02_AnalyzeVisualize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis and Visualization of Sinopia Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%reload_ext lab_black\n",
    "\n",
    "import json\n",
    "\n",
    "import rdflib\n",
    "import requests\n",
    "import kglab\n",
    "import helpers\n",
    "import widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCC BIBFRAME Work-Instance-Item Graph\n",
    "We can use [Sinopia API](https://github.com/ld4p/sinopia_api) to construct a small RDF graph made up of a [BIBFRAME](https://bibframe.org/) Work, Instance, and Item resources and then create a knowledge graph for analysis and visualization of the entities along with their properties.\n",
    "\n",
    "### Step One: Identify Sinopia URLs\n",
    "First we will download the following resources from Sinopia's stage environment and then parse the RDF\n",
    "contained in the resulting JSON.\n",
    "\n",
    "- **BIBFRAME Work**: https://api.stage.sinopia.io/resource/6861fc10-0213-4dfc-b1a0-7df975d235dd\n",
    "- **BIBFRAME Instance**: https://api.stage.sinopia.io/resource/986d673d-f006-44ab-8bf7-b92b279fddfa\n",
    "- **BIBFRAME Item**: https://api.stage.sinopia.io/resource/6767bae3-ee42-4179-8b58-2f3791cb1a6b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sinopia_stage_urls = [\n",
    "    \"https://api.stage.sinopia.io/resource/6861fc10-0213-4dfc-b1a0-7df975d235dd\",\n",
    "    \"https://api.stage.sinopia.io/resource/986d673d-f006-44ab-8bf7-b92b279fddfa\",\n",
    "    \"https://api.stage.sinopia.io/resource/6767bae3-ee42-4179-8b58-2f3791cb1a6b\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step Two: Create RDF Namespaces and Graph\n",
    "\n",
    "Here we create two namespaces for the BIBFRAME and Sinopia vocabularies and bind them to the new Sinopia graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BIBFRAME = rdflib.Namespace(\"http://id.loc.gov/ontologies/bibframe/\")\n",
    "SINOPIA = rdflib.Namespace(\"http://sinopia.io/vocabulary/\")\n",
    "\n",
    "sinopia_pcc_graph = rdflib.Graph()\n",
    "sinopia_pcc_graph.namespace_manager.bind(\"bf\", BIBFRAME)\n",
    "sinopia_pcc_graph.namespace_manager.bind(\"sinopia\", SINOPIA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sinopia_pcc_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step Three: Download and Parse RDF into the Graph\n",
    "\n",
    "We now loop through all of the URLs in the list of Sinopia URLs, query the Sinopoia Stage API endpoint for these resources, and add the JSON-LD to the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting triples in our RDF graph 0\n",
      "Finished ingesting resources, size of graph 127\n"
     ]
    }
   ],
   "source": [
    "print(f\"Starting triples in our RDF graph {len(sinopia_pcc_graph)}\")\n",
    "for sinopia_url in sinopia_stage_urls:\n",
    "    api_result = requests.get(sinopia_url)\n",
    "    if api_result.status_code < 300:\n",
    "        resource_json = api_result.json()\n",
    "        # Need to serialize the JSON to a string for parsing\n",
    "        sinopia_pcc_graph.parse(\n",
    "            data=json.dumps(resource_json[\"data\"]), format=\"json-ld\"\n",
    "        )\n",
    "    else:\n",
    "        print(\n",
    "            f\"ERROR {api_result.status_code} for {sinopia_url}\\nDetail {api_result.text}\"\n",
    "        )\n",
    "print(f\"Finished ingesting resources, size of graph {len(sinopia_pcc_graph)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@prefix bf: <http://id.loc.gov/ontologies/bibframe/> .\n",
      "@prefix ns1: <http://id.loc.gov/ontologies/bflc/> .\n",
      "@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .\n",
      "@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .\n",
      "@prefix sinopia: <http://sinopia.io/vocabulary/> .\n",
      "\n",
      "<https://api.stage.sinopia.io/resource/6767bae3-ee42-4179-8b58-2f3791cb1a6b> a bf:Item ;\n",
      "    bf:enumerationAndChronology [ a bf:Chronology ;\n",
      "            rdfs:label \"2020\"@en ],\n",
      "        [ a bf:Enumeration ;\n",
      "            rdfs:label \"v. 2\"@en ] ;\n",
      "    bf:heldBy <http://id.loc.gov/vocabulary/organizations/dnlm> ;\n",
      "    bf:identifiedBy [ a bf:Barcode ;\n",
      "            rdf:value \"NLM057315341\"@en ] ;\n",
      "    bf:itemOf <https://api.stage.sinopia.io/resource/986d673d-f006-44ab-8bf7-b92b279fddfa> ;\n",
      "    bf:shelfMark [ a bf:ShelfMarkNlm ;\n",
      "            rdf:value \"2022 A-614  v.2\"@en ] ;\n",
      "    bf:sublocation \"GENCOLL\"@en ;\n",
      "    sinopia:hasResourceTemplate \"pcc:bf2:Monograph:Item\" .\n",
      "\n",
      "<https://api.stage.sinopia.io/resource/6861fc10-0213-4dfc-b1a0-7df975d235dd> a bf:Work ;\n",
      "    ns1:relationship [ a ns1:Relationship ;\n",
      "            ns1:relation [ a ns1:Relation ;\n",
      "                    rdfs:label \"translation\"@en ] ] ;\n",
      "    bf:adminMetadata [ a bf:AdminMetadata ;\n",
      "            ns1:catalogerId \"gtruth\" ;\n",
      "            ns1:encodingLevel <http://id.loc.gov/vocabulary/menclvl/f> ;\n",
      "            bf:creationDate \"2022-06-09\" ;\n",
      "            bf:descriptionAuthentication <https://id.loc.gov/vocabulary/marcauthen/pcc> ;\n",
      "            bf:descriptionConventions <https://id.loc.gov/vocabulary/descriptionConventions/rda> ;\n",
      "            bf:descriptionLanguage <https://id.loc.gov/vocabulary/languages/eng> ;\n",
      "            bf:source <http://id.loc.gov/vocabulary/organizations/dnlm> ] ;\n",
      "    bf:classification [ a bf:ClassificationNlm ;\n",
      "            bf:classification \"WB 365\"@en ] ;\n",
      "    bf:content <https://id.loc.gov/vocabulary/contentTypes/txt> ;\n",
      "    bf:contribution [ a ns1:PrimaryContribution ;\n",
      "            bf:role <http://id.loc.gov/vocabulary/relators/edt> ],\n",
      "        [ a bf:Contribution ;\n",
      "            bf:role <http://id.loc.gov/vocabulary/relators/edt> ] ;\n",
      "    bf:hasInstance <https://api.stage.sinopia.io/resource/986d673d-f006-44ab-8bf7-b92b279fddfa> ;\n",
      "    bf:originDate \"2020\"@en ;\n",
      "    bf:subject <http://id.nlm.nih.gov/mesh/D006722>,\n",
      "        <http://id.nlm.nih.gov/mesh/D056705> ;\n",
      "    bf:summary [ a bf:Summary ;\n",
      "            rdfs:label \"Honey typically has a complex chemical and biochemical composition that invariably includes complex sugars, specific proteins, amino acids, phenols, vitamins, and rare minerals. It is reported to be beneficial in the treatment of various diseases, such as those affecting the respiratory, cardiovascular, gastrointestinal, and nervous systems, as well as diabetes mellitus and certain types of cancers; however, there is limited literature describing the use of honey in modern medicine. This book provides evidence-based information on the pharmaceutical potential of honey along with its therapeutic applications and precise mechanisms of action. It discusses in detail the phytochemistry and pharmacological properties of honey, highlighting the economic and culturally significant medicinal uses of honey and comprehensively reviewing the scientific research on the traditional uses, chemical composition, scientific validation, and general pharmacognostical characteristics. Given its scope, it is a valuable tool for researchers and scientists interested in drug discovery and the chemistry and pharmacology of honey.\"@en ] ;\n",
      "    bf:supplementaryContent [ a bf:SupplementaryContent ;\n",
      "            rdfs:label <http://id.loc.gov/vocabulary/msupplcont/bibliography>,\n",
      "                <http://id.loc.gov/vocabulary/msupplcont/index> ] ;\n",
      "    bf:title [ a bf:Title ;\n",
      "            bf:mainTitle \"Therapeutic applications of honey and its phytochemicals\"@en ] ;\n",
      "    sinopia:hasResourceTemplate \"pcc:bf2:Monograph:Work\" .\n",
      "\n",
      "<http://id.loc.gov/authorities/names/n79059023> rdfs:label \"Singapore\"@en .\n",
      "\n",
      "<http://id.loc.gov/authorities/names/nr2005022094> a bf:Agent ;\n",
      "    rdfs:label \"Springer (Firm)\"@en .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/carriers/nc> rdfs:label \"volume\"@en .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/msupplcont/bibliography> rdfs:label \"bibliography\"@en .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/msupplcont/index> rdfs:label \"index\"@en .\n",
      "\n",
      "<http://id.nlm.nih.gov/mesh/D006722> rdfs:label \"Honey\"@en .\n",
      "\n",
      "<http://id.nlm.nih.gov/mesh/D056705> rdfs:label \"Apitherapy\"@en .\n",
      "\n",
      "<https://api.stage.sinopia.io/resource/16061ffa-147a-4b67-9a12-0990964f32d4> rdfs:label \"https://api.stage.sinopia.io/resource/16061ffa-147a-4b67-9a12-0990964f32d4\"@en .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/contentTypes/txt> rdfs:label \"text\"@en .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/issuance/mono> rdfs:label \"multipart monograph\"@en .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/mediaTypes/n> rdfs:label \"unmediated\"@en .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/menclvl/f> rdfs:label \"full\" .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/relators/edt> a bf:Role ;\n",
      "    rdfs:label \"Editor\" .\n",
      "\n",
      "<https://api.stage.sinopia.io/resource/986d673d-f006-44ab-8bf7-b92b279fddfa> a bf:Instance,\n",
      "        bf:Print ;\n",
      "    rdfs:label \"Therapeutic applications of honey and its phytochemicals\"@en ;\n",
      "    bf:adminMetadata [ a bf:AdminMetadata ;\n",
      "            ns1:catalogerId \"gtruth\" ;\n",
      "            ns1:encodingLevel <http://id.loc.gov/vocabulary/menclvl/f> ;\n",
      "            bf:creationDate \"2022-06-09\" ;\n",
      "            bf:descriptionAuthentication <https://id.loc.gov/vocabulary/marcauthen/pcc> ;\n",
      "            bf:descriptionConventions <https://id.loc.gov/vocabulary/descriptionConventions/rda> ;\n",
      "            bf:descriptionLanguage <https://id.loc.gov/vocabulary/languages/eng> ;\n",
      "            bf:source <http://id.loc.gov/vocabulary/organizations/dnlm> ] ;\n",
      "    bf:carrier <http://id.loc.gov/vocabulary/carriers/nc> ;\n",
      "    bf:copyrightDate \"©2020\"@en ;\n",
      "    bf:extent [ a bf:Extent ;\n",
      "            rdfs:label \"2 volumes \"@en ] ;\n",
      "    bf:hasItem <https://api.stage.sinopia.io/resource/16061ffa-147a-4b67-9a12-0990964f32d4> ;\n",
      "    bf:identifiedBy [ a bf:Isbn ;\n",
      "            bf:qualifier \"v.1:  hardbound\"@en ;\n",
      "            rdf:value \"9789811567988  \"@en ],\n",
      "        [ a bf:Isbn ;\n",
      "            bf:qualifier \"v. 2 : hardbound\"@en ;\n",
      "            rdf:value \"9789811573040\"@en ],\n",
      "        [ a bf:Local ;\n",
      "            bf:source [ a bf:Source ;\n",
      "                    rdfs:label \"DNLM\"@en ] ;\n",
      "            rdf:value \"9918334965606676\"@en ],\n",
      "        [ a bf:Local ;\n",
      "            bf:source [ a bf:Source ;\n",
      "                    rdfs:label \"OCLC\"@en ] ;\n",
      "            rdf:value \"1156990712\"@en ] ;\n",
      "    bf:issuance <https://id.loc.gov/vocabulary/issuance/mono> ;\n",
      "    bf:media <https://id.loc.gov/vocabulary/mediaTypes/n> ;\n",
      "    bf:provisionActivity [ a bf:Publication ;\n",
      "            bf:agent <http://id.loc.gov/authorities/names/nr2005022094> ;\n",
      "            bf:date \"[2020]\"@en ;\n",
      "            bf:place <http://id.loc.gov/authorities/names/n79059023> ] ;\n",
      "    bf:provisionActivityStatement \"Singapore : Springer, [2020]\"@en ;\n",
      "    bf:responsibilityStatement \"Muneeb U. Rehman, Sabhiya Majid, editors.\"@en ;\n",
      "    bf:title [ a bf:Title ;\n",
      "            bf:mainTitle \"Therapeutic applications of honey and its phytochemicals\"@en ] ;\n",
      "    sinopia:hasResourceTemplate \"pcc:bf2:Monograph:Instance\" .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/descriptionConventions/rda> rdfs:label \"Resource Description and Access\"@en .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/languages/eng> rdfs:label \"English\"@en .\n",
      "\n",
      "<https://id.loc.gov/vocabulary/marcauthen/pcc> rdfs:label \"Program for Cooperative Cataloging\"@en .\n",
      "\n",
      "<http://id.loc.gov/vocabulary/organizations/dnlm> rdfs:label \"United States, National Library of Medicine (Armed Forces Medical Library)\"@en .\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sinopia_pcc_graph.serialize(format=\"turtle\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step Four: Create a Knowledge Graph\n",
    "\n",
    "Now that we have our BIBFRAME Work, Instance, and Items parsed into our RDF graph, we will then create a knowledge graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sinopia_bf_kg = kglab.KnowledgeGraph(\n",
    "    name=\"Sinopia Stage PCC Knowledge Graph\", import_graph=sinopia_pcc_graph\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step Five: Basic Graph Analysis\n",
    "\n",
    "Our simple PCC BIBFRAME Knowledge Graph provides a number of methods that allow us to analyze the shape and structure RDF through a \"graph\" lens. \n",
    "\n",
    "In graph terminology, a **Node** is the fundamental unit and is represented in RDF:\n",
    "- By a URI or blank-node in the *subject* role \n",
    "- By a URI or blank-node in the *object* role. \n",
    "\n",
    "An **Edge** is a linkage between two nodes. In RDF, the *predicate* is edge connecting the *subject* with the *object*. \n",
    "Because the order in triple matters, RDF is know as a directed graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edges: 127\n",
      "\n",
      "Nodes: 67\n",
      "\n"
     ]
    }
   ],
   "source": [
    "measure = kglab.Measure()\n",
    "measure.measure_graph(sinopia_bf_kg)\n",
    "print(\"Edges: {:,}\\n\".format(measure.get_edge_count()))\n",
    "print(\"Nodes: {:,}\\n\".format(measure.get_node_count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm that the number of edges in our PCC BIBFRAME graph is equal to the number of predicates by creating a list of all of the predicates and then calculate the number of members in that list using the builtin Python `len` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of predicates: 127, \n",
      "         Test for equality: True\n"
     ]
    }
   ],
   "source": [
    "predicates = [p for p in sinopia_pcc_graph.predicates()]\n",
    "print(\n",
    "    f\"\"\"Total number of predicates: {len(predicates)}, \n",
    "         Test for equality: {len(predicates) == measure.get_edge_count()}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "subjects = set([s for s in sinopia_pcc_graph.subjects()])\n",
    "objects = set([o for o in sinopia_pcc_graph.objects() if type(o) != rdflib.Literal])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subjects.union(objects))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of subjects and objects: 67\n",
      "        Test for equality: True\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"\"\"Total number of subjects and objects: {len(subjects.union(objects))}\n",
    "        Test for equality: {len(subjects.union(objects)) == measure.get_node_count()}\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1: Create a Sinopia BIBFRAME Knowledge Graph\n",
    "Using any of the three Sinopia environments (development, stage, or production), find a BIBFRAME Work with a corresponding BIBFRAME Instance and then replicate the steps above to create a Sinopia BIBFRAME Knowledge Graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of the PCC BIBFRAME Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"500px\"\n",
       "            height=\"500px\"\n",
       "            src=\"pcc_bf.fig01.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1273d54e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinopia_pcc_subgraph = kglab.SubgraphTensor(sinopia_bf_kg)\n",
    "pyvis_graph = sinopia_pcc_subgraph.build_pyvis_graph(notebook=True)\n",
    "pyvis_graph.force_atlas_2based()\n",
    "pyvis_graph.show(\"pcc_bf.fig01.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# Sinopia Stage Graph Analysis and Visualizations\n",
    "First we will load the saved knowledge graph and then will do similar analysis on the entire Sinopia stage graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total triples 138,113\n"
     ]
    }
   ],
   "source": [
    "stage_kg = kglab.KnowledgeGraph()\n",
    "stage_kg.load_jsonld(\"data/stage.json\")\n",
    "print(f\"Total triples {len(stage_kg.rdf_graph()):,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let see how many edges and nodes are in all of the resources in Sinopia stage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "stage_measure = kglab.Measure()\n",
    "stage_measure.measure_graph(stage_kg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edges: 138,113\n",
      "\n",
      "Nodes: 40,481\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Edges: {:,}\\n\".format(stage_measure.get_edge_count()))\n",
    "print(\"Nodes: {:,}\\n\".format(stage_measure.get_node_count()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization using Pandas DataFrames\n",
    "\n",
    "With our stage knowledge graph, we can query our graph with SPARQL and return the results as a Panda's DataFrame. DataFrames offer a number of built-in graphs that can be useful for summarizing or visualizing information about the RDF.\n",
    "\n",
    "To start, we'll query the graph to get all of the RDF classes of entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_df = stage_kg.query_as_df(\n",
    "    \"\"\"\n",
    "SELECT ?class\n",
    "WHERE {\n",
    "  ?url rdf:type ?class .\n",
    "}\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the Pandas `classes_df` dataframe, we can see how many classes are in Sinopia stage environment by using the `.info()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27592 entries, 0 to 27591\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   class   27592 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 215.7+ KB\n"
     ]
    }
   ],
   "source": [
    "classes_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `.head()` method, display the first five rows in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      class\n",
       "0  sinopia:PropertyTemplate\n",
       "1  sinopia:PropertyTemplate\n",
       "2  sinopia:PropertyTemplate\n",
       "3  sinopia:PropertyTemplate\n",
       "4  sinopia:PropertyTemplate"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can display a random sample of RDF classes in `classes_df` with the `sample()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21012</th>\n",
       "      <td>sinopia:ResourcePropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13559</th>\n",
       "      <td>bf:Instance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14352</th>\n",
       "      <td>bf:Title</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3514</th>\n",
       "      <td>sinopia:PropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20964</th>\n",
       "      <td>sinopia:ResourcePropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20361</th>\n",
       "      <td>sinopia:ResourcePropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5893</th>\n",
       "      <td>sinopia:ResourceTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7548</th>\n",
       "      <td>bf:Local</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21267</th>\n",
       "      <td>sinopia:ResourcePropertyTemplate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4641</th>\n",
       "      <td>bf:Work</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  class\n",
       "21012  sinopia:ResourcePropertyTemplate\n",
       "13559                       bf:Instance\n",
       "14352                          bf:Title\n",
       "3514           sinopia:PropertyTemplate\n",
       "20964  sinopia:ResourcePropertyTemplate\n",
       "20361  sinopia:ResourcePropertyTemplate\n",
       "5893           sinopia:ResourceTemplate\n",
       "7548                           bf:Local\n",
       "21267  sinopia:ResourcePropertyTemplate\n",
       "4641                            bf:Work"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate a count of each RDF classes use the `.value_counts()` method and display the top 36 RDF classes with the slice notation `[0:35]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sinopia:PropertyTemplate                                           4071\n",
       "bf:AdminMetadata                                                   1679\n",
       "bf:Title                                                           1473\n",
       "sinopia:ResourcePropertyTemplate                                   1439\n",
       "bf:Note                                                            1188\n",
       "sinopia:LookupPropertyTemplate                                     1129\n",
       "bf:Agent                                                           1077\n",
       "bf:Work                                                             978\n",
       "bf:Person                                                           821\n",
       "bf:Contribution                                                     804\n",
       "<https://w3id.org/arm/core/ontology/0.1/Citation>                   738\n",
       "sinopia:ResourceTemplate                                            691\n",
       "bf:Instance                                                         660\n",
       "bf:Publication                                                      621\n",
       "bf:Status                                                           618\n",
       "bf:Extent                                                           602\n",
       "bf:Local                                                            531\n",
       "bf:Source                                                           529\n",
       "bf:GenerationProcess                                                508\n",
       "bf:VariantTitle                                                     466\n",
       "bf:Isbn                                                             409\n",
       "bflc:PrimaryContribution                                            392\n",
       "bf:Text                                                             380\n",
       "bf:ClassificationLcc                                                335\n",
       "bf:Item                                                             296\n",
       "bf:Print                                                            228\n",
       "sinopia:Uri                                                         211\n",
       "mads:Authority                                                      203\n",
       "<http://performedmusicontology.org/ontology/MediumComponent>        202\n",
       "bf:Place                                                            193\n",
       "bf:Script                                                           173\n",
       "bflc:Relationship                                                   160\n",
       "bf:Summary                                                          151\n",
       "bf:Identifier                                                       145\n",
       "<https://w3id.org/arm/custodial_history/ontology/0.1/Ownership>     136\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_df[\"class\"].value_counts()[0:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To generate a bar chart of these values, we call the `.plot` method on the previous `value_counts`  and \n",
    "display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes_df[\"class\"].value_counts()[0:100].plot(kind=\"bar\")"
   ]
  },
  {
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}