ],
     "prompt_number": 395
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now to the content. Here's a few lines that do some data munging with the Tweet texts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk\n",
      "bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
      "stop = nltk.corpus.stopwords.words('english')\n",
      "stop = stop + nltk.corpus.stopwords.words('german')\n",
      "\n",
      "text = {}\n",
      "words = {}\n",
      "\n",
      "for year in tweets:\n",
      "    raw = \" \".join(tweets[year][\"text\"])\n",
      "    tokens = nltk.WordPunctTokenizer().tokenize(raw)\n",
      "    text[year] = nltk.Text(tokens)\n",
      "    words[year] = [w.lower() for w in text[year]]\n",
      "    words[year] = [w for w in words[year] if len(w) > 2]\n",
      "    words[year] = [w for w in words[year] if w not in stop]\n",
      "    words[year] = filter(lambda word: word not in '\"\\'!%,-:()$\\/;?.\u2019\u2013\u201c\u201d#@&', words[year])\n",
      "    words[year] = [w for w in words[year] if w not in [\"://\", \"http\", \"co\", \"rt\", \"va\", \"l\", \"se\", \"...\", \".\\\"\", \n",
      "                                                       \"amp\", \"us\", \"en\", \"el\", \"y\", \"de\", \"que\", \"via\", \"12\", \n",
      "                                                       \"000\", \"hoy\", \"por\", \"les\", \"per\", \"la\", \"los\", \"5\", \"1\", \n",
      "                                                       \".@\", \"con\"]]\n",
      "    words[year] = [w.replace(\"\\xe2\", \"\") for w in words[year]]\n",
      "    words[year] = [w.replace(\"\\xc3\", \"\") for w in words[year]]\n",
      "    words[year] = [w.replace(\"\\xb3\", \"\") for w in words[year]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 396
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How many words have been used in 2013 and 2014? Let's compare the lexical diversity."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numwords = {}\n",
      "uniwords = {}\n",
      "lexi = {}\n",
      "\n",
      "for year in text:\n",
      "    numwords[year] = len(text[year])\n",
      "    uniwords[year] = len(set(text[year]))\n",
      "    lexi[year] = 1.0*numwords[year]/uniwords[year]\n",
      "\n",
      "print numwords\n",
      "print uniwords\n",
      "print lexi"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'2014': 487592, '2013': 195371}\n",
        "{'2014': 24124, '2013': 15607}\n",
        "{'2014': 20.21190515669043, '2013': 12.518164925994746}\n"
       ]
      }
     ],
     "prompt_number": 488
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Printing the Top 25 trending (= growing in frequency) words in 2014"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from prettytable import PrettyTable\n",
      "import codecs\n",
      "\n",
      "for year in numwords:\n",
      "    freq_table[\"Perc_\" + year] = 100.0 * freq_table[\"Freq_\" + year] / numwords[year]\n",
      "\n",
      "for year in [\"2014\"]:\n",
      "    freq_table[\"Growth_\" + year] = 100.0 * freq_table[\"Perc_\" + year] / freq_table[\"Perc_\" + str(int(year)-1)]\n",
      "\n",
      "    tb = freq_table[freq_table['Perc_' + str(year)] >= 0.09].sort(columns=\"Growth_\" + str(year), \n",
      "                                                                  ascending=False)[[\"Word\", \"Freq_\" + str(year), \n",
      "                                                                                    \"Perc_\" + str(year), \n",
      "                                                                                    \"Growth_\" + str(year)]]\n",
      "    tb.columns = [\"Word\", \"Freq\", \"Percent\", \"Index\"]\n",
      "    tb.Index = tb['Index'].round(1)\n",
      "    tb.Percent = tb['Percent'].round(4)\n",
      "    \n",
      "    pt = PrettyTable(field_names=[str(year), 'Frequency', 'Percent', \"Index\"]) \n",
      "    [pt.add_row(a[1]) for a in tb[:25].iterrows()]\n",
      "    pt.align[str(year)], pt.align['Frequency'], pt.align['Percent'], pt.align['Index'] = 'l', 'r', 'r', 'r'\n",
      "\n",
      "    print pt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+---------------+-----------+---------+----------+\n",
        "| 2014          | Frequency | Percent |    Index |\n",
        "+---------------+-----------+---------+----------+\n",
        "| dld14         |     16075 |  3.2968 | 322050.9 |\n",
        "| billion       |       646 |  0.1325 |   2157.0 |\n",
        "| experience    |       492 |  0.1009 |   1159.6 |\n",
        "| founder       |       512 |   0.105 |    820.6 |\n",
        "| anked         |       439 |    0.09 |    586.3 |\n",
        "| user          |       450 |  0.0923 |    487.3 |\n",
        "| bill_gross    |      1919 |  0.3936 |    427.2 |\n",
        "| today         |       644 |  0.1321 |    248.1 |\n",
        "| todo          |       541 |   0.111 |    240.9 |\n",
        "| marketing     |       485 |  0.0995 |    234.1 |\n",
        "| ceo           |       777 |  0.1594 |    225.6 |\n",
        "| google        |       703 |  0.1442 |    199.8 |\n",
        "| mobile        |       748 |  0.1534 |    168.4 |\n",
        "| internet      |       594 |  0.1218 |    165.3 |\n",
        "| day           |       644 |  0.1321 |    160.3 |\n",
        "| great         |       810 |  0.1661 |    144.9 |\n",
        "| digital       |       877 |  0.1799 |    129.7 |\n",
        "| jeffjarvis    |       470 |  0.0964 |    121.5 |\n",
        "| people        |       612 |  0.1255 |    116.8 |\n",
        "| live          |       545 |  0.1118 |     97.1 |\n",
        "| dldconference |      1134 |  0.2326 |     72.9 |\n",
        "| munich        |       472 |  0.0968 |     66.1 |\n",
        "| new           |       498 |  0.1021 |     56.4 |\n",
        "| dld           |      1399 |  0.2869 |     48.0 |\n",
        "| data          |       623 |  0.1278 |     44.4 |\n",
        "+---------------+-----------+---------+----------+\n"
       ]
      }
     ],
     "prompt_number": 399
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Top words in 2014"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tb = freq_table.sort(columns=\"Perc_2014\", ascending=False)[[\"Word\", \"Freq_\" + str(year), \"Perc_\" + str(year), \n",
      "                                                            \"Growth_\" + str(year)]]\n",
      "tb.columns = [\"Word\", \"Freq\", \"Percent\", \"Index\"]\n",
      "tb.Index = tb['Index'].round(1)\n",
      "tb.Percent = tb['Percent'].round(4)\n",
      "\n",
      "pt = PrettyTable(field_names=[\"Top 2014\", 'Frequency', 'Percent', \"Index\"]) \n",
      "[pt.add_row(a[1]) for a in tb[:25].iterrows()]\n",
      "pt.align[\"Top 2014\"], pt.align['Frequency'], pt.align['Percent'], pt.align['Index'] = 'l', 'r', 'r', 'r'\n",
      "\n",
      "print pt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+---------------+-----------+---------+----------+\n",
        "| Top 2014      | Frequency | Percent |    Index |\n",
        "+---------------+-----------+---------+----------+\n",
        "| dld14         |     16075 |  3.2968 | 322050.9 |\n",
        "| bill_gross    |      1919 |  0.3936 |    427.2 |\n",
        "| dld           |      1399 |  0.2869 |     48.0 |\n",
        "| dldconference |      1134 |  0.2326 |     72.9 |\n",
        "| digital       |       877 |  0.1799 |    129.7 |\n",
        "| great         |       810 |  0.1661 |    144.9 |\n",
        "| ceo           |       777 |  0.1594 |    225.6 |\n",
        "| mobile        |       748 |  0.1534 |    168.4 |\n",
        "| google        |       703 |  0.1442 |    199.8 |\n",
        "| billion       |       646 |  0.1325 |   2157.0 |\n",
        "| day           |       644 |  0.1321 |    160.3 |\n",
        "| today         |       644 |  0.1321 |    248.1 |\n",
        "| data          |       623 |  0.1278 |     44.4 |\n",
        "| people        |       612 |  0.1255 |    116.8 |\n",
        "| internet      |       594 |  0.1218 |    165.3 |\n",
        "| live          |       545 |  0.1118 |     97.1 |\n",
        "| todo          |       541 |   0.111 |    240.9 |\n",
        "| founder       |       512 |   0.105 |    820.6 |\n",
        "| new           |       498 |  0.1021 |     56.4 |\n",
        "| experience    |       492 |  0.1009 |   1159.6 |\n",
        "| marketing     |       485 |  0.0995 |    234.1 |\n",
        "| munich        |       472 |  0.0968 |     66.1 |\n",
        "| jeffjarvis    |       470 |  0.0964 |    121.5 |\n",
        "| user          |       450 |  0.0923 |    487.3 |\n",
        "| anked         |       439 |    0.09 |    586.3 |\n",
        "+---------------+-----------+---------+----------+\n"
       ]
      }
     ],
     "prompt_number": 400
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Top Words in 2013"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tb = freq_table.sort(columns=\"Perc_2013\", ascending=False)[[\"Word\", \"Freq_2013\", \"Perc_2013\"]]\n",
      "tb.columns = [\"Word\", \"Freq\", \"Percent\"]\n",
      "tb.Percent = tb['Percent'].round(4)\n",
      "\n",
      "pt = PrettyTable(field_names=[\"Top 2013\", 'Frequency', 'Percent']) \n",
      "[pt.add_row(a[1]) for a in tb[:25].iterrows()]\n",
      "pt.align[\"Top 2013\"], pt.align['Frequency'], pt.align['Percent'] = 'l', 'r', 'r'\n",
      "\n",
      "print pt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "+---------------+-----------+---------+\n",
        "| Top 2013      | Frequency | Percent |\n",
        "+---------------+-----------+---------+\n",
        "| dld13         |      7808 |  3.9965 |\n",
        "| dld           |      1167 |  0.5973 |\n",
        "| dldconference |       623 |  0.3189 |\n",
        "| data          |       562 |  0.2877 |\n",
        "| big           |       481 |  0.2462 |\n",
        "| new           |       354 |  0.1812 |\n",
        "| love          |       351 |  0.1797 |\n",
        "| like          |       343 |  0.1756 |\n",
        "| munich        |       286 |  0.1464 |\n",
        "| digital       |       271 |  0.1387 |\n",
        "| live          |       225 |  0.1152 |\n",
        "| great         |       224 |  0.1147 |\n",
        "| see           |       219 |  0.1121 |\n",
        "| sex           |       215 |    0.11 |\n",
        "| media         |       214 |  0.1095 |\n",
        "| future        |       211 |   0.108 |\n",
        "| people        |       210 |  0.1075 |\n",
        "| really        |       195 |  0.0998 |\n",
        "| times         |       193 |  0.0988 |\n",
        "| conference    |       184 |  0.0942 |\n",
        "| york          |       182 |  0.0932 |\n",
        "| bill_gross    |       180 |  0.0921 |\n",
        "| mobile        |       178 |  0.0911 |\n",
        "| social        |       176 |  0.0901 |\n",
        "| day           |       161 |  0.0824 |\n",
        "+---------------+-----------+---------+\n"
       ]
      }
     ],
     "prompt_number": 401
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Top Bigrams in 2014 and 2013"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from prettytable import PrettyTable\n",
      "from nltk.collocations import *\n",
      "\n",
      "bigram_measures = nltk.collocations.BigramAssocMeasures()\n",
      "trigram_measures = nltk.collocations.TrigramAssocMeasures()\n",
      "\n",
      "for year in [\"2013\", \"2014\"]:\n",
      "    words[year] = [w.replace(\"\\x80\", \"\") for w in words[year]]\n",
      "    words[year] = [w.replace(\"\\x99\", \"\") for w in words[year]]\n",
      "    words[year] = [w for w in words[year] if w not in [\"como\", \"oro\", \"las\", \"nadie\", \"cmo\", \"todos\", \"hablan\", \"una\", \"hacerlo\", \n",
      "                                                       \"sabe\", \")\", \"todo\", \"decidir\", \"slo\", \"adida\"]]\n",
      "    print \"Top Bigrams \" + str(year)\n",
      "    finder = BigramCollocationFinder.from_words(words[year])\n",
      "    scored = finder.score_ngrams(bigram_measures.raw_freq)\n",
      "\n",
      "    pt = PrettyTable(field_names=['Bigram', 'Frequency']) \n",
      "    [ pt.add_row([\" \".join(kv[0]), round(kv[1], 4)]) for kv in scored[:35] ]\n",
      "    pt.align['Bigram'], pt.align['Frequency'] = 'l', 'r' # Set column alignment\n",
      "    \n",
      "    print pt\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top Bigrams 2013\n",
        "+---------------------+-----------+\n",
        "| Bigram              | Frequency |\n",
        "+---------------------+-----------+\n",
        "| big data            |     0.005 |\n",
        "| new york            |    0.0024 |\n",
        "| dld13 dldconference |    0.0022 |\n",
        "| york times          |     0.002 |\n",
        "| times dld13         |    0.0019 |\n",
        "| dld13 tenemos       |    0.0018 |\n",
        "| tenemos abrirnos    |    0.0018 |\n",
        "| sulzberger new      |    0.0018 |\n",
        "| abrirnos redes      |    0.0018 |\n",
        "| redes sociales      |    0.0018 |\n",
        "| adolescentes dld13  |    0.0017 |\n",
        "| comparado sexo      |    0.0017 |\n",
        "| entre adolescentes  |    0.0017 |\n",
        "| sexo entre          |    0.0017 |\n",
        "| data comparado      |    0.0017 |\n",
        "| dean ornish         |    0.0016 |\n",
        "| convergencia zorra  |    0.0015 |\n",
        "| dld13 convergencia  |    0.0015 |\n",
        "| partners dld13      |    0.0015 |\n",
        "| kawaja luma         |    0.0015 |\n",
        "| luma partners       |    0.0015 |\n",
        "| dld13 dld           |    0.0014 |\n",
        "| dld13 ltwnds        |    0.0012 |\n",
        "| teenage sex         |    0.0012 |\n",
        "| ben horowitz        |    0.0012 |\n",
        "| jeffjarvis dld13    |    0.0012 |\n",
        "| future authority    |    0.0012 |\n",
        "| like teenage        |    0.0012 |\n",
        "| data like           |    0.0011 |\n",
        "| ozlem_denizmen dld  |    0.0011 |\n",
        "| dld13 ibcmunich     |    0.0011 |\n",
        "| ornish dld13        |     0.001 |\n",
        "| sex everybody       |     0.001 |\n",
        "| dldconference dld13 |     0.001 |\n",
        "| social media        |     0.001 |\n",
        "+---------------------+-----------+"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Top Bigrams 2014"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "+-----------------------+-----------+\n",
        "| Bigram                | Frequency |\n",
        "+-----------------------+-----------+\n",
        "| dld14 bill_gross      |    0.0029 |\n",
        "| rovio dld14           |    0.0025 |\n",
        "| user experience       |    0.0021 |\n",
        "| vesterbacka rovio     |     0.002 |\n",
        "| dld14 user            |     0.002 |\n",
        "| bill_gross whatsapp   |     0.002 |\n",
        "| today dld14           |    0.0017 |\n",
        "| jimmy wales           |    0.0016 |\n",
        "| active users          |    0.0016 |\n",
        "| million active        |    0.0015 |\n",
        "| billion smartphones   |    0.0014 |\n",
        "| earlier today         |    0.0014 |\n",
        "| users engineers       |    0.0014 |\n",
        "| 435 million           |    0.0014 |\n",
        "| engineers 10m         |    0.0014 |\n",
        "| whatsapp 435          |    0.0014 |\n",
        "| 10m actives           |    0.0014 |\n",
        "| actives engineer      |    0.0013 |\n",
        "| dld14 bitcoin         |    0.0013 |\n",
        "| people check          |    0.0012 |\n",
        "| engineer dld14        |    0.0011 |\n",
        "| 150x day              |    0.0011 |\n",
        "| check 150x            |    0.0011 |\n",
        "| smartphones worldwide |    0.0011 |\n",
        "| hubert burda          |    0.0011 |\n",
        "| moore law             |     0.001 |\n",
        "| bitcoin ventaja       |     0.001 |\n",
        "| dldconference dld14   |     0.001 |\n",
        "| ventaja puedes        |     0.001 |\n",
        "| puedes verdad         |     0.001 |\n",
        "| verdad pagar          |     0.001 |\n",
        "| joe schoendorf        |    0.0009 |\n",
        "| cheaper build         |    0.0009 |\n",
        "| day every             |    0.0009 |\n",
        "| dld14 qu              |    0.0009 |\n",
        "+-----------------------+-----------+"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 402
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Find the context for a word."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text[\"2014\"].concordance(\"xenon\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Displaying 25 of 166 matches:\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n",
        "12 , 000 transistors . Intel ' s new Xenon has 5 BILLION and Moore ' s Law is S\n"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Visualizing different words over time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "query = [\"xenon\", \"wales\", \"data\", \"rovio\"]\n",
      "col = [\"red\", \"blue\", \"green\", \"black\"]\n",
      "data = tweets[\"2014\"]\n",
      "data['text'] = data['text'].apply(str.lower)\n",
      "results = {}\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "\n",
      "for q in range(len(query)):\n",
      "    results[q] = data[data[\"text\"].str.contains(query[q])].ix[:, ['Tweets']]\n",
      "    results[q] = results[q].Tweets.resample('30min', how='count')\n",
      "    results[q].ix['2014-01-19':'2014-01-22'].plot(color=col[q], label=query[q])\n",
      "\n",
      "legend = ax.legend(loc='upper right', shadow=True)\n",
      "plt.xlabel('Date')\n",
      "plt.title('#DLD14 Conference Buzz for ' + \", \".join(query))\n",
      "plt.ylabel('Number of Tweets')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 482,
       "text": [
        "<matplotlib.text.Text at 0x2066c4e0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gypQpGD9+PHJyclBWVoaXXnqJs68l3yYkJMDGxgbnz59HeXk5tm3b1q7eSU3bbPpbzs7O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       "text": [
        "<matplotlib.figure.Figure at 0x1f9e24a8>"
       ]
      }
     ],
     "prompt_number": 482
    }
   ],
   "metadata": {}
  }
 ]
}