CSO Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import plotly\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_colwidth', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "downloads_req_df = pd.read_json('data/download_requests.json')\n",
    "topics_req_df = pd.read_json('data/topic_requests.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>exists</th>\n",
       "      <th>format</th>\n",
       "      <th>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>time</th>\n",
       "      <th>topic</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>html</td>\n",
       "      <td>1</td>\n",
       "      <td>51.4964</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>2018-07-20 12:53:30</td>\n",
       "      <td>computer science</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "      <td>html</td>\n",
       "      <td>2</td>\n",
       "      <td>52.3824</td>\n",
       "      <td>4.8995</td>\n",
       "      <td>2018-07-20 13:41:17</td>\n",
       "      <td>computer science</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>html</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-07-20 18:10:27</td>\n",
       "      <td>computer science</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>html</td>\n",
       "      <td>101</td>\n",
       "      <td>51.4964</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>2018-07-25 18:08:09</td>\n",
       "      <td>computer science</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Wellingborough</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>html</td>\n",
       "      <td>1001</td>\n",
       "      <td>52.3027</td>\n",
       "      <td>-0.6945</td>\n",
       "      <td>2018-09-15 11:09:51</td>\n",
       "      <td>binocular</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                city         country  exists format    id      lat     lon  \\\n",
       "0               None  United Kingdom       1   html     1  51.4964 -0.1224   \n",
       "1               None     Netherlands       1   html     2  52.3824  4.8995   \n",
       "10              None            None       1   html    11      NaN     NaN   \n",
       "100             None  United Kingdom       1   html   101  51.4964 -0.1224   \n",
       "1000  Wellingborough  United Kingdom       1   html  1001  52.3027 -0.6945   \n",
       "\n",
       "                     time             topic  user  \n",
       "0     2018-07-20 12:53:30  computer science     1  \n",
       "1     2018-07-20 13:41:17  computer science     2  \n",
       "10    2018-07-20 18:10:27  computer science     3  \n",
       "100   2018-07-25 18:08:09  computer science     1  \n",
       "1000  2018-09-15 11:09:51         binocular     4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_req_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>format</th>\n",
       "      <th>host</th>\n",
       "      <th>id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>time</th>\n",
       "      <th>user</th>\n",
       "      <th>user_email</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>nt</td>\n",
       "      <td>open-webservice-csvip.open.ac.uk</td>\n",
       "      <td>1</td>\n",
       "      <td>51.4964</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>2018-10-29 18:02:57</td>\n",
       "      <td>137.108.200.90</td>\n",
       "      <td>angelo.salatino@open.ac.uk</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>owl</td>\n",
       "      <td>open-webservice-csvip.open.ac.uk</td>\n",
       "      <td>2</td>\n",
       "      <td>51.4964</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>2018-10-29 18:03:00</td>\n",
       "      <td>137.108.200.90</td>\n",
       "      <td>angelo.salatino@open.ac.uk</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ho Chi Minh City</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>csv</td>\n",
       "      <td>unknown</td>\n",
       "      <td>11</td>\n",
       "      <td>10.8142</td>\n",
       "      <td>106.6438</td>\n",
       "      <td>2018-11-06 17:29:40</td>\n",
       "      <td>27.74.254.26</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Leeds</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>csv</td>\n",
       "      <td>cpc95608-seac27-2-0-cust957.7-2.cable.virginm.net</td>\n",
       "      <td>101</td>\n",
       "      <td>53.8000</td>\n",
       "      <td>-1.5833</td>\n",
       "      <td>2019-01-16 18:27:43</td>\n",
       "      <td>80.2.63.190</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>nt</td>\n",
       "      <td>ad96e3e4a.dsl.de.colt.net</td>\n",
       "      <td>102</td>\n",
       "      <td>51.4964</td>\n",
       "      <td>-0.1224</td>\n",
       "      <td>2019-01-18 08:35:49</td>\n",
       "      <td>62.96.11.74</td>\n",
       "      <td></td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 city         country format  \\\n",
       "0                None  United Kingdom     nt   \n",
       "1                None  United Kingdom    owl   \n",
       "10   Ho Chi Minh City         Vietnam    csv   \n",
       "100             Leeds  United Kingdom    csv   \n",
       "101              None  United Kingdom     nt   \n",
       "\n",
       "                                                  host   id      lat  \\\n",
       "0                     open-webservice-csvip.open.ac.uk    1  51.4964   \n",
       "1                     open-webservice-csvip.open.ac.uk    2  51.4964   \n",
       "10                                             unknown   11  10.8142   \n",
       "100  cpc95608-seac27-2-0-cust957.7-2.cable.virginm.net  101  53.8000   \n",
       "101                          ad96e3e4a.dsl.de.colt.net  102  51.4964   \n",
       "\n",
       "          lon                 time            user  \\\n",
       "0     -0.1224  2018-10-29 18:02:57  137.108.200.90   \n",
       "1     -0.1224  2018-10-29 18:03:00  137.108.200.90   \n",
       "10   106.6438  2018-11-06 17:29:40    27.74.254.26   \n",
       "100   -1.5833  2019-01-16 18:27:43     80.2.63.190   \n",
       "101   -0.1224  2019-01-18 08:35:49     62.96.11.74   \n",
       "\n",
       "                     user_email  version  \n",
       "0    angelo.salatino@open.ac.uk      3.0  \n",
       "1    angelo.salatino@open.ac.uk      3.0  \n",
       "10                                   3.0  \n",
       "100                                  3.0  \n",
       "101                                  3.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_req_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of CSO downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly",
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          3,
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         "name": "Version",
         "textposition": "inside",
         "type": "pie",
         "uid": "8de61bb3-535b-4ff3-8dab-4d34a053d22a",
         "values": [
          2,
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       ],
       "layout": {
        "title": "CSO downloads version fragmentation"
       }
      },
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       "<div id=\"3b3129ac-1351-47aa-8904-deb3cd5497ac\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3b3129ac-1351-47aa-8904-deb3cd5497ac\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [1.0, 2.0, 2.1, 3.0, 3.1], \"name\": \"Version\", \"textposition\": \"inside\", \"values\": [2, 10, 16, 211, 169], \"type\": \"pie\", \"uid\": \"8de61bb3-535b-4ff3-8dab-4d34a053d22a\"}], {\"title\": \"CSO downloads version fragmentation\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"3b3129ac-1351-47aa-8904-deb3cd5497ac\"));});</script>"
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       "<div id=\"3b3129ac-1351-47aa-8904-deb3cd5497ac\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3b3129ac-1351-47aa-8904-deb3cd5497ac\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [1.0, 2.0, 2.1, 3.0, 3.1], \"name\": \"Version\", \"textposition\": \"inside\", \"values\": [2, 10, 16, 211, 169], \"type\": \"pie\", \"uid\": \"8de61bb3-535b-4ff3-8dab-4d34a053d22a\"}], {\"title\": \"CSO downloads version fragmentation\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"3b3129ac-1351-47aa-8904-deb3cd5497ac\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "versions = downloads_req_df.groupby('version')[['id']].nunique()\n",
    "\n",
    "pie = go.Pie(values=versions.id,\n",
    "              labels=versions.index,\n",
    "              textposition='inside',\n",
    "              name='Version',\n",
    "              hoverinfo='label+value+name',\n",
    "              hole=.4)\n",
    "\n",
    "donut = go.Layout(title='CSO downloads version fragmentation')\n",
    "fig = go.Figure(data=[pie], layout=donut)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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         "values": [
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       "layout": {
        "title": "CSO downloads format fragmentation"
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      },
      "text/html": [
       "<div id=\"72afeba1-e704-4360-8d7e-9e35dda435d1\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"72afeba1-e704-4360-8d7e-9e35dda435d1\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [\"csv\", \"nt\", \"owl\"], \"name\": \"Format\", \"textposition\": \"inside\", \"values\": [165, 79, 164], \"type\": \"pie\", \"uid\": \"9fcbe48b-531f-448b-a2f6-fb6903f29274\"}], {\"title\": \"CSO downloads format fragmentation\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"72afeba1-e704-4360-8d7e-9e35dda435d1\"));});</script>"
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   ],
   "source": [
    "formats = downloads_req_df.groupby('format')[['id']].nunique()\n",
    "\n",
    "pie = go.Pie(values=formats.id,\n",
    "              labels=formats.index,\n",
    "              textposition='inside',\n",
    "              name='Format',\n",
    "              hoverinfo='label+value+name',\n",
    "              hole=.4)\n",
    "\n",
    "donut = go.Layout(title='CSO downloads format fragmentation')\n",
    "fig = go.Figure(data=[pie], layout=donut)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        {
         "autocolorscale": true,
         "colorbar": {
          "title": "#downloads"
         },
         "locationmode": "country names",
         "locations": [
          "Albania",
          "Algeria",
          "Argentina",
          "Australia",
          "Austria",
          "Belarus",
          "Belgium",
          "Brazil",
          "Bulgaria",
          "Canada",
          "China",
          "Czech Republic",
          "Europe",
          "France",
          "Germany",
          "Greece",
          "Hong Kong",
          "India",
          "Indonesia",
          "Iran, Islamic Republic of",
          "Ireland",
          "Italy",
          "Japan",
          "Malaysia",
          "Mexico",
          "Myanmar",
          "Netherlands",
          "Pakistan",
          "Palestinian Territory",
          "Poland",
          "Portugal",
          "Romania",
          "Russian Federation",
          "Saudi Arabia",
          "Serbia",
          "South Africa",
          "Spain",
          "Sri Lanka",
          "Switzerland",
          "Syrian Arab Republic",
          "Thailand",
          "Ukraine",
          "United Arab Emirates",
          "United Kingdom",
          "United States",
          "Vietnam"
         ],
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       ],
       "layout": {
        "geo": {
         "projection": {
          "type": "equirectangular"
         },
         "showcoastlines": true,
         "showframe": false
        },
        "title": "Download distribution"
       }
      },
      "text/html": [
       "<div id=\"cd496cef-be91-4a70-9e7c-f889c0698395\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"cd496cef-be91-4a70-9e7c-f889c0698395\", [{\"type\": \"choropleth\", \"locationmode\": \"country names\", \"locations\": [\"Albania\", \"Algeria\", \"Argentina\", \"Australia\", \"Austria\", \"Belarus\", \"Belgium\", \"Brazil\", \"Bulgaria\", \"Canada\", \"China\", \"Czech Republic\", \"Europe\", \"France\", \"Germany\", \"Greece\", \"Hong Kong\", \"India\", \"Indonesia\", \"Iran, Islamic Republic of\", \"Ireland\", \"Italy\", \"Japan\", \"Malaysia\", \"Mexico\", \"Myanmar\", \"Netherlands\", \"Pakistan\", \"Palestinian Territory\", \"Poland\", \"Portugal\", \"Romania\", \"Russian Federation\", \"Saudi Arabia\", \"Serbia\", \"South Africa\", \"Spain\", \"Sri Lanka\", \"Switzerland\", \"Syrian Arab Republic\", \"Thailand\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Vietnam\"], \"z\": [1, 2, 4, 1, 2, 1, 1, 11, 2, 15, 18, 2, 10, 21, 26, 9, 4, 30, 2, 4, 5, 33, 6, 1, 1, 3, 4, 7, 1, 5, 1, 1, 2, 1, 1, 2, 12, 2, 1, 2, 2, 1, 2, 64, 70, 10], \"text\": [1, 2, 4, 1, 2, 1, 1, 11, 2, 15, 18, 2, 10, 21, 26, 9, 4, 30, 2, 4, 5, 33, 6, 1, 1, 3, 4, 7, 1, 5, 1, 1, 2, 1, 1, 2, 12, 2, 1, 2, 2, 1, 2, 64, 70, 10], \"autocolorscale\": true, \"reversescale\": false, \"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"colorbar\": {\"title\": \"#downloads\"}}], {\"title\": \"Download distribution\", \"geo\": {\"showframe\": false, \"showcoastlines\": true, \"projection\": {\"type\": \"equirectangular\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"cd496cef-be91-4a70-9e7c-f889c0698395\"));});</script>"
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      "text/vnd.plotly.v1+html": [
       "<div id=\"cd496cef-be91-4a70-9e7c-f889c0698395\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"cd496cef-be91-4a70-9e7c-f889c0698395\", [{\"type\": \"choropleth\", \"locationmode\": \"country names\", \"locations\": [\"Albania\", \"Algeria\", \"Argentina\", \"Australia\", \"Austria\", \"Belarus\", \"Belgium\", \"Brazil\", \"Bulgaria\", \"Canada\", \"China\", \"Czech Republic\", \"Europe\", \"France\", \"Germany\", \"Greece\", \"Hong Kong\", \"India\", \"Indonesia\", \"Iran, Islamic Republic of\", \"Ireland\", \"Italy\", \"Japan\", \"Malaysia\", \"Mexico\", \"Myanmar\", \"Netherlands\", \"Pakistan\", \"Palestinian Territory\", \"Poland\", \"Portugal\", \"Romania\", \"Russian Federation\", \"Saudi Arabia\", \"Serbia\", \"South Africa\", \"Spain\", \"Sri Lanka\", \"Switzerland\", \"Syrian Arab Republic\", \"Thailand\", \"Ukraine\", \"United Arab Emirates\", \"United Kingdom\", \"United States\", \"Vietnam\"], \"z\": [1, 2, 4, 1, 2, 1, 1, 11, 2, 15, 18, 2, 10, 21, 26, 9, 4, 30, 2, 4, 5, 33, 6, 1, 1, 3, 4, 7, 1, 5, 1, 1, 2, 1, 1, 2, 12, 2, 1, 2, 2, 1, 2, 64, 70, 10], \"text\": [1, 2, 4, 1, 2, 1, 1, 11, 2, 15, 18, 2, 10, 21, 26, 9, 4, 30, 2, 4, 5, 33, 6, 1, 1, 3, 4, 7, 1, 5, 1, 1, 2, 1, 1, 2, 12, 2, 1, 2, 2, 1, 2, 64, 70, 10], \"autocolorscale\": true, \"reversescale\": false, \"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"colorbar\": {\"title\": \"#downloads\"}}], {\"title\": \"Download distribution\", \"geo\": {\"showframe\": false, \"showcoastlines\": true, \"projection\": {\"type\": \"equirectangular\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"cd496cef-be91-4a70-9e7c-f889c0698395\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "download_data = downloads_req_df.groupby(['country'])['id'].count().reset_index()\n",
    "downloads_map = [ dict(\n",
    "        type = 'choropleth',\n",
    "        locationmode = 'country names',\n",
    "        locations = download_data['country'],\n",
    "        z = download_data['id'],\n",
    "        text = download_data['id'],\n",
    "        autocolorscale = True,\n",
    "        reversescale = False,\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(180,180,180)',\n",
    "                width = 0.5\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            title = '#downloads'),\n",
    "      ) ]\n",
    "\n",
    "layout = dict(\n",
    "    title = 'Download distribution',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = True,\n",
    "        projection = dict(\n",
    "            type = 'equirectangular'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict(data=downloads_map, layout=layout)\n",
    "plotly.offline.iplot(fig, validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>United States</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Italy</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>India</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Germany</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>China</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Canada</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  id\n",
       "44   United States  70\n",
       "43  United Kingdom  64\n",
       "21           Italy  33\n",
       "17           India  30\n",
       "14         Germany  26\n",
       "13          France  21\n",
       "10           China  18\n",
       "9           Canada  15\n",
       "36           Spain  12\n",
       "7           Brazil  11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_data.sort_values(by='id', ascending=False).head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of CSO topic requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "hole": 0.4,
         "hoverinfo": "label+value+name",
         "labels": [
          "html",
          "jsonld",
          "rdf",
          "triple",
          "turtle"
         ],
         "name": "Format",
         "textposition": "inside",
         "type": "pie",
         "uid": "bb2fbb42-b3ff-4a59-a6b6-a0b5ac2f9d53",
         "values": [
          12010,
          54,
          649,
          34,
          30
         ]
        }
       ],
       "layout": {
        "title": "CSO topic requests format fragmentation"
       }
      },
      "text/html": [
       "<div id=\"7c300eda-047c-473a-a17f-27739f87dd18\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"7c300eda-047c-473a-a17f-27739f87dd18\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [\"html\", \"jsonld\", \"rdf\", \"triple\", \"turtle\"], \"name\": \"Format\", \"textposition\": \"inside\", \"values\": [12010, 54, 649, 34, 30], \"type\": \"pie\", \"uid\": \"bb2fbb42-b3ff-4a59-a6b6-a0b5ac2f9d53\"}], {\"title\": \"CSO topic requests format fragmentation\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"7c300eda-047c-473a-a17f-27739f87dd18\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"7c300eda-047c-473a-a17f-27739f87dd18\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"7c300eda-047c-473a-a17f-27739f87dd18\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [\"html\", \"jsonld\", \"rdf\", \"triple\", \"turtle\"], \"name\": \"Format\", \"textposition\": \"inside\", \"values\": [12010, 54, 649, 34, 30], \"type\": \"pie\", \"uid\": \"bb2fbb42-b3ff-4a59-a6b6-a0b5ac2f9d53\"}], {\"title\": \"CSO topic requests format fragmentation\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"7c300eda-047c-473a-a17f-27739f87dd18\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "formats = topics_req_df.groupby('format')[['id']].nunique()\n",
    "\n",
    "pie = go.Pie(values=formats.id,\n",
    "              labels=formats.index,\n",
    "              textposition='inside',\n",
    "              name='Format',\n",
    "              hoverinfo='label+value+name',\n",
    "              hole=.4)\n",
    "\n",
    "donut = go.Layout(title='CSO topic requests format fragmentation')\n",
    "fig = go.Figure(data=[pie], layout=donut)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "hole": 0.4,
         "hoverinfo": "label+value+name",
         "labels": [
          0,
          1
         ],
         "name": "Hit",
         "textposition": "inside",
         "type": "pie",
         "uid": "d665dc77-452c-4057-bb17-e322a41d6d53",
         "values": [
          468,
          12309
         ]
        }
       ],
       "layout": {
        "title": "CSO topics requests hits"
       }
      },
      "text/html": [
       "<div id=\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [0, 1], \"name\": \"Hit\", \"textposition\": \"inside\", \"values\": [468, 12309], \"type\": \"pie\", \"uid\": \"d665dc77-452c-4057-bb17-e322a41d6d53\"}], {\"title\": \"CSO topics requests hits\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\", [{\"hole\": 0.4, \"hoverinfo\": \"label+value+name\", \"labels\": [0, 1], \"name\": \"Hit\", \"textposition\": \"inside\", \"values\": [468, 12309], \"type\": \"pie\", \"uid\": \"d665dc77-452c-4057-bb17-e322a41d6d53\"}], {\"title\": \"CSO topics requests hits\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"e4de4583-1e1b-46ef-8aab-24b0f3194aac\"));});</script>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "hits = topics_req_df.groupby('exists')[['id']].nunique()\n",
    "\n",
    "pie = go.Pie(values=hits.id,\n",
    "              labels=hits.index,\n",
    "              textposition='inside',\n",
    "              name='Hit',\n",
    "              hoverinfo='label+value+name',\n",
    "              hole=.4)\n",
    "\n",
    "donut = go.Layout(title='CSO topics requests hits')\n",
    "fig = go.Figure(data=[pie], layout=donut)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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         "autocolorscale": true,
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          "title": "#unique users"
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          "Algeria",
          "Argentina",
          "Asia/Pacific Region",
          "Australia",
          "Austria",
          "Bangladesh",
          "Belarus",
          "Belgium",
          "Botswana",
          "Brazil",
          "Bulgaria",
          "Canada",
          "Chile",
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          "Denmark",
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          "Finland",
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          "Ghana",
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          "Guyana",
          "Hong Kong",
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          "Iran, Islamic Republic of",
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          "Israel",
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          "Jamaica",
          "Japan",
          "Jordan",
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          "Korea, Republic of",
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{\"showframe\": false, \"showcoastlines\": true, \"projection\": {\"type\": \"equirectangular\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"e69fc9e3-7773-481c-be75-9119252d5444\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "requests_data = topics_req_df.groupby(['lat', 'lon'])['id'].count().reset_index()\n",
    "users_data = topics_req_df.groupby(['country'])['user'].nunique().reset_index()\n",
    "\n",
    "user_map = dict(\n",
    "        type = 'choropleth',\n",
    "        locationmode = 'country names',\n",
    "        locations = users_data['country'],\n",
    "        z = users_data['user'],\n",
    "        text = users_data['user'],\n",
    "        autocolorscale = True,\n",
    "        reversescale = False,\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(180,180,180)',\n",
    "                width = 0.5\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            title = '#unique users'),\n",
    "      )\n",
    "\n",
    "bubbles = dict(\n",
    "    type = 'scattergeo',\n",
    "    lon = requests_data['lon'],\n",
    "    lat = requests_data['lat'],\n",
    "    text = requests_data['id'],\n",
    "    marker = dict(\n",
    "        size = requests_data['id']/10,\n",
    "        line = dict(width=0.5, color='rgb(40,40,40)'),\n",
    "        sizemode = 'area'\n",
    "    ))\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Unique user and topic requests',\n",
    "        showlegend = False,\n",
    "        geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = True,\n",
    "        projection = dict(\n",
    "            type = 'equirectangular'\n",
    "        )\n",
    "    )\n",
    "    )\n",
    "\n",
    "fig = dict(data=[user_map, bubbles], layout=layout)\n",
    "plotly.offline.iplot(fig, validate=False)"
   ]
  },
  {
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   "execution_count": 12,
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       "<div id=\"e7afa3af-8730-40ca-90cc-89edd9a20a50\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e7afa3af-8730-40ca-90cc-89edd9a20a50\", [{\"x\": [\"computer science\", \"semantic web\", \"artificial intelligence\", \"machine learning\", \"software engineering\", \"data mining\", \"information retrieval\", \"computer vision\", \"internet\", \"human computer interaction\", \"semantics\", \"natural language processing\", \"theoretical computer science\", \"world wide web\", \"bioinformatics\", \"computer programming\", \"ontology\", \"linked data\", \"neural networks\", \"natural language processing systems\", \"computer science education\", \"deep learning\", \"software\", \"cryptography\", \"computer science course\", \"blockchain\", \"information retrieval systems\", \"computer systems\", \"computer security\", \"computer hardware\", \"intelligent tutoring system\", \"operating systems\", \"java\", \"computer network\", \"ontology matching\", \"resource description framework\", \"text processing\", \"ontology engineering\", \"information technology\", \"python\", \"robotics\", \"computer science curricula\", \"computer programming languages\", \"clustering algorithms\", \"computer aided design\", \"database systems\", \"high level languages\", \"intelligence analysis\", \"formal logic\", \"security of data\"], \"y\": [2002, 1095, 345, 175, 90, 71, 71, 68, 64, 62, 59, 56, 52, 51, 51, 49, 47, 43, 42, 41, 41, 40, 39, 35, 34, 34, 33, 32, 31, 31, 29, 29, 28, 28, 26, 26, 26, 25, 24, 24, 24, 24, 22, 22, 22, 22, 22, 21, 21, 20], \"type\": \"bar\", \"uid\": \"c7d08313-9a0c-4ed5-8032-a9b131deee2e\"}], {\"title\": \"Top-50 topics (hits)\", \"xaxis\": {\"automargin\": true, \"tickangle\": -45, \"tickfont\": {\"size\": 9}}, \"yaxis\": {\"title\": \"# requests\", \"type\": \"log\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"e7afa3af-8730-40ca-90cc-89edd9a20a50\"));});</script>"
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   ],
   "source": [
    "bars_data = topics_req_df[topics_req_df['exists'] == 1]\n",
    "bars_data = bars_data.groupby('topic')[['id']].count().sort_values(by='id', ascending=False).reset_index()\n",
    "bars_data = bars_data[0:50]\n",
    "\n",
    "trace0 = go.Bar(\n",
    "    x = bars_data['topic'],\n",
    "    y = bars_data['id'])\n",
    "\n",
    "layout = go.Layout(title='Top-50 topics (hits)',\n",
    "                   xaxis=dict(tickangle=-45,\n",
    "                             automargin=True,\n",
    "                             tickfont=dict(size=9)),\n",
    "                   yaxis=dict(title='# requests',\n",
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    "                   \n",
    "fig = go.Figure(data=[trace0], layout=layout)\n",
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       "<div id=\"9d350162-3133-4858-bbbd-a955a68bd695\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"9d350162-3133-4858-bbbd-a955a68bd695\", [{\"marker\": {\"color\": \"#cf0a5f\"}, \"x\": [\"\", \"sigma point\", \"computer\", \"web 2\", \"component/users/\", \"index.php\", \"multi-layer perceptrons\", \"fracture mechanics\", \"cnv\", \"melanopsin/\", \"sigma\", \"allergens\", \"auv/\", \"coarse-grained\", \"active learning methods\", \"undefinedsearch.php\", \"branching ratio\", \"composite right/index\", \"u03b2-pinene\", \"dermatoglyphics/\", \"yield\", \"SemanticWeb\", \"geotechnical engineering\", \"modulation\", \"web 2.0.0\", \"computer science\", \"turing machine/\", \"composite\", \"beta phellandrene\", \"user interest\", \"automatic differentiation\", \"constraint satisfaction problem (csp)\", \"strawberry\", \"functional magnetic resonance imaging\", \"automatic generation\", \"sigma point/\", \"clostridium\", \"languages\", \"embedded\", \"job analysis\", \"islanding\", \"integration\", \"hydrodistillation\", \"frequency response\", \"iodine 131\", \"pattern\", \"meiosis\", \"single-mode optical fiber/\", \"voice/index\", \"upwelling\"], \"y\": [48, 25, 18, 9, 8, 8, 7, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], \"type\": \"bar\", \"uid\": \"7ebbd1a5-6709-431f-9d17-4956ae0f62ba\"}], {\"title\": \"Top-50 requested but non existing topics (hits)\", \"xaxis\": {\"automargin\": true, \"tickangle\": -45, \"tickfont\": {\"size\": 9}}, \"yaxis\": {\"title\": \"# requests\", \"type\": \"log\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"9d350162-3133-4858-bbbd-a955a68bd695\"));});</script>"
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       "<div id=\"9d350162-3133-4858-bbbd-a955a68bd695\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"9d350162-3133-4858-bbbd-a955a68bd695\", [{\"marker\": {\"color\": \"#cf0a5f\"}, \"x\": [\"\", \"sigma point\", \"computer\", \"web 2\", \"component/users/\", \"index.php\", \"multi-layer perceptrons\", \"fracture mechanics\", \"cnv\", \"melanopsin/\", \"sigma\", \"allergens\", \"auv/\", \"coarse-grained\", \"active learning methods\", \"undefinedsearch.php\", \"branching ratio\", \"composite right/index\", \"u03b2-pinene\", \"dermatoglyphics/\", \"yield\", \"SemanticWeb\", \"geotechnical engineering\", \"modulation\", \"web 2.0.0\", \"computer science\", \"turing machine/\", \"composite\", \"beta phellandrene\", \"user interest\", \"automatic differentiation\", \"constraint satisfaction problem (csp)\", \"strawberry\", \"functional magnetic resonance imaging\", \"automatic generation\", \"sigma point/\", \"clostridium\", \"languages\", \"embedded\", \"job analysis\", \"islanding\", \"integration\", \"hydrodistillation\", \"frequency response\", \"iodine 131\", \"pattern\", \"meiosis\", \"single-mode optical fiber/\", \"voice/index\", \"upwelling\"], \"y\": [48, 25, 18, 9, 8, 8, 7, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], \"type\": \"bar\", \"uid\": \"7ebbd1a5-6709-431f-9d17-4956ae0f62ba\"}], {\"title\": \"Top-50 requested but non existing topics (hits)\", \"xaxis\": {\"automargin\": true, \"tickangle\": -45, \"tickfont\": {\"size\": 9}}, \"yaxis\": {\"title\": \"# requests\", \"type\": \"log\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"9d350162-3133-4858-bbbd-a955a68bd695\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bars_data = topics_req_df[topics_req_df.exists == 0].groupby('topic')[['id']].count().sort_values(by='id', ascending=False).reset_index()\n",
    "bars_data = bars_data[0:50]\n",
    "\n",
    "trace0 = go.Bar(\n",
    "    x = bars_data['topic'],\n",
    "    y = bars_data['id'],\n",
    "    marker = dict(color='#cf0a5f'))\n",
    "\n",
    "layout = go.Layout(title='Top-50 requested but non existing topics (hits)',\n",
    "                   xaxis=dict(tickangle=-45,\n",
    "                             automargin=True,\n",
    "                             tickfont=dict(size=9)),\n",
    "                   yaxis=dict(title='# requests',\n",
    "                              type='log'))\n",
    "\n",
    "fig = go.Figure(data=[trace0], layout=layout)\n",
    "plotly.offline.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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