2.MarketplaceDataAnalysisAndOverview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 2. SSH Open Marketplace data quality overview\n",
    "\n",
    "This notebook offers an overview of the SSH Open Marketplace dataset and was built to help moderators to: \n",
    "1/ gain a better understanding of the SSH Open Marketplace data population; 2/ prioritising their curation tasks.  \n",
    "\n",
    "It is composed of 5 sections:\n",
    "\n",
    "0. Requirements to run the notebook\n",
    "1. SSH Open Marketplace items provenance\n",
    "2. Non-redundancy\n",
    "3. Completeness & metadata quality\n",
    "4. Activity coverage\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Requirements to run the notebook\n",
    "\n",
    "This section gives all the relevant information to \"interact\" with the MP data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.1 libraries\n",
    "*There are a number of external libraries needed to run the notebook* \n",
    "\n",
    "*Furthermore, a dedicated SSH Open Marketplace library - sshmarketplacelib - with customised functions has been created and can be imported using the python import commands.* \n",
    "\n",
    "*Below the libraries import needed to run this notebook*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #to manage dataframes\n",
    "import matplotlib.pyplot as plt #to create histograms and images\n",
    "import seaborn as sns #to create histograms and images\n",
    "import json #to manage json objects\n",
    "#import the MarketPlace Library \n",
    "from sshmarketplacelib import MPData as mpd\n",
    "from sshmarketplacelib import  eval as eva, helper as hel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.2 data(frame) download\n",
    "\n",
    "The Notebook must download all the items that needs to be checked. The function\n",
    "\n",
    "**getMPItems (itemscategory, localrepository, pages)**  \n",
    "\n",
    "returns a dataframe containing all the MP items of the required category, additionally it add the dataframe to the notebook environment. The function parameters are:\n",
    "<ul>\n",
    "    <li> <i>itemscategory</i> the category of items, possible values are: \n",
    "        <ul>\n",
    "            <li>toolsandservices</li>\n",
    "            <li>publications</li>\n",
    "            <li>trainingmaterials</li>\n",
    "            <li>workflows</li>\n",
    "            <li>datasets</li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li> <i>localrepository</i>, a Boolean value. If the localrepository parameter is set to 'False' or is not set, the items are downloaded from the MP dataset, if instead the localrepository parameter is set to 'True' the items are first searched in the local repository and if they are not present they are downloaded from the remote MP dataset. When the items are downloaded from the remote MP dataset they are stored in the local repository.</li>\n",
    "<li><i> pages</i> the number of pages. If the pages parameter is not provided all descriptions are returned, otherwise 20*pages items are returned.</li>\n",
    "</ul>\n",
    "\n",
    "The following cell downloads data for all categories of items, every category of items is assigned to a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getting data from local repository...\n",
      "getting data from local repository...\n",
      "getting data from local repository...\n",
      "getting data from local repository...\n",
      "getting data from local repository...\n"
     ]
    }
   ],
   "source": [
    "mpdata = mpd()\n",
    "df_tool_flat =mpdata.getMPItems (\"toolsandservices\", True)\n",
    "df_publication_flat =mpdata.getMPItems (\"publications\", True)\n",
    "df_trainingmaterials_flat =mpdata.getMPItems (\"trainingmaterials\", True)\n",
    "df_workflows_flat =mpdata.getMPItems (\"workflows\", True)\n",
    "df_datasets_flat =mpdata.getMPItems (\"datasets\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method getMPItems in module sshmarketplacelib.mpdata:\n",
      "\n",
      "getMPItems(itemscategory, localrepository=False, pages=0) method of sshmarketplacelib.mpdata.MPData instance\n",
      "    Loads data from MP dataset. This method creates a dataframe, stores it in a local repository and returns it to the caller.\n",
      "    \n",
      "    Parameters:\n",
      "    -----------\n",
      "    \n",
      "    itemscategory : str\n",
      "        The category of items\n",
      "    localrepository : boolean, optional\n",
      "        Indicates if the user wants the local dataset if present, if not present the remote dataset is used \n",
      "    pages : int, optional\n",
      "        The number of pages, default is all\n",
      "    \n",
      "    Returns:\n",
      "    --------\n",
      "    \n",
      "    DataFrame: Returning value\n",
      "    \n",
      "    If the localrepository parameter is 'False' or is defined, the items are downloaded from the MP dataset, \n",
      "    if the localrepository parameter is 'True' the items are first searched in the local repository \n",
      "    and if they are not present they are downloaded from the remote MP dataset. \n",
      "    When the items are downloaded from the remote MP dataset they are stored in the local repository.\n",
      "    If the pages parameter is not provided all descriptions are returned, otherwise 20*pages items are returned.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mpdata.getMPItems)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.2.1 first look into the data\n",
    "It can be useful to display the structure of the table. In the next cell the structure of the __df_tool_flat__ table (or dataframe) is shown. Note that all tables obtained with the getMPItems function have the same structure. \n",
    "\n",
    "This dataframe returns MP attributes as defined in the MP data model (see: https://doi.org/10.5281/zenodo.5749464)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "\n",
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       "1693  Zotpress is a WordPress plugin that enables us...   \n",
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       "\n",
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       "1691                                                 []   \n",
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tool_flat.tail()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. SSH Open Marketplace items provenance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to know where all the items of the Marketplace come from. Some items have been mass ingested from third parties sources - https://marketplace-api.sshopencloud.eu/api/sources - and the *informationContributor.username* is *System importer*; some items have been created by individual users, in this latter case, the *informationContributor.username* is NOT *System importer*.\n",
    "\n",
    "We want to have an overview of the items provenance: how many items come from source X, how many items have been created by individual users.   \n",
    "\n",
    "\n",
    "### 1.1 Items coming from mass ingest = from 3d party sources\n",
    "\n",
    "We use the function *getAllItemsBySources()*, it returns a *dataframe* (table) that contains two columns: in the first column there is the name of the source, in the second the number of items.  \n",
    "The following code shows also a possible way to print the content of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dblp: DH Publications            2810\n",
       "TAPoR                            1337\n",
       "SSK Zotero Resources              355\n",
       "Humanities Data                   302\n",
       "The Programming Historian         169\n",
       "NA                                114\n",
       "DARIAH-CAMPUS                      80\n",
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       "EOSC Catalogue                     30\n",
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       "Name: source.label, dtype: int64"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils=hel.Util()\n",
    "#get the dataframe\n",
    "df_items_by_sources=utils.getAllItemsBySources()\n",
    "#prints the content of the dataframe\n",
    "df_items_by_sources.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell uses the function *plot* of the dataframe to draw a bar chart. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df_items_by_sources.plot(kind='bar', figsize=(15,6))\n",
    "plt.grid(alpha=0.6)\n",
    "ax.yaxis.set_label_text(\"\")\n",
    "ax.set_title(\"Number of items by sources\", fontsize=15)\n",
    "ax.set_xlabel('Sources', fontsize=14)\n",
    "ax.set_ylabel('Items', fontsize=14);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item Categories by source providers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>source.label</th>\n",
       "      <th>DARIAH-CAMPUS</th>\n",
       "      <th>EOSC Catalogue</th>\n",
       "      <th>Humanities Data</th>\n",
       "      <th>Language Resource Switchboard</th>\n",
       "      <th>SSHopencloud Service Catalogue</th>\n",
       "      <th>SSK Zotero Resources</th>\n",
       "      <th>Standardization Survival Kit</th>\n",
       "      <th>TAPoR</th>\n",
       "      <th>The Programming Historian</th>\n",
       "      <th>dblp: DH Publications</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Categories</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>publication</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tool-or-service</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>training-material</th>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workflow</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "source.label       DARIAH-CAMPUS  EOSC Catalogue  Humanities Data  \\\n",
       "Categories                                                          \n",
       "dataset                      0.0             0.0            302.0   \n",
       "publication                  0.0             0.0              0.0   \n",
       "tool-or-service              0.0            30.0              0.0   \n",
       "training-material           80.0             0.0              0.0   \n",
       "workflow                     0.0             0.0              0.0   \n",
       "\n",
       "source.label       Language Resource Switchboard  \\\n",
       "Categories                                         \n",
       "dataset                                      0.0   \n",
       "publication                                  0.0   \n",
       "tool-or-service                             51.0   \n",
       "training-material                            0.0   \n",
       "workflow                                     0.0   \n",
       "\n",
       "source.label       SSHopencloud Service Catalogue  SSK Zotero Resources  \\\n",
       "Categories                                                                \n",
       "dataset                                       3.0                   0.0   \n",
       "publication                                   0.0                 136.0   \n",
       "tool-or-service                              18.0                 156.0   \n",
       "training-material                             2.0                  63.0   \n",
       "workflow                                      0.0                   0.0   \n",
       "\n",
       "source.label       Standardization Survival Kit   TAPoR  \\\n",
       "Categories                                                \n",
       "dataset                                     0.0     0.0   \n",
       "publication                                 0.0     0.0   \n",
       "tool-or-service                             0.0  1337.0   \n",
       "training-material                           0.0     0.0   \n",
       "workflow                                   25.0     0.0   \n",
       "\n",
       "source.label       The Programming Historian  dblp: DH Publications  \n",
       "Categories                                                           \n",
       "dataset                                  0.0                    0.0  \n",
       "publication                              0.0                 2810.0  \n",
       "tool-or-service                          0.0                    0.0  \n",
       "training-material                      169.0                    0.0  \n",
       "workflow                                 0.0                    0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categories_bysources=utils.getCategoriesBySources()\n",
    "df_categories_bysources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Items created by individual users \n",
    "\n",
    "*To be added*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Non-redundancy\n",
    "\n",
    "the same entity should only be referenced once in the SSH Open Marketplace. Duplicate items are merged to ensure the coherence of the items showcased in the portal. This notebook is used to IDENTIFY duplicates and get a general idea of the number of potential duplicates present in the MP. **To suggest items merging, see and run notebook 4.1Duplicated_Item_process**\n",
    "\n",
    "### 2.1 Identification of the duplicates\n",
    "\n",
    "Duplicates are individuated for every category by defining the attributes that should be inspected to identify equal items. In the next cell the function __getDuplicates(category, attributes)__ is invoked to get for every category the subset of items having the same value in the 'attribute' `label`, each set of duplicated items is stored in a variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tool_duplicates=utils.getDuplicates(df_tool_flat, 'label')\n",
    "df_publication_duplicates=utils.getDuplicates(df_publication_flat, 'label')\n",
    "df_trainingmaterials_duplicates=utils.getDuplicates(df_trainingmaterials_flat, 'label')\n",
    "df_workflows_duplicates=utils.getDuplicates(df_workflows_flat, 'label')\n",
    "df_datasets_duplicates=utils.getDuplicates(df_datasets_flat, 'label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.1\n",
    "*The  sets of duplicated items can be inspected, in the next cell the number of items with the same label for every category is shown*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are: 14 duplicated tools, 4 duplicated publications, 2 duplicated training materials, 0 duplicated workflows, 0 duplicated datasets\n"
     ]
    }
   ],
   "source": [
    "print (f'There are: {df_tool_duplicates.shape[0]} duplicated tools, {df_publication_duplicates.shape[0]} duplicated publications,'\n",
    "       +f' {df_trainingmaterials_duplicates.shape[0]} duplicated training materials,'+\n",
    "      f' {df_workflows_duplicates.shape[0]} duplicated workflows,'+\n",
    "      f' {df_datasets_duplicates.shape[0]} duplicated datasets')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cells the number of items with the same accessibleAt is individuated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tool_duplicates_3=utils.getDuplicates(df_tool_flat, 'accessibleAt')\n",
    "df_tool_duplicates_3=df_tool_duplicates_3[df_tool_duplicates_3['accessibleAt'].str.len()>0]\n",
    "df_tool_duplicates_3.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>MPUrl</th>\n",
       "      <th>label</th>\n",
       "      <th>accessibleAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>tool-or-service/tAGITn</td>\n",
       "      <td>Alveo</td>\n",
       "      <td>http://alveo.edu.au/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>tool-or-service/sJVV15</td>\n",
       "      <td>Alveo Virtual Laboratory</td>\n",
       "      <td>http://alveo.edu.au/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>tool-or-service/Ntf7jP</td>\n",
       "      <td>CHNM: Scribe</td>\n",
       "      <td>http://chnm.gmu.edu/tools/scribe/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>tool-or-service/pxZXbd</td>\n",
       "      <td>Scribe</td>\n",
       "      <td>http://chnm.gmu.edu/tools/scribe/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>tool-or-service/GQE5se</td>\n",
       "      <td>CorpusSearch 2</td>\n",
       "      <td>http://corpussearch.sourceforge.net/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>tool-or-service/lvtnwT</td>\n",
       "      <td>CorpusSearch</td>\n",
       "      <td>http://corpussearch.sourceforge.net/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>tool-or-service/ShyS5V</td>\n",
       "      <td>LIWC (Linguistic Inquiry and Word Count)</td>\n",
       "      <td>http://liwc.wpengine.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>tool-or-service/grnqNn</td>\n",
       "      <td>Linguistic Inquiry and Word Count</td>\n",
       "      <td>http://liwc.wpengine.com/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>tool-or-service/qUZguv</td>\n",
       "      <td>Stanford Vis Group: Protovis</td>\n",
       "      <td>http://mbostock.github.io/protovis/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>tool-or-service/qDX6aU</td>\n",
       "      <td>Protovis</td>\n",
       "      <td>http://mbostock.github.io/protovis/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>tool-or-service/ogL1S0</td>\n",
       "      <td>Prose (Virtual Muse)</td>\n",
       "      <td>http://oak.conncoll.edu/cohar/Programs.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>tool-or-service/O3Okgg</td>\n",
       "      <td>The Scandroid 1.1</td>\n",
       "      <td>http://oak.conncoll.edu/cohar/Programs.htm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MPUrl                                     label  \\\n",
       "44    tool-or-service/tAGITn                                     Alveo   \n",
       "45    tool-or-service/sJVV15                  Alveo Virtual Laboratory   \n",
       "197   tool-or-service/Ntf7jP                              CHNM: Scribe   \n",
       "1153  tool-or-service/pxZXbd                                    Scribe   \n",
       "289   tool-or-service/GQE5se                            CorpusSearch 2   \n",
       "288   tool-or-service/lvtnwT                              CorpusSearch   \n",
       "759   tool-or-service/ShyS5V  LIWC (Linguistic Inquiry and Word Count)   \n",
       "743   tool-or-service/grnqNn         Linguistic Inquiry and Word Count   \n",
       "1280  tool-or-service/qUZguv              Stanford Vis Group: Protovis   \n",
       "1069  tool-or-service/qDX6aU                                  Protovis   \n",
       "1064  tool-or-service/ogL1S0                      Prose (Virtual Muse)   \n",
       "1388  tool-or-service/O3Okgg                         The Scandroid 1.1   \n",
       "\n",
       "                                    accessibleAt  \n",
       "44                          http://alveo.edu.au/  \n",
       "45                          http://alveo.edu.au/  \n",
       "197            http://chnm.gmu.edu/tools/scribe/  \n",
       "1153           http://chnm.gmu.edu/tools/scribe/  \n",
       "289         http://corpussearch.sourceforge.net/  \n",
       "288         http://corpussearch.sourceforge.net/  \n",
       "759                    http://liwc.wpengine.com/  \n",
       "743                    http://liwc.wpengine.com/  \n",
       "1280         http://mbostock.github.io/protovis/  \n",
       "1069         http://mbostock.github.io/protovis/  \n",
       "1064  http://oak.conncoll.edu/cohar/Programs.htm  \n",
       "1388  http://oak.conncoll.edu/cohar/Programs.htm  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_tool_duplicates_3[['MPUrl', 'label', 'accessibleAt']].sort_values('accessibleAt').head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.2\n",
    "\n",
    "*It's possible to see the values of some of the duplicated items. In the next cell there is a command to show 4 items of the dataset for duplicated training materials.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>MPUrl</th>\n",
       "      <th>id</th>\n",
       "      <th>category</th>\n",
       "      <th>label</th>\n",
       "      <th>persistentId</th>\n",
       "      <th>lastInfoUpdate</th>\n",
       "      <th>status</th>\n",
       "      <th>description</th>\n",
       "      <th>contributors</th>\n",
       "      <th>properties</th>\n",
       "      <th>...</th>\n",
       "      <th>source.url</th>\n",
       "      <th>source.urlTemplate</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>dateLastUpdated</th>\n",
       "      <th>thumbnail.info.mediaId</th>\n",
       "      <th>thumbnail.info.category</th>\n",
       "      <th>thumbnail.info.location.sourceUrl</th>\n",
       "      <th>thumbnail.info.mimeType</th>\n",
       "      <th>thumbnail.info.hasThumbnail</th>\n",
       "      <th>thumbnail.info.filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>training-material/KbYQxy</td>\n",
       "      <td>36300</td>\n",
       "      <td>training-material</td>\n",
       "      <td>ContextCapture tutorials</td>\n",
       "      <td>KbYQxy</td>\n",
       "      <td>2022-01-13T11:48:53+0000</td>\n",
       "      <td>approved</td>\n",
       "      <td>To get started with ContextCapture, we recomme...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'type': {'code': 'language', 'label': 'Langu...</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.zotero.org/groups/427927/ssk-parth...</td>\n",
       "      <td>https://api.zotero.org/groups/427927/items/{so...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>training-material/vySU0m</td>\n",
       "      <td>36303</td>\n",
       "      <td>training-material</td>\n",
       "      <td>ContextCapture tutorials</td>\n",
       "      <td>vySU0m</td>\n",
       "      <td>2022-01-13T11:48:54+0000</td>\n",
       "      <td>approved</td>\n",
       "      <td>To get started with ContextCapture, we recomme...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'type': {'code': 'language', 'label': 'Langu...</td>\n",
       "      <td>...</td>\n",
       "      <td>https://www.zotero.org/groups/427927/ssk-parth...</td>\n",
       "      <td>https://api.zotero.org/groups/427927/items/{so...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MPUrl     id           category  \\\n",
       "45  training-material/KbYQxy  36300  training-material   \n",
       "46  training-material/vySU0m  36303  training-material   \n",
       "\n",
       "                       label persistentId            lastInfoUpdate    status  \\\n",
       "45  ContextCapture tutorials       KbYQxy  2022-01-13T11:48:53+0000  approved   \n",
       "46  ContextCapture tutorials       vySU0m  2022-01-13T11:48:54+0000  approved   \n",
       "\n",
       "                                          description contributors  \\\n",
       "45  To get started with ContextCapture, we recomme...           []   \n",
       "46  To get started with ContextCapture, we recomme...           []   \n",
       "\n",
       "                                           properties  ...  \\\n",
       "45  [{'type': {'code': 'language', 'label': 'Langu...  ...   \n",
       "46  [{'type': {'code': 'language', 'label': 'Langu...  ...   \n",
       "\n",
       "                                           source.url  \\\n",
       "45  https://www.zotero.org/groups/427927/ssk-parth...   \n",
       "46  https://www.zotero.org/groups/427927/ssk-parth...   \n",
       "\n",
       "                                   source.urlTemplate dateCreated  \\\n",
       "45  https://api.zotero.org/groups/427927/items/{so...         NaN   \n",
       "46  https://api.zotero.org/groups/427927/items/{so...         NaN   \n",
       "\n",
       "   dateLastUpdated thumbnail.info.mediaId  thumbnail.info.category  \\\n",
       "45             NaN                    NaN                      NaN   \n",
       "46             NaN                    NaN                      NaN   \n",
       "\n",
       "   thumbnail.info.location.sourceUrl thumbnail.info.mimeType  \\\n",
       "45                               NaN                     NaN   \n",
       "46                               NaN                     NaN   \n",
       "\n",
       "   thumbnail.info.hasThumbnail thumbnail.info.filename  \n",
       "45                         NaN                     NaN  \n",
       "46                         NaN                     NaN  \n",
       "\n",
       "[2 rows x 35 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trainingmaterials_duplicates.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1.3\n",
    "\n",
    "As shown in the section __0.2.1__ of this notebook every row in the table includes 30 different attributes, it may be useful to define a list of attributes and use it when showing the data. In the next cell a list of attributes is defined and used as visualization mask for 6 items in the duplicated trainingmaterials dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>MPUrl</th>\n",
       "      <th>persistentId</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "      <th>source.label</th>\n",
       "      <th>accessibleAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>training-material/KbYQxy</td>\n",
       "      <td>KbYQxy</td>\n",
       "      <td>ContextCapture tutorials</td>\n",
       "      <td>To get started with ContextCapture, we recomme...</td>\n",
       "      <td>SSK Zotero Resources</td>\n",
       "      <td>[https://www.acute3d.com/tutorials/]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>training-material/vySU0m</td>\n",
       "      <td>vySU0m</td>\n",
       "      <td>ContextCapture tutorials</td>\n",
       "      <td>To get started with ContextCapture, we recomme...</td>\n",
       "      <td>SSK Zotero Resources</td>\n",
       "      <td>[https://www.acute3d.com/tutorials/]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       MPUrl persistentId                     label  \\\n",
       "45  training-material/KbYQxy       KbYQxy  ContextCapture tutorials   \n",
       "46  training-material/vySU0m       vySU0m  ContextCapture tutorials   \n",
       "\n",
       "                                          description          source.label  \\\n",
       "45  To get started with ContextCapture, we recomme...  SSK Zotero Resources   \n",
       "46  To get started with ContextCapture, we recomme...  SSK Zotero Resources   \n",
       "\n",
       "                            accessibleAt  \n",
       "45  [https://www.acute3d.com/tutorials/]  \n",
       "46  [https://www.acute3d.com/tutorials/]  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the list of attributes\n",
    "vis_attr=['MPUrl', 'persistentId', 'label','description', 'source.label','accessibleAt']\n",
    "df_trainingmaterials_duplicates[vis_attr].head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list of attributes can be applied to all sets of duplicated items, in the next two cells it is used to filter attributes in the set of duplicated workflows and tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPUrl</th>\n",
       "      <th>persistentId</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "      <th>source.label</th>\n",
       "      <th>accessibleAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [MPUrl, persistentId, label, description, source.label, accessibleAt]\n",
       "Index: []"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_workflows_duplicates[vis_attr].head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>MPUrl</th>\n",
       "      <th>persistentId</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "      <th>source.label</th>\n",
       "      <th>accessibleAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tool-or-service/3NqTBe</td>\n",
       "      <td>3NqTBe</td>\n",
       "      <td>Adobe Acrobat Reader</td>\n",
       "      <td>http://www.wikidata.org/entity/Q2634567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tool-or-service/sc1bNI</td>\n",
       "      <td>sc1bNI</td>\n",
       "      <td>Adobe Acrobat Reader</td>\n",
       "      <td>http://www.wikidata.org/entity/Q60521777</td>\n",
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       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>tool-or-service/4uAhJc</td>\n",
       "      <td>4uAhJc</td>\n",
       "      <td>NodeXL</td>\n",
       "      <td>http://www.wikidata.org/entity/Q16958587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>tool-or-service/mLLrAv</td>\n",
       "      <td>mLLrAv</td>\n",
       "      <td>NodeXL</td>\n",
       "      <td>NodeXL is a free, open source tool for generat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[https://nodexl.com/]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>tool-or-service/89LRtg</td>\n",
       "      <td>89LRtg</td>\n",
       "      <td>Quill</td>\n",
       "      <td>http://www.wikidata.org/entity/Q105972409</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>tool-or-service/6FBpZK</td>\n",
       "      <td>6FBpZK</td>\n",
       "      <td>Quill</td>\n",
       "      <td>http://www.wikidata.org/entity/Q30325191</td>\n",
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       "                       MPUrl persistentId                 label  \\\n",
       "20    tool-or-service/3NqTBe       3NqTBe  Adobe Acrobat Reader   \n",
       "21    tool-or-service/sc1bNI       sc1bNI  Adobe Acrobat Reader   \n",
       "904   tool-or-service/4uAhJc       4uAhJc                NodeXL   \n",
       "905   tool-or-service/mLLrAv       mLLrAv                NodeXL   \n",
       "1090  tool-or-service/89LRtg       89LRtg                 Quill   \n",
       "1091  tool-or-service/6FBpZK       6FBpZK                 Quill   \n",
       "\n",
       "                                            description source.label  \\\n",
       "20              http://www.wikidata.org/entity/Q2634567          NaN   \n",
       "21             http://www.wikidata.org/entity/Q60521777          NaN   \n",
       "904            http://www.wikidata.org/entity/Q16958587          NaN   \n",
       "905   NodeXL is a free, open source tool for generat...          NaN   \n",
       "1090          http://www.wikidata.org/entity/Q105972409          NaN   \n",
       "1091           http://www.wikidata.org/entity/Q30325191          NaN   \n",
       "\n",
       "               accessibleAt  \n",
       "20                       []  \n",
       "21                       []  \n",
       "904                      []  \n",
       "905   [https://nodexl.com/]  \n",
       "1090                     []  \n",
       "1091                     []  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tool_duplicates[vis_attr].head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Completeness & metadata quality\n",
    "\n",
    "We want to know how complete the SSH Open Marketplace dataset is, i.e. what is the level of quality of metadata describing the items populating the Marketplace.\n",
    "As the data model of the Marketplace differentiates between attributes and properties, we want to mix these two different fields in the overview provided in this section.\n",
    "\n",
    "Because not all metadata fields have the same importance depending on the item types, a set of recommended fields have been idenitfied by the Editorial Team (the list is kept up-to-date as part of the front-end code of the MP https://gitlab.gwdg.de/sshoc/sshoc-marketplace-frontend/-/blob/main/config/forms.json).\n",
    "\n",
    "This notebook is only used to provide generic information about the metadata quality. **Dedicated notebooks - 3.2CurationFlagDescription and 3.4CurationFlagCoverage - are built (on the same logic) to inform the MP editorial dashboard, via dedicated curation-flags.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Null values\n",
    "\n",
    "### 3.1.1 Null values in all attributes by item types\n",
    "*There are a number of functions for null values. The function __getNullValues()__ returns a table where the columns are the category and the rows are the attributes. The number in the cell individuated by an attribute and a category indicates how many null values there are in that category for the attribute. The function calculates the table for all the categories downloaded in the notebook. In this sepcific case all datasets downloaded in section __0.2__. The next cell gets the null values and shows them* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cesare/eclipse-workspace/mplib/sshoccurationprod/sshmarketplacelib/helper.py:481: FutureWarning: Passing 'suffixes' which cause duplicate columns {'ts_persistentId_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_items=pd.merge(df_items, df_prop_data_tmp, left_on='persistentId',right_on='ts_persistentId', how = 'outer').fillna(np.nan)\n"
     ]
    },
    {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category                                 dataset  publication  \\\n",
       "property: missed values                                         \n",
       "accessibleAt                                   1           20   \n",
       "composedOf                                   308         2949   \n",
       "concept.candidate                             46           52   \n",
       "concept.code                                  46           52   \n",
       "concept.definition                           307         2947   \n",
       "concept.label                                 46           52   \n",
       "concept.notation                             306         2947   \n",
       "concept.uri                                   46           52   \n",
       "concept.vocabulary.closed                     46           52   \n",
       "concept.vocabulary.code                       46           52   \n",
       "concept.vocabulary.label                      46           52   \n",
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       "id                                             0            0   \n",
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       "label                                          0            0   \n",
       "lastInfoUpdate                                 0            0   \n",
       "media                                        306         2946   \n",
       "persistentId                                   0            0   \n",
       "properties                                    38            5   \n",
       "relatedItems                                 307         2785   \n",
       "source.id                                      3            3   \n",
       "source.label                                   3            3   \n",
       "\n",
       "category                                 tool-or-service  training-material  \\\n",
       "property: missed values                                                       \n",
       "accessibleAt                                         490                 14   \n",
       "composedOf                                          1695                318   \n",
       "concept.candidate                                     70                  1   \n",
       "concept.code                                          70                  1   \n",
       "concept.definition                                  1557                318   \n",
       "concept.label                                         70                  1   \n",
       "concept.notation                                     651                237   \n",
       "concept.uri                                           70                  1   \n",
       "concept.vocabulary.closed                             70                  1   \n",
       "concept.vocabulary.code                               70                  1   \n",
       "concept.vocabulary.label                              70                  1   \n",
       "concept.vocabulary.namespace                          70                  1   \n",
       "concept.vocabulary.scheme                             70                  1   \n",
       "contributors                                         226                 57   \n",
       "dateCreated                                         1695                101   \n",
       "dateLastUpdated                                     1695                151   \n",
       "description                                           55                 26   \n",
       "externalIds                                         1550                147   \n",
       "id                                                     0                  0   \n",
       "informationContributor.config                          0                  0   \n",
       "informationContributor.displayName                     0                  0   \n",
       "informationContributor.email                           0                  0   \n",
       "informationContributor.id                              0                  0   \n",
       "informationContributor.registrationDate                0                  0   \n",
       "informationContributor.role                            0                  0   \n",
       "informationContributor.status                          0                  0   \n",
       "informationContributor.username                        0                  0   \n",
       "label                                                  0                  0   \n",
       "lastInfoUpdate                                         0                  0   \n",
       "media                                                450                225   \n",
       "persistentId                                           0                  0   \n",
       "properties                                            64                  0   \n",
       "relatedItems                                        1509                257   \n",
       "source.id                                            103                  4   \n",
       "source.label                                         103                  4   \n",
       "\n",
       "category                                 workflow  \n",
       "property: missed values                            \n",
       "accessibleAt                                    1  \n",
       "composedOf                                      0  \n",
       "concept.candidate                               0  \n",
       "concept.code                                    0  \n",
       "concept.definition                             18  \n",
       "concept.label                                   0  \n",
       "concept.notation                                9  \n",
       "concept.uri                                     0  \n",
       "concept.vocabulary.closed                       0  \n",
       "concept.vocabulary.code                         0  \n",
       "concept.vocabulary.label                        0  \n",
       "concept.vocabulary.namespace                    0  \n",
       "concept.vocabulary.scheme                       0  \n",
       "contributors                                    0  \n",
       "dateCreated                                    26  \n",
       "dateLastUpdated                                26  \n",
       "description                                     1  \n",
       "externalIds                                    26  \n",
       "id                                              0  \n",
       "informationContributor.config                   0  \n",
       "informationContributor.displayName              0  \n",
       "informationContributor.email                    0  \n",
       "informationContributor.id                       0  \n",
       "informationContributor.registrationDate         0  \n",
       "informationContributor.role                     0  \n",
       "informationContributor.status                   0  \n",
       "informationContributor.username                 0  \n",
       "label                                           0  \n",
       "lastInfoUpdate                                  0  \n",
       "media                                           1  \n",
       "persistentId                                    0  \n",
       "properties                                      0  \n",
       "relatedItems                                   26  \n",
       "source.id                                       1  \n",
       "source.label                                    1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null_values=utils.getNullValues()\n",
    "df_null_values[0].head(35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Null values for a given set of attributes\n",
    "*The function __getNullValues(attributes)__ returns a table where the columns are the categories and the rows are the attributes listed in the invocation.  The list of attributes should contain 1 or more attribute name separated by ','.\n",
    "The number in the cell individuated by an attribute and a category indicates how many null values there are in that category for the attribute.  \n",
    "The function calculates the table for all the dataset downloaded in the notebook. In this sepcific case all datasets downloaded in section __0.2__. The next cell searches for null values of properties: accessibleAt, description, contributors and relatedItems.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cesare/eclipse-workspace/mplib/sshoccurationprod/sshmarketplacelib/helper.py:481: FutureWarning: Passing 'suffixes' which cause duplicate columns {'ts_persistentId_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_items=pd.merge(df_items, df_prop_data_tmp, left_on='persistentId',right_on='ts_persistentId', how = 'outer').fillna(np.nan)\n",
      "/Users/cesare/eclipse-workspace/mplib/sshoccurationprod/sshmarketplacelib/helper.py:494: FutureWarning: Passing 'suffixes' which cause duplicate columns {'ts_persistentId_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "  df_items=pd.merge(left=df_items, right=tmp, left_on='persistentId', right_on='ts_persistentId', how = 'outer').fillna(np.nan)\n"
     ]
    },
    {
     "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>category</th>\n",
       "      <th>dataset</th>\n",
       "      <th>publication</th>\n",
       "      <th>tool-or-service</th>\n",
       "      <th>training-material</th>\n",
       "      <th>workflow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property: missed values</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>accessibleAt</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>490</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contributors</th>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
       "      <td>226</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relatedItems</th>\n",
       "      <td>307</td>\n",
       "      <td>2785</td>\n",
       "      <td>1509</td>\n",
       "      <td>257</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>0</td>\n",
       "      <td>2862</td>\n",
       "      <td>55</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>discipline</th>\n",
       "      <td>306</td>\n",
       "      <td>2948</td>\n",
       "      <td>1623</td>\n",
       "      <td>237</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <td>65</td>\n",
       "      <td>2944</td>\n",
       "      <td>279</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category                 dataset  publication  tool-or-service  \\\n",
       "property: missed values                                          \n",
       "accessibleAt                   1           20              490   \n",
       "contributors                   3           67              226   \n",
       "relatedItems                 307         2785             1509   \n",
       "description                    0         2862               55   \n",
       "discipline                   306         2948             1623   \n",
       "keyword                       65         2944              279   \n",
       "\n",
       "category                 training-material  workflow  \n",
       "property: missed values                               \n",
       "accessibleAt                            14         1  \n",
       "contributors                            57         0  \n",
       "relatedItems                           257        26  \n",
       "description                             26         1  \n",
       "discipline                             237        17  \n",
       "keyword                                 64         7  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nullpart_values=utils.getNullValues('accessibleAt, contributors, relatedItems, description, discipline, keyword')\n",
    "df_nullpart_values[0].head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "*The function __getItemsWithNullValues(props, all)__ returns all items where the properties indicated in the list have null values.  \n",
    "In the next cell:*\n",
    "\n",
    "<ol>\n",
    "    <li> <i>the function is invoked to get all items where the accessibleAt, contributors and relatedItems have null values, the parameter True means that all the attributes/properties in the list must be null in an item</i></li>\n",
    "    <li> <i>the visualization mask is defined</i></li>\n",
    "    <li> <i>some items are shown</i></li>\n",
    "    </ol>\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPUrl</th>\n",
       "      <th>persistentId</th>\n",
       "      <th>category</th>\n",
       "      <th>label</th>\n",
       "      <th>source.label</th>\n",
       "      <th>accessibleAt</th>\n",
       "      <th>contributors</th>\n",
       "      <th>relatedItems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>tool-or-service/CqCK1n</td>\n",
       "      <td>CqCK1n</td>\n",
       "      <td>tool-or-service</td>\n",
       "      <td>API for Generations and Gender Programme (GGP)</td>\n",
       "      <td>SSHopencloud Service Catalogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>tool-or-service/wLzD4H</td>\n",
       "      <td>wLzD4H</td>\n",
       "      <td>tool-or-service</td>\n",
       "      <td>Audio Survey Module Questionnaire and Fieldwor...</td>\n",
       "      <td>SSHopencloud Service Catalogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>tool-or-service/m5jJ5s</td>\n",
       "      <td>m5jJ5s</td>\n",
       "      <td>tool-or-service</td>\n",
       "      <td>Code for data exchange between TMT and open so...</td>\n",
       "      <td>SSHopencloud Service Catalogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>tool-or-service/8lXz8g</td>\n",
       "      <td>8lXz8g</td>\n",
       "      <td>tool-or-service</td>\n",
       "      <td>Data Confidentiality Schema for Sensitive Data</td>\n",
       "      <td>SSHopencloud Service Catalogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>tool-or-service/9McIgG</td>\n",
       "      <td>9McIgG</td>\n",
       "      <td>tool-or-service</td>\n",
       "      <td>ERAN Pilot: Specifications for Secure Remote A...</td>\n",
       "      <td>SSHopencloud Service Catalogue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      MPUrl persistentId         category  \\\n",
       "74   tool-or-service/CqCK1n       CqCK1n  tool-or-service   \n",
       "93   tool-or-service/wLzD4H       wLzD4H  tool-or-service   \n",
       "229  tool-or-service/m5jJ5s       m5jJ5s  tool-or-service   \n",
       "320  tool-or-service/8lXz8g       8lXz8g  tool-or-service   \n",
       "414  tool-or-service/9McIgG       9McIgG  tool-or-service   \n",
       "\n",
       "                                                 label  \\\n",
       "74     API for Generations and Gender Programme (GGP)    \n",
       "93   Audio Survey Module Questionnaire and Fieldwor...   \n",
       "229  Code for data exchange between TMT and open so...   \n",
       "320     Data Confidentiality Schema for Sensitive Data   \n",
       "414  ERAN Pilot: Specifications for Secure Remote A...   \n",
       "\n",
       "                       source.label accessibleAt contributors relatedItems  \n",
       "74   SSHopencloud Service Catalogue          NaN          NaN          NaN  \n",
       "93   SSHopencloud Service Catalogue          NaN          NaN          NaN  \n",
       "229  SSHopencloud Service Catalogue          NaN          NaN          NaN  \n",
       "320  SSHopencloud Service Catalogue          NaN          NaN          NaN  \n",
       "414  SSHopencloud Service Catalogue          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1\n",
    "df_items_null_values=utils.getItemsWithNullValues('accessibleAt, contributors, relatedItems', True)\n",
    "#2\n",
    "vis_items_mask=['MPUrl', 'persistentId', 'category', 'label', 'source.label','accessibleAt','contributors', 'relatedItems']\n",
    "#3\n",
    "df_items_null_values[vis_items_mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 A few functions to go further in the identification of items with null values in attributes\n",
    "\n",
    "*The variable df_items_null_values contains the list of items where the values for accessibleAt, contributors and relatedItems are all null, we can inspect the dataset.  \n",
    "How many items are in the dataset?  \n",
    "The cell below prints the number of items in the dataset*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " There are 15 items where the values in attributes accessibleAt, contributors, relatedItems are null \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print (f'\\n There are {df_items_null_values.shape[0]} items where the values in attributes accessibleAt, contributors, relatedItems are null \\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What are the sources?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source.label\n",
       "SSHopencloud Service Catalogue    12\n",
       "TAPoR                              1\n",
       "The Programming Historian          1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=df_items_null_values.groupby('source.label').count()['label']\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >MPUrl</th>\n",
       "      <th class=\"col_heading level0 col1\" >category</th>\n",
       "      <th class=\"col_heading level0 col2\" >source.label</th>\n",
       "      <th class=\"col_heading level0 col3\" >label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row0\" class=\"row_heading level0 row0\" >74</th>\n",
       "      <td id=\"T_239c8_row0_col0\" class=\"data row0 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/CqCK1n\">tool-or-service/CqCK1n</a></td>\n",
       "      <td id=\"T_239c8_row0_col1\" class=\"data row0 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row0_col2\" class=\"data row0 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row0_col3\" class=\"data row0 col3\" >API for Generations and Gender Programme (GGP) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row1\" class=\"row_heading level0 row1\" >93</th>\n",
       "      <td id=\"T_239c8_row1_col0\" class=\"data row1 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/wLzD4H\">tool-or-service/wLzD4H</a></td>\n",
       "      <td id=\"T_239c8_row1_col1\" class=\"data row1 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row1_col2\" class=\"data row1 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row1_col3\" class=\"data row1 col3\" >Audio Survey Module Questionnaire and Fieldwork Implementation Guideline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row2\" class=\"row_heading level0 row2\" >229</th>\n",
       "      <td id=\"T_239c8_row2_col0\" class=\"data row2 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/m5jJ5s\">tool-or-service/m5jJ5s</a></td>\n",
       "      <td id=\"T_239c8_row2_col1\" class=\"data row2 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row2_col2\" class=\"data row2 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row2_col3\" class=\"data row2 col3\" >Code for data exchange between TMT and open source CAT software</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row3\" class=\"row_heading level0 row3\" >320</th>\n",
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       "      <td id=\"T_239c8_row3_col1\" class=\"data row3 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row3_col2\" class=\"data row3 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row3_col3\" class=\"data row3 col3\" >Data Confidentiality Schema for Sensitive Data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row4\" class=\"row_heading level0 row4\" >414</th>\n",
       "      <td id=\"T_239c8_row4_col0\" class=\"data row4 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/9McIgG\">tool-or-service/9McIgG</a></td>\n",
       "      <td id=\"T_239c8_row4_col1\" class=\"data row4 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row4_col2\" class=\"data row4 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row4_col3\" class=\"data row4 col3\" >ERAN Pilot: Specifications for Secure Remote Access to Sensitive Research Data </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row5\" class=\"row_heading level0 row5\" >487</th>\n",
       "      <td id=\"T_239c8_row5_col0\" class=\"data row5 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/a47VVu\">tool-or-service/a47VVu</a></td>\n",
       "      <td id=\"T_239c8_row5_col1\" class=\"data row5 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row5_col2\" class=\"data row5 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row5_col3\" class=\"data row5 col3\" >Framework for data use agreement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row6\" class=\"row_heading level0 row6\" >640</th>\n",
       "      <td id=\"T_239c8_row6_col0\" class=\"data row6 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/CzWHtv\">tool-or-service/CzWHtv</a></td>\n",
       "      <td id=\"T_239c8_row6_col1\" class=\"data row6 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row6_col2\" class=\"data row6 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row6_col3\" class=\"data row6 col3\" >Interoperability Hub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row7\" class=\"row_heading level0 row7\" >661</th>\n",
       "      <td id=\"T_239c8_row7_col0\" class=\"data row7 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/NvN0y8\">tool-or-service/NvN0y8</a></td>\n",
       "      <td id=\"T_239c8_row7_col1\" class=\"data row7 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row7_col2\" class=\"data row7 col2\" >TAPoR</td>\n",
       "      <td id=\"T_239c8_row7_col3\" class=\"data row7 col3\" >iTunesCyrFix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row8\" class=\"row_heading level0 row8\" >775</th>\n",
       "      <td id=\"T_239c8_row8_col0\" class=\"data row8 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/2KpJe6\">tool-or-service/2KpJe6</a></td>\n",
       "      <td id=\"T_239c8_row8_col1\" class=\"data row8 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row8_col2\" class=\"data row8 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row8_col3\" class=\"data row8 col3\" >Making Data Findable by being Citable - Tools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row9\" class=\"row_heading level0 row9\" >1258</th>\n",
       "      <td id=\"T_239c8_row9_col0\" class=\"data row9 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/AOHiq8\">tool-or-service/AOHiq8</a></td>\n",
       "      <td id=\"T_239c8_row9_col1\" class=\"data row9 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row9_col2\" class=\"data row9 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row9_col3\" class=\"data row9 col3\" >SSH Data repository service on EOSC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row10\" class=\"row_heading level0 row10\" >1259</th>\n",
       "      <td id=\"T_239c8_row10_col0\" class=\"data row10 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/tsbNWP\">tool-or-service/tsbNWP</a></td>\n",
       "      <td id=\"T_239c8_row10_col1\" class=\"data row10 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row10_col2\" class=\"data row10 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row10_col3\" class=\"data row10 col3\" >SSH GDPR Code of Conduct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row11\" class=\"row_heading level0 row11\" >1341</th>\n",
       "      <td id=\"T_239c8_row11_col0\" class=\"data row11 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/Zztxus\">tool-or-service/Zztxus</a></td>\n",
       "      <td id=\"T_239c8_row11_col1\" class=\"data row11 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_239c8_row11_col2\" class=\"data row11 col2\" >nan</td>\n",
       "      <td id=\"T_239c8_row11_col3\" class=\"data row11 col3\" >test vocabularies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row12\" class=\"row_heading level0 row12\" >4853</th>\n",
       "      <td id=\"T_239c8_row12_col0\" class=\"data row12 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/training-material/BvZp6o\">training-material/BvZp6o</a></td>\n",
       "      <td id=\"T_239c8_row12_col1\" class=\"data row12 col1\" >training-material</td>\n",
       "      <td id=\"T_239c8_row12_col2\" class=\"data row12 col2\" >The Programming Historian</td>\n",
       "      <td id=\"T_239c8_row12_col3\" class=\"data row12 col3\" >OCR with Tesseract and ScanTailor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row13\" class=\"row_heading level0 row13\" >4920</th>\n",
       "      <td id=\"T_239c8_row13_col0\" class=\"data row13 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/training-material/m6qr2d\">training-material/m6qr2d</a></td>\n",
       "      <td id=\"T_239c8_row13_col1\" class=\"data row13 col1\" >training-material</td>\n",
       "      <td id=\"T_239c8_row13_col2\" class=\"data row13 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row13_col3\" class=\"data row13 col3\" >Training Professionals Network</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_239c8_level0_row14\" class=\"row_heading level0 row14\" >5008</th>\n",
       "      <td id=\"T_239c8_row14_col0\" class=\"data row14 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/dataset/u11tRS\">dataset/u11tRS</a></td>\n",
       "      <td id=\"T_239c8_row14_col1\" class=\"data row14 col1\" >dataset</td>\n",
       "      <td id=\"T_239c8_row14_col2\" class=\"data row14 col2\" >SSHopencloud Service Catalogue</td>\n",
       "      <td id=\"T_239c8_row14_col3\" class=\"data row14 col3\" >Audio Transcript Data</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x14d7fe500>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend_nv_table_df=df_items_null_values[['MPUrl', 'category', 'source.label','label']]\n",
    "clickable_nv_table = rend_nv_table_df.style.format({'MPUrl': utils.make_clickable})\n",
    "clickable_nv_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Description lengh\n",
    "For the `description` attribute, there should not be any null_values (this is a mandatory attribute and it is automatically filled with \"No description provided\" when it is empty at the ingest stage), so we want to list the items with a description length less than 25 and more than 1500 characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df_all_items=pd.concat([df_tool_flat, df_publication_flat, df_trainingmaterials_flat, df_workflows_flat, df_datasets_flat], ignore_index=True)\n",
    "df_items = df_all_items.replace(utils.empty_description, np.nan)\n",
    "# need to be added after last ingestion\n",
    "tempemptystring=utils.empty_description.replace('.','')\n",
    "df_items = df_items.replace(tempemptystring, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "ax.hist(df_items['description'].str.len(),  bins=100)\n",
    "ax.set_title('Description Length')\n",
    "ax.set_xlabel('Characters in description')\n",
    "ax.set_ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "There are 2944 items with empty descriptions,  3 items with description shorter than 25 characters, 41 Items with description longer than 1500 characters,  2308 Items with description between 25 and 1500 characters.\n"
     ]
    }
   ],
   "source": [
    "minchars=25\n",
    "maxchars=1500\n",
    "\n",
    "df_items_d = df_items[(df_items['description'].notnull()) & (df_items['description'].str.len()>=minchars) & (df_items['description'].str.len()<=maxchars)]\n",
    "\n",
    "df_items_min = df_items[(df_items['description'].notnull()) & (df_items['description'].str.len()<minchars)]\n",
    "\n",
    "df_items_max = df_items[(df_items['description'].notnull()) & (df_items['description'].str.len()>maxchars)]\n",
    "\n",
    "print (f\"\\nThere are {df_items['description'].isna().sum()} items with empty descriptions, \"+\n",
    "       f\" {df_items_min['description'].count()} items with description shorter than {minchars} characters,\"+\n",
    "f\" {df_items_max['description'].count()} Items with description longer than {maxchars} characters, \"+\n",
    "      f\" {df_items_d['description'].count()} Items with description between {minchars} and {maxchars} characters.\")\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of empty descriptions in items by item sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source.label\n",
       "SSK Zotero Resources             131\n",
       "Standardization Survival Kit       1\n",
       "TAPoR                              4\n",
       "The Programming Historian          2\n",
       "dblp: DH Publications           2806\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_empty_desc=df_items[df_items['description'].isna()]\n",
    "df_empty_desc.groupby('source.label').count()['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The next cell creates a table with a clickable url for items having description shorter than 25 characters*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_713fc_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >MPUrl</th>\n",
       "      <th class=\"col_heading level0 col1\" >category</th>\n",
       "      <th class=\"col_heading level0 col2\" >source.label</th>\n",
       "      <th class=\"col_heading level0 col3\" >label</th>\n",
       "      <th class=\"col_heading level0 col4\" >description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_713fc_level0_row0\" class=\"row_heading level0 row0\" >194</th>\n",
       "      <td id=\"T_713fc_row0_col0\" class=\"data row0 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/o2X2Sl\">tool-or-service/o2X2Sl</a></td>\n",
       "      <td id=\"T_713fc_row0_col1\" class=\"data row0 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_713fc_row0_col2\" class=\"data row0 col2\" >TAPoR</td>\n",
       "      <td id=\"T_713fc_row0_col3\" class=\"data row0 col3\" >Chartle</td>\n",
       "      <td id=\"T_713fc_row0_col4\" class=\"data row0 col4\" >Create simple charts.\n",
       "</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_713fc_level0_row1\" class=\"row_heading level0 row1\" >917</th>\n",
       "      <td id=\"T_713fc_row1_col0\" class=\"data row1 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/tool-or-service/5jMQ6a\">tool-or-service/5jMQ6a</a></td>\n",
       "      <td id=\"T_713fc_row1_col1\" class=\"data row1 col1\" >tool-or-service</td>\n",
       "      <td id=\"T_713fc_row1_col2\" class=\"data row1 col2\" >SSK Zotero Resources</td>\n",
       "      <td id=\"T_713fc_row1_col3\" class=\"data row1 col3\" >Offline Getting Started and Manual (pdf)</td>\n",
       "      <td id=\"T_713fc_row1_col4\" class=\"data row1 col4\" >Last Update: 14.12.2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_713fc_level0_row2\" class=\"row_heading level0 row2\" >3406</th>\n",
       "      <td id=\"T_713fc_row2_col0\" class=\"data row2 col0\" ><a target=\"_blank\" href=\"https://marketplace-api.sshopencloud.eu/publication/vwL2JV\">publication/vwL2JV</a></td>\n",
       "      <td id=\"T_713fc_row2_col1\" class=\"data row2 col1\" >publication</td>\n",
       "      <td id=\"T_713fc_row2_col2\" class=\"data row2 col2\" >SSK Zotero Resources</td>\n",
       "      <td id=\"T_713fc_row2_col3\" class=\"data row2 col3\" >Multitemporal 3D modelling for cultural heritage emergency during seismic events: Damage assesment of S. Agostino church in Amatrice (RI)</td>\n",
       "      <td id=\"T_713fc_row2_col4\" class=\"data row2 col4\" >6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x138c13b20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend_desc_table_df=df_items_min[['MPUrl', 'category', 'source.label','label', 'description']]\n",
    "clickable_shortdesc_table = rend_desc_table_df.style.format({'MPUrl': utils.make_clickable})\n",
    "clickable_shortdesc_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Activity coverage\n",
    "\n",
    "We want to see an overview of the activity coverage in the Marketplace dataset, in a dedicated section of the notebook, because this is an important field. The dynamic property `activity` is using a the TaDIRAH taxonomy: https://vocabs.dariah.eu/tadirah/ We want to see the number of items per concept used for the property `activity` and the number of items without values in activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the type.code value used as filter value\n",
    "typecode='activity'\n",
    "df_list_of_properties_sources=utils.getAllPropertiesBySources()\n",
    "#filter properties by type.code\n",
    "df_list_of_activity_properties=df_list_of_properties_sources[df_list_of_properties_sources[\"type.code\"]==typecode]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 29395 entries, 0 to 29394\n",
      "Data columns (total 25 columns):\n",
      " #   Column                        Non-Null Count  Dtype \n",
      "---  ------                        --------------  ----- \n",
      " 0   MPUrl                         29395 non-null  object\n",
      " 1   type.code                     29395 non-null  object\n",
      " 2   type.label                    29395 non-null  object\n",
      " 3   type.type                     29395 non-null  object\n",
      " 4   type.groupName                29395 non-null  object\n",
      " 5   type.hidden                   29395 non-null  bool  \n",
      " 6   type.ord                      29395 non-null  int64 \n",
      " 7   type.allowedVocabularies      29395 non-null  object\n",
      " 8   concept.code                  19584 non-null  object\n",
      " 9   concept.vocabulary.code       19584 non-null  object\n",
      " 10  concept.vocabulary.scheme     19584 non-null  object\n",
      " 11  concept.vocabulary.namespace  19584 non-null  object\n",
      " 12  concept.vocabulary.label      19584 non-null  object\n",
      " 13  concept.vocabulary.closed     19584 non-null  object\n",
      " 14  concept.label                 19584 non-null  object\n",
      " 15  concept.notation              19584 non-null  object\n",
      " 16  concept.uri                   19584 non-null  object\n",
      " 17  concept.candidate             19584 non-null  object\n",
      " 18  value                         9811 non-null   object\n",
      " 19  concept.definition            251 non-null    object\n",
      " 20  ts_label                      29395 non-null  object\n",
      " 21  persistentId                  29395 non-null  object\n",
      " 22  label                         29395 non-null  object\n",
      " 23  source.label                  28528 non-null  object\n",
      " 24  category                      29395 non-null  object\n",
      "dtypes: bool(1), int64(1), object(23)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_list_of_properties_sources.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_a_df=df_list_of_activity_properties.groupby(['concept.label', 'source.label']).count()['label'].unstack('source.label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#change values in the next lines to resize the table\n",
    "fig, ax = plt.subplots(figsize=(12, 18))\n",
    "sns.heatmap(tt_a_df, annot=True, linewidths=0.1, fmt='.0f', ax=ax, cbar=None, cmap='Blues', linecolor='gray')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right')\n",
    "ax.set_yticklabels(ax.get_yticklabels(), rotation=0)\n",
    "ax.invert_yaxis()\n",
    "ax.set_xlabel(None)\n",
    "ax.set_ylabel(None)\n",
    "#sns.heatmap(df, linewidths=2, linecolor='yellow')\n",
    "title = '\"'+typecode+'\" by Source provider\\n'\n",
    "plt.title(title, loc='left', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}