sharepa Tutorial

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "983714"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import json\n",
    "\n",
    "from elasticsearch_dsl import F, A, Q\n",
    "\n",
    "from sharepa import ShareSearch, basic_search, bucket_to_dataframe, merge_dataframes, source_counts\n",
    "\n",
    "# This is just a helper function for pretty printing dictionaries\n",
    "def pretty_print(d):\n",
    "    print json.dumps(d, indent=4)\n",
    "\n",
    "# We include a basic search, that gets all the documents and a simple aggregation by source\n",
    "basic_search.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# <search>.execute sends the actual query to the SHARE API\n",
    "results = basic_search.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avian community structure and incidence of human West Nile infection\n",
      "Rat12_a\n",
      "Non compact continuum limit of two coupled Potts models\n",
      "\n",
      "Simultaneous Localization, Mapping, and Manipulation for Unsupervised\n",
      "  Object Discovery\n",
      "Synthesis of High-Temperature Self-lubricating Wear Resistant Composite Coating on Ti6Al4V Alloy by Laser Deposition\n",
      "Comparative Studies of Silicon Dissolution in Molten Aluminum Under Different Flow Conditions, Part I: Single-Phase Flow\n",
      "Scrambling of data in all-optical domain\n",
      "Keyboard Debut Series, November 12, 1998\n",
      "The effect of inclusions in brittle material\n"
     ]
    }
   ],
   "source": [
    "# Iterating through the results is easy!\n",
    "for hit in results:\n",
    "    print hit.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Synthesis of High-Temperature Self-lubricating Wear Resistant Composite Coating on Ti6Al4V Alloy by Laser Deposition\n",
      "Comparative Studies of Silicon Dissolution in Molten Aluminum Under Different Flow Conditions, Part I: Single-Phase Flow\n",
      "Scrambling of data in all-optical domain\n",
      "Keyboard Debut Series, November 12, 1998\n",
      "The effect of inclusions in brittle material\n"
     ]
    }
   ],
   "source": [
    "# If we don't want 10 results, or we want to offset the results, we can use slices\n",
    "results = basic_search[5:10].execute()\n",
    "for hit in results:\n",
    "    print hit.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1121da790>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAFACAYAAAC4MdtMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121a7690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting is also not hard, you just need to pass a bucket to this function!\n",
    "my_data_frame = bucket_to_dataframe('# documents by source', results.aggregations.sourceAgg.buckets)\n",
    "my_data_frame.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Of course, you can make your own search object as well\n",
    "my_search = ShareSearch()\n",
    "\n",
    "# And adding queries to it is pretty simple!\n",
    "my_search = my_search.query(\n",
    "    'query_string', # This query will accept a lucene query string\n",
    "    query='NOT tags:*', # This lucene query string will find all documents that don't have tags\n",
    "    analyze_wildcard=True  # This will make elasticsearch pay attention to the asterisk (which matches anything)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"query\": {\n",
      "        \"query_string\": {\n",
      "            \"analyze_wildcard\": true, \n",
      "            \"query\": \"NOT tags:*\"\n",
      "        }\n",
      "    }, \n",
      "    \"aggs\": {\n",
      "        \"sources\": {\n",
      "            \"terms\": {\n",
      "                \"field\": \"_type\", \n",
      "                \"min_doc_count\": 0, \n",
      "                \"size\": 0\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Aggregations, which are useful for data analysis, follow a similar pattern\n",
    "my_search.aggs.bucket(\n",
    "    'sources',  # Every aggregation needs a name\n",
    "    'terms',  # There are many kinds of aggregations, terms is a pretty useful one though\n",
    "    field='_type',  # We store the source of a document in its type, so this will aggregate by source\n",
    "    size=0,  # These are just to make sure we get numbers for all the sources, to make it easier to combine graphs\n",
    "    min_doc_count=0\n",
    ")\n",
    "\n",
    "# We can see what query is actually going to be sent to elasticsearch\n",
    "pretty_print(my_search.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"query\": {\n",
      "        \"query_string\": {\n",
      "            \"analyze_wildcard\": true, \n",
      "            \"query\": \"NOT tags:*\"\n",
      "        }\n",
      "    }, \n",
      "    \"aggs\": {\n",
      "        \"sources\": {\n",
      "            \"terms\": {\n",
      "                \"field\": \"_type\", \n",
      "                \"min_doc_count\": 0, \n",
      "                \"size\": 0\n",
      "            }\n",
      "        }, \n",
      "        \"missingTitle\": {\n",
      "            \"aggs\": {\n",
      "                \"sourceAgg\": {\n",
      "                    \"terms\": {\n",
      "                        \"field\": \"_type\", \n",
      "                        \"min_doc_count\": 0, \n",
      "                        \"size\": 0\n",
      "                    }\n",
      "                }\n",
      "            }, \n",
      "            \"filters\": {\n",
      "                \"filters\": {\n",
      "                    \"missingTitle\": {\n",
      "                        \"fquery\": {\n",
      "                            \"query\": {\n",
      "                                \"query_string\": {\n",
      "                                    \"query\": \"NOT title:*\", \n",
      "                                    \"analyze_wildcard\": true\n",
      "                                }\n",
      "                            }\n",
      "                        }\n",
      "                    }\n",
      "                }\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Let's make a more interesting aggregation. Let's look at the documents that are missing titles, by source\n",
    "my_search.aggs.bucket(\n",
    "    'missingTitle',  # so here we will see how many documents will be missing titles\n",
    "    'filters', # We'll want to filter all the documents that have titles\n",
    "    filters={ \n",
    "        'missingTitle': F(  # F defines a filter\n",
    "            'fquery',  # This is a query filter which takes a query and filters document by it\n",
    "            query=Q(  # Q can define a query\n",
    "                'query_string', \n",
    "                query='NOT title:*',  # This will match all documents that don't have content in the title field\n",
    "                analyze_wildcard=True,\n",
    "            )\n",
    "        ) \n",
    "    }\n",
    ").metric(  # but wait, that's not enough! We need to break it down by source as well\n",
    "    'sourceAgg',\n",
    "    'terms',\n",
    "    field='_type',\n",
    "    size=0,\n",
    "    min_doc_count=0\n",
    ")\n",
    "\n",
    "pretty_print(my_search.to_dict()) # Wow this query has gotten big! Good thing we don't have to define it by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# just need to execute the search\n",
    "my_results = my_search.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avian community structure and incidence of human West Nile infection []\n",
      "Non compact continuum limit of two coupled Potts models None\n",
      " []\n",
      "Simultaneous Localization, Mapping, and Manipulation for Unsupervised\n",
      "  Object Discovery None\n",
      "Synthesis of High-Temperature Self-lubricating Wear Resistant Composite Coating on Ti6Al4V Alloy by Laser Deposition None\n",
      "Comparative Studies of Silicon Dissolution in Molten Aluminum Under Different Flow Conditions, Part I: Single-Phase Flow None\n",
      "Scrambling of data in all-optical domain None\n",
      "Keyboard Debut Series, November 12, 1998 None\n",
      "The effect of inclusions in brittle material None\n",
      "Non-Gaussian bias: insights from discrete density peaks None\n"
     ]
    }
   ],
   "source": [
    "# let's look at those hits!\n",
    "for hit in my_results:\n",
    "    print hit.title, hit.get('tags')  # we can see there are no tags in our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Let's pull out those buckets and turn them into dataframes\n",
    "missing_title = bucket_to_dataframe('missingTitle', my_results.aggregations.missingTitle.buckets.missingTitle.sourceAgg.buckets)\n",
    "matches = bucket_to_dataframe('matches', my_results.aggregations.sources.buckets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>total_source_counts</th>\n",
       "      <th>matches</th>\n",
       "      <th>missingTitle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arxiv_oai</th>\n",
       "      <td>168305</td>\n",
       "      <td>168305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asu</th>\n",
       "      <td>12089</td>\n",
       "      <td>12089</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bhl</th>\n",
       "      <td>7359</td>\n",
       "      <td>7359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biomedcentral</th>\n",
       "      <td>8461</td>\n",
       "      <td>8461</td>\n",
       "      <td>8133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calhoun</th>\n",
       "      <td>1696</td>\n",
       "      <td>1696</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calpoly</th>\n",
       "      <td>730</td>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caltech</th>\n",
       "      <td>1860</td>\n",
       "      <td>1860</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clinicaltrials</th>\n",
       "      <td>26954</td>\n",
       "      <td>8975</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmu</th>\n",
       "      <td>1551</td>\n",
       "      <td>1551</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cogprints</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>columbia</th>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crossref</th>\n",
       "      <td>143999</td>\n",
       "      <td>143999</td>\n",
       "      <td>837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuscholar</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dash</th>\n",
       "      <td>548</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataone</th>\n",
       "      <td>207514</td>\n",
       "      <td>162310</td>\n",
       "      <td>161109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doepages</th>\n",
       "      <td>2631</td>\n",
       "      <td>2631</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dryad</th>\n",
       "      <td>3827</td>\n",
       "      <td>3827</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figshare</th>\n",
       "      <td>132340</td>\n",
       "      <td>132340</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iowaresearch</th>\n",
       "      <td>2882</td>\n",
       "      <td>2882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit</th>\n",
       "      <td>23471</td>\n",
       "      <td>23471</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opensiuc</th>\n",
       "      <td>465</td>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osf</th>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plos</th>\n",
       "      <td>21042</td>\n",
       "      <td>21042</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubmedcentral</th>\n",
       "      <td>153524</td>\n",
       "      <td>153524</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarsbank</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scitech</th>\n",
       "      <td>47885</td>\n",
       "      <td>47885</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shareok</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spdataverse</th>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stcloud</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tdar</th>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texasstate</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trinity</th>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ucescholarship</th>\n",
       "      <td>2367</td>\n",
       "      <td>2367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uiucideals</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upennsylvania</th>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utaustin</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uwashington</th>\n",
       "      <td>5741</td>\n",
       "      <td>5741</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valposcholar</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtech</th>\n",
       "      <td>2521</td>\n",
       "      <td>2521</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waynestate</th>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zenodo</th>\n",
       "      <td>554</td>\n",
       "      <td>554</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                total_source_counts  matches  missingTitle\n",
       "arxiv_oai                    168305   168305             0\n",
       "asu                           12089    12089             0\n",
       "bhl                            7359     7359             1\n",
       "biomedcentral                  8461     8461          8133\n",
       "calhoun                        1696     1696             0\n",
       "calpoly                         730      730             0\n",
       "caltech                        1860     1860             3\n",
       "clinicaltrials                26954     8975             0\n",
       "cmu                            1551     1551             0\n",
       "cogprints                        45       45             0\n",
       "columbia                        653      653             0\n",
       "crossref                     143999   143999           837\n",
       "cuscholar                        92       92             0\n",
       "dash                            548      548             0\n",
       "dataone                      207514   162310        161109\n",
       "doepages                       2631     2631             0\n",
       "dryad                          3827     3827             0\n",
       "figshare                     132340   132340             1\n",
       "iowaresearch                   2882     2882             0\n",
       "mit                           23471    23471             1\n",
       "opensiuc                        465      465             0\n",
       "osf                              86       86             0\n",
       "plos                          21042    21042             0\n",
       "pubmedcentral                153524   153524            22\n",
       "scholarsbank                     51       51             0\n",
       "scitech                       47885    47885             3\n",
       "shareok                          60       60             0\n",
       "spdataverse                      66       66             0\n",
       "stcloud                          71       71             0\n",
       "tdar                           1026     1026             0\n",
       "texasstate                       44       44             0\n",
       "trinity                         165      165             0\n",
       "ucescholarship                 2367     2367             0\n",
       "uiucideals                        6        6             0\n",
       "upennsylvania                   792      792             0\n",
       "utaustin                         61       61             0\n",
       "uwashington                    5741     5741             0\n",
       "valposcholar                     91       91             0\n",
       "vtech                          2521     2521             0\n",
       "waynestate                       89       89             0\n",
       "zenodo                          554      554             0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It will be more useful to analyze these dataframes together\n",
    "# Luckily, we have a handy function for merging dataframes (as long as they have the same indices)\n",
    "merged = merge_dataframes(source_counts(), matches,  missing_title)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112e81a10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAFACAYAAAC4MdtMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/HXI0Wc16oiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112cecd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>total_source_counts</th>\n",
       "      <th>matches</th>\n",
       "      <th>missingTitle</th>\n",
       "      <th>percent_missing_tags_and_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arxiv_oai</th>\n",
       "      <td>168305</td>\n",
       "      <td>168305</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asu</th>\n",
       "      <td>12089</td>\n",
       "      <td>12089</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bhl</th>\n",
       "      <td>7359</td>\n",
       "      <td>7359</td>\n",
       "      <td>1</td>\n",
       "      <td>0.013589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biomedcentral</th>\n",
       "      <td>8461</td>\n",
       "      <td>8461</td>\n",
       "      <td>8133</td>\n",
       "      <td>96.123390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calhoun</th>\n",
       "      <td>1696</td>\n",
       "      <td>1696</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calpoly</th>\n",
       "      <td>730</td>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caltech</th>\n",
       "      <td>1860</td>\n",
       "      <td>1860</td>\n",
       "      <td>3</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clinicaltrials</th>\n",
       "      <td>26954</td>\n",
       "      <td>8975</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cmu</th>\n",
       "      <td>1551</td>\n",
       "      <td>1551</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cogprints</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>columbia</th>\n",
       "      <td>653</td>\n",
       "      <td>653</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crossref</th>\n",
       "      <td>143999</td>\n",
       "      <td>143999</td>\n",
       "      <td>837</td>\n",
       "      <td>0.581254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuscholar</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dash</th>\n",
       "      <td>548</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataone</th>\n",
       "      <td>207514</td>\n",
       "      <td>162310</td>\n",
       "      <td>161109</td>\n",
       "      <td>77.637653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doepages</th>\n",
       "      <td>2631</td>\n",
       "      <td>2631</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dryad</th>\n",
       "      <td>3827</td>\n",
       "      <td>3827</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figshare</th>\n",
       "      <td>132340</td>\n",
       "      <td>132340</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iowaresearch</th>\n",
       "      <td>2882</td>\n",
       "      <td>2882</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mit</th>\n",
       "      <td>23471</td>\n",
       "      <td>23471</td>\n",
       "      <td>1</td>\n",
       "      <td>0.004261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opensiuc</th>\n",
       "      <td>465</td>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osf</th>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plos</th>\n",
       "      <td>21042</td>\n",
       "      <td>21042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubmedcentral</th>\n",
       "      <td>153524</td>\n",
       "      <td>153524</td>\n",
       "      <td>22</td>\n",
       "      <td>0.014330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarsbank</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scitech</th>\n",
       "      <td>47885</td>\n",
       "      <td>47885</td>\n",
       "      <td>3</td>\n",
       "      <td>0.006265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shareok</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spdataverse</th>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stcloud</th>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tdar</th>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texasstate</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trinity</th>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ucescholarship</th>\n",
       "      <td>2367</td>\n",
       "      <td>2367</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uiucideals</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>upennsylvania</th>\n",
       "      <td>792</td>\n",
       "      <td>792</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>utaustin</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uwashington</th>\n",
       "      <td>5741</td>\n",
       "      <td>5741</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valposcholar</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtech</th>\n",
       "      <td>2521</td>\n",
       "      <td>2521</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waynestate</th>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zenodo</th>\n",
       "      <td>554</td>\n",
       "      <td>554</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                total_source_counts  matches  missingTitle  \\\n",
       "arxiv_oai                    168305   168305             0   \n",
       "asu                           12089    12089             0   \n",
       "bhl                            7359     7359             1   \n",
       "biomedcentral                  8461     8461          8133   \n",
       "calhoun                        1696     1696             0   \n",
       "calpoly                         730      730             0   \n",
       "caltech                        1860     1860             3   \n",
       "clinicaltrials                26954     8975             0   \n",
       "cmu                            1551     1551             0   \n",
       "cogprints                        45       45             0   \n",
       "columbia                        653      653             0   \n",
       "crossref                     143999   143999           837   \n",
       "cuscholar                        92       92             0   \n",
       "dash                            548      548             0   \n",
       "dataone                      207514   162310        161109   \n",
       "doepages                       2631     2631             0   \n",
       "dryad                          3827     3827             0   \n",
       "figshare                     132340   132340             1   \n",
       "iowaresearch                   2882     2882             0   \n",
       "mit                           23471    23471             1   \n",
       "opensiuc                        465      465             0   \n",
       "osf                              86       86             0   \n",
       "plos                          21042    21042             0   \n",
       "pubmedcentral                153524   153524            22   \n",
       "scholarsbank                     51       51             0   \n",
       "scitech                       47885    47885             3   \n",
       "shareok                          60       60             0   \n",
       "spdataverse                      66       66             0   \n",
       "stcloud                          71       71             0   \n",
       "tdar                           1026     1026             0   \n",
       "texasstate                       44       44             0   \n",
       "trinity                         165      165             0   \n",
       "ucescholarship                 2367     2367             0   \n",
       "uiucideals                        6        6             0   \n",
       "upennsylvania                   792      792             0   \n",
       "utaustin                         61       61             0   \n",
       "uwashington                    5741     5741             0   \n",
       "valposcholar                     91       91             0   \n",
       "vtech                          2521     2521             0   \n",
       "waynestate                       89       89             0   \n",
       "zenodo                          554      554             0   \n",
       "\n",
       "                percent_missing_tags_and_title  \n",
       "arxiv_oai                             0.000000  \n",
       "asu                                   0.000000  \n",
       "bhl                                   0.013589  \n",
       "biomedcentral                        96.123390  \n",
       "calhoun                               0.000000  \n",
       "calpoly                               0.000000  \n",
       "caltech                               0.161290  \n",
       "clinicaltrials                        0.000000  \n",
       "cmu                                   0.000000  \n",
       "cogprints                             0.000000  \n",
       "columbia                              0.000000  \n",
       "crossref                              0.581254  \n",
       "cuscholar                             0.000000  \n",
       "dash                                  0.000000  \n",
       "dataone                              77.637653  \n",
       "doepages                              0.000000  \n",
       "dryad                                 0.000000  \n",
       "figshare                              0.000756  \n",
       "iowaresearch                          0.000000  \n",
       "mit                                   0.004261  \n",
       "opensiuc                              0.000000  \n",
       "osf                                   0.000000  \n",
       "plos                                  0.000000  \n",
       "pubmedcentral                         0.014330  \n",
       "scholarsbank                          0.000000  \n",
       "scitech                               0.006265  \n",
       "shareok                               0.000000  \n",
       "spdataverse                           0.000000  \n",
       "stcloud                               0.000000  \n",
       "tdar                                  0.000000  \n",
       "texasstate                            0.000000  \n",
       "trinity                               0.000000  \n",
       "ucescholarship                        0.000000  \n",
       "uiucideals                            0.000000  \n",
       "upennsylvania                         0.000000  \n",
       "utaustin                              0.000000  \n",
       "uwashington                           0.000000  \n",
       "valposcholar                          0.000000  \n",
       "vtech                                 0.000000  \n",
       "waynestate                            0.000000  \n",
       "zenodo                                0.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also easily do computations on these columns, and add those to the dataframe\n",
    "merged['percent_missing_tags_and_title'] = (merged.missingTitle / merged.total_source_counts) * 100\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1130c0950>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAFACAYAAAC4MdtMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noUOHDh069OD/AbiYsFp4vxAfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fbe090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we are getting a lot of columns though\n",
    "# If we only want to plot some of the columns, we can do this:\n",
    "merged.loc[:, ['matches', 'missingTitle']].plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}