Chapter 1 - Import Data, Select Cases and Variables, Save DataFrame.ipynb

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 "metadata": {
  "name": "Tutorial - Import Data, Select Cases, Save Dataset"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "This is the first in a series of notebooks designed to show you how to <i>analyze</i> social media data. We assume you have already downloaded the data and are now ready to begin examining it. In this first notebook I will show you how to set up your ipython working environment and import the Twitter data we have downloaded."
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "Chapter 1: Set up Jupyter, Import Twitter Data and Select Cases"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>\n\nFirst, we will import several necessary Python packages. We will be using the <a href=\"http://pandas.pydata.org/\">Python Data Analysis Library,</a> or <i>PANDAS</i>, extensively for our data manipulations. It is invaluable for analyzing datasets. "
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Import packages"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nimport pandas as pd\nfrom pandas import DataFrame\nfrom pandas import Series",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>\n\nPANDAS allows you to set various options for, among other things, inspecting the data. I like to be able to see all of the columns. Therefore, I typically include this line at the top of all my notebooks."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#Set PANDAS to show all columns in DataFrame\npd.set_option('display.max_columns', None)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>\n\nWe can check which version of various packages we're using. You can see I'm running PANDAS 0.13 here."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print pd.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "0.13.1\n"
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Read in data"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "PANDAS can read in data from a variety of different data types. If you've followed some of my earlier tutorials you have downloaded tweets into an SQLite database, then converted to a CSV file. That's what we have here. We have a set of tweets by <i>Fortune 200</i> firms. So, in the following three lines we'll first import the CSV file and assign it to the name 'df' -- short for 'dataframe', the PANDAS name for a dataset. Second, we'll use the <i>len</i> function to see how many rows (tweets) there are in the dataset; there are 34,097 tweets in total. Finally, we will use the <i>head</i> function to show the first two rows of the dataset. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.read_csv('CSR_user_timeline_2013.csv', sep=',', low_memory=False)\nprint len(df)\ndf.head(2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "34097\n"
      },
      {
       "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>rowid</th>\n      <th>query</th>\n      <th>tweet_id</th>\n      <th>tweet_id_str</th>\n      <th>inserted_date</th>\n      <th>truncated</th>\n      <th>language</th>\n      <th>possibly_sensitive</th>\n      <th>coordinates</th>\n      <th>retweeted_status</th>\n      <th>withheld_in_countries</th>\n      <th>withheld_scope</th>\n      <th>created_at_text</th>\n      <th>created_at</th>\n      <th>month</th>\n      <th>year</th>\n      <th>content</th>\n      <th>from_user_screen_name</th>\n      <th>from_user_id</th>\n      <th>from_user_followers_count</th>\n      <th>from_user_friends_count</th>\n      <th>from_user_listed_count</th>\n      <th>from_user_favourites_count</th>\n      <th>from_user_statuses_count</th>\n      <th>from_user_description</th>\n      <th>from_user_location</th>\n      <th>from_user_created_at</th>\n      <th>retweet_count</th>\n      <th>favorite_count</th>\n      <th>entities_urls</th>\n      <th>entities_urls_count</th>\n      <th>entities_hashtags</th>\n      <th>entities_hashtags_count</th>\n      <th>entities_mentions</th>\n      <th>entities_mentions_count</th>\n      <th>in_reply_to_screen_name</th>\n      <th>in_reply_to_status_id</th>\n      <th>source</th>\n      <th>entities_expanded_urls</th>\n      <th>entities_media_count</th>\n      <th>media_expanded_url</th>\n      <th>media_url</th>\n      <th>media_type</th>\n      <th>video_link</th>\n      <th>photo_link</th>\n      <th>twitpic</th>\n      <th>num_characters</th>\n      <th>num_words</th>\n      <th>retweeted_user</th>\n      <th>retweeted_user_description</th>\n      <th>retweeted_user_screen_name</th>\n      <th>retweeted_user_followers_count</th>\n      <th>retweeted_user_listed_count</th>\n      <th>retweeted_user_statuses_count</th>\n      <th>retweeted_user_location</th>\n      <th>retweeted_tweet_created_at</th>\n      <th>Fortune_2012_rank</th>\n      <th>Company</th>\n      <th>CSR_sustainability</th>\n      <th>specific_project_initiative_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 67340</td>\n      <td>  humanavitality</td>\n      <td> 306897327585652736</td>\n      <td> 306897327585652736</td>\n      <td> 2014-03-09 13:46:50.222857</td>\n      <td> 0</td>\n      <td> en</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> Wed Feb 27 22:43:19 +0000 2013</td>\n      <td> 2013-02-27 22:43:19.000000</td>\n      <td> 2</td>\n      <td> 2013</td>\n      <td> @louloushive (Tweet 2) We encourage other empl...</td>\n      <td>  humanavitality</td>\n      <td> 274041023</td>\n      <td> 2859</td>\n      <td> 440</td>\n      <td> 38</td>\n      <td> 25</td>\n      <td> 1766</td>\n      <td> This is the official Twitter account for Human...</td>\n      <td>    NaN</td>\n      <td> Tue Mar 29 16:23:02 +0000 2011</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td>              NaN</td>\n      <td> 0</td>\n      <td> louloushive</td>\n      <td> 1</td>\n      <td> louloushive</td>\n      <td> 3.062183e+17</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 121</td>\n      <td> 19</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 79</td>\n      <td> Humana</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 39454</td>\n      <td> FundacionPfizer</td>\n      <td> 308616393706844160</td>\n      <td> 308616393706844160</td>\n      <td> 2014-03-09 13:38:20.679967</td>\n      <td> 0</td>\n      <td> es</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> Mon Mar 04 16:34:17 +0000 2013</td>\n      <td> 2013-03-04 16:34:17.000000</td>\n      <td> 3</td>\n      <td> 2013</td>\n      <td> \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...</td>\n      <td> FundacionPfizer</td>\n      <td> 188384056</td>\n      <td> 2464</td>\n      <td> 597</td>\n      <td> 50</td>\n      <td> 11</td>\n      <td> 2400</td>\n      <td> Noticias sobre Responsabilidad Social y Fundac...</td>\n      <td> M\u00e9xico</td>\n      <td> Wed Sep 08 16:14:11 +0000 2010</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> vacuna, neumon\u00eda</td>\n      <td> 2</td>\n      <td>         NaN</td>\n      <td> 0</td>\n      <td>         NaN</td>\n      <td>          NaN</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 138</td>\n      <td> 20</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 40</td>\n      <td> Pfizer</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows \u00d7 60 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": "   rowid            query            tweet_id        tweet_id_str  \\\n0  67340   humanavitality  306897327585652736  306897327585652736   \n1  39454  FundacionPfizer  308616393706844160  308616393706844160   \n\n                inserted_date  truncated language  possibly_sensitive  \\\n0  2014-03-09 13:46:50.222857          0       en                 NaN   \n1  2014-03-09 13:38:20.679967          0       es                 NaN   \n\n  coordinates retweeted_status  withheld_in_countries  withheld_scope  \\\n0         NaN              NaN                    NaN             NaN   \n1         NaN              NaN                    NaN             NaN   \n\n                  created_at_text                  created_at  month  year  \\\n0  Wed Feb 27 22:43:19 +0000 2013  2013-02-27 22:43:19.000000      2  2013   \n1  Mon Mar 04 16:34:17 +0000 2013  2013-03-04 16:34:17.000000      3  2013   \n\n                                             content from_user_screen_name  \\\n0  @louloushive (Tweet 2) We encourage other empl...        humanavitality   \n1  \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...       FundacionPfizer   \n\n   from_user_id  from_user_followers_count  from_user_friends_count  \\\n0     274041023                       2859                      440   \n1     188384056                       2464                      597   \n\n   from_user_listed_count  from_user_favourites_count  \\\n0                      38                          25   \n1                      50                          11   \n\n   from_user_statuses_count  \\\n0                      1766   \n1                      2400   \n\n                               from_user_description from_user_location  \\\n0  This is the official Twitter account for Human...                NaN   \n1  Noticias sobre Responsabilidad Social y Fundac...             M\u00e9xico   \n\n             from_user_created_at  retweet_count  favorite_count  \\\n0  Tue Mar 29 16:23:02 +0000 2011              0               0   \n1  Wed Sep 08 16:14:11 +0000 2010              1               0   \n\n  entities_urls  entities_urls_count entities_hashtags  \\\n0           NaN                    0               NaN   \n1           NaN                    0  vacuna, neumon\u00eda   \n\n   entities_hashtags_count entities_mentions  entities_mentions_count  \\\n0                        0       louloushive                        1   \n1                        2               NaN                        0   \n\n  in_reply_to_screen_name  in_reply_to_status_id source  \\\n0             louloushive           3.062183e+17    web   \n1                     NaN                    NaN    web   \n\n  entities_expanded_urls  entities_media_count media_expanded_url media_url  \\\n0                    NaN                   NaN                NaN       NaN   \n1                    NaN                   NaN                NaN       NaN   \n\n  media_type  video_link  photo_link  twitpic  num_characters  num_words  \\\n0        NaN           0           0        0             121         19   \n1        NaN           0           0        0             138         20   \n\n   retweeted_user retweeted_user_description retweeted_user_screen_name  \\\n0             NaN                        NaN                        NaN   \n1             NaN                        NaN                        NaN   \n\n   retweeted_user_followers_count  retweeted_user_listed_count  \\\n0                             NaN                          NaN   \n1                             NaN                          NaN   \n\n   retweeted_user_statuses_count retweeted_user_location  \\\n0                            NaN                     NaN   \n1                            NaN                     NaN   \n\n  retweeted_tweet_created_at  Fortune_2012_rank Company  CSR_sustainability  \\\n0                        NaN                 79  Humana                   0   \n1                        NaN                 40  Pfizer                   0   \n\n   specific_project_initiative_area  \n0                                 1  \n1                                 1  \n\n[2 rows x 60 columns]"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>\n\nList all the columns in the DataFrame"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.columns",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": "Index([u'rowid', u'query', u'tweet_id', u'tweet_id_str', u'inserted_date', u'truncated', u'language', u'possibly_sensitive', u'coordinates', u'retweeted_status', u'withheld_in_countries', u'withheld_scope', u'created_at_text', u'created_at', u'month', u'year', u'content', u'from_user_screen_name', u'from_user_id', u'from_user_followers_count', u'from_user_friends_count', u'from_user_listed_count', u'from_user_favourites_count', u'from_user_statuses_count', u'from_user_description', u'from_user_location', u'from_user_created_at', u'retweet_count', u'favorite_count', u'entities_urls', u'entities_urls_count', u'entities_hashtags', u'entities_hashtags_count', u'entities_mentions', u'entities_mentions_count', u'in_reply_to_screen_name', u'in_reply_to_status_id', u'source', u'entities_expanded_urls', u'entities_media_count', u'media_expanded_url', u'media_url', u'media_type', u'video_link', u'photo_link', u'twitpic', u'num_characters', u'num_words', u'retweeted_user', u'retweeted_user_description', u'retweeted_user_screen_name', u'retweeted_user_followers_count', u'retweeted_user_listed_count', u'retweeted_user_statuses_count', u'retweeted_user_location', u'retweeted_tweet_created_at', u'Fortune_2012_rank', u'Company', u'CSR_sustainability', u'specific_project_initiative_area'], dtype='object')"
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br> We can use the <i>len</i> function again here to see how many columns there are in the dataframe: 60."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(df.columns)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": "60"
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br> We should also inspect the format for our columns. We can see that some are integers, some are 'float' (can have a decimal), and some are 'objects' (text). If you have a identifying text variable that has accidentally been imported as a float, for instance, that could cause problems down the road, so you should fix it before continuing."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.dtypes",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": "rowid                                 int64\nquery                                object\ntweet_id_str                          int64\ninserted_date                        object\nlanguage                             object\ncoordinates                          object\nretweeted_status                     object\ncreated_at                           object\nmonth                                 int64\nyear                                  int64\ncontent                              object\nfrom_user_screen_name                object\nfrom_user_id                          int64\nfrom_user_followers_count             int64\nfrom_user_friends_count               int64\nfrom_user_listed_count                int64\nfrom_user_favourites_count            int64\nfrom_user_statuses_count              int64\nfrom_user_description                object\nfrom_user_location                   object\nfrom_user_created_at                 object\nretweet_count                         int64\nfavorite_count                        int64\nentities_urls                        object\nentities_urls_count                   int64\nentities_hashtags                    object\nentities_hashtags_count               int64\nentities_mentions                    object\nentities_mentions_count               int64\nin_reply_to_screen_name              object\nin_reply_to_status_id               float64\nsource                               object\nentities_expanded_urls               object\nentities_media_count                float64\nmedia_expanded_url                   object\nmedia_url                            object\nmedia_type                           object\nvideo_link                            int64\nphoto_link                            int64\ntwitpic                               int64\nnum_characters                        int64\nnum_words                             int64\nretweeted_user                      float64\nretweeted_user_description           object\nretweeted_user_screen_name           object\nretweeted_user_followers_count      float64\nretweeted_user_listed_count         float64\nretweeted_user_statuses_count       float64\nretweeted_user_location              object\nretweeted_tweet_created_at           object\nFortune_2012_rank                     int64\nCompany                              object\nCSR_sustainability                    int64\nspecific_project_initiative_area      int64\nLength: 54, dtype: object"
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Remove Unneeded Columns"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Every researcher will have different preferences about keeping or deleting unneeded columns. You might want to delete variables to make your dataset cleaner, you might want to save memory, or you might want a smaller dataset for some specific analyses. In any case, we can use the <i>drop</i> command to delete individual columns. Let's drop six that are not needed here. The first two have duplicate columns in another format, while the latter four all have zero variation (all are blank).\t    "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = df.drop('created_at_text',1)\ndf = df.drop('tweet_id',1)\ndf = df.drop('withheld_in_countries',1)\ndf = df.drop('withheld_scope',1)\ndf = df.drop('truncated',1)\ndf = df.drop('possibly_sensitive',1)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>There are now 54 columns in the dataframe."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(df.columns)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": "54"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.head(2)",
     "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>rowid</th>\n      <th>query</th>\n      <th>tweet_id_str</th>\n      <th>inserted_date</th>\n      <th>language</th>\n      <th>coordinates</th>\n      <th>retweeted_status</th>\n      <th>created_at</th>\n      <th>month</th>\n      <th>year</th>\n      <th>content</th>\n      <th>from_user_screen_name</th>\n      <th>from_user_id</th>\n      <th>from_user_followers_count</th>\n      <th>from_user_friends_count</th>\n      <th>from_user_listed_count</th>\n      <th>from_user_favourites_count</th>\n      <th>from_user_statuses_count</th>\n      <th>from_user_description</th>\n      <th>from_user_location</th>\n      <th>from_user_created_at</th>\n      <th>retweet_count</th>\n      <th>favorite_count</th>\n      <th>entities_urls</th>\n      <th>entities_urls_count</th>\n      <th>entities_hashtags</th>\n      <th>entities_hashtags_count</th>\n      <th>entities_mentions</th>\n      <th>entities_mentions_count</th>\n      <th>in_reply_to_screen_name</th>\n      <th>in_reply_to_status_id</th>\n      <th>source</th>\n      <th>entities_expanded_urls</th>\n      <th>entities_media_count</th>\n      <th>media_expanded_url</th>\n      <th>media_url</th>\n      <th>media_type</th>\n      <th>video_link</th>\n      <th>photo_link</th>\n      <th>twitpic</th>\n      <th>num_characters</th>\n      <th>num_words</th>\n      <th>retweeted_user</th>\n      <th>retweeted_user_description</th>\n      <th>retweeted_user_screen_name</th>\n      <th>retweeted_user_followers_count</th>\n      <th>retweeted_user_listed_count</th>\n      <th>retweeted_user_statuses_count</th>\n      <th>retweeted_user_location</th>\n      <th>retweeted_tweet_created_at</th>\n      <th>Fortune_2012_rank</th>\n      <th>Company</th>\n      <th>CSR_sustainability</th>\n      <th>specific_project_initiative_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 67340</td>\n      <td>  humanavitality</td>\n      <td> 306897327585652736</td>\n      <td> 2014-03-09 13:46:50.222857</td>\n      <td> en</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 2013-02-27 22:43:19.000000</td>\n      <td> 2</td>\n      <td> 2013</td>\n      <td> @louloushive (Tweet 2) We encourage other empl...</td>\n      <td>  humanavitality</td>\n      <td> 274041023</td>\n      <td> 2859</td>\n      <td> 440</td>\n      <td> 38</td>\n      <td> 25</td>\n      <td> 1766</td>\n      <td> This is the official Twitter account for Human...</td>\n      <td>    NaN</td>\n      <td> Tue Mar 29 16:23:02 +0000 2011</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td>              NaN</td>\n      <td> 0</td>\n      <td> louloushive</td>\n      <td> 1</td>\n      <td> louloushive</td>\n      <td> 3.062183e+17</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 121</td>\n      <td> 19</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 79</td>\n      <td> Humana</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 39454</td>\n      <td> FundacionPfizer</td>\n      <td> 308616393706844160</td>\n      <td> 2014-03-09 13:38:20.679967</td>\n      <td> es</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 2013-03-04 16:34:17.000000</td>\n      <td> 3</td>\n      <td> 2013</td>\n      <td> \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...</td>\n      <td> FundacionPfizer</td>\n      <td> 188384056</td>\n      <td> 2464</td>\n      <td> 597</td>\n      <td> 50</td>\n      <td> 11</td>\n      <td> 2400</td>\n      <td> Noticias sobre Responsabilidad Social y Fundac...</td>\n      <td> M\u00e9xico</td>\n      <td> Wed Sep 08 16:14:11 +0000 2010</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> vacuna, neumon\u00eda</td>\n      <td> 2</td>\n      <td>         NaN</td>\n      <td> 0</td>\n      <td>         NaN</td>\n      <td>          NaN</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 138</td>\n      <td> 20</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 40</td>\n      <td> Pfizer</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows \u00d7 54 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": "   rowid            query        tweet_id_str               inserted_date  \\\n0  67340   humanavitality  306897327585652736  2014-03-09 13:46:50.222857   \n1  39454  FundacionPfizer  308616393706844160  2014-03-09 13:38:20.679967   \n\n  language coordinates retweeted_status                  created_at  month  \\\n0       en         NaN              NaN  2013-02-27 22:43:19.000000      2   \n1       es         NaN              NaN  2013-03-04 16:34:17.000000      3   \n\n   year                                            content  \\\n0  2013  @louloushive (Tweet 2) We encourage other empl...   \n1  2013  \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...   \n\n  from_user_screen_name  from_user_id  from_user_followers_count  \\\n0        humanavitality     274041023                       2859   \n1       FundacionPfizer     188384056                       2464   \n\n   from_user_friends_count  from_user_listed_count  \\\n0                      440                      38   \n1                      597                      50   \n\n   from_user_favourites_count  from_user_statuses_count  \\\n0                          25                      1766   \n1                          11                      2400   \n\n                               from_user_description from_user_location  \\\n0  This is the official Twitter account for Human...                NaN   \n1  Noticias sobre Responsabilidad Social y Fundac...             M\u00e9xico   \n\n             from_user_created_at  retweet_count  favorite_count  \\\n0  Tue Mar 29 16:23:02 +0000 2011              0               0   \n1  Wed Sep 08 16:14:11 +0000 2010              1               0   \n\n  entities_urls  entities_urls_count entities_hashtags  \\\n0           NaN                    0               NaN   \n1           NaN                    0  vacuna, neumon\u00eda   \n\n   entities_hashtags_count entities_mentions  entities_mentions_count  \\\n0                        0       louloushive                        1   \n1                        2               NaN                        0   \n\n  in_reply_to_screen_name  in_reply_to_status_id source  \\\n0             louloushive           3.062183e+17    web   \n1                     NaN                    NaN    web   \n\n  entities_expanded_urls  entities_media_count media_expanded_url media_url  \\\n0                    NaN                   NaN                NaN       NaN   \n1                    NaN                   NaN                NaN       NaN   \n\n  media_type  video_link  photo_link  twitpic  num_characters  num_words  \\\n0        NaN           0           0        0             121         19   \n1        NaN           0           0        0             138         20   \n\n   retweeted_user retweeted_user_description retweeted_user_screen_name  \\\n0             NaN                        NaN                        NaN   \n1             NaN                        NaN                        NaN   \n\n   retweeted_user_followers_count  retweeted_user_listed_count  \\\n0                             NaN                          NaN   \n1                             NaN                          NaN   \n\n   retweeted_user_statuses_count retweeted_user_location  \\\n0                            NaN                     NaN   \n1                            NaN                     NaN   \n\n  retweeted_tweet_created_at  Fortune_2012_rank Company  CSR_sustainability  \\\n0                        NaN                 79  Humana                   0   \n1                        NaN                 40  Pfizer                   0   \n\n   specific_project_initiative_area  \n0                                 1  \n1                                 1  \n\n[2 rows x 54 columns]"
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>If you have only a few columns to delete you can use the <i>drop</i> command as shown above. On the other hand, if you only want to <i>keep</i> a few columns, you can create a new version of the dataframe with only those columns you like. Note that the double square brackets -- \"[[...]]\" -- in PANDAS forms a dataframe representation. In the following example, I am creating a new dataframe with only three variables. You can see that this new dataframe has the same number of tweets but fewer columns (variables)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df2 = df[['created_at', 'from_user_screen_name', 'retweet_count']]\nprint len(df2)\ndf2.head(2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "34097\n"
      },
      {
       "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>created_at</th>\n      <th>from_user_screen_name</th>\n      <th>retweet_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2013-02-27 22:43:19.000000</td>\n      <td>  humanavitality</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013-03-04 16:34:17.000000</td>\n      <td> FundacionPfizer</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows \u00d7 3 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": "                   created_at from_user_screen_name  retweet_count\n0  2013-02-27 22:43:19.000000        humanavitality              0\n1  2013-03-04 16:34:17.000000       FundacionPfizer              1\n\n[2 rows x 3 columns]"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "View Twitter Accounts Represented in DF "
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We can use the <i>unique</i> function to find how many unique Twitter accounts are represented in the dataset. First, I'll show you what  <i>unique</i> function does -- it creates an array of all the <i>screen_names</i> of the Twitter accounts."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pd.unique(df.from_user_screen_name.ravel())",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": "array(['humanavitality', 'FundacionPfizer', 'Intelinvolved', 'CiscoCSR',\n       'IBMSmartCities', 'citizenIBM', 'BofA_Community', 'PG_CSDW',\n       'mathmovesu', 'PPGIdeascapes', 'gehealthy', 'msftcitizenship',\n       'TICalculators', 'CiscoEDU', 'DuPont_ability', 'Dell4Good',\n       'verizongiving', 'DellEDU', 'SprintGreenNews', 'TeachingMoney',\n       'WalmartGreen', 'ecomagination', 'WalmartAction', 'Microsoft_Green',\n       'Comcastdreambig', 'DE_Youtility', 'hpglobalcitizen',\n       'CitizenDisney', 'AmgenFoundation', 'ATTAspire', 'AlcoaFoundation',\n       'nikebetterworld', 'HoneywellBuild', 'googlestudents',\n       '3M_FoodSafety', 'HeartRescue', 'EnviroSears', 'ClickToEmpower',\n       'GreenIBM', 'PromesaPepsiCo', 'FordDriveGreen', 'JNJStories'], dtype=object)"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>Note again how we can use <i>len</i> to find out how many accounts are in the array: 42"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(pd.unique(df.from_user_screen_name.ravel()))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": "42"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Remove Tweets from One Specific Account"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We want to get rid of all tweets by <i>TICalculators</i> from the dataframe. Unlike the other 41 Twitter accounts in the dataset, this account is not a CSR-related account.  First, we can use the <i>len</i> function combined with a dataframe query to count the number of tweets that are not sent by <i>TICalculators</i>: 32,300"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(df[df['from_user_screen_name'] != 'TICalculators'])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": "32330"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>We should then also check how many tweets <i>are</i> sent by <i>TICalculators</i>: 1,767"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(df[df['from_user_screen_name'] == 'TICalculators'])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": "1767"
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>We can use Python to do \"math.\" Let's use this to show whether the two numbers returned in the above steps add up to the total number of tweets in our dataframe. They do. While this may seem like an unnecessary step, it is always critical to perform such basic data checks in order to avert unexpected data disasters."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "1767 + 32330",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": "34097"
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>We can also do this another way"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "(1767 + 32330) - len(df)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": "0"
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>Or even"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(df[df['from_user_screen_name'] != 'TICalculators']) + len(df[df['from_user_screen_name'] == 'TICalculators']) - len(df)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": "0"
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Remove Tweets by TICalculators"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "In the next block of code we will create a new version of our dataframe, this time limiting it to only those tweets that are not sent by <i>TICalculators</i>. As we can see, there are now 32,300 tweets in this dataframe, the same number as we calculated above. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = df[df['from_user_screen_name'] != 'TICalculators']\nprint len(df)\ndf.head(2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "32330\n"
      },
      {
       "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>rowid</th>\n      <th>query</th>\n      <th>tweet_id_str</th>\n      <th>inserted_date</th>\n      <th>language</th>\n      <th>coordinates</th>\n      <th>retweeted_status</th>\n      <th>created_at</th>\n      <th>month</th>\n      <th>year</th>\n      <th>content</th>\n      <th>from_user_screen_name</th>\n      <th>from_user_id</th>\n      <th>from_user_followers_count</th>\n      <th>from_user_friends_count</th>\n      <th>from_user_listed_count</th>\n      <th>from_user_favourites_count</th>\n      <th>from_user_statuses_count</th>\n      <th>from_user_description</th>\n      <th>from_user_location</th>\n      <th>from_user_created_at</th>\n      <th>retweet_count</th>\n      <th>favorite_count</th>\n      <th>entities_urls</th>\n      <th>entities_urls_count</th>\n      <th>entities_hashtags</th>\n      <th>entities_hashtags_count</th>\n      <th>entities_mentions</th>\n      <th>entities_mentions_count</th>\n      <th>in_reply_to_screen_name</th>\n      <th>in_reply_to_status_id</th>\n      <th>source</th>\n      <th>entities_expanded_urls</th>\n      <th>entities_media_count</th>\n      <th>media_expanded_url</th>\n      <th>media_url</th>\n      <th>media_type</th>\n      <th>video_link</th>\n      <th>photo_link</th>\n      <th>twitpic</th>\n      <th>num_characters</th>\n      <th>num_words</th>\n      <th>retweeted_user</th>\n      <th>retweeted_user_description</th>\n      <th>retweeted_user_screen_name</th>\n      <th>retweeted_user_followers_count</th>\n      <th>retweeted_user_listed_count</th>\n      <th>retweeted_user_statuses_count</th>\n      <th>retweeted_user_location</th>\n      <th>retweeted_tweet_created_at</th>\n      <th>Fortune_2012_rank</th>\n      <th>Company</th>\n      <th>CSR_sustainability</th>\n      <th>specific_project_initiative_area</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 67340</td>\n      <td>  humanavitality</td>\n      <td> 306897327585652736</td>\n      <td> 2014-03-09 13:46:50.222857</td>\n      <td> en</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 2013-02-27 22:43:19.000000</td>\n      <td> 2</td>\n      <td> 2013</td>\n      <td> @louloushive (Tweet 2) We encourage other empl...</td>\n      <td>  humanavitality</td>\n      <td> 274041023</td>\n      <td> 2859</td>\n      <td> 440</td>\n      <td> 38</td>\n      <td> 25</td>\n      <td> 1766</td>\n      <td> This is the official Twitter account for Human...</td>\n      <td>    NaN</td>\n      <td> Tue Mar 29 16:23:02 +0000 2011</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td>              NaN</td>\n      <td> 0</td>\n      <td> louloushive</td>\n      <td> 1</td>\n      <td> louloushive</td>\n      <td> 3.062183e+17</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 121</td>\n      <td> 19</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 79</td>\n      <td> Humana</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 39454</td>\n      <td> FundacionPfizer</td>\n      <td> 308616393706844160</td>\n      <td> 2014-03-09 13:38:20.679967</td>\n      <td> es</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 2013-03-04 16:34:17.000000</td>\n      <td> 3</td>\n      <td> 2013</td>\n      <td> \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...</td>\n      <td> FundacionPfizer</td>\n      <td> 188384056</td>\n      <td> 2464</td>\n      <td> 597</td>\n      <td> 50</td>\n      <td> 11</td>\n      <td> 2400</td>\n      <td> Noticias sobre Responsabilidad Social y Fundac...</td>\n      <td> M\u00e9xico</td>\n      <td> Wed Sep 08 16:14:11 +0000 2010</td>\n      <td> 1</td>\n      <td> 0</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> vacuna, neumon\u00eda</td>\n      <td> 2</td>\n      <td>         NaN</td>\n      <td> 0</td>\n      <td>         NaN</td>\n      <td>          NaN</td>\n      <td> web</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 0</td>\n      <td> 138</td>\n      <td> 20</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td> NaN</td>\n      <td> NaN</td>\n      <td> 40</td>\n      <td> Pfizer</td>\n      <td> 0</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows \u00d7 54 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": "   rowid            query        tweet_id_str               inserted_date  \\\n0  67340   humanavitality  306897327585652736  2014-03-09 13:46:50.222857   \n1  39454  FundacionPfizer  308616393706844160  2014-03-09 13:38:20.679967   \n\n  language coordinates retweeted_status                  created_at  month  \\\n0       en         NaN              NaN  2013-02-27 22:43:19.000000      2   \n1       es         NaN              NaN  2013-03-04 16:34:17.000000      3   \n\n   year                                            content  \\\n0  2013  @louloushive (Tweet 2) We encourage other empl...   \n1  2013  \u00bfSabes por qu\u00e9 la #vacuna contra la #neumon\u00eda ...   \n\n  from_user_screen_name  from_user_id  from_user_followers_count  \\\n0        humanavitality     274041023                       2859   \n1       FundacionPfizer     188384056                       2464   \n\n   from_user_friends_count  from_user_listed_count  \\\n0                      440                      38   \n1                      597                      50   \n\n   from_user_favourites_count  from_user_statuses_count  \\\n0                          25                      1766   \n1                          11                      2400   \n\n                               from_user_description from_user_location  \\\n0  This is the official Twitter account for Human...                NaN   \n1  Noticias sobre Responsabilidad Social y Fundac...             M\u00e9xico   \n\n             from_user_created_at  retweet_count  favorite_count  \\\n0  Tue Mar 29 16:23:02 +0000 2011              0               0   \n1  Wed Sep 08 16:14:11 +0000 2010              1               0   \n\n  entities_urls  entities_urls_count entities_hashtags  \\\n0           NaN                    0               NaN   \n1           NaN                    0  vacuna, neumon\u00eda   \n\n   entities_hashtags_count entities_mentions  entities_mentions_count  \\\n0                        0       louloushive                        1   \n1                        2               NaN                        0   \n\n  in_reply_to_screen_name  in_reply_to_status_id source  \\\n0             louloushive           3.062183e+17    web   \n1                     NaN                    NaN    web   \n\n  entities_expanded_urls  entities_media_count media_expanded_url media_url  \\\n0                    NaN                   NaN                NaN       NaN   \n1                    NaN                   NaN                NaN       NaN   \n\n  media_type  video_link  photo_link  twitpic  num_characters  num_words  \\\n0        NaN           0           0        0             121         19   \n1        NaN           0           0        0             138         20   \n\n   retweeted_user retweeted_user_description retweeted_user_screen_name  \\\n0             NaN                        NaN                        NaN   \n1             NaN                        NaN                        NaN   \n\n   retweeted_user_followers_count  retweeted_user_listed_count  \\\n0                             NaN                          NaN   \n1                             NaN                          NaN   \n\n   retweeted_user_statuses_count retweeted_user_location  \\\n0                            NaN                     NaN   \n1                            NaN                     NaN   \n\n  retweeted_tweet_created_at  Fortune_2012_rank Company  CSR_sustainability  \\\n0                        NaN                 79  Humana                   0   \n1                        NaN                 40  Pfizer                   0   \n\n   specific_project_initiative_area  \n0                                 1  \n1                                 1  \n\n[2 rows x 54 columns]"
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>Now let's check again for all the unique accounts in the dataframe -- as you can see, <i>TICalculators</i> is gone and there are now 41 accounts."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pd.unique(df.from_user_screen_name.ravel())",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": "array(['humanavitality', 'FundacionPfizer', 'Intelinvolved', 'CiscoCSR',\n       'IBMSmartCities', 'citizenIBM', 'BofA_Community', 'PG_CSDW',\n       'mathmovesu', 'PPGIdeascapes', 'gehealthy', 'msftcitizenship',\n       'CiscoEDU', 'DuPont_ability', 'Dell4Good', 'verizongiving',\n       'DellEDU', 'SprintGreenNews', 'TeachingMoney', 'WalmartGreen',\n       'ecomagination', 'WalmartAction', 'Microsoft_Green',\n       'Comcastdreambig', 'DE_Youtility', 'hpglobalcitizen',\n       'CitizenDisney', 'AmgenFoundation', 'ATTAspire', 'AlcoaFoundation',\n       'nikebetterworld', 'HoneywellBuild', 'googlestudents',\n       '3M_FoodSafety', 'HeartRescue', 'EnviroSears', 'ClickToEmpower',\n       'GreenIBM', 'PromesaPepsiCo', 'FordDriveGreen', 'JNJStories'], dtype=object)"
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "len(pd.unique(df.from_user_screen_name.ravel()))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": "41"
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>We now have our new dataframe without TICalculators. We are left with 32,330 tweets sent by 41 companies over the course of 2013."
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": "Save New DataFrame"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We will now save the dataframe in PANDAS' native format. It's called 'pickling' a file, so we'll give it the typical 'pkl' extension."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df.to_pickle('CSR tweets - 2013 by 41 accounts.pkl')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "<br>\n\nFor more Notebooks as well as additional Python and Big Data tutorials, please visit http://social-metrics.org or follow me on Twitter <a href='https://twitter.com/gregorysaxton'>@gregorysaxton</a>"
    }
   ],
   "metadata": {}
  }
 ]
}