Helper_Functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Imports__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "import pandas as pd  # for easy and effective catalogue manipulation\n",
    "import numpy as np  # for mathematic stuff\n",
    "\n",
    "from ipywidgets import widgets  # for easy implement UI directly in notebook\n",
    "\n",
    "import plotly.graph_objs as go\n",
    "import plotly.io as pio\n",
    "from plotly.offline import init_notebook_mode, iplot  # for beautifull plot\n",
    "\n",
    "pio.renderers.default = \"colab\"\n",
    "\n",
    "import cufflinks as cf  # to directly bind pandas and plotly\n",
    "\n",
    "import requests  # for dealing with API\n",
    "import json  # to deal with json inputs/outputs\n",
    "import pprint  # for more friendly console formatting\n",
    "import operator  # often faster than lambda expression\n",
    "\n",
    "import sklearn.metrics.pairwise as skdist\n",
    "import statistics\n",
    "\n",
    "cf.go_offline()  # set plotly to offline mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Download and rename datasets (catalogue and user data)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove 'x5gon_catelogue.tsv*': No such file or directory\n",
      "rm: cannot remove 'x5gon_user_data.psv*': No such file or directory\n",
      "mkdir: cannot create directory ‘datasets’: File exists\n",
      "--2020-02-25 11:07:37--  https://gitlab.univ-nantes.fr/x5gon/x5gon-hackathon-datasets/raw/master/datasets/x5gon_catelogue.tsv\n",
      "Loaded CA certificate '/etc/ssl/certs/ca-certificates.crt'\n",
      "Resolving gitlab.univ-nantes.fr (gitlab.univ-nantes.fr)... 193.52.101.66\n",
      "Connecting to gitlab.univ-nantes.fr (gitlab.univ-nantes.fr)|193.52.101.66|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 63850145 (61M) [text/plain]\n",
      "Saving to: ‘x5gon_catelogue.tsv’\n",
      "\n",
      "x5gon_catelogue.tsv 100%[===================>]  60.89M  19.0MB/s    in 3.2s    \n",
      "\n",
      "2020-02-25 11:07:42 (19.0 MB/s) - ‘x5gon_catelogue.tsv’ saved [63850145/63850145]\n",
      "\n",
      "--2020-02-25 11:07:42--  https://gitlab.univ-nantes.fr/x5gon/x5gon-hackathon-datasets/raw/master/datasets/x5gon_user_data.psv\n",
      "Loaded CA certificate '/etc/ssl/certs/ca-certificates.crt'\n",
      "Resolving gitlab.univ-nantes.fr (gitlab.univ-nantes.fr)... 193.52.101.66\n",
      "Connecting to gitlab.univ-nantes.fr (gitlab.univ-nantes.fr)|193.52.101.66|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 255593745 (244M) [text/plain]\n",
      "Saving to: ‘x5gon_user_data.psv’\n",
      "\n",
      "x5gon_user_data.psv 100%[===================>] 243.75M  17.1MB/s    in 20s     \n",
      "\n",
      "2020-02-25 11:08:05 (12.1 MB/s) - ‘x5gon_user_data.psv’ saved [255593745/255593745]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "! rm x5gon_catelogue.tsv*\n",
    "! rm x5gon_user_data.psv*\n",
    "! mkdir datasets\n",
    "! wget https://gitlab.univ-nantes.fr/x5gon/x5gon-hackathon-datasets/raw/master/datasets/x5gon_catelogue.tsv\n",
    "! wget https://gitlab.univ-nantes.fr/x5gon/x5gon-hackathon-datasets/raw/master/datasets/x5gon_user_data.psv\n",
    "! mv x5gon_catelogue.tsv datasets/catalogue.tsv\n",
    "! mv x5gon_user_data.psv datasets/user_data.psv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Preview catalogue__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>language</th>\n",
       "      <th>type</th>\n",
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       "      <th>concepts</th>\n",
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       "      <th>3904</th>\n",
       "      <td>Electromagnetic Fields, Forces, and Motion</td>\n",
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       "      <td>[north korea,  korea,  china,  north,  pyongya...</td>\n",
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       "      <th>5757</th>\n",
       "      <td>Statistics for Brain and Cognitive Science</td>\n",
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       "      <th>23191</th>\n",
       "      <td>Medical Decision Support</td>\n",
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       "      <td>['http://en.wikipedia.org/wiki/P-value',  'htt...</td>\n",
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       "      <th>40540</th>\n",
       "      <td>Distinguishing Causes from Effects using Nonli...</td>\n",
       "      <td>fr</td>\n",
       "      <td>mp4</td>\n",
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       "      <th>43621</th>\n",
       "      <td>Computer Graphics</td>\n",
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       "    <tr>\n",
       "      <th>48869</th>\n",
       "      <td>Microeconomic Theory III</td>\n",
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       "      <th>51007</th>\n",
       "      <td>Designing and Sustaining Technology Innovation...</td>\n",
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       "      <td>pdf</td>\n",
       "      <td>[jeff,  health,  clinical,  harvard,  technolo...</td>\n",
       "      <td>['http://en.wikipedia.org/wiki/Massachusetts_I...</td>\n",
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       "    <tr>\n",
       "      <th>53259</th>\n",
       "      <td>New Enterprises</td>\n",
       "      <td>en</td>\n",
       "      <td>pdf</td>\n",
       "      <td>[new enterprises,  enterprises,  team,  founde...</td>\n",
       "      <td>['http://en.wikipedia.org/wiki/Business',  'ht...</td>\n",
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       "      <th>57981</th>\n",
       "      <td>Mathematics for Computer Science</td>\n",
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       "      <td>['http://en.wikipedia.org/wiki/Truth_table',  ...</td>\n",
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       "      <th>37227</th>\n",
       "      <td>String Theory for Undergraduates</td>\n",
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       "      <td>['http://en.wikipedia.org/wiki/Antonov',  'htt...</td>\n",
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       "      <th>70407</th>\n",
       "      <td>Optimizing Multi-Class Spatio-Spectral Filters...</td>\n",
       "      <td>en</td>\n",
       "      <td>pdf</td>\n",
       "      <td>[eeg,  experiments conclusions,  conclusions, ...</td>\n",
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       "      <td>en</td>\n",
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       "      <th>80431</th>\n",
       "      <td>Nonlinear Mappings for Generative Kernels on L...</td>\n",
       "      <td>en</td>\n",
       "      <td>pdf</td>\n",
       "      <td>[generative,  kernel,  hmm,  normalization,  n...</td>\n",
       "      <td>['http://en.wikipedia.org/wiki/Nonlinear_syste...</td>\n",
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      "text/plain": [
       "                                                   title language type  \\\n",
       "id                                                                       \n",
       "59260                          C7 - Computing with Space       en  pdf   \n",
       "3904          Electromagnetic Fields, Forces, and Motion       en  pdf   \n",
       "4796                                    Uncertain Allies       en  pdf   \n",
       "5757          Statistics for Brain and Cognitive Science       en  pdf   \n",
       "6930   Classification of Web Documents Using a Graph-...       en  mp4   \n",
       "8160          Advanced Fluid Dynamics of the Environment       en  pdf   \n",
       "11812  Lecture 5 - Work-Energy Theorem and Law of Con...       en  mov   \n",
       "23191                           Medical Decision Support       en  pdf   \n",
       "40540  Distinguishing Causes from Effects using Nonli...       fr  mp4   \n",
       "43621                                  Computer Graphics       en  pdf   \n",
       "48869                           Microeconomic Theory III       en  pdf   \n",
       "51007  Designing and Sustaining Technology Innovation...       en  pdf   \n",
       "53259                                    New Enterprises       en  pdf   \n",
       "57981                   Mathematics for Computer Science       en  pdf   \n",
       "37227                   String Theory for Undergraduates       en  pdf   \n",
       "69377  An Introduction to Intelligent Transportation ...       en  pdf   \n",
       "70407  Optimizing Multi-Class Spatio-Spectral Filters...       en  pdf   \n",
       "79827  Approaches of Systems Biology of Metabolically...       en  pdf   \n",
       "80431  Nonlinear Mappings for Generative Kernels on L...       en  pdf   \n",
       "86736  IntroducciÃ³n a la plataforma para mÃ³viles An...       es  mp4   \n",
       "\n",
       "                                                keywords  \\\n",
       "id                                                         \n",
       "59260  [space,  omicini,  c7,  omicini disi,  disi un...   \n",
       "3904   [forces motion,  fields forces,  electromagnet...   \n",
       "4796   [north korea,  korea,  china,  north,  pyongya...   \n",
       "5757   [pr,  pr pr,  probability,  probability theory...   \n",
       "6930   [graph,  subgraph,  document,  contrast,  clas...   \n",
       "8160   [fluid,  mei,  layer,  temperature,  water sur...   \n",
       "11812  [force,  velocity,  minus,  energy,  function,...   \n",
       "23191  [clinical,  database,  db,  risk,  report,  la...   \n",
       "40540  [causal,  nonlinear,  model,  ica,  disturbanc...   \n",
       "43621  [ray,  dir,  intersection,  y1,  y2,  traverse...   \n",
       "48869  [ann,  asset,  utility,  problem,  iii,  grade...   \n",
       "51007  [jeff,  health,  clinical,  harvard,  technolo...   \n",
       "53259  [new enterprises,  enterprises,  team,  founde...   \n",
       "57981  [proposition,  true,  mcs ftl,  ftl page,  ftl...   \n",
       "37227  [point problem,  string,  problem point,  cyli...   \n",
       "69377  [transportation,  joseph sussman,  sussman,  i...   \n",
       "70407  [eeg,  experiments conclusions,  conclusions, ...   \n",
       "79827  [ho,  metabolic,  et al,  pk,  al,  et,  ho ho...   \n",
       "80431  [generative,  kernel,  hmm,  normalization,  n...   \n",
       "86736  [android,  mobile,  tap,  platform,  year,  vi...   \n",
       "\n",
       "                                                concepts  \n",
       "id                                                        \n",
       "59260  ['http://en.wikipedia.org/wiki/Bologna',  'htt...  \n",
       "3904   ['http://en.wikipedia.org/wiki/Massachusetts_I...  \n",
       "4796   ['http://en.wikipedia.org/wiki/North_Korea',  ...  \n",
       "5757   ['http://en.wikipedia.org/wiki/Probability_the...  \n",
       "6930   ['http://en.wikipedia.org/wiki/Hello',  'http:...  \n",
       "8160   ['http://en.wikipedia.org/wiki/Homework',  'ht...  \n",
       "11812  ['http://en.wikipedia.org/wiki/Conservation_of...  \n",
       "23191  ['http://en.wikipedia.org/wiki/P-value',  'htt...  \n",
       "40540  ['http://en.wikipedia.org/wiki/Nonlinear_syste...  \n",
       "43621  ['http://en.wikipedia.org/wiki/K-d_tree',  'ht...  \n",
       "48869  ['http://en.wikipedia.org/wiki/Microeconomics'...  \n",
       "51007  ['http://en.wikipedia.org/wiki/Massachusetts_I...  \n",
       "53259  ['http://en.wikipedia.org/wiki/Business',  'ht...  \n",
       "57981  ['http://en.wikipedia.org/wiki/Truth_table',  ...  \n",
       "37227  ['http://en.wikipedia.org/wiki/Homework',  'ht...  \n",
       "69377  ['http://en.wikipedia.org/wiki/Antonov',  'htt...  \n",
       "70407  ['http://en.wikipedia.org/wiki/Electroencephal...  \n",
       "79827  ['http://en.wikipedia.org/wiki/Carotenoid',  '...  \n",
       "80431  ['http://en.wikipedia.org/wiki/Nonlinear_syste...  \n",
       "86736  ['http://en.wikipedia.org/wiki/Machine_learnin...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_parser = lambda x: x[1:-1].split(\",\")\n",
    "catalogue = pd.read_csv(\n",
    "    \"datasets/catalogue.tsv\",\n",
    "    sep=\"\\t\",\n",
    "    converters={\"keywords\": list_parser, \"concepts\": list_parser},\n",
    ")\n",
    "# This is added in case initial dataset hasn't the right columns names:\n",
    "catalogue.columns = [\"id\", \"title\", \"language\", \"type\", \"keywords\", \"concepts\"]\n",
    "catalogue.set_index(\"id\", inplace=True)\n",
    "catalogue.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Preview users__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2020-02-05 16:54:55.407645</th>\n",
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       "      <th>2020-02-05 16:54:55.410975</th>\n",
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       "      <th>2020-02-06 14:45:36.547601</th>\n",
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       "      <th>2020-02-07 00:49:01.311032</th>\n",
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       "      <th>2020-02-08 17:54:43.255974</th>\n",
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       "    <tr>\n",
       "      <th>2020-02-05 16:54:55.414236</th>\n",
       "      <td>2</td>\n",
       "      <td>85880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05 17:29:13.414236</th>\n",
       "      <td>2</td>\n",
       "      <td>83438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05 17:29:43.414236</th>\n",
       "      <td>2</td>\n",
       "      <td>83438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05 16:54:55.417606</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06 07:00:18.417606</th>\n",
       "      <td>3</td>\n",
       "      <td>14495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06 07:17:35.417606</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06 07:29:41.417606</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07 11:14:16.695300</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 06:07:38.873074</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 10:17:15.424700</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11 11:57:57.760750</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12 13:56:19.108033</th>\n",
       "      <td>3</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05 16:54:55.420881</th>\n",
       "      <td>4</td>\n",
       "      <td>41467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05 18:23:20.739967</th>\n",
       "      <td>4</td>\n",
       "      <td>41467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id     url\n",
       "timestamp                             \n",
       "2020-02-05 16:54:55.407645   0   85079\n",
       "2020-02-05 21:29:13.219211   0   85079\n",
       "2020-02-05 16:54:55.410975   1  141207\n",
       "2020-02-06 14:45:36.547601   1  141207\n",
       "2020-02-07 00:49:01.311032   1  141207\n",
       "2020-02-08 17:54:43.255974   1  141207\n",
       "2020-02-05 16:54:55.414236   2   85880\n",
       "2020-02-05 17:29:13.414236   2   83438\n",
       "2020-02-05 17:29:43.414236   2   83438\n",
       "2020-02-05 16:54:55.417606   3    2099\n",
       "2020-02-06 07:00:18.417606   3   14495\n",
       "2020-02-06 07:17:35.417606   3    2099\n",
       "2020-02-06 07:29:41.417606   3    2099\n",
       "2020-02-07 11:14:16.695300   3    2099\n",
       "2020-02-11 06:07:38.873074   3    2099\n",
       "2020-02-11 10:17:15.424700   3    2099\n",
       "2020-02-11 11:57:57.760750   3    2099\n",
       "2020-02-12 13:56:19.108033   3    2099\n",
       "2020-02-05 16:54:55.420881   4   41467\n",
       "2020-02-05 18:23:20.739967   4   41467"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_parser = lambda x: x[1:-1].split(\"|\")\n",
    "users = pd.read_csv(\n",
    "    \"datasets/user_data.psv\",\n",
    "    sep=\"|\",\n",
    "    converters={\"keywords\": list_parser, \"concepts\": list_parser},\n",
    ")\n",
    "# This is added in case initial dataset hasn't the right columns names:\n",
    "users.columns = [\"id\", \"url\", \"timestamp\"]\n",
    "users.set_index(\"timestamp\", inplace=True)\n",
    "users.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Search query example__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>66731</th>\n",
       "      <td>31.1591</td>\n",
       "      <td>The Multi-layer Perceptron</td>\n",
       "      <td>This presentation describes the multilayer per...</td>\n",
       "      <td>2008-01-21T10:26:07.000Z</td>\n",
       "      <td>2018-08-02T11:32:58.041Z</td>\n",
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       "      <td>[173471]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65029</th>\n",
       "      <td>30.1091</td>\n",
       "      <td>The Projectron: a Bounded Kernel-Based Perceptron</td>\n",
       "      <td>We present a discriminative online algorithm w...</td>\n",
       "      <td>2008-07-08T11:30:00.000Z</td>\n",
       "      <td>2018-08-02T11:44:18.703Z</td>\n",
       "      <td>text</td>\n",
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       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[171743]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137065</th>\n",
       "      <td>30.0063</td>\n",
       "      <td>Fast learning of Document Ranking Functions wi...</td>\n",
       "      <td></td>\n",
       "      <td>2008-02-11T13:45:00.000Z</td>\n",
       "      <td>2019-10-29T10:41:13.248Z</td>\n",
       "      <td>text</td>\n",
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       "      <td>http://hydro.ijs.si/v002/40/iahak5ao5zin3gmtpx...</td>\n",
       "      <td>http://videolectures.net/wsdm08_elsas_fldr/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[278370]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>29.0144</td>\n",
       "      <td>The Projectron: a Bounded Kernel-Based Perceptron</td>\n",
       "      <td>We present a discriminative online algorithm w...</td>\n",
       "      <td>2008-07-08T11:30:00.000Z</td>\n",
       "      <td>2018-08-02T11:44:18.703Z</td>\n",
       "      <td>video</td>\n",
       "      <td>video/mp4</td>\n",
       "      <td>http://hydro.ijs.si/v002/d0/2ci2ggjinmnmra4g3j...</td>\n",
       "      <td>http://videolectures.net/icml08_orabona_pbk/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[28053, 28054, 28055, 28056, 28057, 28058, 280...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8542</th>\n",
       "      <td>28.1756</td>\n",
       "      <td>The Multi-layer Perceptron</td>\n",
       "      <td>This presentation describes the multilayer per...</td>\n",
       "      <td>2008-01-21T10:26:07.000Z</td>\n",
       "      <td>2018-08-02T11:32:58.041Z</td>\n",
       "      <td>video</td>\n",
       "      <td>video/mp4</td>\n",
       "      <td>http://hydro.ijs.si/v002/0d/bxku65bxlnzaudo3ux...</td>\n",
       "      <td>http://videolectures.net/epsrcws08_harison_tmp/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[19374, 19375, 267102, 19376, 19377, 267103, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8343</th>\n",
       "      <td>27.6245</td>\n",
       "      <td>Fast learning of Document Ranking Functions wi...</td>\n",
       "      <td>None</td>\n",
       "      <td>2008-02-11T13:45:00.000Z</td>\n",
       "      <td>2018-08-02T12:52:08.217Z</td>\n",
       "      <td>video</td>\n",
       "      <td>video/mp4</td>\n",
       "      <td>http://hydro.ijs.si/v002/4e/jyygewqam7awndgsph...</td>\n",
       "      <td>http://videolectures.net/wsdm08_elsas_fldr/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[18791, 18792, 18793, 18794, 18795, 18796, 187...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79325</th>\n",
       "      <td>22.8812</td>\n",
       "      <td>Surrogate Functions for Maximizing Precision a...</td>\n",
       "      <td>The problem of maximizing precision at the top...</td>\n",
       "      <td>2015-07-08T13:44:45.000Z</td>\n",
       "      <td>2018-08-02T11:45:56.622Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v012/4b/jnajx6667vfgohvwaz...</td>\n",
       "      <td>http://videolectures.net/icml2015_kar_surrogat...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[183869]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72072</th>\n",
       "      <td>22.7092</td>\n",
       "      <td>On-line learning algorithms: theory and practice</td>\n",
       "      <td>None</td>\n",
       "      <td>2007-10-24T09:00:00.000Z</td>\n",
       "      <td>2020-01-13T13:40:23.592Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v001/2b/fmyf4tqf6hu3rpokyu...</td>\n",
       "      <td>http://videolectures.net/aop07_cesa_bianchi_onl/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[308921, 308917, 308922, 308915, 308918, 30891...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69429</th>\n",
       "      <td>22.6157</td>\n",
       "      <td>Online Similarity Prediction of Networked Data...</td>\n",
       "      <td>We consider online similarity prediction probl...</td>\n",
       "      <td>2013-06-13T15:00:00.000Z</td>\n",
       "      <td>2020-01-14T22:16:48.739Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v00d/38/hckovwpuc6i5subzwu...</td>\n",
       "      <td>http://videolectures.net/colt2013_herbster_gra...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[316761, 316757, 316762, 316755, 316758, 31675...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71650</th>\n",
       "      <td>22.6097</td>\n",
       "      <td>On-line Statistical Learning</td>\n",
       "      <td>None</td>\n",
       "      <td>2007-01-31T00:00:00.000Z</td>\n",
       "      <td>2020-01-16T02:37:22.089Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v001/13/cnjeui6xgpsje3gkcs...</td>\n",
       "      <td>http://videolectures.net/stw07_bianchi_lsl/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[327282, 327278, 327283, 327276, 327279, 32728...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37091</th>\n",
       "      <td>22.592</td>\n",
       "      <td>Statistical Learning Theory and Applications</td>\n",
       "      <td>This course is for upper-level graduate studen...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T15:04:27.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/brain-and-cognitiv...</td>\n",
       "      <td>https://ocw.mit.edu/courses/brain-and-cognitiv...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[145565]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72947</th>\n",
       "      <td>22.5189</td>\n",
       "      <td>Algorithms for Predicting Structured Data</td>\n",
       "      <td>Structured prediction is the problem of predic...</td>\n",
       "      <td>2010-09-20T14:00:00.000Z</td>\n",
       "      <td>2018-08-02T11:30:00.034Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v009/12/ck3743qxhusvzv7flx...</td>\n",
       "      <td>http://videolectures.net/ecmlpkdd2010_gartner_...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[179917]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73088</th>\n",
       "      <td>22.5063</td>\n",
       "      <td>Exploiting feature covariance in high-dimensio...</td>\n",
       "      <td>Some online algorithms for linear classificati...</td>\n",
       "      <td>2010-05-14T18:43:00.000Z</td>\n",
       "      <td>2018-08-02T11:14:36.445Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v008/e1/4gmkkwhoavkp2chd3p...</td>\n",
       "      <td>http://videolectures.net/aistats2010_ma_efcih/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[180048]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13063</th>\n",
       "      <td>22.4973</td>\n",
       "      <td>Advanced Natural Language Processing</td>\n",
       "      <td>This course is a graduate introduction to natu...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T13:55:30.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[195202]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79155</th>\n",
       "      <td>22.4849</td>\n",
       "      <td>Efficient Bandit Algorithms for Online Multicl...</td>\n",
       "      <td>This paper introduces the Banditron, a variant...</td>\n",
       "      <td>2008-07-08T11:55:00.000Z</td>\n",
       "      <td>2020-01-14T14:26:48.759Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v002/bd/xxvcnw7uwtbedrfs6y...</td>\n",
       "      <td>http://videolectures.net/icml08_shwartz_eba/</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[314217, 314213, 314218, 314211, 314214, 31421...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44874</th>\n",
       "      <td>22.4532</td>\n",
       "      <td>Machine Learning</td>\n",
       "      <td>6.867 is an introductory course on machine lea...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T13:23:01.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[151816]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42647</th>\n",
       "      <td>22.4532</td>\n",
       "      <td>Artificial Intelligence</td>\n",
       "      <td>This course introduces students to the basic k...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T15:26:50.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[149649]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63767</th>\n",
       "      <td>22.4115</td>\n",
       "      <td>Learning Prediction Suffix Trees with Winnow</td>\n",
       "      <td>Prediction suffix trees (PSTs) are a popular t...</td>\n",
       "      <td>2009-06-15T11:45:00.000Z</td>\n",
       "      <td>2018-08-02T11:44:44.694Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>http://hydro.ijs.si/v005/1c/dqoe3innucx6hcmosj...</td>\n",
       "      <td>http://videolectures.net/icml09_karampatziakis...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'by-nc-nd', 'typed_name': ['by'...</td>\n",
       "      <td>{'id': 1, 'name': 'videolectures.net', 'domain...</td>\n",
       "      <td>[170510]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39212</th>\n",
       "      <td>22.3919</td>\n",
       "      <td>Introduction to Neural Networks</td>\n",
       "      <td>This course explores the organization of synap...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T15:13:37.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/brain-and-cognitiv...</td>\n",
       "      <td>https://ocw.mit.edu/courses/brain-and-cognitiv...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[147103]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64079</th>\n",
       "      <td>22.313</td>\n",
       "      <td>Machine Learning</td>\n",
       "      <td>6.867 is an introductory course on machine lea...</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-06-14T13:23:01.000Z</td>\n",
       "      <td>text</td>\n",
       "      <td>application/pdf</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>https://ocw.mit.edu/courses/electrical-enginee...</td>\n",
       "      <td>en</td>\n",
       "      <td>{'short_name': 'X5GON recommends the use of th...</td>\n",
       "      <td>{'id': 10, 'name': 'mit opencourseware', 'doma...</td>\n",
       "      <td>[170802]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weight                                              title  \\\n",
       "66731   31.1591                         The Multi-layer Perceptron   \n",
       "65029   30.1091  The Projectron: a Bounded Kernel-Based Perceptron   \n",
       "137065  30.0063  Fast learning of Document Ranking Functions wi...   \n",
       "11997   29.0144  The Projectron: a Bounded Kernel-Based Perceptron   \n",
       "8542    28.1756                         The Multi-layer Perceptron   \n",
       "8343    27.6245  Fast learning of Document Ranking Functions wi...   \n",
       "79325   22.8812  Surrogate Functions for Maximizing Precision a...   \n",
       "72072   22.7092   On-line learning algorithms: theory and practice   \n",
       "69429   22.6157  Online Similarity Prediction of Networked Data...   \n",
       "71650   22.6097                       On-line Statistical Learning   \n",
       "37091    22.592       Statistical Learning Theory and Applications   \n",
       "72947   22.5189          Algorithms for Predicting Structured Data   \n",
       "73088   22.5063  Exploiting feature covariance in high-dimensio...   \n",
       "13063   22.4973               Advanced Natural Language Processing   \n",
       "79155   22.4849  Efficient Bandit Algorithms for Online Multicl...   \n",
       "44874   22.4532                                   Machine Learning   \n",
       "42647   22.4532                            Artificial Intelligence   \n",
       "63767   22.4115       Learning Prediction Suffix Trees with Winnow   \n",
       "39212   22.3919                    Introduction to Neural Networks   \n",
       "64079    22.313                                   Machine Learning   \n",
       "\n",
       "                                              description  \\\n",
       "66731   This presentation describes the multilayer per...   \n",
       "65029   We present a discriminative online algorithm w...   \n",
       "137065                                                      \n",
       "11997   We present a discriminative online algorithm w...   \n",
       "8542    This presentation describes the multilayer per...   \n",
       "8343                                                 None   \n",
       "79325   The problem of maximizing precision at the top...   \n",
       "72072                                                None   \n",
       "69429   We consider online similarity prediction probl...   \n",
       "71650                                                None   \n",
       "37091   This course is for upper-level graduate studen...   \n",
       "72947   Structured prediction is the problem of predic...   \n",
       "73088   Some online algorithms for linear classificati...   \n",
       "13063   This course is a graduate introduction to natu...   \n",
       "79155   This paper introduces the Banditron, a variant...   \n",
       "44874   6.867 is an introductory course on machine lea...   \n",
       "42647   This course introduces students to the basic k...   \n",
       "63767   Prediction suffix trees (PSTs) are a popular t...   \n",
       "39212   This course explores the organization of synap...   \n",
       "64079   6.867 is an introductory course on machine lea...   \n",
       "\n",
       "                   creation_date            retrieved_date   type  \\\n",
       "66731   2008-01-21T10:26:07.000Z  2018-08-02T11:32:58.041Z   text   \n",
       "65029   2008-07-08T11:30:00.000Z  2018-08-02T11:44:18.703Z   text   \n",
       "137065  2008-02-11T13:45:00.000Z  2019-10-29T10:41:13.248Z   text   \n",
       "11997   2008-07-08T11:30:00.000Z  2018-08-02T11:44:18.703Z  video   \n",
       "8542    2008-01-21T10:26:07.000Z  2018-08-02T11:32:58.041Z  video   \n",
       "8343    2008-02-11T13:45:00.000Z  2018-08-02T12:52:08.217Z  video   \n",
       "79325   2015-07-08T13:44:45.000Z  2018-08-02T11:45:56.622Z   text   \n",
       "72072   2007-10-24T09:00:00.000Z  2020-01-13T13:40:23.592Z   text   \n",
       "69429   2013-06-13T15:00:00.000Z  2020-01-14T22:16:48.739Z   text   \n",
       "71650   2007-01-31T00:00:00.000Z  2020-01-16T02:37:22.089Z   text   \n",
       "37091                       None  2018-06-14T15:04:27.000Z   text   \n",
       "72947   2010-09-20T14:00:00.000Z  2018-08-02T11:30:00.034Z   text   \n",
       "73088   2010-05-14T18:43:00.000Z  2018-08-02T11:14:36.445Z   text   \n",
       "13063                       None  2018-06-14T13:55:30.000Z   text   \n",
       "79155   2008-07-08T11:55:00.000Z  2020-01-14T14:26:48.759Z   text   \n",
       "44874                       None  2018-06-14T13:23:01.000Z   text   \n",
       "42647                       None  2018-06-14T15:26:50.000Z   text   \n",
       "63767   2009-06-15T11:45:00.000Z  2018-08-02T11:44:44.694Z   text   \n",
       "39212                       None  2018-06-14T15:13:37.000Z   text   \n",
       "64079                       None  2018-06-14T13:23:01.000Z   text   \n",
       "\n",
       "               mimetype                                                url  \\\n",
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       "11997         video/mp4  http://hydro.ijs.si/v002/d0/2ci2ggjinmnmra4g3j...   \n",
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       "8343          video/mp4  http://hydro.ijs.si/v002/4e/jyygewqam7awndgsph...   \n",
       "79325   application/pdf  http://hydro.ijs.si/v012/4b/jnajx6667vfgohvwaz...   \n",
       "72072   application/pdf  http://hydro.ijs.si/v001/2b/fmyf4tqf6hu3rpokyu...   \n",
       "69429   application/pdf  http://hydro.ijs.si/v00d/38/hckovwpuc6i5subzwu...   \n",
       "71650   application/pdf  http://hydro.ijs.si/v001/13/cnjeui6xgpsje3gkcs...   \n",
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       "72947   application/pdf  http://hydro.ijs.si/v009/12/ck3743qxhusvzv7flx...   \n",
       "73088   application/pdf  http://hydro.ijs.si/v008/e1/4gmkkwhoavkp2chd3p...   \n",
       "13063   application/pdf  https://ocw.mit.edu/courses/electrical-enginee...   \n",
       "79155   application/pdf  http://hydro.ijs.si/v002/bd/xxvcnw7uwtbedrfs6y...   \n",
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       "42647   application/pdf  https://ocw.mit.edu/courses/electrical-enginee...   \n",
       "63767   application/pdf  http://hydro.ijs.si/v005/1c/dqoe3innucx6hcmosj...   \n",
       "39212   application/pdf  https://ocw.mit.edu/courses/brain-and-cognitiv...   \n",
       "64079   application/pdf  https://ocw.mit.edu/courses/electrical-enginee...   \n",
       "\n",
       "                                                  website language  \\\n",
       "66731     http://videolectures.net/epsrcws08_harison_tmp/       en   \n",
       "65029        http://videolectures.net/icml08_orabona_pbk/       en   \n",
       "137065        http://videolectures.net/wsdm08_elsas_fldr/       en   \n",
       "11997        http://videolectures.net/icml08_orabona_pbk/       en   \n",
       "8542      http://videolectures.net/epsrcws08_harison_tmp/       en   \n",
       "8343          http://videolectures.net/wsdm08_elsas_fldr/       en   \n",
       "79325   http://videolectures.net/icml2015_kar_surrogat...       en   \n",
       "72072    http://videolectures.net/aop07_cesa_bianchi_onl/       en   \n",
       "69429   http://videolectures.net/colt2013_herbster_gra...       en   \n",
       "71650         http://videolectures.net/stw07_bianchi_lsl/       en   \n",
       "37091   https://ocw.mit.edu/courses/brain-and-cognitiv...       en   \n",
       "72947   http://videolectures.net/ecmlpkdd2010_gartner_...       en   \n",
       "73088      http://videolectures.net/aistats2010_ma_efcih/       en   \n",
       "13063   https://ocw.mit.edu/courses/electrical-enginee...       en   \n",
       "79155        http://videolectures.net/icml08_shwartz_eba/       en   \n",
       "44874   https://ocw.mit.edu/courses/electrical-enginee...       en   \n",
       "42647   https://ocw.mit.edu/courses/electrical-enginee...       en   \n",
       "63767   http://videolectures.net/icml09_karampatziakis...       en   \n",
       "39212   https://ocw.mit.edu/courses/brain-and-cognitiv...       en   \n",
       "64079   https://ocw.mit.edu/courses/electrical-enginee...       en   \n",
       "\n",
       "                                                  license  \\\n",
       "66731   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
       "65029   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
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       "71650   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
       "37091   {'short_name': 'X5GON recommends the use of th...   \n",
       "72947   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
       "73088   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
       "13063   {'short_name': 'X5GON recommends the use of th...   \n",
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       "44874   {'short_name': 'X5GON recommends the use of th...   \n",
       "42647   {'short_name': 'X5GON recommends the use of th...   \n",
       "63767   {'short_name': 'by-nc-nd', 'typed_name': ['by'...   \n",
       "39212   {'short_name': 'X5GON recommends the use of th...   \n",
       "64079   {'short_name': 'X5GON recommends the use of th...   \n",
       "\n",
       "                                                 provider  \\\n",
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       "13063   {'id': 10, 'name': 'mit opencourseware', 'doma...   \n",
       "79155   {'id': 1, 'name': 'videolectures.net', 'domain...   \n",
       "44874   {'id': 10, 'name': 'mit opencourseware', 'doma...   \n",
       "42647   {'id': 10, 'name': 'mit opencourseware', 'doma...   \n",
       "63767   {'id': 1, 'name': 'videolectures.net', 'domain...   \n",
       "39212   {'id': 10, 'name': 'mit opencourseware', 'doma...   \n",
       "64079   {'id': 10, 'name': 'mit opencourseware', 'doma...   \n",
       "\n",
       "                                              content_ids  \n",
       "66731                                            [173471]  \n",
       "65029                                            [171743]  \n",
       "137065                                           [278370]  \n",
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       "79325                                            [183869]  \n",
       "72072   [308921, 308917, 308922, 308915, 308918, 30891...  \n",
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       "37091                                            [145565]  \n",
       "72947                                            [179917]  \n",
       "73088                                            [180048]  \n",
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       "44874                                            [151816]  \n",
       "42647                                            [149649]  \n",
       "63767                                            [170510]  \n",
       "39212                                            [147103]  \n",
       "64079                                            [170802]  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The X5GON API is available at:\n",
    "PLATFORM_URL = \"https://platform.x5gon.org/api/v1/\"\n",
    "function = \"search/\"\n",
    "parameter = \"text=\"\n",
    "keyword = \"perceptron\"\n",
    "response = requests.get(PLATFORM_URL + function + \"?\" + parameter + keyword)\n",
    "r_json = response.json()\n",
    "\n",
    "# store query results in a dict of dicts {material_d: {material_data}}\n",
    "# also remove duplicates by comparing material descriptions\n",
    "materials = {}\n",
    "for result in r_json[\"rec_materials\"]:\n",
    "    materials[result[\"material_id\"]] = result\n",
    "    materials[result[\"material_id\"]].pop(\"material_id\", None)\n",
    "\n",
    "df_materials = pd.DataFrame(materials)\n",
    "df_materials = df_materials.T\n",
    "df_materials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Given a keyword search display users that accessed this content__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The material 137065 - Fast learning of Document Ranking Functions with the Committee Perceptron was accessed by:\n",
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      " 953161 955825 959484 960344 965313 981445 985702 991238 998919]\n",
      "\n",
      "The material 79325 - Surrogate Functions for Maximizing Precision at the Top was accessed by:\n",
      "[     8      8      8     38     38     38     38     38     38  16828\n",
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      " 673228 673228 673228 676999 676999 676999 689911 689911 689911 689911\n",
      " 689911 707770 707770 707770 707770 707770 707770 719869 719869 719869\n",
      " 727221 727221 727221 727221 727221 742339 742339 742339 742339 742339\n",
      " 742339 742339 742339 745264 745264 745264 747599 747599 747599 770324\n",
      " 770324 770324 770324 770324 771271 771271 771271 771271 771271 773714\n",
      " 773714 773714 773714 788376 788376 788376 788675 788675 788675 788675\n",
      " 798552 798552 798552 799258 799258 799258 800981 800981 800981 800981\n",
      " 800981 802565 802565 802565 805050 805050 805050 805050 807411 807411\n",
      " 807411 807411 807411 807411 808666 808666 808666 808666 808666 813093\n",
      " 813093 813093 813093 813093 815953 815953 815953 818946 818946 818946\n",
      " 826837 826837 826837 826837 826837 826837 844211 844211 844211 844211\n",
      " 847167 847167 847167 850835 850835 850835 867216 867216 867216 898906\n",
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      " 921093 921093 927506 927506 927506 928394 928394 928394 930352 930352\n",
      " 930352 930352 930352 930352 938257 938257 938257 939283 939283 939283\n",
      " 939283 956230 956230 956230 961706 961706 961706 963203 963203 963203\n",
      " 965568 965568 965568 965568 965568 970722 970722 970722 970722]\n",
      "\n",
      "The material 72072 - On-line learning algorithms: theory and practice was accessed by:\n",
      "[  1172   1172   1172   1172  16637  16637  25498  25498  25498  25498\n",
      "  25498  29164  29164  29164  29164  29164  29164  29164  35474  35474\n",
      "  35474  35474  35474  35474  35474  39133  39133  39133  39133  48963\n",
      "  48963  48963  59181  59181  59181  59181  71995  71995  71995  78125\n",
      "  78125  78125  78125  94328  94328  94328  94328  96509  96509  96509\n",
      "  96509  96509 105523 105523 107624 107624 107624 124234 124234 195351\n",
      " 195351 195351 195351 199673 199673 199673 199673 199673 201577 201577\n",
      " 201840 201840 201840 201840 201840 201840 201840 202659 202659 202659\n",
      " 202659 202659 202659 202659 202659 202659 222254 222254 222254 222254\n",
      " 222254 222254 222254 222254 222254 222254 227562 227562 234244 234244\n",
      " 234244 234244 234244 234244 244660 244660 244660 246584 246584 246584\n",
      " 247020 247020 247020 247020 247020 247020 248014 248014 248014 249074\n",
      " 249074 249128 249128 249128 249128 249128 249128 249128 273667 273667\n",
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      " 281134 283508 283508 283508 288047 288047 288047 301697 301697 315889\n",
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      " 319605 319605 319605 319605 342385 342385 342385 342385 342385 342385\n",
      " 349768 349768 349768 349768 369115 369115 369115 370229 370229 377009\n",
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      " 421773 421773 424197 424197 424197 424197 424197 424197 424197 424197\n",
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      " 967059 967059 971873 971873 971873 971873 971873 971873 997741 997741]\n",
      "\n",
      "The material 72947 - Algorithms for Predicting Structured Data was accessed by:\n",
      "[  4240  23875  27714  36282  39523  44649  63311  63860  67304  76172\n",
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      " 916821 937655 944729 948710 955570 961971 983534 994946 997779]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in df_materials.index:\n",
    "    res = users[users.url == key].id\n",
    "    if not res.empty:\n",
    "        print(f\"The material {key} - {materials[key]['title']} was accessed by:\")\n",
    "        print(users[users.url == key].id.values)\n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### __Save the list of the content accessed, ordered by from access count__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = []\n",
    "for key in catalogue.index:\n",
    "    res = users[users.url == key].index\n",
    "    if not res.empty:\n",
    "        count.append((len(res), catalogue[catalogue.index == key].title))\n",
    "\n",
    "accessed = []\n",
    "for c in count:\n",
    "    accessed.append([c[0], c[1].keys()[0], c[1].values[0]])\n",
    "\n",
    "test = pd.Series(accessed)\n",
    "test = test.sort_values()\n",
    "viewed = pd.DataFrame([[a[0], a[1], a[2]] for a in test])\n",
    "viewed.to_csv(\"accessed.tsv\", sep=\"\\t\", encoding=\"utf-8\", index=False)"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
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