F4_Doc.ipynb

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    "# F4 Group - Data Science Project\n",
    "This documentation regards the final project developed for the \"Data Science\" course (second module of the \"Computational Management of Data\" course, a.a. 2021/2022) within the Digital Humanities and Digital Knowledge Master's Degree at Alma Mater Studiorum - University of Bologna.\n",
    "\n",
    "## The project\n",
    "The goal of the project is to <b>develop a software</b> able to:\n",
    "<ul>\n",
    "<li><b>Process data</b> stored in <b>different formats</b> (such as CSV and JSON files)</li>\n",
    "<li><b>Upload</b> the processed data in a <b>database</b> (relational or triplestore)</li>\n",
    "<li><b>Query</b> the databases <b>simultaneously</b> using predefined operations</li>\n",
    "</ul>\n",
    "\n",
    "### The division of the workflow\n",
    "The structure of the software can be seen in the following image:\n",
    "# ![<Workflow structure>](imgs/workflow.png)\n",
    "Initial creation of the class structure representing the data model in python was together as a group and later,the workflow was divided as follows:\n",
    "<ul>\n",
    "<li><b>Anita Liviabella</b> developed the RelationalDataProcessor and RelationalProcessor as well as the statistical analysis section</li>\n",
    "<li><b>Giusy Grieco</b> developed the TriplestoreDataProcessor and TriplestoreProcessor as well as half of the queries of the TriplestoreQueryProcessor</li>\n",
    "<li><b>Manu Srivastava</b> developed half of the queries of the TriplestoreQueryProcessor and the main part of the GenericQueryProcessor (main attributes and half of the methods)</li>\n",
    "<li><b>Orsola Maria Borrini</b> developed all the queries of the RelationalQueryProcessor and half of the queries of the GenericQueryProcessor</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The software\n",
    "The software was represented through a UML data model:\n",
    "# ![<Software UML>](imgs/uml2.png)\n",
    "As we can see, the expected results of the GenericQueryProcessor's queries are Python objects. The hierarchy of their classes and internal structures was also described through a UML data model:\n",
    "# ![<Objects UML>](imgs/uml.png)\n",
    "These Python classes have been implemented in the `dataModelClasses.py` file.\n",
    "\n",
    "### Implementation of Python classes\n",
    "\n",
    "The Python classes were defined in the `dataModelClasses.py` file. As it can be seen from the UML data model, all of the classes are dependant upon some other class <i>except for</i> the `IdentifiableEntity` class. For this reason, this was the first class to be implemented, while the order of the following implementations follows a \"hierarchical\" order: first, the four classes directly depending on the `IdentifiableEntity` class:\n",
    "<ul>\n",
    "<li>Organisation class</li>\n",
    "<li>Venue class</li>\n",
    "<li>Publication class</li>\n",
    "<li>Person class</li>\n",
    "</ul>\n",
    "\n",
    "# ![<First layer objects UML>](imgs/UMLpy1.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
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   "source": [
    "class IdentifiableEntity(object):\n",
    "    def __init__(self, ids):\n",
    "        self.id = set()\n",
    "        for item in ids:\n",
    "            self.id.add(item)\n",
    "\n",
    "    def getIds(self):\n",
    "        return list(self.id)\n",
    "\n",
    "class Person(IdentifiableEntity):\n",
    "    def __init__(self, ids, givenName, familyName):\n",
    "        self.givenName = givenName\n",
    "        self.familyName = familyName\n",
    "        super().__init__(ids)\n",
    "    \n",
    "    def getGivenName(self):\n",
    "        return self.givenName\n",
    "    \n",
    "    def getFamilyName(self):\n",
    "        return self.familyName\n",
    "\n",
    "class Organisation(IdentifiableEntity):\n",
    "    def __init__(self, ids, name):\n",
    "        self.name = name\n",
    "        super().__init__(ids)\n",
    "    \n",
    "    def getName(self):\n",
    "        return self.name\n",
    "\n",
    "class Venue(IdentifiableEntity):\n",
    "    def __init__(self, ids, title, publisher):\n",
    "        self.title = title\n",
    "        self.publisher = publisher\n",
    "        super().__init__(ids)\n",
    "    \n",
    "    def getTitle(self):\n",
    "        return self.title\n",
    "    \n",
    "    def getPublisher(self):\n",
    "        return self.publisher\n",
    "\n",
    "class Publication(IdentifiableEntity):\n",
    "    def __init__(self, ids, publicationYear, title, authors, publicationVenue, pcites):\n",
    "        self.publicationYear = publicationYear\n",
    "        self.title = title\n",
    "        self.author = set()\n",
    "        for aut in authors:\n",
    "            self.author.add(aut)\n",
    "        self.publicationVenue = publicationVenue\n",
    "        self.cites = set()\n",
    "        for cit in pcites:\n",
    "            self.cites.add(cit)\n",
    "        super().__init__(ids)\n",
    "    \n",
    "    def getPublicationYear(self):\n",
    "        return self.publicationYear\n",
    "    \n",
    "    def getTitle(self):\n",
    "        return self.title\n",
    "    \n",
    "    def getCitedPublications(self):\n",
    "        return list(self.cites)\n",
    "    \n",
    "    def getPublicationVenue(self):\n",
    "        return self.publicationVenue\n",
    "    \n",
    "    def getAuthors(self):\n",
    "        return self.author"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Then, the classes that depend on these classes just defined (specifically, they depend on the classes `Publication` and `Venue`)\n",
    "\n",
    "<ul>\n",
    "<li>Publication class -> from this class depend other three classes: JournalArticle, BookChapter and ProceedingsPaper</li>\n",
    "<li>Venue class -> from this class depend other three classes: Journal, Book and Proceedings</li>\n",
    "</ul>\n",
    "\n",
    "# ![<Second layer objects UML>](imgs/UMLpy2.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "class JournalArticle(Publication):\n",
    "    def __init__(self, ids, publicationYear, title, authors, publicationVenue, pcites, issue, volume):\n",
    "        self.issue = issue\n",
    "        self.volume = volume\n",
    "        super().__init__(ids, publicationYear, title, authors, publicationVenue, pcites)\n",
    "    \n",
    "    def getIssue(self):\n",
    "        return self.issue\n",
    "    \n",
    "    def getVolume(self):\n",
    "        return self.volume\n",
    "\n",
    "class BookChapter(Publication):\n",
    "    def __init__(self, ids, publicationYear, title, authors, publicationVenue, pcites, chapterNumber):\n",
    "        self.chapterNumber = chapterNumber\n",
    "        super().__init__(ids, publicationYear, title, authors, publicationVenue, pcites)\n",
    "    \n",
    "    def getChapterNumber(self):\n",
    "        return self.chapterNumber\n",
    "\n",
    "class ProceedingsPaper(Publication):\n",
    "    pass\n",
    "\n",
    "class Journal(Venue):\n",
    "    pass\n",
    "\n",
    "class Book(Venue):\n",
    "    pass\n",
    "\n",
    "class Proceedings(Venue):\n",
    "    def __init__(self, ids, title, publisher, event):\n",
    "        self.event = event\n",
    "        super().__init__(ids, title, publisher)\n",
    "    \n",
    "    def getEvent(self):\n",
    "        return self.event"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, it is worth pointing out that we also defined in the same file `dataModelClasses.py` the `QueryProcessor` class, that will then need to be imported in the files `extraGraphClasses.py` and `extraRelationalClasses.py` in order to correctly define and implement the classes `RelationalQueryProcessor` and `TriplestoreQueryProcessor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ===== QUERYPROCESSOR DEFINITION SUPERCLASS OF THE RELATIONALQUERYPROCESSOR AND THE TRIPLESTOREQUERYPROCESSOR\n",
    "class QueryProcessor(object):\n",
    "    def __init__(self):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tests for this part of the software can be found in the folder `testFiles > TEST_dataModelClasses.py`, in the repository of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Relational part\n",
    "### RelationalDataProcessor and RelationalProcessor\n",
    "\n",
    "The definition of these two classes can be found in the `extraRelationalClasses.py` file.\n",
    "\n",
    "The development of this section of the software started from the definition of the `RelationalProcessor` class, which is the `RelationalDataProcessor`'s superclass, and therefore must be defined before the last one.\n",
    "\n",
    "# ![Relational UML part 1>](imgs/UMLrel1.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from logging import raiseExceptions\n",
    "from re import X\n",
    "import sqlite3 as sql3\n",
    "from unittest import result\n",
    "from wsgiref.validate import IteratorWrapper\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "class RelationalProcessor(object):\n",
    "    def __init__(self): #constructor\n",
    "        self.dbPath = \"\" #path of the database\n",
    "    \n",
    "    def getDbPath(self): \n",
    "        return self.dbPath #method that returns the path of the database.\n",
    "    \n",
    "    def setDbPath(self,path):\n",
    "        if type(path)==str:\n",
    "            self.dbPath = path   #set a new path for the database\n",
    "            return True\n",
    "        else:\n",
    "            return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've defined the class within its constructur and methods: the variable `dbPath` contains the path of the database (a string), initially set as an empty string, that will be updated with the method `setDbPath` . The `getDbPath` returns the path of the database (returns a string), the `setDbPath` enables to set a new path for the database to handle (returns a boolean)\n",
    "Then, I was able to define the `RelationalDataProcessor` subclass within its contructur and `super().__init__()` function, which returns a temporary object of the parent class. This allows us to call the methods from inside the parent class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalDataProcessor(RelationalProcessor):\n",
    "    def __init__(self):\n",
    "        super().__init__() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I've moved to the `uploadData` method which enables to upload the collection of data specified in the input file path (either in CSV or JSON) into the relational database.\n",
    "First of all, the method take as parameters `self` and `filepath`, then I check if the input filepath it's not a string, in case it's a string I am able to proceed. \n",
    "Moving forward, I specified the format in which the data are stored, if it's a CSV file or a JSON file, in order to do this, I used the `endswith` method for checking whether the `filepah` string ends with `\".csv\"` or `\".json\"`. For an appropriate description of these file section, go to the Data Analysis section of this Notebook."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then I proceed according to the file format which the sofware will receive as an input file.\n",
    "Our purpuse in the `uploadData` is to build a relational database, which is based on the relational model of data, relation in this context is a synonym of \"table\", the idea is that a relational database uses tables with columns and rows, where each row is idenfified by a unique key (an identifier, even implicit). Each table, in principle, represnt a type of entity (e.g publication or venue), where rows idenfity instances of that type of entity (e.g the article  with DOI “10.1162/qss_a_00023” or the venue “Quantitative Science Studies”) and columns represents values that can be attributed to that instance. \n",
    "Moving to the process of building our relational database:\n",
    "\n",
    "#### If the filepath is a CSV file:\n",
    "First of all, in order to translate our UML Data Model into a relational database, we need to implement it as a series of tables related to each other. The professor's suggestion was to:\n",
    "\n",
    "Create one table for each concrete class (the classes that do not have any subclass), those are:\n",
    "\n",
    "From `Publication` superclass:\n",
    "<ul>\n",
    "<li>JournalArticle</li>\n",
    "<li>BookChapter</li>\n",
    "<li>ProceedingsPaper</li>\n",
    "</ul>\n",
    "\n",
    "From `Venue` superclass:\n",
    "<ul>\n",
    "<li>Journal</li>\n",
    "<li>Book</li>\n",
    "<li>Proceedings</li>\n",
    "</ul>\n",
    "\n",
    "I've used pandas library in order to implement tables as dataframes. For each of these tables/classes, I consider them having 1 column for each attribute and relation.\n",
    "In our case we are talking about the classes: `JournalArticle, BookChapter,ProceedingsPaper, Journal, Book, Proceedings, Organisation, Person`\n",
    "\n",
    "First I put the CSV file into the python enviroment thanks to the `read_csv` funcion provided by pandas, I specify as parameters the `filepath` and `na_filter=False`, the last parameter is able to convert all the NaN values to blank spaces. I store all inside the `df_publications` variable, which can be printed as a dataframe.\n",
    "Then, I am building my empty dataframes, specifying all the columns and the appropriate dtype. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For what concerns the `JournalArticle` dataframe, for example, I am specifying as columns all the attributes and relations from the subclass and also the others that it inherits from the superclass.\n",
    "I am doing the same for all the other dataframes. \n",
    "Then I am going to fill all the columns with data that I am able to extract from the `df_publication` dataframe.\n",
    "I decided not to use a for loop because I read that it might be computationally more expensive than other procedures, so I decided to fill each column in a different way.\n",
    "For instance, let's see again the `journal_article_df`:\n",
    "I'll take singularly each single column of my dataframe (`issue, volume, publication_year, title, publication_venue, id, pub_type`) in order to fill them with data:\n",
    "I take from df_publications the columns `\"type\"` which have values equal to `\"journal-article\"`.\n",
    "I specify the column that I am looking for according to that type of publication.\n",
    "I am using the astype method, in order to cast a pandas object to a specified `dtype`, furthermore, the` astype()` function also provides the capability to convert any suitable existing column to categorical type.\n",
    "I am also replacing with the replace method all the `NaN` values with empty strings.\n",
    "I am doing this for all the other dataframes. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After creating such dataframes (one for each table), we can then store them in the relational database, using the `pandas.DataFrame.to_sql` method.\n",
    "The package that we should use is `sqlite3`, a classic Python package that allows you to get in contact with the database for running querying, creating tables, managing data etc...\n",
    "The function `connect` allows us to connect with an existing database or to create a new one, it works specifying in input a path to a file existing in your file system, that file is where you will store the tables of your database and the data related.\n",
    "Opening the connection with the database will automatically create one if it does not exist yet.\n",
    "\n",
    "I am opening the connection to my relational database, using the funcion connect, if the file is not existing it will create the `db.file` for us.\n",
    "In the end I am committing all the operations. \n",
    "\n",
    "The tables that I've obtained are named:\n",
    "- JournalArticle\n",
    "- BookChapter\n",
    "- ProceedingsPaper\n",
    "- JournalTable\n",
    "- BookTable\n",
    "- ProceedingsTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If the filepath is a JSON file\n",
    "I am using the function `open` and the method `load` in order to handle the json file. \n",
    "The `load` method is used to read JSON encoded data from a file and convert it into Python dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I obtained a dictionary stored inside the jsondata variable, through which I am going to build the dataframes. \n",
    "In summary, I am taking each single key of the dictionary, those are:\n",
    "- authors\n",
    "- venues_id\n",
    "- references \n",
    "- publishers\n",
    "\n",
    "Taking `authors_df` as an example, I am building the empty dataframe using each authors key as a column and specifying the corrisponding `dtype`.\n",
    "Then I am creating empty lists for each authors key.\n",
    "In the next step I am specifying the authors variable, in which I will store the values of the key \"authors\" from the `jsondata` dictionary.\n",
    "Then I am using two for loops in order to append values to the empty lists that we've created:\n",
    "\n",
    "The external for loop is ciclying all the author's keys, the most external keys are doi_authors, in which inside are present more keys, those are: `family`, `given` and `orcid`.\n",
    "So, for each `doi_authors` and for each value of the `doi_authors` I am appending all the values in the appropriate column (family, given, orcid).\n",
    "\n",
    "In the end I am taking the dataframe columns and store the elements of the lists inside them.\n",
    "\n",
    "I am proceding in the same way following the different structure of each key of the `jsondata` dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the end I am again opening the database connection, with the `sql3.connect` method and storing all the tables with the `to_sql` method and then committing all the transactions with the `commit` method. \n",
    "\n",
    "The names of the tables that I've obtained are:\n",
    "- AuthorsTable\n",
    "- VenuesIdTable\n",
    "- ReferencesTable\n",
    "- PublishersTable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalDataProcessor(RelationalProcessor):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "    \n",
    "    def uploadData(self,filepath):\n",
    "        if type(filepath) != str:   # Is it a string?\n",
    "            return False\n",
    "        else:   # Check the file format\n",
    "            # =============== CSV UPLOAD DATA ===============\n",
    "            if filepath.endswith(\".csv\"):\n",
    "                df_publications = pd.read_csv(filepath,na_filter=False)\n",
    "\n",
    "                # =============== PUBLICATION DATAFRAMES ===============\n",
    "\n",
    "                journal_article_df = pd.DataFrame({\n",
    "                    \"issue\": pd.Series(dtype=\"str\"),\n",
    "                    \"volume\": pd.Series(dtype=\"str\"),\n",
    "                    \"publication_year\": pd.Series(dtype=\"int\"),\n",
    "                    \"title\": pd.Series(dtype=\"str\"),\n",
    "                    \"publication_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"id\": pd.Series(dtype=\"str\"),\n",
    "                    \"pub_type\": pd.Series(dtype=\"str\")\n",
    "                })\n",
    "\n",
    "                book_chapter_df = pd.DataFrame({\n",
    "                    \"chapter_number\": pd.Series(dtype=\"str\"),\n",
    "                    \"publication_year\": pd.Series(dtype=\"int\"),\n",
    "                    \"title\": pd.Series(dtype=\"str\"),\n",
    "                    \"publication_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"id\": pd.Series(dtype=\"str\"),\n",
    "                    \"pub_type\": pd.Series(dtype=\"str\")\n",
    "                })\n",
    "\n",
    "                proceeding_paper_df = pd.DataFrame({\n",
    "                    \"publication_year\": pd.Series(dtype=\"int\"),\n",
    "                    \"title\": pd.Series(dtype=\"str\"),\n",
    "                    \"publication_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"id\": pd.Series(dtype=\"str\"),\n",
    "                    \"pub_type\": pd.Series(dtype=\"str\")\n",
    "                })\n",
    "\n",
    "                journal_article_df['issue'] = df_publications[df_publications['type']== \"journal-article\"]['issue'].astype('str')\n",
    "                journal_article_df['volume'] = df_publications[df_publications['type']== \"journal-article\"]['volume'].astype('str')\n",
    "                journal_article_df['publication_year'] = df_publications[df_publications['type']== \"journal-article\"]['publication_year'].astype('int')\n",
    "                journal_article_df['title'] = df_publications[df_publications['type']== \"journal-article\"]['title'].astype('str')\n",
    "                journal_article_df['publication_venue'] = df_publications[df_publications['type']== \"journal-article\"]['publication_venue'].astype('str')\n",
    "                journal_article_df['id'] = df_publications[df_publications['type']== \"journal-article\"]['id'].astype('str')\n",
    "                journal_article_df['pub_type'] = df_publications[df_publications['type']== \"journal-article\"]['type'].astype('str')\n",
    "                journal_article_df.replace(to_replace=\"nan\",value=\"\")\n",
    "\n",
    "                book_chapter_df['publication_year'] = df_publications[df_publications['type']== \"book-chapter\"]['publication_year'].astype('int')\n",
    "                book_chapter_df['title'] = df_publications[df_publications['type']== \"book-chapter\"]['title'].astype('str')\n",
    "                book_chapter_df['chapter_number'] = df_publications[df_publications['type']== \"book-chapter\"]['chapter'].astype('str')\n",
    "                book_chapter_df['publication_venue'] = df_publications[df_publications['type']== \"book-chapter\"]['publication_venue'].astype('str')\n",
    "                book_chapter_df['id'] = df_publications[df_publications['type']== \"book-chapter\"]['id'].astype('str')\n",
    "                book_chapter_df['pub_type'] = df_publications[df_publications['type']== \"book-chapter\"]['type'].astype('str')\n",
    "                book_chapter_df.replace(to_replace=\"nan\",value=\"\")\n",
    "                \n",
    "\n",
    "                proceeding_paper_df['publication_year'] = df_publications[df_publications['type']== \"proceedings-paper\"]['publication_year'].astype('int')\n",
    "                proceeding_paper_df['title'] = df_publications[df_publications['type']== \"proceedings-paper\"]['title'].astype('str')\n",
    "                proceeding_paper_df['publication_venue'] = df_publications[df_publications['type']== \"proceedings-paper\"]['publication_venue'].astype('str')\n",
    "                proceeding_paper_df['id'] = df_publications[df_publications['type'] == \"proceedings-paper\"]['id'].astype('str')\n",
    "                proceeding_paper_df['pub_type'] = df_publications[df_publications['type']== \"proceedings-paper\"]['type'].astype('str')\n",
    "                proceeding_paper_df.replace(to_replace=\"nan\",value=\"\")\n",
    "\n",
    "                # =============== VENUES DATAFRAMES ===============\n",
    "\n",
    "                journal_df = pd.DataFrame({\n",
    "                    \"name_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"publisher\": pd.Series(dtype=\"str\"),\n",
    "                    \"id_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"venue_type\": pd.Series(dtype=\"str\")\n",
    "                })\n",
    "\n",
    "                book_df = pd.DataFrame({\n",
    "                    \"name_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"publisher\": pd.Series(dtype=\"str\"),\n",
    "                    \"id_venue\": pd.Series(dtype=\"str\"),\n",
    "                    \"venue_type\": pd.Series(dtype=\"str\")\n",
    "                })\n",
    "\n",
    "                proceedings_df = pd.DataFrame({\"event\": pd.Series(dtype=\"str\"),\n",
    "                                               \"name_venue\": pd.Series(dtype=\"str\"),\n",
    "                                               \"publisher\": pd.Series(dtype=\"str\"),\n",
    "                                               \"id_venue\": pd.Series(dtype=\"str\"),\n",
    "                                                \"venue_type\": pd.Series(dtype=\"str\")\n",
    "\n",
    "                })\n",
    "\n",
    "                journal_df['name_venue'] = df_publications[df_publications['venue_type'] == \"journal\"]['publication_venue'].astype('str')\n",
    "                journal_df['publisher'] = df_publications[df_publications['venue_type'] == \"journal\"]['publisher'].astype('str')\n",
    "                journal_df['id_venue'] = df_publications[df_publications['venue_type'] == \"journal\"]['id'].astype('str')\n",
    "                journal_df['venue_type'] = df_publications[df_publications['venue_type'] == \"journal\"]['venue_type'].astype('str')\n",
    "                journal_df.replace(to_replace=\"nan\",value=\"\")\n",
    "\n",
    "                book_df['name_venue'] = df_publications[df_publications['venue_type'] == \"book\"]['publication_venue'].astype('str')\n",
    "                book_df['publisher'] = df_publications[df_publications['venue_type'] == \"book\"]['publisher'].astype('str')\n",
    "                book_df['id_venue'] = df_publications[df_publications['venue_type'] == \"book\"]['id'].astype('str')\n",
    "                book_df['venue_type'] = df_publications[df_publications['venue_type'] == \"book\"]['venue_type'].astype('str')\n",
    "                book_df.replace(to_replace=\"nan\",value=\"\")\n",
    "\n",
    "                proceedings_df['event'] = df_publications[df_publications['venue_type'] == \"proceedings\"]['event'].astype('str')\n",
    "                proceedings_df['name_venue'] = df_publications[df_publications['venue_type'] == \"proceedings\"]['publication_venue'].astype('str')\n",
    "                proceedings_df['publisher'] = df_publications[df_publications['venue_type'] == \"proceedings\"]['publisher'].astype('str')\n",
    "                proceedings_df['id_venue'] = df_publications[df_publications['venue_type'] == \"proceedings\"]['id'].astype('str')\n",
    "                proceedings_df['venue_type'] = df_publications[df_publications['venue_type'] == \"proceedings\"]['venue_type'].astype('str')\n",
    "                proceedings_df.replace(to_replace=\"nan\",value=\"\")\n",
    "\n",
    "                # =============== DATABASE CONNECTION ===============\n",
    "\n",
    "                with sql3.connect(self.dbPath) as rdb:\n",
    "                    journal_article_df.to_sql(\"JournalArticleTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    book_chapter_df.to_sql(\"BookChapterTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    proceeding_paper_df.to_sql(\"ProceedingsPaperTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    journal_df.to_sql(\"JournalTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    book_df.to_sql(\"BookTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    proceedings_df.to_sql(\"ProceedingsTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    rdb.commit()\n",
    "            \n",
    "            # =============== JSON UPLOAD DATA ===============\n",
    "\n",
    "            elif filepath.endswith(\".json\"):\n",
    "                with open(filepath, \"r\", encoding=\"utf-8\") as file:\n",
    "                    jsondata = json.load(file)\n",
    "                    \n",
    "                    # =============== AUTHORS DATAFRAME ===============\n",
    "                    authors_df = pd.DataFrame({\n",
    "                        \"doi_authors\": pd.Series(dtype=\"str\"),\n",
    "                        \"family\": pd.Series(dtype=\"str\"),\n",
    "                        \"given\": pd.Series(dtype=\"str\"),\n",
    "                        \"orcid\": pd.Series(dtype=\"str\")\n",
    "                    })\n",
    "\n",
    "                    family = []\n",
    "                    given = []\n",
    "                    orcid = []\n",
    "                    doi_authors = []\n",
    "\n",
    "                    authors = jsondata['authors']\n",
    "                    for key in authors:\n",
    "                        for value in authors[key]:\n",
    "                            doi_authors.append(key)\n",
    "                            family.append(value['family'])\n",
    "                            given.append(value['given'])\n",
    "                            orcid.append(value['orcid'])\n",
    "\n",
    "                    authors_df['doi_authors'] = doi_authors\n",
    "                    authors_df['family'] = family\n",
    "                    authors_df['given'] = given\n",
    "                    authors_df['orcid'] = orcid\n",
    "\n",
    "                    # =============== VENUES DATAFRAME ===============\n",
    "\n",
    "                    venues_id_df = pd.DataFrame({\n",
    "                        \"doi_venues_id\": pd.Series(dtype=\"str\"),\n",
    "                        \"issn_isbn\": pd.Series(dtype=\"str\"),\n",
    "                    })\n",
    "\n",
    "                    doi_venues_id = []\n",
    "                    issn_isbn = []\n",
    "\n",
    "                    venues_id = jsondata[\"venues_id\"]\n",
    "                    for key in venues_id:\n",
    "                        for value in venues_id[key]:\n",
    "                            doi_venues_id.append(key)\n",
    "                            issn_isbn.append(value)\n",
    "\n",
    "                    venues_id_df[\"doi_venues_id\"] = doi_venues_id\n",
    "                    venues_id_df[\"issn_isbn\"] = pd.Series(issn_isbn)\n",
    "\n",
    "                    # =============== REFERENCES DATAFRAME ===============\n",
    "\n",
    "                    references_df = pd.DataFrame({\n",
    "                        \"og_doi\": pd.Series(dtype=\"str\"),\n",
    "                        \"ref_doi\": pd.Series(dtype=\"str\"),\n",
    "                    })\n",
    "\n",
    "                    og_doi = []\n",
    "                    ref_doi = []\n",
    "\n",
    "                    references = jsondata[\"references\"]\n",
    "                    for key in references:\n",
    "                        for value in references[key]:\n",
    "                            og_doi.append(key)\n",
    "                            ref_doi.append(value)\n",
    "\n",
    "                    references_df[\"og_doi\"] = pd.Series(og_doi)\n",
    "                    references_df[\"ref_doi\"] = pd.Series(ref_doi)\n",
    "\n",
    "                    # =============== PUBLISHER DATAFRAME ===============\n",
    "\n",
    "                    publishers_df = pd.DataFrame({\n",
    "                        \"crossref\": pd.Series(dtype=\"str\"),\n",
    "                        \"id_crossref\": pd.Series(dtype=\"str\"),\n",
    "                        \"name_pub\": pd.Series(dtype=\"str\")\n",
    "                    })\n",
    "\n",
    "                    crossref = []\n",
    "                    id_crossref = []\n",
    "                    name_pub = []\n",
    "\n",
    "                    publishers = jsondata[\"publishers\"]\n",
    "                    for key in publishers:\n",
    "                        crossref.append(key)\n",
    "                        id_crossref.append(publishers[key][\"id\"])\n",
    "                        name_pub.append(publishers[key][\"name\"])\n",
    "\n",
    "                    publishers_df[\"crossref\"] = pd.Series(crossref)\n",
    "                    publishers_df[\"id_crossref\"] = pd.Series(id_crossref)\n",
    "                    publishers_df[\"name_pub\"] = pd.Series(name_pub)\n",
    "                \n",
    "                # =============== DATABASE CONNECTION ===============\n",
    "\n",
    "                with sql3.connect(self.dbPath) as rdb:\n",
    "                    authors_df.to_sql(\"AuthorsTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    venues_id_df.to_sql(\"VenuesIdTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    references_df.to_sql(\"ReferencesTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    publishers_df.to_sql(\"PublishersTable\", rdb, if_exists=\"append\", index=False)\n",
    "                    rdb.commit()\n",
    "            return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RelationalQueryProcessor\n",
    "\n",
    "The structure of the `RelationalQueryProcessor` is described in the UML data model in the following way:\n",
    "# ![Relational UML part 2>](imgs/UMLrel2.png)\n",
    "\n",
    "As it can be seen, it doesn't have its own attributes or relations, but inherits everything from the two super-classes (basically only `RelationalProcessor`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalQueryProcessor(QueryProcessor,RelationalProcessor):\n",
    "    def __init__(self):\n",
    "        super().__init__()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The SQL queries\n",
    "\n",
    "For what concerns the queries, we have decided to implement them in such a way that the resulting pandas dataframes would contain <b>all the information necessary for creating the corresponding object</b> in the next step (the `GenericQueryProcessor`).\n",
    "We will analyse this logic for the first query, `getPublicationsPublishedInYear`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `getPublicationsPublishedInYear` Query\n",
    "\n",
    "This query takes in input an integer (representing a certain year), and returns a DataFrame.\n",
    "As in the `GenericQueryProcessor` this same query requires, as output, a list of `Publication` objects, we need to gather all the information needed to build such objects here.\n",
    "This was done by referring to the `dataModelClasses.py` file, where the Python classes are defined: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Publication(IdentifiableEntity):\n",
    "    def __init__(self, ids, publicationYear, title, authors, publicationVenue, pcites):\n",
    "        self.publicationYear = publicationYear\n",
    "        self.title = title\n",
    "        self.author = set()\n",
    "        for aut in authors:\n",
    "            self.author.add(aut)\n",
    "        self.publicationVenue = publicationVenue\n",
    "        self.cites = set()\n",
    "        for cit in pcites:\n",
    "            self.cites.add(cit)\n",
    "        super().__init__(ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that a `Publication` object requires:\n",
    "<ul>\n",
    "<li>A list of ids (strings, they will be dois)</li>\n",
    "<li>A publication year (integer)</li>\n",
    "<li>A title (string)</li>\n",
    "<li>A list of authors (Person objects, at least one)</li>\n",
    "- As the authors are objects of class Person, we need to create such objects and therefore need the necessary information: a list of ids (strings, they will be orcids), a familyName (string) and a givenName (string)\n",
    "<li>A publication venue (a Venue object, at most one)</li>\n",
    "    - As the publication venue is an object of class Venue, we need to create such object and therefore need the necessary information: a list of ids (strings, they will be issn and/or isbn), a title (string), and a publisher (as the publisher is an Organisation object, we need its information: a ist of ids (strings) and a name (string))\n",
    "<li>A list of cited publications (a list of Publication objects)</li>\n",
    "    - For what concerns the cited publications, in the RelationalQueryProcessor we will select them only by their id (doi)\n",
    "</ul>\n",
    "\n",
    "To actually implement the query we then need to:\n",
    "1. Open a connection to the database\n",
    "2. Select, from the dataframes contained in the database, all the columns that contain the aforementioned informations\n",
    "    - For what concerns \"generic\" queries such as those involving a Publication or a Venue we will need to make a UNION of queries, each involving one specific Publication or Venue table\n",
    "        - In our case, we need to make a UNION between the same query applied on JournalArticleTable, BookChapterTable and ProceedingsPaperTable\n",
    "        - In the case of a Venue, we need to make a UNION between the same query applied on JournalTable, BookTable and ProceedingsTable\n",
    "3. When we have an input parameter that is being decided by the user, we need to consider it as a variable: inside the query we will use a placeholder whose value will then be substituted by whatever input parameter is passed (of course, even before the opening of the connection to the database, a check on the type of the input parameter is done)\n",
    "4. We then execute the query, fetch all rows and transform the list of tuples (that is the result of a SQL query) in a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalQueryProcessor(QueryProcessor,RelationalProcessor):\n",
    "    def getPublicationsPublishedInYear(self,year):\n",
    "        if type(year) == int:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalTable.id_venue==VenuesIdTable.doi_venues_id WHERE publication_year='{pub_year}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookTable.id_venue==VenuesIdTable.doi_venues_id WHERE publication_year='{pub_year}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsTable.id_venue==VenuesIdTable.doi_venues_id WHERE publication_year='{pub_year}'\".format(pub_year=year)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not an integer!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The other queries\n",
    "The same is done for the other queries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalQueryProcessor(QueryProcessor,RelationalProcessor):\n",
    "    def getPublicationsByAuthorId(self,id):\n",
    "        if type(id) == str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE orcid='{orcid}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookChapterTable.id==VenuesIdTable.doi_venues_id WHERE orcid='{orcid}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsPaperTable.id==VenuesIdTable.doi_venues_id WHERE orcid='{orcid}'\".format(orcid=id)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not a string!\")\n",
    "    \n",
    "    def getVenuesByPublisherId(self,id):\n",
    "        if type(id) == str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT issn_isbn, NULL AS event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN JournalTable ON VenuesIdTable.doi_venues_id==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref WHERE publisher='{pub_id}' UNION SELECT issn_isbn, NULL AS event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN BookTable ON VenuesIdTable.doi_venues_id==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref WHERE publisher='{pub_id}' UNION SELECT issn_isbn, event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN ProceedingsTable ON VenuesIdTable.doi_venues_id==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref WHERE publisher='{pub_id}'\".format(pub_id=id)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result, columns=[\"issn_isbn\", \"event\", \"name_venue\", \"publisher\", \"name_pub\", \"venue_type\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not string!\")\n",
    "\n",
    "    def getPublicationInVenue(self,venueId):\n",
    "        if type(venueId)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE issn_isbn='{venue_id}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookChapterTable.id==VenuesIdTable.doi_venues_id WHERE issn_isbn='{venue_id}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsPaperTable.id==VenuesIdTable.doi_venues_id WHERE issn_isbn='{venue_id}'\".format(venue_id=venueId)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"])        \n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not a string!\")\n",
    "\n",
    "    def getJournalArticlesInIssue(self,issue,volume,journalId):\n",
    "        if type(issue)==str and type(volume)==str and type(journalId)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE issue='{issue}' AND volume='{volume}' AND issn_isbn='{journal_id}'\".format(issue=issue, volume=volume, journal_id=journalId)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"]) \n",
    "        else:\n",
    "            raiseExceptions(\"One or all the input parameter are not strings!\")\n",
    "\n",
    "    def getJournalArticlesInVolume(self,volume,journalId):\n",
    "        if type(volume)==str and type(journalId)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE volume='{volume}' AND issn_isbn='{journal_id}'\".format(volume=volume, journal_id=journalId)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"]) \n",
    "        else:\n",
    "            raiseExceptions(\"One or all the input parameter are not strings!\")\n",
    "\n",
    "    def getJournalArticlesInJournal(self,journalId):\n",
    "        if type(journalId)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE issn_isbn='{journal_id}'\".format(journal_id=journalId)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"]) \n",
    "        else:\n",
    "            raiseExceptions(\"One or all the input parameter are not strings!\")\n",
    "\n",
    "    def getProceedingsByEvent(self,eventPartialName):\n",
    "        if type(eventPartialName)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT event, name_venue, publisher, issn_isbn, name_pub, venue_type FROM ProceedingsTable LEFT JOIN VenuesIdTable ON ProceedingsTable.id_venue==VenuesIdTable.doi_venues_id LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref WHERE event LIKE '%{event}%'\".format(event=eventPartialName)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"event\",\"name_venue\",\"publisher\",\"issn_isbn\", \"name_pub\", \"venue_type\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not string!\")\n",
    "\n",
    "    def getPublicationAuthors(self,publicationId):\n",
    "        if type(publicationId)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT doi_authors, family, given, orcid FROM AuthorsTable WHERE doi_authors='{pub_doi}'\".format(pub_doi=publicationId)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"doi_authors\", \"family\", \"given\", \"orcid\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not string!\")\n",
    "\n",
    "    def getPublicationsByAuthorName(self,authorPartialName):\n",
    "        if type(authorPartialName)==str:\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                query = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id WHERE family LIKE '%{family}%' OR given LIKE '%{given}%' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookChapterTable.id==VenuesIdTable.doi_venues_id WHERE family LIKE '%{family}%' OR given LIKE '%{given}%' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsPaperTable.id==VenuesIdTable.doi_venues_id WHERE family LIKE '%{family}%' OR given LIKE '%{given}%'\".format(family=authorPartialName,given=authorPartialName)\n",
    "                cur.execute(query)\n",
    "                result = cur.fetchall()\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"]) \n",
    "        else:\n",
    "            raiseExceptions(\"One or all the input parameter are not strings!\")\n",
    "            \n",
    "    def getDistinctPublishersOfPublications(self,pubIdList):\n",
    "        if type(pubIdList) == list and all(isinstance(n, str) for n in pubIdList):\n",
    "            with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                cur = qrdb.cursor()\n",
    "                result = list()\n",
    "                for item in pubIdList:\n",
    "                    query = \"SELECT name_venue, id_venue, crossref, name_pub, venue_type FROM JournalTable LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref WHERE id_venue='{doi}' UNION SELECT name_venue, id_venue, crossref, name_pub, venue_type FROM BookTable LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref WHERE id_venue='{doi}' UNION SELECT name_venue, id_venue, crossref, name_pub, venue_type FROM ProceedingsTable LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref WHERE id_venue='{doi}'\".format(doi = item)\n",
    "                    cur.execute(query)\n",
    "                    result_q = cur.fetchall()\n",
    "                    result.extend(result_q)\n",
    "                qrdb.commit()\n",
    "            return pd.DataFrame(data=result, columns=[\"name_venue\", \"id_venue\", \"crossref\", \"name_pub\", \"venue_type\"])\n",
    "        else:\n",
    "            raiseExceptions(\"The input parameter is not a list or it is not a list of strings!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a couple of \"particular\" queries: the `getMostCitedPublication()`, the `getMostCitedVenue()` and the additional query `getPublicationsFromDOI(pubIdList)` (which is needed in the `GenericQueryProcessor`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `getMostCitedPublication` and the `getMostCitedVenue` Query\n",
    "For the development of these two queries, given our starting requirement for each query (getting, as a result, a dataframe containing all the necessary information for building the expected Python objects in the `GenericQueryProcessor`), two different SQL queries were written and their results then merged.\n",
    "\n",
    "1. In the first query we select select all the cited Publication dois and the number of citations involving them (ordering them in descending order)\n",
    "2. We cannot be sure that the most cited Publication is the first row of the resulting dataframe, as there could be many Publications that deserve the top-place (being cited the same number of times)\n",
    "3. We then save these top-cited Publications in a list and transform it into a dataframe\n",
    "4. In the second query we retrieve all the information needed to create these Publication objects\n",
    "5. At the end we merge the two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalQueryProcessor(QueryProcessor,RelationalProcessor):\n",
    "    def getMostCitedPublication(self):\n",
    "        with sql3.connect(self.getDbPath()) as qrdb:\n",
    "            cur = qrdb.cursor()\n",
    "            query1 = \"SELECT ref_doi, COUNT(ref_doi) AS num_citations FROM ReferencesTable GROUP BY ref_doi ORDER BY num_citations DESC\"\n",
    "            cur.execute(query1)\n",
    "            result_q1 = cur.fetchall()\n",
    "            max = result_q1[0][1]\n",
    "            result1 = list()\n",
    "            for item in result_q1:\n",
    "                index = result_q1.index(item)\n",
    "                if result_q1[index][1] == max:\n",
    "                    tpl = tuple((result_q1[index][0],max))\n",
    "                    result1.append(tpl)\n",
    "            df1 = pd.DataFrame(data=result1,columns=[\"ref_doi\",\"num_citations\"])\n",
    "            query2 = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, family, given, orcid, ref_doi, name_venue, issn_isbn, publisher, name_pub FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalArticleTable.id==VenuesIdTable.doi_venues_id UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, family, given, orcid, ref_doi, name_venue, issn_isbn, publisher, name_pub FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookChapterTable.id==VenuesIdTable.doi_venues_id UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, family, given, orcid, ref_doi, name_venue, issn_isbn, publisher, name_pub FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsPaperTable.id==VenuesIdTable.doi_venues_id\"\n",
    "            cur.execute(query2)\n",
    "            result_q2 = cur.fetchall()\n",
    "            df2 = pd.DataFrame(data=result_q2,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"name_venue\", \"issn_isbn\", \"publisher\", \"name_pub\"])\n",
    "            final_result = pd.merge(left=df2, right=df1, left_on=\"id\", right_on=\"ref_doi\")\n",
    "            qrdb.commit()\n",
    "        return final_result\n",
    "    \n",
    "    def getMostCitedVenue(self):\n",
    "        with sql3.connect(self.getDbPath()) as qrdb:\n",
    "            cur = qrdb.cursor()\n",
    "            query1 = \"SELECT name_venue, COUNT(name_venue) as num_cit FROM JournalTable LEFT JOIN ReferencesTable ON JournalTable.id_venue==ReferencesTable.ref_doi WHERE ref_doi IS NOT NULL GROUP BY name_venue UNION SELECT name_venue, COUNT(name_venue) as num_cit FROM BookTable LEFT JOIN ReferencesTable ON BookTable.id_venue==ReferencesTable.ref_doi WHERE ref_doi IS NOT NULL GROUP BY name_venue UNION SELECT name_venue, COUNT(name_venue) as num_cit FROM ProceedingsTable LEFT JOIN ReferencesTable ON ProceedingsTable.id_venue==ReferencesTable.ref_doi WHERE ref_doi IS NOT NULL GROUP BY name_venue ORDER BY num_cit DESC\"\n",
    "            cur.execute(query1)\n",
    "            result_q1 = cur.fetchall()\n",
    "            max = result_q1[0][1]\n",
    "            result1 = list()\n",
    "            for item in result_q1:\n",
    "                index = result_q1.index(item)\n",
    "                if result_q1[index][1] == max:\n",
    "                    result1.append(result_q1[index][0])\n",
    "            df1 = pd.DataFrame(data=result1,columns=[\"name_venue\"])\n",
    "            query2 = \"SELECT issn_isbn, NULL AS event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN JournalTable ON VenuesIdTable.doi_venues_id==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref UNION SELECT issn_isbn, NULL AS event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN BookTable ON VenuesIdTable.doi_venues_id==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref UNION SELECT issn_isbn, event, name_venue, publisher, name_pub, venue_type FROM VenuesIdTable LEFT JOIN ProceedingsTable ON VenuesIdTable.doi_venues_id==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref\"\n",
    "            cur.execute(query2)\n",
    "            result_q2 = cur.fetchall()\n",
    "            df2 = pd.DataFrame(data=result_q2, columns=[\"issn_isbn\", \"event\", \"name_venue\", \"publisher\", \"name_pub\", \"venue_type\"])\n",
    "            final_result = pd.merge(left=df2, right=df1, left_on=\"name_venue\", right_on=\"name_venue\")\n",
    "            qrdb.commit()\n",
    "        return final_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `getPublicationsFromDOI`\n",
    "We have also implemented this additional query that will be needed in the `GenericQueryProcessor`: what it does is it retrieves all the Publications' information starting from a list of ids (doi strings)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RelationalQueryProcessor(QueryProcessor,RelationalProcessor):\n",
    "    def getPublicationsFromDOI(self,pubIdList):\n",
    "            if type(pubIdList) == list and all(isinstance(n, str) for n in pubIdList):\n",
    "                with sql3.connect(self.getDbPath()) as qrdb:\n",
    "                    cur = qrdb.cursor()\n",
    "                    result = list()\n",
    "                    for item in pubIdList:\n",
    "                        query = \"SELECT publication_year, title, publication_venue, id, issue, volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM JournalArticleTable LEFT JOIN AuthorsTable ON JournalArticleTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN JournalTable ON ReferencesTable.og_doi==JournalTable.id_venue LEFT JOIN PublishersTable ON JournalTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON JournalTable.id_venue==VenuesIdTable.doi_venues_id WHERE id='{doi}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM BookChapterTable LEFT JOIN AuthorsTable ON BookChapterTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN BookTable ON ReferencesTable.og_doi==BookTable.id_venue LEFT JOIN PublishersTable ON BookTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON BookTable.id_venue==VenuesIdTable.doi_venues_id WHERE id='{doi}' UNION SELECT publication_year, title, publication_venue, id, NULL AS issue, NULL AS volume, NULL AS chapter_number, pub_type, family, given, orcid, ref_doi, issn_isbn, publisher, name_pub, venue_type FROM ProceedingsPaperTable LEFT JOIN AuthorsTable ON ProceedingsPaperTable.id==AuthorsTable.doi_authors LEFT JOIN ReferencesTable ON AuthorsTable.doi_authors==ReferencesTable.og_doi LEFT JOIN ProceedingsTable ON ReferencesTable.og_doi==ProceedingsTable.id_venue LEFT JOIN PublishersTable ON ProceedingsTable.publisher==PublishersTable.crossref LEFT JOIN VenuesIdTable ON ProceedingsTable.id_venue==VenuesIdTable.doi_venues_id WHERE id='{doi}'\".format(doi=item)\n",
    "                        cur.execute(query)\n",
    "                        result_q = cur.fetchall()\n",
    "                        result.extend(result_q)\n",
    "                    qrdb.commit()\n",
    "                return pd.DataFrame(data=result,columns=[\"publication_year\", \"title\", \"publication_venue\", \"id\", \"issue\", \"volume\", \"chapter_number\", \"pub_type\", \"family\", \"given\", \"orcid\", \"ref_doi\", \"issn_isbn\", \"publisher\", \"name_pub\", \"venue_type\"])\n",
    "            else:\n",
    "                raiseExceptions(\"The input parameter is not a list or it is not a list of strings!\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tests for this part of the software can be found in the folder `testFiles > TEST_extraRelationalClasses.py`, in the repository of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Triplestore part\n",
    "### TriplestoreDataProcessor and TriplestoreProcessor\n",
    "\n",
    "TRIPLESTORE PROCESSOR\n",
    "\n",
    "My(Guisy) personal experience in this project taught me one thing above all: is not how much you know \"how to code\", but how you create and practice your logic. As the professor said at the very beginning of the course, I found myself practicing the hacking style (in the best sense of the term of course) at the highest level here.\n",
    "Indeed, when I asked for an external help, I understood some things that to me were not so clear before, even if I moved well on Pandas dataframes, rdf lib syntax and csv and json structure in general. \n",
    "\n",
    "In particular, what I figured it out is that: 1) while using RDFLIb, you can effectively query the SPARQL Store, after opening and closing the connection to it: this allows you to navigate the graph literally. 2)At a certain point in my research I thought i needed some async functions importing the time library; one thing I understood is that, when there is the ingestion of some files uploaded by the user, you can think of the situation of the software as a blak-or-white-situation: before the user input, after the user input. This means that, instead of thinking of the upload in itself, I relied on two different situations: if the graph was already populated with some triples or not. And then I acted in consequence. 3)I acted in consequence with what I call in the comments \"caches\", i.e. initially empty dictionaries, and empty lists. These two were the most powerful instruments to deal with structured (.csv) and hierarchical (.json) data.\n",
    "\n",
    "Expected result: use of multliple methods called by the main uploadData, to set the steps of the process (ingesting, cleaning, populating a graph, output). Avoiding Spaghetti Coding. Coding well all the time. A function beginning in an elegant way.\n",
    "\n",
    "<b>Pain:</b> How to deal with different files, in different formats (basically two) coming in the same time from the user - How to store this in triple-based forms - How to query inside the graph while populating it.\n",
    "\n",
    "<b>Gain:</b> the developing part relies on the main structure and methods offered by Pandas, rdflib and SPARQL query.\n",
    "\n",
    "<b>Result:</b> spaghetti coding still there (if-else loop dominate the scenario). The software provides reliable outputs.\n",
    "\n",
    " # ![Triplestore UML part 1>](imgs/UMLgraph1.png)\n",
    "\n",
    "For the interaction with the Blazegraph, these are the chosen URIRef and the base url:\n",
    "\n",
    "JournalArticle = URIRef(\"https://schema.org/ScholarlyArticle\")\n",
    "BookChapter = URIRef(\"https://schema.org/Chapter\")\n",
    "Journal = URIRef(\"https://schema.org/Periodical\")\n",
    "Book = URIRef(\"https://schema.org/Book\")\n",
    "Proceedings = URIRef(\"https://schema.org/EventSeries\")\n",
    "ProceedingsPaper = URIRef(\"http://purl.org/spar/fabio/ProceedingsPaper\")\n",
    "doi = URIRef(\"https://schema.org/identifier\") #valid also for issn/isbn\n",
    "publicationYear = URIRef(\"https://dbpedia.org/ontology/year\")\n",
    "title = URIRef(\"https://schema.org/name\")\n",
    "issue = URIRef(\"https://schema.org/issueNumber\")\n",
    "volume = URIRef(\"https://schema.org/volumeNumber\")\n",
    "chapter_num = URIRef(\"https://schema.org/numberedPosition\")\n",
    "publisher = URIRef(\"https://schema.org/publisher\")\n",
    "publicationVenue = URIRef(\"https://schema.org/isPartOf\")\n",
    "author = URIRef(\"https://schema.org/author\")\n",
    "name = URIRef(\"https://schema.org/givenName\")\n",
    "surname = URIRef(\"https://schema.org/familyName\")\n",
    "citation = URIRef(\"https://schema.org/citation\")\n",
    "event = URIRef(\"https://schema.org/event\")\n",
    "\n",
    "base_url = \"https://FF.github.io/res/\"\n",
    "\n",
    "The above assignement if URIs for our class objects is done in the ```extraGraphClasses.py``` file\n",
    "\n",
    "### THE CODE\n",
    "What to do before instantiating a TripleStore Processor: please remember to open the connection to the BlazegraphDB. For more, follow Silvio Peroni indications on this topic here: https://github.com/comp-data/2021-2022/blob/main/docs/handson/06/06-Interacting_with_databases_using_Pandas.ipynb\n",
    "\n",
    "<b>How to read it:</b> you will find comments in the .py file.\n",
    "\n",
    "To check the real structure of files for the ingesting of data, see: https://github.com/comp-data/2021-2022/tree/main/docs/project\n",
    "\n",
    "We begin by instanciating the main classes: Triplestore Processor and Triplestore Data Processor.\n",
    "As one can notice, the Triplestore processor, by default, doen not return anything, except for the method setEndopointUrl, that takes in input the user-provided data path.\n",
    "On the other hand, the Triple Store Data Processor is described by a method, called uploadData, which takes in input the user provided data path and returns a boolean value, in accordance with the correct uploading of data, and collateral populating of the graph.\n",
    "The function is mainly divided in two parts: one for the csv data upload and one for the json data upload. This is possible through a conditional loop.\n",
    "In each part, we have two possible scenarios taken in consideration, stored in a variable \"storedTriples\"; basically through the method __len__ we are able to know if in the store there are some triples or not.\n",
    "\n",
    "There are 4 previously instanciated variables, that are empty dicitonaries and a Literal, that work as basic cache/counter which help for the storing process of the venues and publications.\n",
    "\n",
    "<b>CSV FILE UPLOAD: FIRST CASE SCENARIO</b>\n",
    "\n",
    "Let's now take as reference the case of a .csv uploding with an empty SPARQL store. Here the machine will go iterating over the dataframe obtained through the compulsive action of the read_csv method.\n",
    "\n",
    "For every publication, the machine will set a unique identifier for local purposes, associated to the base_url of the FF resource path on github.\n",
    "The value obtained by the concatenation of these two strings will end up being the subject of our first triple, and so on with the other publications.\n",
    "In particular, the triples built from the csv file are read here in a Publication and Venue centric point of view. \n",
    "That's because we want that data stored in the \"publications\" dataframe becoming information in the triples.\n",
    "So, the doi column data will become the object of the triple, according to the UML model, and the same will be for the title column and the publicationYear, that in the dataframe has been set as an integer value.\n",
    "\n",
    "Again, according to the data model, we instantiate a conditional loop to tell the machine to add other triples, related to the subclasses of publication (journal article, book chapter, proceedings paper), according to the type of subclass, that we can read from the publications dataframe in the column \"type\".\n",
    "The same thing happens for the venue class, in the sense that, after checking in my global variable (cache) if other venues have been stored, putting the case in which there are no other venues, I'll  populate venues-related triples: the subject will be the string obtained by the base_url of Fantastic Four GitHub resource + the number of the index. The number of the index is here obtained checking for the lenght of the relative cache (everytime a venue will be read, it will be authomatically put in the cache). Since the subclasses of Venue class are venue type, journal, book and proceedings, the machine will create the venue-related triples with the values obtained by the .csv data in the column \"venue_type\", both in the form of Literals and the subclass.\n",
    "\n",
    "The exactly same process of \n",
    "<ul>\n",
    "1) checking the cache \n",
    "2) updating the cache \n",
    "3) creating the triple, has been set for the publisher value, i.e. for the name of the Organization.\n",
    "</ul>\n",
    "\n",
    "<b>CSV FILE UPLOAD: SECOND CASE SCENARIO</b>\n",
    "\n",
    "The second scenario (if storedTriples > 0) works more in a dynamic way in accordance with the store, that has been instantiated at the beginning.\n",
    "Through basic SPARQL query syntax, we're able to access the information already present in the store, and consequentially act, populating the database, ideally again.\n",
    "In the first query we're asking the machine to search for publications in the SPARQL store (put in the variable store), and we're retrieving from the obtained dataframe (named publicationOutput) the \"lenght\" (i.e. the number of publications) of the store through the method shape(). Since the shape() method gives as output a tuple of two values, we select only the first element of it.\n",
    "\n",
    "Next step is to compare what publication doi we have in the user input .csv and in the store. So we create another query, that works upon the sample technique, as documented in the rdflib SPARQL documentation the value is retrieved through the format() method.\n",
    "If (conditional loop) there are no dois stored in the output dataframe from the previous query, we instantiate a new subject, whose identifier is anyway obtained by the sum of the lenght of my publication counter (variable instantiated at the very beginning as a sort of global variable, even if it is not properly a global variable) and the lenght of my last query output dataframe.\n",
    "\n",
    "In the same conditional loop of no results between the comparison of query-output-dataframe and my counter of publications (if comparePublicationsOutput.empty), we instantiate another query, to check the venues situation.\n",
    "\n",
    "We expect that usually there will be no venue value stored yet, unless it comes from another csv file uploading operation. Here we ask the machine to compare the publication venues already in the store (if any) and the publication venues in the input data. Here we check if in our cache there are already some of the venues we're retrieving, and in this case we instantiate it as the subject. Of course this is again a for each loop, meaning that the just described passage suits every single venue.\n",
    "The other side of the conditional loop is that the venue value read is already stored in the graph. In this case, we're telling the machine to create triples related to the subclasses of that value: as before, we're talking about journal, book and proceedings. Since the class proceedings present one attribute, i.e. \"event\", the machine will also add a triple according this.\n",
    "\n",
    "Coming out from the conditional loop of the venues, we are back on the conditional loop, the publication-related one, according to which there are already some dois in the graph registered in a subject node. Now the work of the machine will be instead focused on to check if the current doi is already linked to a venue object node: this is done as usual through a SPARQL query. The result from this query is the \"comparePublicationsAndVenues\" dataframe. A set of conditional loops is instanciated: if the aforementioned dataframe is empty, then if the value provided by the .csv data is not in our cache, the machine will add it.\n",
    "\n",
    "Otherwise, we'll instanciate another query \"compareVenues\" to check if the venues are already or not in our store and in our cache.\n",
    "A similar comparison is done also for publishers, always in a publication-centric point of view.\n",
    "\n",
    "<b>JSON FILE UPLOAD: FIRST AND SECOND CASE SCENARIO.</b>\n",
    "\n",
    "In the second case, that of .json files, we retrieve the four main json objects: \"authors\", \"venues\", \"references\" and \"publishers\". The choosen way to explore the objects here is by instantiating some dataframes from the uploaded data.\n",
    "For the very first object, we create four empty lists, that will become the series of the authors_df: one for doi, one for the name, one for the surname and one for the related orcid. Furthermore, we create also other three empty lists, to keep the counting of the family, given and orcid. These latest lists will be appended to the main list (family, given, orcid).\n",
    "\n",
    "For the venues object, we will do basically the same operation (list->Series->Dataframe), creating one list more to store in general the issn/isbn of the venue to keep track of them (as a cache) and one list more with the same purpose containing the related dois.\n",
    "A similar process follows for publisher object and references object.\n",
    "Finally, since the dois retrieved from the object authors could be related to some venues (remember that the main key of the object venue is, indeed, a doi) we decide to (out) merge the venuesids dataframe, together with the authors dataframe, in case they could be linked together in a triple. Taking the output dataframe, we want to merge it with with the references, to check whether the (venues) dois can be linked with that one of the references.\n",
    "Next step is to instanciate four dictionaries, one for every object rendered in a dataframe.\n",
    "As before we will have the two scenarios: storedTriples == 0 and storedTriples > 0. In both cases, we'll continue using a publication/venue-centred approach, in the sense that, also for the \"additional information\" coming from the json file, I'll first of all retrieve the relative doi/issn or isbn, check whether they are or not already in my cache, and act on consequence, maintaining the publication and the venue as subjects of the triples.\n",
    "\n",
    "At the very end of the function, we're able to open again the connection to the Blazegraph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TriplestoreQueryProcessor\n",
    "\n",
    "The triplestore query processor acts based on a series of methods to retrieve information, through a SPARQL+rdflib syntax. This class acts as an extension of the Triplestore processor throuoh which we can access the content of the triplestore databases. It has not attributes of its own but has access to the endpoint URI through its parent class.\n",
    "\n",
    " #### The SPARQL queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following queries can be executed from the triplestoreQueryProcessor — \n",
    "\n",
    " # ![Triplestore UML part 1>](imgs/UMLgraph2.png)|\n",
    " \n",
    " The top seven queries were scripted, in accordance with the workload distribution, by Giusy and the bottom six were scripted by Manu. In its current state, the bottom six queries do not work because of the error in uploading the data and to avoid giving errors from the generic query processor, we simply use the `pass` command in the bottom six queries. \n",
    " \n",
    "  Once can see the code for the SPARQL queries in the `extraGraphClasses.py` file in the github repository. To see how to the queries are supposed to work and what arguments are accepted by them, head over to the <b>Classes RelationalQueryProcessor and TriplestoreQueryProcessor section</b> on this link - https://github.com/comp-data/2021-2022/tree/main/docs/project\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tests for this part of the software can be found in the folder `testFiles > TEST_extraGraphClasses.py`, in the repository of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GenericQueryProcessor\n",
    "\n",
    "The `GenericQueryProcessor` is structured as follows:\n",
    " # ![Generic UML part 1>](imgs/UMLgen.png)\n",
    "\n",
    "`queryProcessor`: the variable containing the list of QueryProcessor objects to involve when one of the get methods below is executed. In practice, every time a get method is executed, the method will call the related method on all the `QueryProcessor` objects included in the variable queryProcessor, before combining the results and returning the requested object.\n",
    "The definition of these two classes can be found in the `GenericQueryProcessor.py` file.\n",
    "\n",
    "This section of the software will need, to work, the importation of the classes defined in the `extraRelationalClasses.py`, `extraGraphClasses.py` and `dataModelClasses.py` files.\n",
    "\n",
    "### cleanQueryProcessors\n",
    "This function is used to clean the list of queryProcessor from all the QueryProcessor objects it includes. The Code can be seen below -\n",
    "### addQueryProcessor \n",
    "This function is used to append the input `QueryProcessor object` to the list queryProcessor which is an attribute of the genericQueryProcessor class. The genericQP can work with two different types of query processors, namely `RelationaQueryProcessor` and `TriplestoreQueryProcessor` which have been defined previously in the `extraRelationalClasses.py` and `extraGraphClasses.py` file. \n",
    "\n",
    "It allows the possiblity to work with multiple query processor objects but needs atleast one to exectue the queries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenericQueryProcessor(object):\n",
    "    def __init__(self):\n",
    "        self.queryProcessor = list()\n",
    "\n",
    "    def cleanQueryProcessors(self):\n",
    "        self.queryProcessor = []\n",
    "        return True\n",
    "\n",
    "    def addQueryProcessor(self,processor):\n",
    "        pClass = type(processor)\n",
    "        if issubclass (pClass,QueryProcessor):\n",
    "            self.queryProcessor.append(processor)\n",
    "            return True\n",
    "        else:\n",
    "            return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### The Queries\n",
    "The queries of the `GenericQueryProcessor` were developed by Manu Srivastava and Orsola Maria Borrini, the former dealing with:\n",
    "<ul>\n",
    "<li>getPublicationsPublishedInYear</li>\n",
    "<li>getPublicationsByAuthorId</li>\n",
    "<li>getMostCitedPublication</li>\n",
    "<li>getPublicationInVenue</li>\n",
    "<li>getJournalArticlesInIssue</li>\n",
    "<li>getJournalArticlesInVolume</li>\n",
    "<li>getJournalArticlesInJournal</li>\n",
    "<li>getPublicationsByAuthorName</li>\n",
    "</ul>\n",
    "\n",
    "While the latter mainly focused on those queries which did not involve any object of class `Publication`:\n",
    "<ul>\n",
    "<li>getMostCitedVenue</li>\n",
    "<li>getVenuesByPublisherId</li>\n",
    "<li>getProceedingsByEvent</li>\n",
    "<li>getPublicationAuthors</li>\n",
    "<li>getDistinctPublishersOfPublication</li>\n",
    "</ul>\n",
    "\n",
    "Aside from some specificities (analysed in the following paragraphs), the queries were structured as follows:\n",
    "<ol>\n",
    "<li>An empty dataframe (final_DF) was created to contain the results of the queries from the various query processors passed as input of the GenericQueryProcessor object </li>\n",
    "<li>Iterate over the list of query processors (self.queryProcessor)</li>\n",
    "<li>For each of these query processors (that could be of class RelationalQueryProcessor or TriplestoreQueryProcessor), the query is called and its resulting Dataframe saved in a variable result_DF</li>\n",
    "<li>Each of these results is then concatenated to the final_DF which, at the end of the iteration, will contain <i>all</i> the results of the queries in <i>all</i> the query processors contained in the list</li>\n",
    "<li>As the queries require, as output, a list of Python objects, the result list is then created (of course, empty)</li>\n",
    "</ol>\n",
    "From this point onwards, the logic of the queries changes depending on the requested output object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `Publication` and `JournalArticle` Queries\n",
    "\n",
    "The queries scripted by Manu under the GenericQueryProcessor(GQP) deal with the creation of `Publication` and `JournalArticle` objects out of the retured results from the different query processors provided as argument to the GQP using the `addQueryProcessor` class method.\n",
    "\n",
    "According to the definition of the constructor of the `Publication` and `JournalArticle` in the `dataModelClasses.py` file, we see that they require the citations for class instance creation and this required us to script a function to create `Publication` and `JournalArticle` class objects which requires some level recursive logic.\n",
    "\n",
    "The constructor function code can be found in the `pubMemory_full.py` file. \n",
    "\n",
    "The functions work as follows, the genericQueryProcessor queries like `getPublicationsPublisherInYear` or `getJournalArticlesinJournal` which need to return the <b>publication</b> object and <b>JournalArticle</b> objects will be calling the the `creatPubobj` function which takes in the argument <b>DOI</b> you can see the script of the function in the code block below -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "citlist = set()\n",
    "def MANUalrecursion(doilist):\n",
    "    \n",
    "    for item in doilist:\n",
    "        if item == None:\n",
    "            Noauth = Person(\"NO-Orcid\",\"NO-Givenname\",\"No-Familyname\")\n",
    "            autlist = set()\n",
    "            autlist.add(Noauth)\n",
    "            NoVenue = Venue(\"NO-VenueId\",\"NO-VenueTitle\",Organisation(\"NO-OrganisationID\",\"NO-OrganisationTitle\"))\n",
    "            ender = Publication(\"NO-PubID\",\"NO-PubYear\",\"NO-Title\",autlist,NoVenue,set())\n",
    "            citlist.add(ender)\n",
    "            pass\n",
    "        else:\n",
    "            citlist.add(creatPubobj(item))\n",
    "    return citlist\n",
    "\n",
    "def creatPubobj(DOI):   #creates class objects \n",
    "    Final_obj = {}\n",
    "    Final_obj.update(mem[DOI])\n",
    "    #print(Final_obj)\n",
    "    #now we use the Final_obj dict to create our class object\n",
    "    #first we create dependency objects like person object, venue object, etc\n",
    "    # 1 - author object by iterating over the dict in the Final_obj dict\n",
    "    aut = []\n",
    "    for item in Final_obj['authors']:\n",
    "        person = Person(item['orcid'],item['given'],item['family'])\n",
    "        aut.append(person)\n",
    "    #print(Final_obj['venue']['id'])\n",
    "    if Final_obj['venue']['id'] == None:\n",
    "        pass\n",
    "    else:\n",
    "        orgdata = {}\n",
    "        orgdata.update(Final_obj['venue']['publisher'])\n",
    "        publisherobj = Organisation(orgdata['id'],orgdata['title'])\n",
    "        venueobj = Venue(Final_obj['venue']['id'],Final_obj['venue']['title'],publisherobj)\n",
    "    \n",
    "    if Final_obj['cites'] == None:\n",
    "        pub_obj = Publication(Final_obj['doi'],Final_obj['year'],Final_obj['title'],aut,venueobj,set())\n",
    "    else:\n",
    "        pub_obj = Publication(Final_obj['doi'],Final_obj['year'],Final_obj['title'],aut,venueobj,MANUalrecursion(Final_obj['cites']))\n",
    "    \n",
    "    return pub_obj     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, the `Final_obj` is used to store the data from the `mem` variable which is populated with all the publication data using the function named `createPubobjsdict` which taken in a <b>Dataframe</b> which hold all the data required to create a publication object. \n",
    "\n",
    "Similarly, we have another function called `creatJAobj` which uses similar logic to create `JournalArticle` objects instead of `Publication` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createPubobjsdict(df):     # This DF contains all our publication data for one all our databases.\n",
    "    for idx, item in df.iterrows():\n",
    "        if item['id'] in mem:  #checks if we have alreay dealt with the the DOI in the df argument\n",
    "            pass\n",
    "        else:\n",
    "            obj = {\n",
    "                    \"doi\": [item['id']],\n",
    "                    \"title\": item['title'],\n",
    "                    \"year\":item['publication_year'],\n",
    "                    \"issue\":item['issue'],\n",
    "                    \"volume\":item['volume']\n",
    "            }\n",
    "            # ADDING AUTH LIST CREATED IN THE AUTHORLIST FUNCTION\n",
    "            if item['id'] in tutti_author:    #check key\n",
    "                auth_list = tutti_author[item['id']]  # json might have incomplete data\n",
    "                obj.update({\"authors\":auth_list}) #adding dict of authors for this obj\n",
    "            else:\n",
    "                auth_list = {\n",
    "                            \"family\": \"No Data available\",\n",
    "                            \"given\": \"No Data available\",\n",
    "                            \"orcid\": \"No Data available\"}\n",
    "                obj.update({\"authors\":auth_list}) #adding dict of authors for this obj\n",
    "\n",
    "            # CREATING AND ADDING VENUE AND PUBLISHER OBJECT\n",
    "            if item['publication_venue'] == NULL:   #check syntax\n",
    "                empt_publisher = {\"id\":\"none//data nt available\",\"title\":\"none//data nt available\"}\n",
    "                empt_venue = {\"id\":\"none/data nt available\", \"title\":\"none/data nt available\",\"publisher\":empt_publisher}\n",
    "                obj.update({'venue': empt_venue })\n",
    "            else:\n",
    "                org_data = {\"id\":[item[\"publisher\"]],\"title\":item[\"name_pub\"]}\n",
    "                ven_data = {\"id\":[item['issn_isbn']],\"title\": item['publication_venue'], \"publisher\": org_data}\n",
    "                obj.update({\"venue\":ven_data})\n",
    "            mem.update({item['id']:obj})   #here we add the incomple obj to the mem dict\n",
    "            \n",
    "            #creating cites list\n",
    "            cites_list = []\n",
    "            cites_list.append(item['ref_doi'])\n",
    "            obj.update({'cites': cites_list })\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "This dataframe used by the above shown function is created by another function called `df_creator` which takes in input a `dataprocessor` (code shown below). This function is called during the addintion of the queryProcessor objects to the GQP. It iterrates over the dictionay `tutti_author` which populated during the upload of the data from .json file uploaded using the upload data method of either the `relationalDataProcessor` or the `triplestoreDataProcessor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_creator(z):\n",
    "    sumDF = pd.DataFrame()\n",
    "    for key in tutti_author:\n",
    "        singDF1 = z.getPublicationsFromDOI([key])\n",
    "        #singDF2 = c.getPublicationsFromDOI([key])  #this function is written but the query takes too long and probably goes on infinitely\n",
    "        \n",
    "        #sumDF_temp = pd.concat([singDF1,singDF2])\n",
    "        sumDF = pd.concat([singDF1,sumDF])\n",
    "        #print(\"This query takes time, please wait with a hot cup of coffee\")\n",
    "    createPubobjsdict(sumDF)\n",
    "    return "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `Venue`, `Person`, `Organisation` Queries\n",
    "What we mean here is those queries that request, as output, a list of objects of `Venue`, `Person`, `Organisation` class:\n",
    "<ul>\n",
    "<li>getMostCitedVenue -> a Venue object </li>\n",
    "<li>getVenuesByPublisherId -> list of Venue objects </li>\n",
    "<li>getProceedingsByEvent -> list of Proceedings objects (Venue)</li>\n",
    "<li>getPublicationAuthors -> list of Person objects </li>\n",
    "<li>getDistinctPublishersOfPublication -> list of Organisation objects</li>\n",
    "</ul>\n",
    "\n",
    "After calling the query in the list of query processors (`self.queryProcessor`), we create an empty list (the `result` list) which will contain the python objects.\n",
    "\n",
    "We then start iterating over the `final_DF` dataframe, saving all the information needed for each object:\n",
    "<ul>\n",
    "<li>Venue objects need a list of ids (strings, they will be issn and/or isbn), a title (string), and a publisher</li>\n",
    "    - As the publisher is an Organisation object, we need to create such object and therefore need the necessary information: a list of ids (strings, they will be crossref) and a name (string)\n",
    "<li>Proceedings objects need all the information needed by a Venue object and some additional information too: the event (string) </li> \n",
    "<li>Person objects need a list of ids (strings, they will be orcids), a familyName (string) and a givenName (string)</li>\n",
    "<li>Organisation objects, as we have already seen with Venues, need a list of ids (strings, they will be crossref) and a name (string) </li>\n",
    "</ul>\n",
    "\n",
    "To save this information we scroll through the rows of the `final_DF` and save the values we need (which will be saved under specific columns).\n",
    "Some information is required to be in the form of a list (ids):\n",
    "<ul>\n",
    "<li>Create an empty list which will contain all the values needed for each specific object</li>\n",
    "<li>Save the first value in a variable</li>\n",
    "<li>Start another iteration on the same dataframe</li>\n",
    "<li>By using meaningful information that remains static for each object (for example, the name of the Venue or the id of the Publication), we can compare it between the two different iterations: if this static meaningful information stays the same, it means we are still in the same occurrence of object (and we add the needed information to the empty list), otherwise we are dealing with another occurrence (and don't add anything to the list)</li>\n",
    "</ul>\n",
    "\n",
    "Before moving on with another \"main\" iteration over the `final_DF`, we create the Python object and append it to the `result` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenericQueryProcessor(object):\n",
    "    def getVenuesByPublisherId(self,id):\n",
    "        # The id is going to be a crossref\n",
    "        final_DF = pd.DataFrame()\n",
    "        \n",
    "        for item in self.queryProcessor:\n",
    "            result_DF = item.getVenuesByPublisherId(id)\n",
    "            final_DF = pd.concat([final_DF,result_DF])\n",
    "\n",
    "        result = list()\n",
    "        for idx,row in final_DF.iterrows():\n",
    "            title = row[\"name_venue\"] # -> \"title\"(of the Venue)\n",
    "            ids_list = list() # -> [\"ids\"](=issn_isbn)\n",
    "            for idx2,row2 in final_DF.iterrows():\n",
    "                title2 = row2[\"name_venue\"]\n",
    "                if title2 == title:\n",
    "                    # We are in the same Venue!\n",
    "                    issn_isbn = row2[\"issn_isbn\"]\n",
    "                    ids_list.append(issn_isbn)\n",
    "            crossref_list = list() # -> [\"ids\"](=crossref)\n",
    "            for idx3,row3 in final_DF.iterrows():\n",
    "                title3 = row3[\"name_venue\"]\n",
    "                if title3 == title:\n",
    "                    crossref = row3[\"publisher\"]\n",
    "                    crossref_list.append(crossref)\n",
    "            name_org = row[\"name_pub\"]\n",
    "            publ_obj = Organisation(crossref_list,name_org)\n",
    "            venue_obj = Venue(ids_list,title,publ_obj)\n",
    "            result.append(venue_obj)\n",
    "        return result\n",
    "        #list[Venue]\n",
    "\n",
    "\n",
    "    def getProceedingsByEvent(self,eventPartialName):\n",
    "        final_DF = pd.DataFrame()\n",
    "        \n",
    "        for item in self.queryProcessor:\n",
    "            result_DF = item.getProceedingsByEvent(eventPartialName)\n",
    "            final_DF = pd.concat([final_DF,result_DF])\n",
    "\n",
    "        result = list()\n",
    "        for idx,row in final_DF.iterrows():\n",
    "            event = row[\"event\"] # -> additional info \"event\" str\n",
    "            title = row[\"name_venue\"] # -> \"title\"(of the Venue)\n",
    "            ids_list = list() # -> [\"ids\"](=issn_isbn)\n",
    "            for idx2,row2 in final_DF.iterrows():\n",
    "                title2 = row2[\"name_venue\"]\n",
    "                if title2 == title:\n",
    "                    # We are in the same Venue!\n",
    "                    issn_isbn = row2[\"issn_isbn\"]\n",
    "                    ids_list.append(issn_isbn)\n",
    "            crossref_list = list() # -> [\"ids\"](=crossref)\n",
    "            for idx3,row3 in final_DF.iterrows():\n",
    "                title3 = row3[\"name_venue\"]\n",
    "                if title3 == title:\n",
    "                    crossref = row3[\"publisher\"]\n",
    "                    crossref_list.append(crossref)\n",
    "            name_org = row[\"name_pub\"]\n",
    "            publ_obj = Organisation(crossref_list,name_org)\n",
    "            proc_obj = Proceedings(ids_list,title,publ_obj,event)\n",
    "            result.append(proc_obj)\n",
    "        return result\n",
    "        #list[Proceeding]\n",
    "\n",
    "    def getPublicationAuthors(self,publicationId):\n",
    "        # The id is going to be a doi\n",
    "        final_DF = pd.DataFrame()\n",
    "        \n",
    "        for item in self.queryProcessor:\n",
    "            result_DF = item.getPublicationAuthors(publicationId)\n",
    "            final_DF = pd.concat([final_DF,result_DF])\n",
    "\n",
    "        result = list()\n",
    "        for idx,row in final_DF.iterrows():\n",
    "            #[\"ids\"](=orcid), \"givenName\", \"familyName\"\n",
    "            doi = row[\"doi_authors\"]\n",
    "            ids_list = list() # -> [\"ids\"](=orcid)\n",
    "            for idx2,row2 in final_DF.iterrows():\n",
    "                doi2 = row2[\"doi_authors\"]\n",
    "                if doi2 == doi:\n",
    "                    # We are in the same publication\n",
    "                    id = row2[\"orcid\"]\n",
    "                    ids_list.append(id)\n",
    "            given = row[\"given\"] # -> \"givenName\"\n",
    "            family = row[\"family\"] # -> \"familyName\"\n",
    "            pers_obj = Person(ids_list,given,family)\n",
    "            result.append(pers_obj)\n",
    "        return result\n",
    "        #list[Person]\n",
    "    \n",
    "    def getDistinctPublishersOfPublications(self,pubIdList):\n",
    "        # The ids are going to be dois\n",
    "        final_DF = pd.DataFrame()\n",
    "        \n",
    "        for item in self.queryProcessor:\n",
    "            result_DF = item.getDistinctPublishersOfPublications(pubIdList)\n",
    "            final_DF = pd.concat([final_DF,result_DF])\n",
    "\n",
    "        result = list()\n",
    "        for idx,row in final_DF.iterrows():\n",
    "            #[\"ids\"](=crossref), \"name\"(of the Organisation)\n",
    "            doi = row[\"id_venue\"]\n",
    "            ids_list = list() # -> [\"ids\"](=crossref)\n",
    "            for idx2,row2 in final_DF.iterrows():\n",
    "                doi2 = row2[\"id_venue\"]\n",
    "                if doi2 == doi:\n",
    "                    # We are in the same publication\n",
    "                    id = row2[\"crossref\"]\n",
    "                    ids_list.append(id)\n",
    "            name = row[\"name_pub\"] # -> \"name\"(of the Organisation)\n",
    "            pub_obj = Organisation(ids_list,name)\n",
    "            result.append(pub_obj)\n",
    "        return result\n",
    "        #list[Organisation]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `getMostCitedVenue` Query\n",
    "In the UML data model this query requires, as output, a single `Venue` object (and not a list of `Venue` objects).\n",
    "However, we have decided to ignore this instruction and to return a list of Venue objects: this is because there could be more than one `Venue` having the same number of citations (that number being the top one).\n",
    "The same can be said for the `getMostCitedPublication` query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GenericQueryProcessor(object):\n",
    "    def getMostCitedVenue(self):            # CHANGE THIS QUERY \n",
    "        final_DF = pd.DataFrame()\n",
    "        \n",
    "        for item in self.queryProcessor:\n",
    "            result_DF = item.getMostCitedVenue()\n",
    "            final_DF = pd.concat([final_DF,result_DF])\n",
    "        \n",
    "        result = list()\n",
    "        for idx,row in final_DF.iterrows():\n",
    "            title = row[\"name_venue\"] # -> \"title\"(of the Venue)\n",
    "            ids_list = list() # -> [\"ids\"](=issn_isbn)\n",
    "            for idx2,row2 in final_DF.iterrows():\n",
    "                title2 = row2[\"name_venue\"]\n",
    "                if title2 == title:\n",
    "                    # We are in the same Venue!\n",
    "                    issn_isbn = row2[\"issn_isbn\"]\n",
    "                    ids_list.append(issn_isbn)\n",
    "            crossref_list = list() # -> [\"ids\"](=crossref)\n",
    "            for idx3,row3 in final_DF.iterrows():\n",
    "                title3 = row3[\"name_venue\"]\n",
    "                if title3 == title:\n",
    "                    crossref = row3[\"publisher\"]\n",
    "                    crossref_list.append(crossref)\n",
    "            name_org = row[\"name_pub\"]\n",
    "            publ_obj = Organisation(crossref_list,name_org)\n",
    "            venue_obj = Venue(ids_list,title,publ_obj)\n",
    "            result.append(venue_obj)\n",
    "        return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tests for this part of the software can be found in the folder `testFiles > TEST_extraGraphClasses.py`, in the repository of the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "Our examplar data provided for the project are:\n",
    "- \"relational_publications.csv\" and \"relational_other_data.json\" for what concerns the relational part\n",
    "- \"graph_publications.csv\" and \"graph_other_data.json\" for what concerns the graph part\n",
    "\n",
    "In this section we will provide a bunch of information for each file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### relational_publications.csv\n",
    "Our CSV file (Comma-Separated Values) contains data about publications.\n",
    "If seen as a table, it has 11 columns:   \n",
    "1. `id` contains string values (doi)\n",
    "2. `title` contains string values\n",
    "3. `type` contains string values (journal-article, book-chapter, eventually proceedings-paper)\n",
    "4. `publication_year` contains integer values\n",
    "5. `issue` contains integer values (1, 1-2 or empty)\n",
    "6. `volume` contains integer values (121 or empty)\n",
    "7. `chapter` contains integer values (1 or empty)\n",
    "8. `publication_venue` contains string values\n",
    "9. `venue_type` contains string values (journal, book, eventually proceedings)\n",
    "10. `publisher` contains string values (crossref)\n",
    "11. `event` eventually contains string values\n",
    "\n",
    "First of all we should introduce our data format in the python enviroment, this could be done usind the `read_csv` funcion, which is part of the pandas library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Plos Biology</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:340</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>doi:10.3390/admsci10030069</td>\n",
       "      <td>Performance Analysis And Science Mapping Of In...</td>\n",
       "      <td>journal-article</td>\n",
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       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Administrative Sciences</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:1968</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>doi:10.1186/s12888-020-02825-4</td>\n",
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       "      <td>journal-article</td>\n",
       "      <td>2020</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Bmc Psychiatry</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:297</td>\n",
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       "      <th>497</th>\n",
       "      <td>doi:10.1080/00472778.2020.1776578</td>\n",
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       "      <td>journal-article</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Journal Of Small Business Management</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:301</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>journal</td>\n",
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       "      <th>499</th>\n",
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       "      <td>NaN</td>\n",
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       "<p>500 rows × 11 columns</p>\n",
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       "                                    id  \\\n",
       "0              doi:10.1162/qss_a_00023   \n",
       "1       doi:10.1007/s11192-019-03217-6   \n",
       "2       doi:10.1007/s11192-019-03311-9   \n",
       "3            doi:10.1038/sdata.2016.18   \n",
       "4     doi:10.1371/journal.pbio.3000385   \n",
       "..                                 ...   \n",
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       "496     doi:10.1186/s12888-020-02825-4   \n",
       "497  doi:10.1080/00472778.2020.1776578   \n",
       "498     doi:10.1007/s11301-020-00196-4   \n",
       "499  doi:10.1080/08989621.2020.1836620   \n",
       "\n",
       "                                                 title             type  \\\n",
       "0    Opencitations, An Infrastructure Organization ...  journal-article   \n",
       "1    Software Review: Coci, The Opencitations Index...  journal-article   \n",
       "2    Nine Million Book Items And Eleven Million Cit...  journal-article   \n",
       "3    The Fair Guiding Principles For Scientific Dat...  journal-article   \n",
       "4    The Nih Open Citation Collection: A Public Acc...  journal-article   \n",
       "..                                                 ...              ...   \n",
       "495  Performance Analysis And Science Mapping Of In...  journal-article   \n",
       "496  Mapping The Literature On Parents With Mental ...  journal-article   \n",
       "497  Evolution Of The Entrepreneurship And Innovati...  journal-article   \n",
       "498  Intellectual Structure Of Management Innovatio...  journal-article   \n",
       "499  A Bibliometric Analysis Of Academic Misconduct...  journal-article   \n",
       "\n",
       "     publication_year issue volume  chapter  \\\n",
       "0                2020     1      1      NaN   \n",
       "1                2019     2    121      NaN   \n",
       "2                2019     2    122      NaN   \n",
       "3                2016     1      3      NaN   \n",
       "4                2019    10     17      NaN   \n",
       "..                ...   ...    ...      ...   \n",
       "495              2020     3     10      NaN   \n",
       "496              2020     1     20      NaN   \n",
       "497              2020   NaN    NaN      NaN   \n",
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       "499              2020     6     28      NaN   \n",
       "\n",
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       "4                            Plos Biology    journal   crossref:340    NaN  \n",
       "..                                    ...        ...            ...    ...  \n",
       "495               Administrative Sciences    journal  crossref:1968    NaN  \n",
       "496                        Bmc Psychiatry    journal   crossref:297    NaN  \n",
       "497  Journal Of Small Business Management    journal   crossref:301    NaN  \n",
       "498           Management Review Quarterly    journal   crossref:297    NaN  \n",
       "499            Accountability In Research    journal   crossref:301    NaN  \n",
       "\n",
       "[500 rows x 11 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relational_publications_csv = pd.read_csv(\"testData/relational_publications.csv\") \n",
    "relational_publications_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to this function we're able to visualize our data in a DataFrame (as shown above), which is made up of 500 rows and 11 columns. \n",
    "\n",
    "The suggestion from the professor was to avoid using `keep_default_na=False` in the `read_csv` funciotn, we must leave the system handle the empy cells as it wants, otherwise it will fill the cells with empty strings which are still values ad the cells will be full, even if we don't put charcaters in it.\n",
    "\n",
    "Then, we may want to get other information about it, so we could apply the following method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 500 non-null    object \n",
      " 1   title              500 non-null    object \n",
      " 2   type               500 non-null    object \n",
      " 3   publication_year   500 non-null    int64  \n",
      " 4   issue              347 non-null    object \n",
      " 5   volume             443 non-null    object \n",
      " 6   chapter            22 non-null     float64\n",
      " 7   publication_venue  498 non-null    object \n",
      " 8   venue_type         498 non-null    object \n",
      " 9   publisher          498 non-null    object \n",
      " 10  event              0 non-null      float64\n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "info_rp_csv = relational_publications_csv.info()\n",
    "info_rp_csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're able to see a summary of our DataFrame.\n",
    "As we didn't use `keep_default_na=False` we are able to see which columns have always a non-null value specified:\n",
    "\n",
    "`id`, `title`, `type` and `publication_year` don't present empty cells.\n",
    "`issue`, `volume`, `chapter`, `publication_venue`, `venue_type`, `publisher`, `event` do present empty cells. \n",
    "\n",
    "The `dtype` is specified as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next step, might be analyze the values of each column by using the describe method.\n",
    "In fact, this one allows to obtain an overall view contained in a new data frame, describing the one in which the method is called. \n",
    "The suggestion is to call such a method using the optional input named parameter: `include=\"all\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>1.512353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.294245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  \\\n",
       "count                       500   \n",
       "unique                      500   \n",
       "top     doi:10.1162/qss_a_00023   \n",
       "freq                          1   \n",
       "mean                        NaN   \n",
       "std                         NaN   \n",
       "min                         NaN   \n",
       "25%                         NaN   \n",
       "50%                         NaN   \n",
       "75%                         NaN   \n",
       "max                         NaN   \n",
       "\n",
       "                                                    title             type  \\\n",
       "count                                                 500              500   \n",
       "unique                                                500                2   \n",
       "top     Opencitations, An Infrastructure Organization ...  journal-article   \n",
       "freq                                                    1              478   \n",
       "mean                                                  NaN              NaN   \n",
       "std                                                   NaN              NaN   \n",
       "min                                                   NaN              NaN   \n",
       "25%                                                   NaN              NaN   \n",
       "50%                                                   NaN              NaN   \n",
       "75%                                                   NaN              NaN   \n",
       "max                                                   NaN              NaN   \n",
       "\n",
       "        publication_year issue volume    chapter publication_venue venue_type  \\\n",
       "count         500.000000   347    443  22.000000               498        498   \n",
       "unique               NaN    31    149        NaN               295          2   \n",
       "top                  NaN     1     11        NaN    Scientometrics    journal   \n",
       "freq                 NaN    69     29        NaN                50        478   \n",
       "mean         2019.558000   NaN    NaN   0.909091               NaN        NaN   \n",
       "std             1.512353   NaN    NaN   0.294245               NaN        NaN   \n",
       "min          2014.000000   NaN    NaN   0.000000               NaN        NaN   \n",
       "25%          2018.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "50%          2020.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "75%          2021.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "max          2021.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "\n",
       "           publisher  event  \n",
       "count            498    0.0  \n",
       "unique            32    NaN  \n",
       "top     crossref:297    NaN  \n",
       "freq             145    NaN  \n",
       "mean             NaN    NaN  \n",
       "std              NaN    NaN  \n",
       "min              NaN    NaN  \n",
       "25%              NaN    NaN  \n",
       "50%              NaN    NaN  \n",
       "75%              NaN    NaN  \n",
       "max              NaN    NaN  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description = relational_publications_csv.describe(include=\"all\")\n",
    "description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method is really interesting becase we're able to see some information about our data without looking at them in details.\n",
    "Looking at the rows we can retrieve something usefull:\n",
    "\n",
    "- We have variables like `\"count\"`, the values that belogs to `\"count\"` fills all the columns. \n",
    "- The variable `\"unique\"` shows us that each id and title of the publications are unique.\n",
    "- We have two different types and the most used one is `\"journal_article\"`, same for `\"venue_type\"` (journal, book).\n",
    "- The oldest publication year is 2014, while the most recent the 2021.\n",
    "- The column `\"event\"` is empty.\n",
    "- We can notice different statistics about each single column of the original data, some of them applies to a certain columns while other to not. For example all the statistics about number manipulations like \"mean\", \"st\", \"min\" etc.. do not apply with strings an thus NaN is returned in these cases.\n",
    "\n",
    "After we've observed our data we may want to run some queries in order to get specific information and obtain answers about our questions. \n",
    "The output of the query will be a real dataframe, the index will be a string that defines statistical dimensions. If we'll have a `NaN` value it means that something can't be computed, for instance, we can't compute the mean of a string. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some examples of possible queries:\n",
    "\n",
    "q1) For our first query we we may want to ask the system to retrieve all the \"Scientometrics\" publication venues (50), as we noticed that it is the top publication venue value in the column \"publication_venue\" of the previuous DataFrame.\n",
    "\n",
    "q2) Then I want to obtain all the publications that have the value \"book\" as venue type.\n",
    "q3) I want to get all the publications that have publisher = \"crossref:297\" which is the top publisher value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 id  \\\n",
      "1    doi:10.1007/s11192-019-03217-6   \n",
      "2    doi:10.1007/s11192-019-03311-9   \n",
      "6    doi:10.1007/s11192-020-03397-6   \n",
      "12   doi:10.1007/s11192-020-03690-4   \n",
      "16   doi:10.1007/s11192-021-04079-7   \n",
      "19   doi:10.1007/s11192-021-04097-5   \n",
      "41    doi:10.1007/s11192-016-1879-4   \n",
      "42    doi:10.1007/s11192-016-1971-9   \n",
      "43    doi:10.1007/s11192-016-2071-6   \n",
      "44    doi:10.1007/s11192-016-2132-x   \n",
      "45    doi:10.1007/s11192-016-2194-9   \n",
      "46    doi:10.1007/s11192-016-2215-8   \n",
      "47    doi:10.1007/s11192-017-2436-5   \n",
      "48    doi:10.1007/s11192-017-2449-0   \n",
      "49    doi:10.1007/s11192-017-2452-5   \n",
      "50    doi:10.1007/s11192-017-2481-0   \n",
      "51    doi:10.1007/s11192-017-2538-0   \n",
      "52    doi:10.1007/s11192-017-2604-7   \n",
      "53    doi:10.1007/s11192-017-2616-3   \n",
      "54    doi:10.1007/s11192-017-2636-z   \n",
      "55    doi:10.1007/s11192-018-2651-8   \n",
      "56    doi:10.1007/s11192-018-2705-y   \n",
      "57    doi:10.1007/s11192-018-2734-6   \n",
      "58    doi:10.1007/s11192-018-2740-8   \n",
      "59    doi:10.1007/s11192-018-2748-0   \n",
      "60    doi:10.1007/s11192-018-2752-4   \n",
      "61    doi:10.1007/s11192-018-2761-3   \n",
      "62    doi:10.1007/s11192-018-2765-z   \n",
      "63    doi:10.1007/s11192-018-2775-x   \n",
      "64    doi:10.1007/s11192-018-2786-7   \n",
      "65    doi:10.1007/s11192-018-2796-5   \n",
      "66    doi:10.1007/s11192-018-2803-x   \n",
      "67    doi:10.1007/s11192-018-2809-4   \n",
      "68    doi:10.1007/s11192-018-2841-4   \n",
      "69    doi:10.1007/s11192-018-2844-1   \n",
      "70    doi:10.1007/s11192-018-2856-x   \n",
      "71    doi:10.1007/s11192-018-2936-y   \n",
      "72    doi:10.1007/s11192-018-2977-2   \n",
      "73    doi:10.1007/s11192-018-2990-5   \n",
      "74   doi:10.1007/s11192-019-03047-6   \n",
      "75   doi:10.1007/s11192-019-03057-4   \n",
      "76   doi:10.1007/s11192-019-03087-y   \n",
      "77   doi:10.1007/s11192-019-03116-w   \n",
      "78   doi:10.1007/s11192-019-03201-0   \n",
      "79   doi:10.1007/s11192-019-03213-w   \n",
      "191  doi:10.1007/s11192-020-03805-x   \n",
      "272  doi:10.1007/s11192-019-03222-9   \n",
      "273  doi:10.1007/s11192-019-03230-9   \n",
      "274  doi:10.1007/s11192-019-03234-5   \n",
      "275  doi:10.1007/s11192-019-03238-1   \n",
      "\n",
      "                                                 title             type  \\\n",
      "1    Software Review: Coci, The Opencitations Index...  journal-article   \n",
      "2    Nine Million Book Items And Eleven Million Cit...  journal-article   \n",
      "6    The Practice Of Self-Citations: A Longitudinal...  journal-article   \n",
      "12   Google Scholar, Microsoft Academic, Scopus, Di...  journal-article   \n",
      "16   Universal And Specific Features Of Ukrainian E...  journal-article   \n",
      "19   A Qualitative And Quantitative Analysis Of Ope...  journal-article   \n",
      "41   A Bibliometric Analysis To Illustrate The Role...  journal-article   \n",
      "42   Quantifying The Changing Role Of Past Publicat...  journal-article   \n",
      "43   Mapping And Classification Of Agriculture In W...  journal-article   \n",
      "44   Measuring The Match Between Evaluators And Eva...  journal-article   \n",
      "45   Ruling Out Static Latent Homophily In Citation...  journal-article   \n",
      "46   Sleeping Beauties Cited In Patents: Is There A...  journal-article   \n",
      "47   Tracing The Knowledge-Building Dynamics In New...  journal-article   \n",
      "48   Mapping Patent Classifications: Portfolio And ...  journal-article   \n",
      "49         Tracking The Emergence Of Synthetic Biology  journal-article   \n",
      "50   Discovering Interdisciplinary Interactions Bet...  journal-article   \n",
      "51   An Overview Of The Web Of Science Record Of Sc...  journal-article   \n",
      "52   Identifying The “Ghost City” Of Domain Topics ...  journal-article   \n",
      "53   Usage Pattern Comparison Of The Same Scholarly...  journal-article   \n",
      "54   The Presence Of The Encyclicals In Web Of Scie...  journal-article   \n",
      "55   Bibliometric Analysis To Identify An Emerging ...  journal-article   \n",
      "56   A Look Back Over The Past 40 Years Of Female E...  journal-article   \n",
      "57   Discontinuities In Citation Relations Among Jo...  journal-article   \n",
      "58   An Eu Without The Uk: Mapping The Uk’S Changin...  journal-article   \n",
      "59   Mapping The Knowledge Domain And The Theme Evo...  journal-article   \n",
      "60   Delayed Recognition Of Judah Folkman’S Hypothe...  journal-article   \n",
      "61   The Evolutions Of The Rich Get Richer And The ...  journal-article   \n",
      "62   The Application Of Bibliometric Analysis: Disc...  journal-article   \n",
      "63   A Comparison Of Cognitive And Organizational C...  journal-article   \n",
      "64   A Bibliometric Analysis Of Highly Cited Papers...  journal-article   \n",
      "65   Accumulation Of Knowledge In Para-Scientific A...  journal-article   \n",
      "66   Litstoryteller+: An Interactive System For Mul...  journal-article   \n",
      "67   Entrepreneurial Cognition And Socially Situate...  journal-article   \n",
      "68   Overlapping Thematic Structures Extraction Wit...  journal-article   \n",
      "69   A Comparative Study Of Citations To Chemical E...  journal-article   \n",
      "70   Twenty Years Of Statistical Learning: From Lan...  journal-article   \n",
      "71   Exploring The Common Denominator Between Micro...  journal-article   \n",
      "72   What’S In A Name? Exploring The Conceptual Str...  journal-article   \n",
      "73   Digital Competences, Computer Skills And Infor...  journal-article   \n",
      "74   Research And Partnership In Studies Of Sugarca...  journal-article   \n",
      "75   A Research Framework To Explore Knowledge Evol...  journal-article   \n",
      "76   Bibliometric Analysis Of Publications From Pos...  journal-article   \n",
      "77   Opening The Black Box Of Academic Entrepreneur...  journal-article   \n",
      "78   Innovation In Latin America Through The Lens O...  journal-article   \n",
      "79   Software Survey: Scientopy, A Scientometric To...  journal-article   \n",
      "191  Mapping The Research History, Collaborations A...  journal-article   \n",
      "272  Investigating The Applications Of Artificial I...  journal-article   \n",
      "273  The Role Of South African Researchers In Inter...  journal-article   \n",
      "274  Bibliometric Analysis In Motorcycle Accident R...  journal-article   \n",
      "275  The Data Source Of This Study Is Web Of Scienc...  journal-article   \n",
      "\n",
      "     publication_year issue volume  chapter publication_venue venue_type  \\\n",
      "1                2019     2    121      NaN    Scientometrics    journal   \n",
      "2                2019     2    122      NaN    Scientometrics    journal   \n",
      "6                2020     1    123      NaN    Scientometrics    journal   \n",
      "12               2020     1    126      NaN    Scientometrics    journal   \n",
      "16               2021     9    126      NaN    Scientometrics    journal   \n",
      "19               2021    10    126      NaN    Scientometrics    journal   \n",
      "41               2016     1    107      NaN    Scientometrics    journal   \n",
      "42               2016     2    108      NaN    Scientometrics    journal   \n",
      "43               2016     2    109      NaN    Scientometrics    journal   \n",
      "44               2016     3    109      NaN    Scientometrics    journal   \n",
      "45               2016     2    110      NaN    Scientometrics    journal   \n",
      "46               2017     3    110      NaN    Scientometrics    journal   \n",
      "47               2017     3    112      NaN    Scientometrics    journal   \n",
      "48               2017     3    112      NaN    Scientometrics    journal   \n",
      "49               2017     3    112      NaN    Scientometrics    journal   \n",
      "50               2017     1    113      NaN    Scientometrics    journal   \n",
      "51               2017     3    113      NaN    Scientometrics    journal   \n",
      "52               2017     3    114      NaN    Scientometrics    journal   \n",
      "53               2017     1    115      NaN    Scientometrics    journal   \n",
      "54               2018     1    115      NaN    Scientometrics    journal   \n",
      "55               2018     3    115      NaN    Scientometrics    journal   \n",
      "56               2018     2    115      NaN    Scientometrics    journal   \n",
      "57               2018     1    116      NaN    Scientometrics    journal   \n",
      "58               2018     3    115      NaN    Scientometrics    journal   \n",
      "59               2018     1    116      NaN    Scientometrics    journal   \n",
      "60               2018     1    116      NaN    Scientometrics    journal   \n",
      "61               2018     1    116      NaN    Scientometrics    journal   \n",
      "62               2018     1    116      NaN    Scientometrics    journal   \n",
      "63               2018     2    116      NaN    Scientometrics    journal   \n",
      "64               2018     2    116      NaN    Scientometrics    journal   \n",
      "65               2018     2    116      NaN    Scientometrics    journal   \n",
      "66               2018     3    116      NaN    Scientometrics    journal   \n",
      "67               2018     3    116      NaN    Scientometrics    journal   \n",
      "68               2018     1    117      NaN    Scientometrics    journal   \n",
      "69               2018     1    117      NaN    Scientometrics    journal   \n",
      "70               2018     1    117      NaN    Scientometrics    journal   \n",
      "71               2018     3    117      NaN    Scientometrics    journal   \n",
      "72               2018     2    118      NaN    Scientometrics    journal   \n",
      "73               2018     2    118      NaN    Scientometrics    journal   \n",
      "74               2019     1    119      NaN    Scientometrics    journal   \n",
      "75               2019     2    119      NaN    Scientometrics    journal   \n",
      "76               2019     2    119      NaN    Scientometrics    journal   \n",
      "77               2019     1    120      NaN    Scientometrics    journal   \n",
      "78               2019     2    121      NaN    Scientometrics    journal   \n",
      "79               2019     2    121      NaN    Scientometrics    journal   \n",
      "191              2021     2    126      NaN    Scientometrics    journal   \n",
      "272              2019     2    121      NaN    Scientometrics    journal   \n",
      "273              2019     3    121      NaN    Scientometrics    journal   \n",
      "274              2019     2    121      NaN    Scientometrics    journal   \n",
      "275              2019     3    121      NaN    Scientometrics    journal   \n",
      "\n",
      "        publisher  event  \n",
      "1    crossref:297    NaN  \n",
      "2    crossref:297    NaN  \n",
      "6    crossref:297    NaN  \n",
      "12   crossref:297    NaN  \n",
      "16   crossref:297    NaN  \n",
      "19   crossref:297    NaN  \n",
      "41   crossref:297    NaN  \n",
      "42   crossref:297    NaN  \n",
      "43   crossref:297    NaN  \n",
      "44   crossref:297    NaN  \n",
      "45   crossref:297    NaN  \n",
      "46   crossref:297    NaN  \n",
      "47   crossref:297    NaN  \n",
      "48   crossref:297    NaN  \n",
      "49   crossref:297    NaN  \n",
      "50   crossref:297    NaN  \n",
      "51   crossref:297    NaN  \n",
      "52   crossref:297    NaN  \n",
      "53   crossref:297    NaN  \n",
      "54   crossref:297    NaN  \n",
      "55   crossref:297    NaN  \n",
      "56   crossref:297    NaN  \n",
      "57   crossref:297    NaN  \n",
      "58   crossref:297    NaN  \n",
      "59   crossref:297    NaN  \n",
      "60   crossref:297    NaN  \n",
      "61   crossref:297    NaN  \n",
      "62   crossref:297    NaN  \n",
      "63   crossref:297    NaN  \n",
      "64   crossref:297    NaN  \n",
      "65   crossref:297    NaN  \n",
      "66   crossref:297    NaN  \n",
      "67   crossref:297    NaN  \n",
      "68   crossref:297    NaN  \n",
      "69   crossref:297    NaN  \n",
      "70   crossref:297    NaN  \n",
      "71   crossref:297    NaN  \n",
      "72   crossref:297    NaN  \n",
      "73   crossref:297    NaN  \n",
      "74   crossref:297    NaN  \n",
      "75   crossref:297    NaN  \n",
      "76   crossref:297    NaN  \n",
      "77   crossref:297    NaN  \n",
      "78   crossref:297    NaN  \n",
      "79   crossref:297    NaN  \n",
      "191  crossref:297    NaN  \n",
      "272  crossref:297    NaN  \n",
      "273  crossref:297    NaN  \n",
      "274  crossref:297    NaN  \n",
      "275  crossref:297    NaN  \n",
      "                                   id  \\\n",
      "8    doi:10.1007/978-3-030-61244-3_16   \n",
      "9     doi:10.1007/978-3-030-61244-3_6   \n",
      "10    doi:10.1007/978-3-030-54956-5_2   \n",
      "11   doi:10.1007/978-3-030-55814-7_15   \n",
      "13   doi:10.1007/978-3-030-62466-8_28   \n",
      "15   doi:10.1007/978-3-030-77385-4_37   \n",
      "20   doi:10.1007/978-3-030-84825-5_11   \n",
      "26   doi:10.1007/978-3-030-16187-3_20   \n",
      "29   doi:10.1007/978-3-030-59194-6_37   \n",
      "30    doi:10.1007/978-3-030-61244-3_7   \n",
      "32    doi:10.1007/978-3-030-54956-5_9   \n",
      "33   doi:10.1007/978-3-030-71903-6_32   \n",
      "35   doi:10.1007/978-3-030-91669-5_24   \n",
      "294   doi:10.1007/978-3-319-91473-2_1   \n",
      "371   doi:10.1007/978-981-16-4760-4_1   \n",
      "395  doi:10.1007/978-981-16-6128-0_25   \n",
      "400   doi:10.1007/978-3-030-75722-9_7   \n",
      "417   doi:10.1007/978-3-030-78570-3_4   \n",
      "482  doi:10.1007/978-3-030-58799-4_49   \n",
      "484  doi:10.1007/978-3-030-53036-5_30   \n",
      "\n",
      "                                                 title          type  \\\n",
      "8    Researchflow: Understanding The Knowledge Flow...  book-chapter   \n",
      "9    Ontologies Supporting Research-Related Informa...  book-chapter   \n",
      "10    Question Answering On Scholarly Knowledge Graphs  book-chapter   \n",
      "11   Dingo: An Ontology For Projects And Grants Lin...  book-chapter   \n",
      "13                        The Opencitations Data Model  book-chapter   \n",
      "15   Kgbench: A Collection Of Knowledge Graph Datas...  book-chapter   \n",
      "20   Lobd: Linked Data Dashboard For Marine Biodive...  book-chapter   \n",
      "26   Using The Spar Ontology Network To Represent T...  book-chapter   \n",
      "29   Construction And Leverage Scientific Knowledge...  book-chapter   \n",
      "30   A Unified Nanopublication Model For Effective ...  book-chapter   \n",
      "32   Ontology Design For Pharmaceutical Research Ou...  book-chapter   \n",
      "33                 A Linked Data Model For Data Scopes  book-chapter   \n",
      "35   Federating Scholarly Infrastructures With Graphql  book-chapter   \n",
      "294  A Bibliometric Analysis Of The Explainable Art...  book-chapter   \n",
      "371  Assessing Eu'S Progress And Performance With R...  book-chapter   \n",
      "395  Fault Diagnosis In Industries: How To Improve ...  book-chapter   \n",
      "400  Computational Tools For Literature Review, Ana...  book-chapter   \n",
      "417  Eco-Innovation And Digital Transformation Rela...  book-chapter   \n",
      "482  Digital Signature In The Xades Standard As A R...  book-chapter   \n",
      "484  The Use Of Artificial Intelligence For Clinica...  book-chapter   \n",
      "\n",
      "     publication_year issue volume  chapter  \\\n",
      "8                2020   NaN    NaN      1.0   \n",
      "9                2020   NaN    NaN      1.0   \n",
      "10               2020   NaN    NaN      1.0   \n",
      "11               2020   NaN    NaN      1.0   \n",
      "13               2020   NaN    NaN      1.0   \n",
      "15               2021   NaN    NaN      1.0   \n",
      "20               2021   NaN    NaN      1.0   \n",
      "26               2019   NaN    NaN      1.0   \n",
      "29               2020   NaN    NaN      1.0   \n",
      "30               2020   NaN    NaN      1.0   \n",
      "32               2020   NaN    NaN      1.0   \n",
      "33               2021   NaN    NaN      1.0   \n",
      "35               2021   NaN    NaN      1.0   \n",
      "294              2018   NaN    NaN      1.0   \n",
      "371              2021   NaN    NaN      1.0   \n",
      "395              2021   NaN    NaN      1.0   \n",
      "400              2021   NaN    NaN      1.0   \n",
      "417              2021   NaN    NaN      1.0   \n",
      "482              2020   NaN    NaN      1.0   \n",
      "484              2020   NaN    NaN      1.0   \n",
      "\n",
      "                                     publication_venue venue_type  \\\n",
      "8    Lecture Notes In Computer Science - Knowledge ...       book   \n",
      "9    Lecture Notes In Computer Science - Knowledge ...       book   \n",
      "10   Digital Libraries For Open Knowledge - Lecture...       book   \n",
      "11   Adbis, Tpdl And Eda 2020 Common Workshops And ...       book   \n",
      "13   Lecture Notes In Computer Science - The Semant...       book   \n",
      "15   The Semantic Web - Lecture Notes In Computer S...       book   \n",
      "20   Communications In Computer And Information Sci...       book   \n",
      "26   Advances In Intelligent Systems And Computing ...       book   \n",
      "29   Systems And Information Sciences - Advances In...       book   \n",
      "30   Lecture Notes In Computer Science - Knowledge ...       book   \n",
      "32   Digital Libraries For Open Knowledge - Lecture...       book   \n",
      "33   Metadata And Semantic Research - Communication...       book   \n",
      "35   Lecture Notes In Computer Science - Towards Op...       book   \n",
      "294  Communications In Computer And Information Sci...       book   \n",
      "371  Industrial Ecology - Sustainable Production An...       book   \n",
      "395  Sustainable Design And Manufacturing - Smart I...       book   \n",
      "400                                 Literature Reviews       book   \n",
      "417  Industrial Engineering And Operations Manageme...       book   \n",
      "482  Computational Science And Its Applications – I...       book   \n",
      "484  Advances In Intelligent Systems And Computing ...       book   \n",
      "\n",
      "        publisher  event  \n",
      "8    crossref:297    NaN  \n",
      "9    crossref:297    NaN  \n",
      "10   crossref:297    NaN  \n",
      "11   crossref:297    NaN  \n",
      "13   crossref:297    NaN  \n",
      "15   crossref:297    NaN  \n",
      "20   crossref:297    NaN  \n",
      "26   crossref:297    NaN  \n",
      "29   crossref:297    NaN  \n",
      "30   crossref:297    NaN  \n",
      "32   crossref:297    NaN  \n",
      "33   crossref:297    NaN  \n",
      "35   crossref:297    NaN  \n",
      "294  crossref:297    NaN  \n",
      "371  crossref:297    NaN  \n",
      "395  crossref:297    NaN  \n",
      "400  crossref:297    NaN  \n",
      "417  crossref:297    NaN  \n",
      "482  crossref:297    NaN  \n",
      "484  crossref:297    NaN  \n",
      "                                 id  \\\n",
      "1    doi:10.1007/s11192-019-03217-6   \n",
      "2    doi:10.1007/s11192-019-03311-9   \n",
      "3         doi:10.1038/sdata.2016.18   \n",
      "6    doi:10.1007/s11192-020-03397-6   \n",
      "7    doi:10.1186/s13321-020-00448-1   \n",
      "..                              ...   \n",
      "487  doi:10.1007/s40200-020-00606-0   \n",
      "488  doi:10.1007/s00521-020-05395-4   \n",
      "489  doi:10.1007/s11695-020-05058-2   \n",
      "496  doi:10.1186/s12888-020-02825-4   \n",
      "498  doi:10.1007/s11301-020-00196-4   \n",
      "\n",
      "                                                 title             type  \\\n",
      "1    Software Review: Coci, The Opencitations Index...  journal-article   \n",
      "2    Nine Million Book Items And Eleven Million Cit...  journal-article   \n",
      "3    The Fair Guiding Principles For Scientific Dat...  journal-article   \n",
      "6    The Practice Of Self-Citations: A Longitudinal...  journal-article   \n",
      "7    Adoption Of The Citation Typing Ontology By Th...  journal-article   \n",
      "..                                                 ...              ...   \n",
      "487  Contribution Of Iran In Covid-19 Studies: A Bi...  journal-article   \n",
      "488  Automatic Clustering Algorithms: A Systematic ...  journal-article   \n",
      "489  Latin American Research On Bariatric Surgery: ...  journal-article   \n",
      "496  Mapping The Literature On Parents With Mental ...  journal-article   \n",
      "498  Intellectual Structure Of Management Innovatio...  journal-article   \n",
      "\n",
      "     publication_year issue volume  chapter  \\\n",
      "1                2019     2    121      NaN   \n",
      "2                2019     2    122      NaN   \n",
      "3                2016     1      3      NaN   \n",
      "6                2020     1    123      NaN   \n",
      "7                2020     1     12      NaN   \n",
      "..                ...   ...    ...      ...   \n",
      "487              2020     2     19      NaN   \n",
      "488              2020    11     33      NaN   \n",
      "489              2020     4     31      NaN   \n",
      "496              2020     1     20      NaN   \n",
      "498              2020     3     71      NaN   \n",
      "\n",
      "                             publication_venue venue_type     publisher  event  \n",
      "1                               Scientometrics    journal  crossref:297    NaN  \n",
      "2                               Scientometrics    journal  crossref:297    NaN  \n",
      "3                              Scientific Data    journal  crossref:297    NaN  \n",
      "6                               Scientometrics    journal  crossref:297    NaN  \n",
      "7                   Journal Of Cheminformatics    journal  crossref:297    NaN  \n",
      "..                                         ...        ...           ...    ...  \n",
      "487  Journal Of Diabetes & Metabolic Disorders    journal  crossref:297    NaN  \n",
      "488          Neural Computing And Applications    journal  crossref:297    NaN  \n",
      "489                            Obesity Surgery    journal  crossref:297    NaN  \n",
      "496                             Bmc Psychiatry    journal  crossref:297    NaN  \n",
      "498                Management Review Quarterly    journal  crossref:297    NaN  \n",
      "\n",
      "[145 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "q1 = relational_publications_csv.query('publication_venue ==\"Scientometrics\"')\n",
    "print(q1)\n",
    "q2 = relational_publications_csv.query('venue_type == \"book\"')\n",
    "print(q2)\n",
    "q3 = relational_publications_csv.query('publisher == \"crossref:297\"')\n",
    "print(q3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply, also, some Series methods to our data.\n",
    "The most intuitive one is the method to compute the median value of a certain series of numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = relational_publications_csv[\"publication_year\"].median()\n",
    "median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output abowe represents the average value of the `\"publication_year\"` Series of numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving forward, we have the unique method, which identifies what are all the unique values in a series.\n",
    "For instance, we want the unique values from the `\"issue\"` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1' '2' '10' '1-2' nan '9' '799' '3' '21' '7' '4' '23' '16' '6' '12' '5'\n",
      " '11' '13' '15' '17' '9-10' '20' '18' '8' '22' '14' '76' '96' '9-12' '95'\n",
      " '32' 'ahead-of-print']\n",
      "['journal-article' 'book-chapter']\n"
     ]
    }
   ],
   "source": [
    "issue_unique = relational_publications_csv[\"issue\"].unique()\n",
    "print(issue_unique)\n",
    "type_unique = relational_publications_csv[\"type\"].unique()\n",
    "print(type_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another method that we've learned about is the `value_counts()`, it returns the number of times each string appears in the series, where the unique string of the series become the index of the new series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "journal    478\n",
      "book        20\n",
      "Name: venue_type, dtype: int64\n",
      "2021    209\n",
      "2019    109\n",
      "2018     83\n",
      "2020     56\n",
      "2017     24\n",
      "2016     15\n",
      "2014      3\n",
      "2015      1\n",
      "Name: publication_year, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "venue_type_count = relational_publications_csv[\"venue_type\"].value_counts()\n",
    "print(venue_type_count)\n",
    "publication_year_count = relational_publications_csv[\"publication_year\"].value_counts()\n",
    "print(publication_year_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics gives us the chance to visualize, with simple portions of code, some usefull graphs.\n",
    "For instance, the series above can be plotted as a bar chart easily.\n",
    "The index labels will ne the categories shown in the x-axis, while the number of times each string is represented in the original series is the value in the y-axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.plotting._core.PlotAccessor object at 0x00000256A3B9E320>\n"
     ]
    },
    {
     "data": {
      "image/png": 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DkldOdypJ0+RlmQaSfAF4SVXtHfszwEd97w5p9fKZew+P2x/24S78byutat4t08OHk3wEeNfYvwD4tynOI2nKvCzTRJJfAV44dv+zqv51iuNImjLj/hiW5JNV9aIk32HuPvdMHP4BcDfwp1X1V1MZUNLUGPfGkhwPfKqqnrXgyZJaMe7NJTmpqu6c9hySjizjLkkNebucJDVk3CWpIeMuSQ0Zd0lqyLhLUkP/B9tjrryNrHqTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "venue_type_count = relational_publications_csv[\"venue_type\"].value_counts()\n",
    "\n",
    "venue_type_count.plot(kind=\"bar\")\n",
    "print(venue_type_count.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.plotting._core.PlotAccessor object at 0x00000256A3B9DBD0>\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAT0ElEQVR4nO3df5DkdX3n8ecLMNZFPAGZ20JgM+gtXCCHq04R6owpcuQSflig1hXKpZR46modVLQudbmN3p3WVSW1d6exksqF1CqcWGVQFFEucB4ciVImQVlgxcUF+ZFFdrMuGzBg1LKy8L4/+jvSO87szkx/Z3r64/NR1TXdn2/3t1/0LK/59qe//f2mqpAkteWIcQeQJPXPcpekBlnuktQgy12SGmS5S1KDLHdJatBR4w4AcPzxx9f09PS4Y0jSRLnrrrv+tqqm5lu2Jsp9enqabdu2jTuGJE2UJI8utMxpGUlqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KD1sSXmJZievNNva9z15YLe1+nJI2TW+6S1CDLXZIaZLlLUoMsd0lq0GHLPcnJSf48yTeS3Jfk3d34cUluTfJg9/PYbjxJ/iDJQ0nuTfLKlf6PkCQdbDFb7geA36yq04GzgcuTnA5sBm6rqg3Abd1tgPOBDd1lE3Bl76klSYd02HKvqr1VdXd3/bvATuBE4GLgmu5u1wCv665fDHy8Bu4AjklyQt/BJUkLW9Kce5Jp4BXAV4B1VbW3W/RtYF13/UTgsaGH7e7GJEmrZNHlnuRo4HrgPVX19PCyqiqglvLESTYl2ZZk2/79+5fyUEnSYSyq3JM8j0Gxf6KqPtsN75udbul+Pt6N7wFOHnr4Sd3YQapqa1XNVNXM1NS8pwCUJC3TYvaWCXAVsLOqfm9o0Y3AZd31y4DPD42/pdtr5mzgqaHpG0nSKljMsWVeDbwZ+HqS7d3Ye4EtwHVJ3gY8ClzSLbsZuAB4CPg+8NY+A0uSDu+w5V5VXwaywOJz57l/AZePmEuSNAK/oSpJDbLcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJatBiTrN3dZLHk+wYGvtUku3dZdfsGZqSTCf5wdCyP17B7JKkBSzmNHsfA/4Q+PjsQFW9cfZ6kg8BTw3d/+Gq2thTPknSMizmNHu3J5meb1l38uxLgH/Zcy5J0ghGnXN/DbCvqh4cGjslyT1JvpTkNSOuX5K0DIuZljmUS4Frh27vBdZX1RNJXgV8LskZVfX03Acm2QRsAli/fv2IMSRJw5a95Z7kKOANwKdmx6rqh1X1RHf9LuBh4NT5Hl9VW6tqpqpmpqamlhtDkjSPUaZlfhm4v6p2zw4kmUpyZHf9pcAG4JHRIkqSlmoxu0JeC/wVcFqS3Une1i16EwdPyQD8InBvt2vkZ4B3VdWTPeaVJC3CYvaWuXSB8V+fZ+x64PrRY0mSRuE3VCWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWqQ5S5JDVrMmZiuTvJ4kh1DYx9IsifJ9u5ywdCy307yUJIHkvzqSgWXJC1sMVvuHwPOm2f8w1W1sbvcDJDkdAan3zuje8wfzZ5TVZK0eg5b7lV1O7DY86BeDHyyqn5YVX8NPAScNUI+SdIyjDLnfkWSe7tpm2O7sROBx4bus7sbkyStouWW+5XAy4CNwF7gQ0tdQZJNSbYl2bZ///5lxpAkzWdZ5V5V+6rqmap6FvgIz0297AFOHrrrSd3YfOvYWlUzVTUzNTW1nBiSpAUsq9yTnDB08/XA7J40NwJvSvL8JKcAG4CvjhZRkrRURx3uDkmuBc4Bjk+yG3g/cE6SjUABu4B3AlTVfUmuA74BHAAur6pnViS5JGlBhy33qrp0nuGrDnH/3wF+Z5RQkqTR+A1VSWqQ5S5JDbLcJalBlrskNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDDlvuSa5O8niSHUNj/yPJ/UnuTXJDkmO68ekkP0iyvbv88QpmlyQtYDFb7h8Dzpszdivwc1V1JvBN4LeHlj1cVRu7y7v6iSlJWorDlntV3Q48OWfslqo60N28AzhpBbJJkpapjzn3fwv8n6HbpyS5J8mXkrymh/VLkpbosCfIPpQk7wMOAJ/ohvYC66vqiSSvAj6X5Iyqenqex24CNgGsX79+lBiSpDmWveWe5NeB1wK/VlUFUFU/rKonuut3AQ8Dp873+KraWlUzVTUzNTW13BiSpHksq9yTnAf8FnBRVX1/aHwqyZHd9ZcCG4BH+ggqSVq8w07LJLkWOAc4Pslu4P0M9o55PnBrEoA7uj1jfhH4r0n+AXgWeFdVPTnviiVJK+aw5V5Vl84zfNUC970euH7UUJKk0fgNVUlqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNGumQv1rY9Oabel/nri0X9r5OSW1yy12SGmS5S1KDLHdJapDlLkkNstwlqUGLKvckVyd5PMmOobHjktya5MHu57HdeJL8QZKHktyb5JUrFV6SNL/Fbrl/DDhvzthm4Laq2gDc1t0GOJ/BuVM3AJuAK0ePKUlaikWVe1XdDsw9F+rFwDXd9WuA1w2Nf7wG7gCOSXJCD1klSYs0ypz7uqra213/NrCuu34i8NjQ/XZ3YwdJsinJtiTb9u/fP0IMSdJcvXygWlUF1BIfs7WqZqpqZmpqqo8YkqTOKOW+b3a6pfv5eDe+Bzh56H4ndWOSpFUySrnfCFzWXb8M+PzQ+Fu6vWbOBp4amr6RJK2CRR04LMm1wDnA8Ul2A+8HtgDXJXkb8ChwSXf3m4ELgIeA7wNv7TmzJOkwFlXuVXXpAovOnee+BVw+SihJ0mj8hqokNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktSgRR1bRm2a3nxT7+vcteXC3tcpaenccpekBlnuktQgy12SGrTsOfckpwGfGhp6KfBfgGOAdwCzZ71+b1XdvNznkSQt3bLLvaoeADYCJDmSwXlSb2Bw5qUPV9UH+wgoSVq6vqZlzgUerqpHe1qfJGkEfZX7m4Brh25fkeTeJFcnOban55AkLdLI5Z7kp4CLgE93Q1cCL2MwZbMX+NACj9uUZFuSbfv375/vLpKkZepjy/184O6q2gdQVfuq6pmqehb4CHDWfA+qqq1VNVNVM1NTUz3EkCTN6qPcL2VoSibJCUPLXg/s6OE5JElLMNLhB5K8APhXwDuHhv97ko1AAbvmLJMkrYKRyr2qvge8eM7Ym0dKJEkamd9QlaQGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ2y3CWpQZa7JDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNGumQv9JqmN58U+/r3LXlwt7XKa0lbrlLUoNG3nJPsgv4LvAMcKCqZpIcB3wKmGZwNqZLquo7oz6XJGlx+tpy/6Wq2lhVM93tzcBtVbUBuK27LUlaJSs1LXMxcE13/RrgdSv0PJKkefRR7gXckuSuJJu6sXVVtbe7/m1gXQ/PI0lapD72lvmFqtqT5J8Atya5f3hhVVWSmvug7g/BJoD169f3EEOSNGvkLfeq2tP9fBy4ATgL2JfkBIDu5+PzPG5rVc1U1czU1NSoMSRJQ0Yq9yQvSPLC2evArwA7gBuBy7q7XQZ8fpTnkSQtzajTMuuAG5LMrutPquoLSe4ErkvyNuBR4JIRn0eStAQjlXtVPQK8fJ7xJ4BzR1m3JGn5/IaqJDXIcpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUIMtdkhpkuUtSgyx3SWqQ5S5JDbLcJalByy73JCcn+fMk30hyX5J3d+MfSLInyfbuckF/cSVJizHKmZgOAL9ZVXd351G9K8mt3bIPV9UHR48nSVqOZZd7Ve0F9nbXv5tkJ3BiX8EkScvXy5x7kmngFcBXuqErktyb5Ookx/bxHJKkxRu53JMcDVwPvKeqngauBF4GbGSwZf+hBR63Kcm2JNv2798/agxJ0pBR5txJ8jwGxf6JqvosQFXtG1r+EeBP53tsVW0FtgLMzMzUKDmktWB68029r3PXlgt7X6d+Moyyt0yAq4CdVfV7Q+MnDN3t9cCO5ceTJC3HKFvurwbeDHw9yfZu7L3ApUk2AgXsAt45wnNIkpZhlL1lvgxknkU3Lz+OJKkPfkNVkhpkuUtSgyx3SWqQ5S5JDbLcJalBI32JSdLk8ctWPxnccpekBlnuktQgy12SGmS5S1KDLHdJapDlLkkNstwlqUGWuyQ1yHKXpAZZ7pLUoBU7/ECS84DfB44EPlpVW1bquSS1x8MkjGZFttyTHAn8T+B84HQGp947fSWeS5L041Zqy/0s4KGqegQgySeBi4FvrNDzSdJYrNV3GKmqHqLMWWnyr4Hzqurt3e03Az9fVVcM3WcTsKm7eRrwQM8xjgf+tud1rgRz9suc/ZqEnJOQEVYm589U1dR8C8Z2yN+q2gpsXan1J9lWVTMrtf6+mLNf5uzXJOSchIyw+jlXam+ZPcDJQ7dP6sYkSatgpcr9TmBDklOS/BTwJuDGFXouSdIcKzItU1UHklwB/F8Gu0JeXVX3rcRzHcKKTfn0zJz9Mme/JiHnJGSEVc65Ih+oSpLGy2+oSlKDLHdJapDlLkkNstwlqUFj+xLTakny1qr6X+POMSvJS4E3MPgewDPAN4E/qaqnxxpsyNDuq39TVf8vyb8B/gWwE9haVf8w1oBDkvwzBoe2OLEb2gPcWFU7x5fqOUl+A7ihqh4bd5ZDSfLzwM6qejrJPwI2A69kcMiQ362qp8Ya8BCSfLyq3jLuHIeS5BcYHJZlR1XdsirP2freMkm+VVXrx50DfvQ/+muB24ELgHuAvwNeD/y7qvri2MINSfIJBn/4f5pBvqOBzwLnMvg3c9n40j0nyX8ELgU+Cezuhk9i8Ifpk2vhSKRJngK+BzwMXAt8uqr2jzfVj0tyH/DybjfmrcD3gc8w+J2/vKreMNaAnSRzvy8T4JeAPwOoqotWPdQ8kny1qs7qrr8DuBy4AfgV4H+vxr/NJso9yb0LLQJOrarnr2aehST5OrCxqp5J8tPAzVV1TpL1wOer6hVjjggMXs+qOjPJUQy2hF/SZQ7wtao6c8wRAUjyTeCMue8kunce91XVhvEkOyjLPcCrgF8G3ghcBNzFoOg/W1XfHWO8H0mys6p+trt+d1W9cmjZ9qraOLZwQ5LczeDdxEeBYvD/+LUM/qBTVV8aX7rnJLln9v/nJHcCF1TV/iQvAO6oqn++0hlamZZZB/wq8J054wH+cvXjHNJRDKZjns9gi5iq+laS54011cGO6AryBQy23l8EPMkg81rK+SzwEuDROeMndMvWgqqqZ4FbgFu63/P5DN5xfBCY96BPY7BjaArza0lmqmpbklOBNTMNB8wA7wbeB/yHqtqe5AdrpdSHHJHkWAafa2b23VpVfS/JgdUI0Eq5/ylwdFVtn7sgyRdXPc3CPgrcmeQrwGuA/waQZIpBea4VVwH3M/h28fuATyd5BDibwRTIWvEe4LYkDwKzc9rrgX8KXLHQg1ZZhm907zJuBG7s3r2tFW8Hfj/Jf2Jw5MK/SvIYg9f17WNNNqT7Q/nhJJ/ufu5jbfbYixi8QwtQSU6oqr1JjmbOv4mV0sS0zCRJcgbwsww+WLl/3HkWkuQlAFX1N0mOYTCt8K2q+upYg82R5AgGH1QNf6B6Z1U9M75Uz0lyalV9c9w5FivJPwZOYVCYu6tq35gjHVKSC4FXV9V7x51lMbo/6Ouq6q9X/Lks97UhydFV9ffjznE4k5JzEkzKa2nOfq1Wzib2c09yZpI7kjyWZGs31zW7bE1taR7CpJylas3kbOD3vmZey8MwZ79WJedanKtajj8CPgDcwWB+8MtJLqqqh1lDHwAm+fcLLaL7cHUtmJScTMDvfVJeS3P2ay3kbGLLHXhhVX2hqv6uqj7I4MO0LyQ5m8HuUmvF7wLHAi+cczmatfW7mJSck/B7n5TX0pz9Gn/Oqpr4C/A14EVzxs4EHgSeGHe+oUx/CbxqgWWPjTvfBOZc87/3CXotzdlYziY+UO2+Hv9IVd0xZ3w98J+r6h3jSXawJKcBT9Y831BMsq7WyJ4JE5Rzzf/eJ+i1NGeP1kLOJspdknSwtTRHtWxJXpRkS5L7kzyZ5IkkO7uxY8adb5Y5+zUJOSchI5izb2shZxPlDlzH4NAD51TVcVX1YgYHE/pOt2ytMGe/JiHnJGQEc/Zt7DmbmJZJ8kBVnbbUZavNnP2ahJyTkBHM2be1kLOVLfdHk/xWknWzA0nWZXBI2LV0HG1z9msSck5CRjBn38aes5VyfyPwYuBLSb6T5Engi8BxwCXjDDaHOfs1CTknISOYs29jz9nEtAz86Iw8JzE4VvLfD42fV1VfGF+yg5mzX5OQcxIygjn7Nvac49rJv88L8BvAA8DngF3AxUPL7h53PnP+5OachIzmbDPn2F+Enl7IrzM4njvANLANeHd3+55x5zPnT27OSchozjZztnLgsCOqe9tTVbuSnAN8JsnPsEoHxl8kc/ZrEnJOQkYwZ9/GnrOVD1T3Jdk4e6N7UV8LHA+s+LkKl8Cc/ZqEnJOQEczZt7HnbOID1SQnAQeq6tvzLHt1Vf3FGGL9GHP2axJyTkJGMGff1kLOJspdknSwVqZlJElDLHdJapDlLkkNstwlqUGWuyQ16P8DyAxYjZV6QZAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "publication_year_count = relational_publications_csv[\"publication_year\"].value_counts()\n",
    "publication_year_count.plot(kind=\"bar\")\n",
    "print(publication_year_count.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11     29\n",
       "13     22\n",
       "10     18\n",
       "18     12\n",
       "9      11\n",
       "14     11\n",
       "116    10\n",
       "16     10\n",
       "2       9\n",
       "28      9\n",
       "121     8\n",
       "7       8\n",
       "21      8\n",
       "12      8\n",
       "27      6\n",
       "15      6\n",
       "23      6\n",
       "115     6\n",
       "99      5\n",
       "33      5\n",
       "Name: volume, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_volume = relational_publications_csv[\"volume\"].value_counts()[:20]\n",
    "top_volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_volume.plot(kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Scientometrics                                                       50\n",
       "Sustainability                                                       30\n",
       "International Journal Of Environmental Research And Public Health    14\n",
       "Journal Of Informetrics                                               9\n",
       "Journal Of Cleaner Production                                         9\n",
       "                                                                     ..\n",
       "Current Psychology                                                    1\n",
       "Geoderma                                                              1\n",
       "European Journal Of Education                                         1\n",
       "Architectural Science Review                                          1\n",
       "Accountability In Research                                            1\n",
       "Name: publication_venue, Length: 295, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publications_per_venue = relational_publications_csv[\"publication_venue\"].value_counts()\n",
    "publications_per_venue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3D Printing In Medicine                                                                                                   1\n",
       "Accident Analysis & Prevention                                                                                            1\n",
       "Accountability In Research                                                                                                1\n",
       "Adbis, Tpdl And Eda 2020 Common Workshops And Doctoral Consortium - Communications In Computer And Information Science    1\n",
       "Administrative Sciences                                                                                                   3\n",
       "                                                                                                                         ..\n",
       "Utilities Policy                                                                                                          1\n",
       "Water, Air, & Soil Pollution                                                                                              2\n",
       "Work                                                                                                                      1\n",
       "World Journal Of Entrepreneurship, Management And Sustainable Development                                                 1\n",
       "World Journal Of Microbiology And Biotechnology                                                                           1\n",
       "Name: publication_venue, Length: 295, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "publications_per_venue_sorted = publications_per_venue.sort_index()\n",
    "publications_per_venue_sorted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### relational_other_data.json \n",
    "The file is a JSON (JavaScript Object Notation).\n",
    "It is a dictionary containing 4 keys:\n",
    "1. Key `\"authors\"` has a dictionary with many keys, it conncecs a publication (doi) with its authors(s)\n",
    "     - Key `\"doi\"` has value a list of dictionaries:\n",
    "         - Each dictionary has 3 keys: `\"famly\"`, `\"given\"`, `\"orcid\"`.\n",
    "             - Each of these keys has a value a string.\n",
    "2. Key `\"venues_id\"` has a value a dictionary with many keys, it conncects a publication (doi) with its publication venue\n",
    "    - Key `\"doi\"` has value a lists of strings\n",
    "        - The strings are either `\"issn:...\"` or `\"isbn:...\"`\n",
    "            - ISSN = International Standard Serial Number\n",
    "            - ISBN = International Standard Book Number\n",
    "3. Key `\"references\"` has value a dictionary with many keys, it conncects a publication (doi) with other publication(s), these are the publications that are cited by our starting publication\n",
    "    - Key `\"doi\"` has value a list of strings\n",
    "        - The list can be empty or can contain any number of strings\n",
    "            - The strings are `\"doi:...\"`\n",
    "4. Key `\"publishers\"` has value a dictionary with many keys, it connects a publisher (crosssref) with its id (also crossref) and its name.\n",
    "    - Key `\"crossref:...\"` has value a dictionary with 2 keys:\n",
    "        - Crossref provides digital object identifiers (DOIs) for journal content (but not only this)\n",
    "            - It was founded in 2000 by a group of publishers who needed an efficient method to connect their journal articles, and so began the use of DOIs to provide a way to link references between articles\n",
    "        - Key `\"id\"` and key `\"name\"` have value a string\n",
    "\n",
    "Since the structure is pretty different from the .csv file we might want to create a dataframe for each keys of our file. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUTHORS_DF                             doi_authors    family           given  \\\n",
      "0               doi:10.1162/qss_a_00023    Peroni          Silvio   \n",
      "1               doi:10.1162/qss_a_00023   Shotton           David   \n",
      "2        doi:10.1007/s11192-019-03217-6     Heibi            Ivan   \n",
      "3        doi:10.1007/s11192-019-03217-6    Peroni          Silvio   \n",
      "4        doi:10.1007/s11192-019-03217-6   Shotton           David   \n",
      "...                                 ...       ...             ...   \n",
      "1133  doi:10.1080/00472778.2020.1776578    Amorós      J. Ernesto   \n",
      "1134     doi:10.1007/s11301-020-00196-4     Simao  Lurdes Barroso   \n",
      "1135     doi:10.1007/s11301-020-00196-4  Carvalho    Luísa Cagica   \n",
      "1136     doi:10.1007/s11301-020-00196-4   Madeira      Maria José   \n",
      "1137  doi:10.1080/08989621.2020.1836620       Ali           Imran   \n",
      "\n",
      "                    orcid  \n",
      "0     0000-0003-0530-4305  \n",
      "1     0000-0001-5506-523X  \n",
      "2     0000-0001-5366-5194  \n",
      "3     0000-0003-0530-4305  \n",
      "4     0000-0001-5506-523X  \n",
      "...                   ...  \n",
      "1133  0000-0001-9601-0892  \n",
      "1134  0000-0002-9703-1481  \n",
      "1135  0000-0002-9804-7813  \n",
      "1136  0000-0003-1722-6148  \n",
      "1137  0000-0002-8912-5760  \n",
      "\n",
      "[1138 rows x 4 columns]\n",
      "VENUES_ID_DF                          doi_venues_id       issn_isbn\n",
      "0              doi:10.1162/qss_a_00023  issn:2641-3337\n",
      "1       doi:10.1007/s11192-019-03217-6  issn:0138-9130\n",
      "2       doi:10.1007/s11192-019-03217-6  issn:1588-2861\n",
      "3       doi:10.1007/s11192-019-03311-9  issn:0138-9130\n",
      "4       doi:10.1007/s11192-019-03311-9  issn:1588-2861\n",
      "..                                 ...             ...\n",
      "736  doi:10.1080/00472778.2020.1776578  issn:1540-627X\n",
      "737     doi:10.1007/s11301-020-00196-4  issn:2198-1620\n",
      "738     doi:10.1007/s11301-020-00196-4  issn:2198-1639\n",
      "739  doi:10.1080/08989621.2020.1836620  issn:0898-9621\n",
      "740  doi:10.1080/08989621.2020.1836620  issn:1545-5815\n",
      "\n",
      "[741 rows x 2 columns]\n",
      "REFERENCES_DF                              og_doi                           ref_doi\n",
      "0           doi:10.1162/qss_a_00023    doi:10.1007/s11192-019-03217-6\n",
      "1           doi:10.1162/qss_a_00023    doi:10.1007/s11192-019-03311-9\n",
      "2           doi:10.1162/qss_a_00023         doi:10.1038/sdata.2016.18\n",
      "3           doi:10.1162/qss_a_00023  doi:10.1371/journal.pbio.3000385\n",
      "4           doi:10.1162/qss_a_00023             doi:10.3233/ds-190016\n",
      "..                              ...                               ...\n",
      "196       doi:10.3390/polym13121957  doi:10.1016/j.autcon.2019.01.010\n",
      "197  doi:10.1007/s00521-020-05395-4     doi:10.1007/s00500-018-3168-z\n",
      "198  doi:10.1007/s00521-020-05395-4    doi:10.1016/j.asoc.2018.03.041\n",
      "199  doi:10.1007/s00521-020-05395-4    doi:10.1016/j.cmpb.2019.105075\n",
      "200    doi:10.1108/jic-02-2020-0054     doi:10.1007/s10961-017-9637-1\n",
      "\n",
      "[201 rows x 2 columns]\n",
      "PUBLISHERS_DF           crossref     id_crossref  \\\n",
      "0    crossref:6228   crossref:6228   \n",
      "1    crossref:4297   crossref:4297   \n",
      "2     crossref:297    crossref:297   \n",
      "3      crossref:98     crossref:98   \n",
      "4     crossref:530    crossref:530   \n",
      "5    crossref:9677   crossref:9677   \n",
      "6    crossref:2373   crossref:2373   \n",
      "7     crossref:179    crossref:179   \n",
      "8    crossref:7132   crossref:7132   \n",
      "9      crossref:30     crossref:30   \n",
      "10    crossref:311    crossref:311   \n",
      "11   crossref:2209   crossref:2209   \n",
      "12    crossref:316    crossref:316   \n",
      "13    crossref:281    crossref:281   \n",
      "14   crossref:2560   crossref:2560   \n",
      "15    crossref:140    crossref:140   \n",
      "16   crossref:6188   crossref:6188   \n",
      "17   crossref:1010   crossref:1010   \n",
      "18    crossref:340    crossref:340   \n",
      "19   crossref:4443   crossref:4443   \n",
      "20  crossref:16117  crossref:16117   \n",
      "21     crossref:56     crossref:56   \n",
      "22     crossref:78     crossref:78   \n",
      "23    crossref:292    crossref:292   \n",
      "24   crossref:7437   crossref:7437   \n",
      "25    crossref:286    crossref:286   \n",
      "26   crossref:2258   crossref:2258   \n",
      "27    crossref:266    crossref:266   \n",
      "28    crossref:239    crossref:239   \n",
      "29   crossref:1968   crossref:1968   \n",
      "30   crossref:1709   crossref:1709   \n",
      "31    crossref:301    crossref:301   \n",
      "\n",
      "                                             name_pub  \n",
      "0                                  Codon Publications  \n",
      "1                           Riga Technical University  \n",
      "2             Springer Science and Business Media LLC  \n",
      "3                                     Hindawi Limited  \n",
      "4                                 FapUNIFESP (SciELO)  \n",
      "5   The Russian Presidential Academy of National E...  \n",
      "6                                      Editorial CSIC  \n",
      "7                                   SAGE Publications  \n",
      "8                            Universidad de Antioquia  \n",
      "9          American Society of Civil Engineers (ASCE)  \n",
      "10                                              Wiley  \n",
      "11             Vilnius Gediminas Technical University  \n",
      "12                    American Chemical Society (ACS)  \n",
      "13                               MIT Press - Journals  \n",
      "14                                 F1000 Research Ltd  \n",
      "15                                            Emerald  \n",
      "16                                    Sergii Iermakov  \n",
      "17                             JMIR Publications Inc.  \n",
      "18                   Public Library of Science (PLoS)  \n",
      "19                                              PeerJ  \n",
      "20                 Association of Austrian Librarians  \n",
      "21                   Cambridge University Press (CUP)  \n",
      "22                                        Elsevier BV  \n",
      "23                   Royal Society of Chemistry (RSC)  \n",
      "24                                          IOS Press  \n",
      "25                      Oxford University Press (OUP)  \n",
      "26                                 Pensoft Publishers  \n",
      "27                                     IOP Publishing  \n",
      "28                                                BMJ  \n",
      "29                                            MDPI AG  \n",
      "30       Ediciones Profesionales de la Informacion SL  \n",
      "31                                 Informa UK Limited  \n"
     ]
    }
   ],
   "source": [
    "with open(\"testData/relational_other_data.json\", \"r\", encoding=\"utf-8\") as file:\n",
    "    jsondata = json.load(file)\n",
    "\n",
    "    # =============== AUTHORS DATAFRAME ===============\n",
    "    authors_df = pd.DataFrame({\n",
    "        \"doi_authors\": pd.Series(dtype=\"str\"),\n",
    "        \"family\": pd.Series(dtype=\"str\"),\n",
    "        \"given\": pd.Series(dtype=\"str\"),\n",
    "        \"orcid\": pd.Series(dtype=\"str\")\n",
    "    })\n",
    "\n",
    "    family = []\n",
    "    given = []\n",
    "    orcid = []\n",
    "    doi_authors = []\n",
    "\n",
    "    authors = jsondata['authors']\n",
    "    for key in authors:\n",
    "        for value in authors[key]:\n",
    "            doi_authors.append(key)\n",
    "            family.append(value['family'])\n",
    "            given.append(value['given'])\n",
    "            orcid.append(value['orcid'])\n",
    "\n",
    "    authors_df['doi_authors'] = doi_authors\n",
    "    authors_df['family'] = family\n",
    "    authors_df['given'] = given\n",
    "    authors_df['orcid'] = orcid\n",
    "\n",
    "    # =============== VENUES DATAFRAME ===============\n",
    "\n",
    "    venues_id_df = pd.DataFrame({\n",
    "        \"doi_venues_id\": pd.Series(dtype=\"str\"),\n",
    "        \"issn_isbn\": pd.Series(dtype=\"str\"),\n",
    "    })\n",
    "\n",
    "    doi_venues_id = []\n",
    "    issn_isbn = []\n",
    "\n",
    "    venues_id = jsondata[\"venues_id\"]\n",
    "    for key in venues_id:\n",
    "        for value in venues_id[key]:\n",
    "            doi_venues_id.append(key)\n",
    "            issn_isbn.append(value)\n",
    "\n",
    "    venues_id_df[\"doi_venues_id\"] = doi_venues_id\n",
    "    venues_id_df[\"issn_isbn\"] = pd.Series(issn_isbn)\n",
    "\n",
    "    # =============== REFERENCES DATAFRAME ===============\n",
    "\n",
    "    references_df = pd.DataFrame({\n",
    "        \"og_doi\": pd.Series(dtype=\"str\"),\n",
    "        \"ref_doi\": pd.Series(dtype=\"str\"),\n",
    "    })\n",
    "\n",
    "    og_doi = []\n",
    "    ref_doi = []\n",
    "\n",
    "    references = jsondata[\"references\"]\n",
    "    for key in references:\n",
    "        for value in references[key]:\n",
    "            og_doi.append(key)\n",
    "            ref_doi.append(value)\n",
    "\n",
    "    references_df[\"og_doi\"] = pd.Series(og_doi)\n",
    "    references_df[\"ref_doi\"] = pd.Series(ref_doi)\n",
    "\n",
    "    # =============== PUBLISHER DATAFRAME ===============\n",
    "\n",
    "    publishers_df = pd.DataFrame({\n",
    "        \"crossref\": pd.Series(dtype=\"str\"),\n",
    "        \"id_crossref\": pd.Series(dtype=\"str\"),\n",
    "        \"name_pub\": pd.Series(dtype=\"str\")\n",
    "    })\n",
    "\n",
    "    crossref = []\n",
    "    id_crossref = []\n",
    "    name_pub = []\n",
    "\n",
    "    publishers = jsondata[\"publishers\"]\n",
    "    for key in publishers:\n",
    "        crossref.append(key)\n",
    "        id_crossref.append(publishers[key][\"id\"])\n",
    "        name_pub.append(publishers[key][\"name\"])\n",
    "\n",
    "    publishers_df[\"crossref\"] = pd.Series(crossref)\n",
    "    publishers_df[\"id_crossref\"] = pd.Series(id_crossref)\n",
    "    publishers_df[\"name_pub\"] = pd.Series(name_pub)\n",
    "\n",
    "print(\"AUTHORS_DF\", authors_df)\n",
    "print(\"VENUES_ID_DF\", venues_id_df)\n",
    "print(\"REFERENCES_DF\", references_df)\n",
    "print(\"PUBLISHERS_DF\", publishers_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what we've done for our upload Method. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we obtained our tables we are able to apply our methods to each of them.\n",
    "First of all, in order to obtain an overview we are going to use the describe method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      doi_authors family      given                orcid\n",
      "count                        1138   1138       1138                 1138\n",
      "unique                        508    927        916                 1017\n",
      "top     doi:10.1038/sdata.2016.18   Wang  Francisco  0000-0001-6593-7986\n",
      "freq                           46     10          9                    8\n",
      "                            doi_venues_id       issn_isbn\n",
      "count                                 741             741\n",
      "unique                                498             451\n",
      "top     doi:10.1080/08989621.2020.1836620  issn:1588-2861\n",
      "freq                                    2              50\n",
      "                         og_doi                  ref_doi\n",
      "count                       201                      201\n",
      "unique                      134                       91\n",
      "top     doi:10.1162/qss_a_00023  doi:10.1162/qss_a_00023\n",
      "freq                          5                       20\n",
      "             crossref    id_crossref            name_pub\n",
      "count              32             32                  32\n",
      "unique             32             32                  32\n",
      "top     crossref:6228  crossref:6228  Codon Publications\n",
      "freq                1              1                   1\n"
     ]
    }
   ],
   "source": [
    "print(authors_df.describe(include=\"all\"))\n",
    "print(venues_id_df.describe(include=\"all\"))\n",
    "print(references_df.describe(include=\"all\"))\n",
    "print(publishers_df.describe(include=\"all\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're not able to compute values as \"mean\", \"st\", \"min\" etc.. because the values are not comparable. \n",
    "Now that we've our first description we may want to run some queeries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------query for authors_df-----------------------------------------------\n",
      "                              doi_authors family             given  \\\n",
      "254                doi:10.3390/su10030682   Wang              Peng   \n",
      "453   doi:10.1016/j.socscimed.2019.112552   Wang              Yuxi   \n",
      "585            doi:10.3390/ijerph18147358   Wang           Jinmiao   \n",
      "588          doi:10.3390/recycling6020034   Wang  Cynthia Changxin   \n",
      "720                  doi:10.1002/rra.3431   Wang               Jun   \n",
      "723                     doi:10.2196/12625   Wang            Shimin   \n",
      "760            doi:10.1002/adom.202100519   Wang              Zhen   \n",
      "834         doi:10.1177/03128962211035470   Wang           Nengmin   \n",
      "992               doi:10.3390/jmse9020218   Wang          Tianzhen   \n",
      "1062               doi:10.1002/cjce.24127   Wang              Xiao   \n",
      "\n",
      "                    orcid  \n",
      "254   0000-0002-7842-9144  \n",
      "453   0000-0002-9897-7316  \n",
      "585   0000-0002-7587-5763  \n",
      "588   0000-0001-6414-3228  \n",
      "720   0000-0003-2481-1409  \n",
      "723   0000-0003-4247-6317  \n",
      "760   0000-0002-0331-8271  \n",
      "834   0000-0002-6543-1309  \n",
      "992   0000-0002-7525-8466  \n",
      "1062  0000-0003-1624-8230  \n",
      "--------------------------------------query for venues_id_df------------------------------------------\n",
      "                         doi_venues_id       issn_isbn\n",
      "739  doi:10.1080/08989621.2020.1836620  issn:0898-9621\n",
      "740  doi:10.1080/08989621.2020.1836620  issn:1545-5815\n",
      "---------------------------------------query for references_df----------------------------------------\n",
      "                    og_doi                           ref_doi\n",
      "0  doi:10.1162/qss_a_00023    doi:10.1007/s11192-019-03217-6\n",
      "1  doi:10.1162/qss_a_00023    doi:10.1007/s11192-019-03311-9\n",
      "2  doi:10.1162/qss_a_00023         doi:10.1038/sdata.2016.18\n",
      "3  doi:10.1162/qss_a_00023  doi:10.1371/journal.pbio.3000385\n",
      "4  doi:10.1162/qss_a_00023             doi:10.3233/ds-190016\n",
      "---------------------------------------query for publisher_df---------------------------------------\n",
      "        crossref    id_crossref            name_pub\n",
      "0  crossref:6228  crossref:6228  Codon Publications\n"
     ]
    }
   ],
   "source": [
    "print(\"-------------------------------------query for authors_df-----------------------------------------------\")\n",
    "aq = authors_df.query('family ==\"Wang\"')\n",
    "print(aq)\n",
    "print(\"--------------------------------------query for venues_id_df------------------------------------------\")\n",
    "vq = venues_id_df.query('doi_venues_id == \"doi:10.1080/08989621.2020.1836620\"')\n",
    "print(vq)\n",
    "print(\"---------------------------------------query for references_df----------------------------------------\")\n",
    "rq = references_df.query('og_doi == \"doi:10.1162/qss_a_00023\"')\n",
    "print(rq)\n",
    "print(\"---------------------------------------query for publisher_df---------------------------------------\")\n",
    "pq = publishers_df.query('name_pub == \"Codon Publications\"')\n",
    "print(pq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now apply the unique method in order to retrieve all the unique given names in the `authors_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Silvio' 'David' 'Ivan' 'Yongjun' 'Mark D.' 'Michel' 'Ijsbrand Jan'\n",
      " 'Gabrielle' 'Myles' 'Arie' 'Niklas' 'Jan-Willem' 'Luiz Bonino'\n",
      " 'Philip E.' 'Anthony J.' 'Tim' 'Mercè' 'Ingrid' 'Olivier' 'Scott'\n",
      " 'Chris T.' 'Richard' 'Alejandra' 'Alasdair J.G.' 'Paul' 'Carole'\n",
      " 'Jeffrey S.' 'Jaap' 'Peter A.C' 'Rob' 'Tobias' 'Joost' 'Scott J.'\n",
      " 'Maryann E.' 'Abel L.' 'Bengt' 'Philippe' 'Marco' 'Susanna-Assunta'\n",
      " 'Erik' 'Thierry' 'Ted' 'Morris A.' 'Mark' 'Jan' 'Andra' 'Katherine' 'Jun'\n",
      " 'Barend' 'B. Ian' 'Kirk L.' 'Matthew T.' 'Mario A.' 'Stephen A.'\n",
      " 'George M.' 'Paolo' 'Aldo' 'Andrea Giovanni' 'Francesco' 'Valentina'\n",
      " 'Egon' 'Angelo' 'Enrico' 'Viet Bach' 'Vojtěch' 'Gollam' 'Oscar'\n",
      " 'Mohamad Yaser' 'Markus' 'Sören' 'Diego' 'Alexis-Michel' 'Alberto' 'Mike'\n",
      " 'Enrique' 'Emilio' 'Marilena' 'Giovanni' 'Behnam' 'Anne' 'Philipp'\n",
      " 'Matteo' 'Kevin W.' 'Peter' 'Xander' 'Lucas' 'Victor' 'O.' 'Martijn'\n",
      " 'Nees Jan' 'Ludo' 'Bilal H.' 'Muhammad' 'Roderic D. M.' 'Marcos' 'Carlos'\n",
      " 'José Luis' 'Josh M.' 'Milo' 'Patrice' 'Ashish' 'Domenic' 'Neves P.'\n",
      " 'Sean C.' 'Christian' 'Serhii' 'Franziska' 'Nataliia' 'Hongbing'\n",
      " 'Mariela' 'Janneth' 'Paola' 'Idafen' 'Konstantinos I.' 'Teresa'\n",
      " 'Cristina-Iulia' 'Davide' 'Roderic' 'Zeynep' 'Said' 'Sahar' 'Jens'\n",
      " 'Marijn' 'Rik' 'Mariya' 'Viktor' 'Teodor' 'Georgi' 'Lyubomir'\n",
      " 'Kheir Eddine' 'Antonina' 'Gabriele' 'Alessandro' 'Tiziano' 'Barbara'\n",
      " 'Ina' 'Bastian' 'Lambert' 'Raul' 'Brian W.' 'Illés J.' 'Péter' 'Tomaz'\n",
      " 'A. I. M. Jakaria' 'Anthony F. J.' 'Alfonso' 'Loet' 'Philip' 'Thara'\n",
      " 'Pitambar' 'Kai' 'Bikun' 'Rafael' 'Alba' 'Carolina' 'Rosa María' 'Gina'\n",
      " 'Zhigang' 'Yaowu' 'Guillermo Armando' 'Ole' 'Raf' 'Qiang' 'Eugenio'\n",
      " 'Qing' 'Giacomo' 'Shuo' 'Robert' 'Toni' 'Juliana A.' 'Alexander S.'\n",
      " 'Matthias' 'Manuel' 'Michela' 'Elona' 'Karmen' 'Tomaž' 'Ivone' 'Rhewter'\n",
      " 'Stela' 'Isabela Pavanelli' 'Rosane Garcia' 'Shahadat' 'Andrey' 'Elena'\n",
      " 'Igors' 'Julián David' 'Juan' 'Gustavo' 'Jesus' 'Yawei' 'Ahmad Watsiq'\n",
      " 'Anis' 'Adi' 'Mohamed M.' 'Francisco' 'Allan Dahl' 'Weishu' 'Yanchao'\n",
      " 'Giangiacomo' 'Jian' 'José M' 'Flavio S.' 'Nicolás' 'Federico' 'Marcello'\n",
      " 'Pierfrancesco' 'Simone' 'Corrado' 'Han Woo' 'Miroslav' 'Laurent'\n",
      " 'Mauricio' 'Sang-Bing' 'Peng' 'José' 'Luis' 'Isabel' 'Francesca' 'Junzhi'\n",
      " 'Katia' 'Miguel-Angel' 'Hongyun' 'Ana-Beatriz' 'Maria' 'Laura'\n",
      " 'M. Glòria' 'Laia' 'Hongping' 'Maria Elena' 'Marta' 'Biagia' 'Rosaria'\n",
      " 'Vichita' 'Pedro' 'Luís' 'Angels' 'María-Victoria' 'Mohan'\n",
      " 'Mohammad Jaber' 'Jean-Luc' 'Fabrizio' 'Mohammad' 'Gregory S.' 'Katia A.'\n",
      " 'Joel' 'Noé' 'Cinzia' 'Kristiaan H.J.' 'Thyago Celso Cavalcante' 'Robin'\n",
      " 'Adrián A.' 'Niccolò' 'Bruna De Paula' 'Élisabeth' 'Peter A.' 'Jiangen'\n",
      " 'Wen' 'Chaomei' 'Jose A.' 'Enzo Morosini' 'Patrícia Ianelli' 'Hong'\n",
      " 'Ruoyu' 'Yang' 'David John' 'M. Reza' 'Monica' 'Hangjung' 'Taciano L.'\n",
      " 'Jan C' 'William F' 'Sabine' 'Jérôme' 'Thorben' 'Wagner' 'Gregory F'\n",
      " 'Joeri' 'Pete' 'Njål' 'Artur' 'Silvestre' 'Atanas G.' 'Aşkın' 'Güzin'\n",
      " 'Mert' 'Muhammet' 'Muhammad Jamaluddin' 'Sander' 'Paulo' 'Jie' 'Alan L.'\n",
      " 'Sergio' 'Gustavo Dambiski Gomes' 'Carla Cristiane' 'Wesley Vieira'\n",
      " 'Hélio Gomes' 'Rafael Vignoli' 'Antonio Carlos' 'Claudimar Pereira' 'N.'\n",
      " 'Hexu' 'Mohamed' 'Carles' 'José M.' 'Jianli' 'Hongzhang' 'Andrea'\n",
      " 'Mohammad Kabir' 'Salman' 'Chu' 'André' 'Xinyu' 'Dou' 'Andy Wai Kan'\n",
      " 'Faezeh' 'Sobhan' 'Johanna' 'Kari' 'Gefei' 'Sa’Ed H.' 'Edmund' 'Joshua'\n",
      " 'Matthew L.' 'Ismael' 'Dejian' 'Norzita' 'Joao J.' 'Ayyoob'\n",
      " 'Bhakti Stephan' 'H. Niles' 'Jason' 'Julián' 'Naoki' 'Zhongyu' 'Giustina'\n",
      " 'Güvenç' 'Mónica' 'Chiara' 'Myriam' 'Ya' 'Wei' 'Zeshui'\n",
      " 'Edmundas Kazimieras' 'Alfredas' 'Mariana M. M.' 'Eduardo' 'Rodrigo N.'\n",
      " 'Pavel' 'José M. M.' 'Giuseppina' 'Vincenzo' 'Mario' 'Raffaele' 'Brodie'\n",
      " 'Nikunja Mohan' 'Georgios' 'Arzu Tugce' 'Benjamín' 'Zaida' 'Amos' 'Armen'\n",
      " 'Gabriel' 'Thorsten' 'Xin' 'E.' 'Ö.F.' 'A.' 'Luisa F.' 'Chun Liang'\n",
      " 'Weina' 'Apollinaire' 'Bernard' 'Hui' 'Ling' 'Pranab K.' 'Rúben'\n",
      " 'Shui-Long' 'Annan' 'Yuxi' 'Martin' 'Aleksandra' 'Dieter' 'Hao' 'Jianhua'\n",
      " 'Mehran' 'Piotr' 'Jerónimo' 'Susanna E.' 'Jiayin' 'Mohmed Isaqali'\n",
      " 'Manahil' 'Paras' 'Muqthadir Siddiqui Mohammed' 'Anand' 'Adith'\n",
      " 'Giuseppe Alessandro' 'Pietro' 'Tahir Yusuf' 'Sandra Leonara'\n",
      " 'Luis Felipe Dias' 'Fabiola' 'Mateus R.N.' 'Paula' 'Jih‐Mirn' 'Israel E.'\n",
      " 'Marcio Pereira' 'Valdecy' 'Max William Coelho Moreira'\n",
      " 'Antonio Fernandes' 'Orlinda Claudia Rosa De' 'Satish' 'Katarzyna'\n",
      " 'Ahmed' 'Ibrahim A.' 'Ah-Ram' 'Ning-Sheng' 'Ming-Chi' 'Hsiu-Hua'\n",
      " 'Hui-Chin' 'Chia-Wen' 'Kuang-Yung' 'Chien-Hsueh' 'Bao-Bao' 'Cheng-Han'\n",
      " 'Malcolm' 'Nader' 'Preet' 'Suresh' 'Gitanjali' 'Jianfei' 'Yeongjoo'\n",
      " 'Shintaro' 'Xitong' 'Kota' 'Giulio Paolo' 'Maria Grazia' 'Miguel Angel'\n",
      " 'Adriano' 'Mariana' 'Maiane' 'Mohsen' 'Mariana D.'\n",
      " 'Ronney Arismel Mancebo' 'Augusto' 'Eyko Medeiros' 'Janaína'\n",
      " 'Laene Oliveira' 'Vanessa Aparecida' 'Danielle Rodrigues' 'Frank W.'\n",
      " 'Carly' 'John N' 'Christopher' 'Aliakbar' 'Şule Taşlı' 'Bao‐Zhong' 'F.'\n",
      " 'O. A.' 'M. A.' 'B.' 'F. J.' 'Dania' 'Justin-Paul' 'Luis Daniel'\n",
      " 'Haruna Luz' 'Karel' 'Marko' 'Manoel Bastos' 'Lucas Emmanuel Nascimento'\n",
      " 'Sérgio Henrique De Oliveira' 'Rebeca Da Rocha' 'Lauren A.' 'Anton'\n",
      " 'Jason R.' 'Joseph A.' 'Anthony R.' 'Juan-José' 'Jesús' 'Gaurav' 'Claus'\n",
      " 'Saurav' 'Sidhartha' 'Shriram' 'Matilde' 'Estela' 'Lucas Santos'\n",
      " 'Gabriel Araújo E Silva' 'Giuseppe' 'Yiran' 'Mila' 'Alfredo' 'Esther'\n",
      " 'P.H.A.J.M.' 'Julián D.' 'Mariana-Daniela' 'Dongping' 'Jinmiao' 'Kamyar'\n",
      " 'Cynthia Changxin' 'Gurunathan' 'Shanmugam' 'Jeremy Y.' 'Byeong Je'\n",
      " 'Seunghoo' 'Ji-Bum' 'Leonardo Bertolin' 'Michele Kremer' 'Liane Mahlmann'\n",
      " 'José Ricardo' 'Manuel J.' 'Antonio' 'Francesco Paolo' 'Antonio Messeni'\n",
      " 'Idris' 'Juan Carlos' 'Clara' 'Holman' 'Keivan' 'André Luiz' 'Junguo'\n",
      " 'Jun-Jie' 'Voon-Hsien' 'Keng-Boon' 'Binshan' 'Kathleen M' 'Akram'\n",
      " 'Greg J.' 'Marlene' 'Daniel' 'Andrew D.' 'David C.' 'Liz' 'Ana I.'\n",
      " 'Ricardo A.' 'Jorge M.' 'Miguel N.' 'Nicolò' 'Amandeep' 'Jose M.' 'Ciro'\n",
      " 'Monika' 'Ming' 'Maliwan' 'Anderson J.' 'Claure N.' 'Nelson A.'\n",
      " 'María Del Carmen' 'Vicente Javier' 'Yawen' 'Eva-María' 'Camilo'\n",
      " 'Juan P.' 'José A.' 'José L.' 'Juan M.' 'Joaquín' 'Begoña' 'Ambrosio'\n",
      " 'Javier' 'Félix' 'Coral' 'Liaquat' 'René' 'Stephen' 'Ilan' 'Bersant'\n",
      " 'Egeria' 'Maysam' 'Shengbo' 'Magnus' 'Leonid' 'Mona' 'Gerardo' 'Mason'\n",
      " 'Yidong' 'Natalia' 'Patrick' 'Howard' 'Qw' 'Carmen' 'Fabio Castro'\n",
      " 'Dasapta' 'Lane' 'Finn' 'Daniel J.' 'Silvia' 'Roberto' 'Simon' 'Lining'\n",
      " 'Shimin' 'Zhiguo' 'Bach Xuan' 'Carl A' 'Noha' 'Katherina' 'Giang Thu'\n",
      " 'Wilson W S' 'Ngai-Man' 'Huong Lan Thi' 'Cyrus S H' 'Roger C M'\n",
      " 'Mehmet Ali' 'Brian' 'Chao' 'Jidong' 'José Antonio' 'Kaifeng' 'Virpi'\n",
      " 'Abdul Hannan' 'Alessandra' 'Eliana B.' 'Ginevra' 'Antonello' 'Chengyu'\n",
      " 'Burhan Fatih' 'Ahmet' 'Ting-Hao' 'Ewelina P.' 'Cheng-Chih' 'Zhen'\n",
      " 'Chun Lam Clement' 'Tianheng H.' 'Richard M.' 'Rano' 'Ahmed Zainul'\n",
      " 'Shahryar' 'Shubham' 'Usha' 'Donald T.' 'Hammoda' 'Alex'\n",
      " 'Siti Hamimah Sheikh Abdul' 'Adnan' 'Dean' 'Jean-Vasile' 'Marius'\n",
      " 'Ignacio' 'Walter' 'Mihaela' 'Johannes M.' 'Amanda Lange'\n",
      " 'Felicia Motunrayo' 'Ilija' 'Rosley' 'Izabela' 'Felix'\n",
      " 'Maria Alzira Pimenta' 'Maris' 'Göran' 'Martin Munashe' 'Petra'\n",
      " 'Salil K.' 'Erandathie' 'Ramona' 'Mariusz' 'Magdalena' 'Bogna' 'Paweł'\n",
      " 'Mateusz' 'Krzysztof' 'Yuh-Shan' 'Faris' 'Mosab' 'Sa’Ed H' 'Zhonggen'\n",
      " 'Jinhui' 'Marcello M.' 'Waleed M.' 'Quangang' 'M. Kabir' 'Muneer M.'\n",
      " 'Osama F.' 'Zora' 'Ionela' 'Raluca' 'Bogdan Mihai' 'Bao-Zhong'\n",
      " 'Nubia Yaneth' 'Orlando' 'Alba Lorena' 'Nengmin' 'Guwen' 'Qi' 'Pengfei'\n",
      " 'Prashanti' 'Nakul' 'Kesang' 'Francesco Gabriele' 'Faisal' 'Syed'\n",
      " 'Manuela' 'Luigi' 'Recep' 'Rui' 'Turgut' 'Ramazan' 'Stamatios'\n",
      " 'Tuncay Yavuz' 'Murat' 'Ana Paula' 'D. P.' 'Maria Isabel W. M.' 'Leandro'\n",
      " 'Doris' 'Dörthe' 'David A' 'Ahmet Faruk' 'Nicholas' 'Joseph H. K.'\n",
      " 'Albertina' 'Jamil' 'Aqsa' 'Roberta' 'Teresa Maria' 'Alireza' 'Soheil'\n",
      " 'Seyed Alireza' 'Theoklis' 'M. C.' 'T. T.' 'S.' 'William G.' 'Solange I.'\n",
      " 'Tânia' 'Menta' 'Celeste' 'Pilar' 'Gopaiah' 'Enda' 'Cronan'\n",
      " 'Ricardo Ángel' 'Gilmar' 'Vidalina' 'Renán Dilton' 'Jorge' 'Andrés'\n",
      " 'Jun Sik' 'Motoi' 'Haisong' 'Ali' 'Adriane' 'João' 'Taab Ahmad' 'Yusra'\n",
      " 'Agnieszka' 'Longxi' 'Michelle E.' 'Seppo' 'Fuzhen' 'Kee-Hung' 'Blanca'\n",
      " 'Milton M.' 'Ruijun' 'Li' 'Simonetta' 'Erika Kajiyama' 'Yiming'\n",
      " 'Susan E. P.' 'David W.' 'Yi' 'Yong' 'Andrzej' 'Bereket Abera' 'Hervé'\n",
      " 'Yannick' 'Lúcia Lima' 'Mochamad Arief' 'Marcin' 'Abolfazl' 'Pasquale'\n",
      " 'Pablo' 'Jantiene' 'Cristiano' 'Devraj' 'Songchao' 'Andreas' 'Amelie'\n",
      " 'Konstantinos' 'Ivan Lizaga' 'Dinesh' 'Edouard' 'Cristian Valeriu'\n",
      " 'Raquel' 'Mohammed' 'Giovanni Francesco' 'Calogero' 'Vasileios'\n",
      " 'Diogo Noses' 'Paulo Tarso' 'Resham' 'Jae E.' 'Shuiqing' 'Panos'\n",
      " 'Kleopatra' 'Nikolaos' 'Athanasia' 'Aneta' 'Xingyu' 'Xianhai' 'Min'\n",
      " 'Ke-Hu' 'Michael J' 'Dagnachew Leta' 'Denise Santos' 'Fabian'\n",
      " 'Héctor Javier' 'Lisu' 'Tianzhen' 'Ziaul Haque' 'Okan' 'Enna'\n",
      " 'Luciana Fighera' 'Fabíola' 'Vânia Medianeira Flores' 'Vanessa' 'Rosa'\n",
      " 'Aditi' 'Debra' 'Susanne Marieke' 'Tiago F. A. C.' 'Bruno C.' 'Sandra N.'\n",
      " 'Naveen' 'Yatish' 'Saumyaranjan' 'Carlos Antonio Gonçalves'\n",
      " 'Marcio Coutinho' 'Belén' 'Luis Jesús' 'Miguel' 'Chris' 'Arminda'\n",
      " 'Carla Sofia' 'Violeta' 'Lucas Veiga' 'Johannes (Joost)' 'Claudio R.P.'\n",
      " 'Barbara Maria' 'Amanda' 'Izabella' 'Osvaldo L.G.' 'Antonis' 'Man'\n",
      " 'Fabio' 'Huong' 'Trung' 'Thao' 'Trinh' 'Milagros' 'Jagajjit' 'Jiayu'\n",
      " 'Chen' 'Jia-Yan' 'Sascha' 'Vincent' 'Rainer' 'Xiao' 'Manoranjan' 'Desul'\n",
      " 'Celso Augusto Guimarães' 'Shailendra Kumar' 'Dipika' 'Kabita' 'Namita'\n",
      " 'Rocco' 'Massimiliano Matteo' 'Valery' 'Nekane' 'Meisam' 'Michael'\n",
      " 'Zahra' 'Su Shiung' 'Amir M.' 'Mário Nuno' 'Rui Miguel' 'Anabela Batista'\n",
      " 'Sayani' 'Tauseef' 'Mukhtiar' 'Harapan' 'Weng Marc' 'Rafiq'\n",
      " 'Renato Carauta' 'Edna Dias' 'Chris R.' 'J.' 'Milad' 'Oscar Iván'\n",
      " 'Jean David' 'Milton José' 'Diana Carolina' 'Amrollah' 'Mohammad Javad'\n",
      " 'Arash' 'Kazem' 'Noushin' 'Afshin' 'Absalom E.' 'Carlos J.' 'Irene'\n",
      " 'Vijay' 'Angélica Vilas Boas' 'Breno Dias' 'Mohammad Fakhar'\n",
      " 'Massimiliano M.' 'Ricarda' 'Thiago C.' 'Olavo B.' 'Ingunn Olea'\n",
      " 'Christian A.' 'J. Ernesto' 'Lurdes Barroso' 'Luísa Cagica' 'Maria José'\n",
      " 'Imran']\n"
     ]
    }
   ],
   "source": [
    "print(authors_df[\"given\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value counts of og_doi doi:10.1162/qss_a_00023                5\n",
      "doi:10.3390/ijerph18115985             5\n",
      "doi:10.3989/arbor.2021.799007          5\n",
      "doi:10.3145/thinkepi.2021.e15e04       4\n",
      "doi:10.7717/peerj-cs.421               3\n",
      "                                      ..\n",
      "doi:10.1016/j.resconrec.2018.09.029    1\n",
      "doi:10.1016/j.foodchem.2018.06.139     1\n",
      "doi:10.1007/s10668-020-01179-x         1\n",
      "doi:10.1016/j.iref.2019.04.004         1\n",
      "doi:10.1108/jic-02-2020-0054           1\n",
      "Name: og_doi, Length: 134, dtype: int64\n",
      "value counts of ref_doi doi:10.1162/qss_a_00023              20\n",
      "doi:10.1038/sdata.2016.18            14\n",
      "doi:10.1007/s11192-019-03217-6        9\n",
      "doi:10.1016/j.joi.2014.07.006         9\n",
      "doi:10.1016/j.ssci.2019.01.029        5\n",
      "                                     ..\n",
      "doi:10.1016/j.jbusres.2019.02.050     1\n",
      "doi:10.1057/s41599-018-0175-8         1\n",
      "doi:10.1007/s11192-019-03238-1        1\n",
      "doi:10.3390/ijerph16111928            1\n",
      "doi:10.1016/j.cmpb.2019.105075        1\n",
      "Name: ref_doi, Length: 91, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "og_count = references_df[\"og_doi\"].value_counts()\n",
    "ref_count = references_df[\"ref_doi\"].value_counts()\n",
    "print(\"value counts of og_doi\", og_count)\n",
    "print(\"value counts of ref_doi\", ref_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wang                 10\n",
      "Chen                  9\n",
      "Patience              8\n",
      "Peroni                7\n",
      "Liu                   7\n",
      "Hu                    5\n",
      "Leydesdorff           5\n",
      "Xu                    4\n",
      "Li                    4\n",
      "Zhou                  4\n",
      "Merigó                4\n",
      "Huang                 4\n",
      "Manzano-Agugliaro     4\n",
      "Yuan                  4\n",
      "Sharifi               4\n",
      "Shotton               4\n",
      "Jin                   4\n",
      "Yang                  3\n",
      "Nazarovets            3\n",
      "García-Lillo          3\n",
      "Name: family, dtype: int64\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_authors = authors_df[\"family\"].value_counts()[:20]\n",
    "print(best_authors)\n",
    "print(best_authors.plot(kind=\"bar\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.260833,0.125;0.503333x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(best_authors.plot(kind=\"pie\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(best_authors.plot(kind=\"barh\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000-0001-6593-7986    8\n",
      "0000-0003-0530-4305    7\n",
      "0000-0002-4672-6961    5\n",
      "0000-0002-7835-3098    5\n",
      "0000-0001-5506-523X    4\n",
      "                      ..\n",
      "0000-0001-5115-7460    1\n",
      "0000-0002-1608-4478    1\n",
      "0000-0002-7978-6039    1\n",
      "0000-0003-2910-3524    1\n",
      "0000-0002-8912-5760    1\n",
      "Name: orcid, Length: 1017, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "authors_per_orcid = authors_df[\"orcid\"].value_counts()\n",
    "print(authors_per_orcid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000-0001-5009-4242    1\n",
      "0000-0001-5014-8749    1\n",
      "0000-0001-5030-7260    1\n",
      "0000-0001-5037-6485    1\n",
      "0000-0001-5054-4506    1\n",
      "                      ..\n",
      "0000-0003-4882-9726    1\n",
      "0000-0003-4900-3897    1\n",
      "0000-0003-4957-1050    1\n",
      "0000-0003-4968-5728    1\n",
      "0000-0003-4970-0860    1\n",
      "Name: orcid, Length: 1017, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "authors_per_orcid_sorted = authors_per_orcid.sort_index()\n",
    "print(authors_per_orcid_sorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "authors_per_orcid_sorted = authors_per_orcid.sort_index()\n",
    "print(authors_per_orcid_sorted.plot(kind=\"bar\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we're extending the same procedure to the other files, which are  \"graph_publications.csv\" and \"graph_other_data.json.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>type</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>issue</th>\n",
       "      <th>volume</th>\n",
       "      <th>chapter</th>\n",
       "      <th>publication_venue</th>\n",
       "      <th>venue_type</th>\n",
       "      <th>publisher</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>doi:10.1016/j.websem.2021.100655</td>\n",
       "      <td>Crossing The Chasm Between Ontology Engineerin...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Journal Of Web Semantics</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>doi:10.1007/s10115-017-1100-y</td>\n",
       "      <td>Core Techniques Of Question Answering Systems ...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Knowledge And Information Systems</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doi:10.1016/j.websem.2014.03.003</td>\n",
       "      <td>Api-Centric Linked Data Integration: The Open ...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Journal Of Web Semantics</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>doi:10.1093/nar/gkz997</td>\n",
       "      <td>The Monarch Initiative In 2019: An Integrative...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2019</td>\n",
       "      <td>D1</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nucleic Acids Research</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:286</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>doi:10.3390/publications7030050</td>\n",
       "      <td>Dras-Tic Linked Data: Evenly Distributing The ...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Publications</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:1968</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>doi:10.1145/3407194</td>\n",
       "      <td>Early Detection Of Social Media Hoaxes At Scale</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acm Transactions On The Web</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:320</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>doi:10.3390/app10144893</td>\n",
       "      <td>Cognitive Aspects-Based Short Text Representat...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2020</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:1968</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>doi:10.1145/3309547</td>\n",
       "      <td>Temporal Relational Ranking For Stock Prediction</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Acm Transactions On Information Systems</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:320</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>doi:10.1007/978-3-030-58285-2_27</td>\n",
       "      <td>Fast Pathfinding In Knowledge Graphs Using Wor...</td>\n",
       "      <td>book-chapter</td>\n",
       "      <td>2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lecture Notes In Computer Science - Ki 2020: A...</td>\n",
       "      <td>book</td>\n",
       "      <td>crossref:297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>doi:10.1007/s11280-020-00842-7</td>\n",
       "      <td>Using Context Information To Enhance Simple Qu...</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World Wide Web</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:297</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   id  \\\n",
       "0    doi:10.1016/j.websem.2021.100655   \n",
       "1       doi:10.1007/s10115-017-1100-y   \n",
       "2    doi:10.1016/j.websem.2014.03.003   \n",
       "3              doi:10.1093/nar/gkz997   \n",
       "4     doi:10.3390/publications7030050   \n",
       "..                                ...   \n",
       "495               doi:10.1145/3407194   \n",
       "496           doi:10.3390/app10144893   \n",
       "497               doi:10.1145/3309547   \n",
       "498  doi:10.1007/978-3-030-58285-2_27   \n",
       "499    doi:10.1007/s11280-020-00842-7   \n",
       "\n",
       "                                                 title             type  \\\n",
       "0    Crossing The Chasm Between Ontology Engineerin...  journal-article   \n",
       "1    Core Techniques Of Question Answering Systems ...  journal-article   \n",
       "2    Api-Centric Linked Data Integration: The Open ...  journal-article   \n",
       "3    The Monarch Initiative In 2019: An Integrative...  journal-article   \n",
       "4    Dras-Tic Linked Data: Evenly Distributing The ...  journal-article   \n",
       "..                                                 ...              ...   \n",
       "495    Early Detection Of Social Media Hoaxes At Scale  journal-article   \n",
       "496  Cognitive Aspects-Based Short Text Representat...  journal-article   \n",
       "497   Temporal Relational Ranking For Stock Prediction  journal-article   \n",
       "498  Fast Pathfinding In Knowledge Graphs Using Wor...     book-chapter   \n",
       "499  Using Context Information To Enhance Simple Qu...  journal-article   \n",
       "\n",
       "     publication_year issue volume  chapter  \\\n",
       "0                2021   NaN     70      NaN   \n",
       "1                2017     3     55      NaN   \n",
       "2                2014   NaN     29      NaN   \n",
       "3                2019    D1     48      NaN   \n",
       "4                2019     3      7      NaN   \n",
       "..                ...   ...    ...      ...   \n",
       "495              2020     4     14      NaN   \n",
       "496              2020    14     10      NaN   \n",
       "497              2019     2     37      NaN   \n",
       "498              2020   NaN    NaN      1.0   \n",
       "499              2020     1     24      NaN   \n",
       "\n",
       "                                     publication_venue venue_type  \\\n",
       "0                             Journal Of Web Semantics    journal   \n",
       "1                    Knowledge And Information Systems    journal   \n",
       "2                             Journal Of Web Semantics    journal   \n",
       "3                               Nucleic Acids Research    journal   \n",
       "4                                         Publications    journal   \n",
       "..                                                 ...        ...   \n",
       "495                        Acm Transactions On The Web    journal   \n",
       "496                                   Applied Sciences    journal   \n",
       "497            Acm Transactions On Information Systems    journal   \n",
       "498  Lecture Notes In Computer Science - Ki 2020: A...       book   \n",
       "499                                     World Wide Web    journal   \n",
       "\n",
       "         publisher  event  \n",
       "0      crossref:78    NaN  \n",
       "1     crossref:297    NaN  \n",
       "2      crossref:78    NaN  \n",
       "3     crossref:286    NaN  \n",
       "4    crossref:1968    NaN  \n",
       "..             ...    ...  \n",
       "495   crossref:320    NaN  \n",
       "496  crossref:1968    NaN  \n",
       "497   crossref:320    NaN  \n",
       "498   crossref:297    NaN  \n",
       "499   crossref:297    NaN  \n",
       "\n",
       "[500 rows x 11 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_publications_csv = pd.read_csv(\"testData/graph_publications.csv\") \n",
    "graph_publications_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 500 non-null    object \n",
      " 1   title              500 non-null    object \n",
      " 2   type               500 non-null    object \n",
      " 3   publication_year   500 non-null    int64  \n",
      " 4   issue              303 non-null    object \n",
      " 5   volume             391 non-null    object \n",
      " 6   chapter            93 non-null     float64\n",
      " 7   publication_venue  486 non-null    object \n",
      " 8   venue_type         486 non-null    object \n",
      " 9   publisher          486 non-null    object \n",
      " 10  event              0 non-null      float64\n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "info_graph_csv = graph_publications_csv.info() #getting some information from data without entirely looking at them.\n",
    "info_graph_csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>doi:10.1016/j.websem.2021.100655</td>\n",
       "      <td>Supply Chain Coordination Based On Web Service</td>\n",
       "      <td>journal-article</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Applied Sciences</td>\n",
       "      <td>journal</td>\n",
       "      <td>crossref:297</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>407</td>\n",
       "      <td>173</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.849462</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.775636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.359536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>max</th>\n",
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       "      <td>2022.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      id  \\\n",
       "count                                500   \n",
       "unique                               500   \n",
       "top     doi:10.1016/j.websem.2021.100655   \n",
       "freq                                   1   \n",
       "mean                                 NaN   \n",
       "std                                  NaN   \n",
       "min                                  NaN   \n",
       "25%                                  NaN   \n",
       "50%                                  NaN   \n",
       "75%                                  NaN   \n",
       "max                                  NaN   \n",
       "\n",
       "                                                 title             type  \\\n",
       "count                                              500              500   \n",
       "unique                                             497                2   \n",
       "top     Supply Chain Coordination Based On Web Service  journal-article   \n",
       "freq                                                 2              407   \n",
       "mean                                               NaN              NaN   \n",
       "std                                                NaN              NaN   \n",
       "min                                                NaN              NaN   \n",
       "25%                                                NaN              NaN   \n",
       "50%                                                NaN              NaN   \n",
       "75%                                                NaN              NaN   \n",
       "max                                                NaN              NaN   \n",
       "\n",
       "        publication_year issue volume    chapter publication_venue venue_type  \\\n",
       "count         500.000000   303    391  93.000000               486        486   \n",
       "unique               NaN    38    116        NaN               299          2   \n",
       "top                  NaN     1     11        NaN  Applied Sciences    journal   \n",
       "freq                 NaN    63     22        NaN                15        407   \n",
       "mean         2019.132000   NaN    NaN   0.849462               NaN        NaN   \n",
       "std             1.775636   NaN    NaN   0.359536               NaN        NaN   \n",
       "min          2011.000000   NaN    NaN   0.000000               NaN        NaN   \n",
       "25%          2018.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "50%          2019.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "75%          2021.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "max          2022.000000   NaN    NaN   1.000000               NaN        NaN   \n",
       "\n",
       "           publisher  event  \n",
       "count            486    0.0  \n",
       "unique            37    NaN  \n",
       "top     crossref:297    NaN  \n",
       "freq             173    NaN  \n",
       "mean             NaN    NaN  \n",
       "std              NaN    NaN  \n",
       "min              NaN    NaN  \n",
       "25%              NaN    NaN  \n",
       "50%              NaN    NaN  \n",
       "75%              NaN    NaN  \n",
       "max              NaN    NaN  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description_g = graph_publications_csv.describe(include=\"all\")\n",
    "description_g # we're able to see some statistics values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--this is the result of the first query                            id  \\\n",
      "57     doi:10.3390/app9122553   \n",
      "84    doi:10.3390/app11219825   \n",
      "87    doi:10.3390/app12010143   \n",
      "90    doi:10.3390/app12010287   \n",
      "97    doi:10.3390/app11062813   \n",
      "113   doi:10.3390/app11125633   \n",
      "121   doi:10.3390/app11157056   \n",
      "152   doi:10.3390/app10020591   \n",
      "153   doi:10.3390/app10031040   \n",
      "164   doi:10.3390/app10072277   \n",
      "301   doi:10.3390/app11167233   \n",
      "308  doi:10.3390/app112110450   \n",
      "313   doi:10.3390/app10186328   \n",
      "350    doi:10.3390/app9214547   \n",
      "496   doi:10.3390/app10144893   \n",
      "\n",
      "                                                 title             type  \\\n",
      "57   Ontoimm: An Ontology For Product Intelligent M...  journal-article   \n",
      "84   Model And Knowledge Representation For The Reu...  journal-article   \n",
      "87       Ontology-Based Nutritional Recommender System  journal-article   \n",
      "90   Ontology-Driven Cultural Heritage Conservation...  journal-article   \n",
      "97   Ontology-Based Semantic Conceptualisation Of H...  journal-article   \n",
      "113  Semantic Microservice Framework For Digital Twins  journal-article   \n",
      "121  Towards Collaborative And Dynamic Spectrum Sha...  journal-article   \n",
      "152  An Analytical Game For Knowledge Acquisition F...  journal-article   \n",
      "153  Agrient: A Knowledge-Based Web Platform For Ma...  journal-article   \n",
      "164  The Systematic Design Of Industrial Products T...  journal-article   \n",
      "301  An Ontology To Model The International Rules F...  journal-article   \n",
      "308  An Ontology-Based Expert System For Rice Disea...  journal-article   \n",
      "313  Ontology Fixing By Using Software Engineering ...  journal-article   \n",
      "350  Ontology-Based System For Dynamic Risk Managem...  journal-article   \n",
      "496  Cognitive Aspects-Based Short Text Representat...  journal-article   \n",
      "\n",
      "     publication_year issue volume  chapter publication_venue venue_type  \\\n",
      "57               2019    12      9      NaN  Applied Sciences    journal   \n",
      "84               2021    21     11      NaN  Applied Sciences    journal   \n",
      "87               2021     1     12      NaN  Applied Sciences    journal   \n",
      "90               2021     1     12      NaN  Applied Sciences    journal   \n",
      "97               2021     6     11      NaN  Applied Sciences    journal   \n",
      "113              2021    12     11      NaN  Applied Sciences    journal   \n",
      "121              2021    15     11      NaN  Applied Sciences    journal   \n",
      "152              2020     2     10      NaN  Applied Sciences    journal   \n",
      "153              2020     3     10      NaN  Applied Sciences    journal   \n",
      "164              2020     7     10      NaN  Applied Sciences    journal   \n",
      "301              2021    16     11      NaN  Applied Sciences    journal   \n",
      "308              2021    21     11      NaN  Applied Sciences    journal   \n",
      "313              2020    18     10      NaN  Applied Sciences    journal   \n",
      "350              2019    21      9      NaN  Applied Sciences    journal   \n",
      "496              2020    14     10      NaN  Applied Sciences    journal   \n",
      "\n",
      "         publisher  event  \n",
      "57   crossref:1968    NaN  \n",
      "84   crossref:1968    NaN  \n",
      "87   crossref:1968    NaN  \n",
      "90   crossref:1968    NaN  \n",
      "97   crossref:1968    NaN  \n",
      "113  crossref:1968    NaN  \n",
      "121  crossref:1968    NaN  \n",
      "152  crossref:1968    NaN  \n",
      "153  crossref:1968    NaN  \n",
      "164  crossref:1968    NaN  \n",
      "301  crossref:1968    NaN  \n",
      "308  crossref:1968    NaN  \n",
      "313  crossref:1968    NaN  \n",
      "350  crossref:1968    NaN  \n",
      "496  crossref:1968    NaN  \n",
      "--this is the result of the second query                                       id  \\\n",
      "125  doi:10.4018/978-1-5225-7501-6.ch036   \n",
      "136  doi:10.4018/978-1-5225-0956-1.ch009   \n",
      "\n",
      "                                              title          type  \\\n",
      "125  Supply Chain Coordination Based On Web Service  book-chapter   \n",
      "136  Supply Chain Coordination Based On Web Service  book-chapter   \n",
      "\n",
      "     publication_year issue volume  chapter publication_venue venue_type  \\\n",
      "125              2019   NaN    NaN      0.0               NaN        NaN   \n",
      "136              2017   NaN    NaN      0.0               NaN        NaN   \n",
      "\n",
      "    publisher  event  \n",
      "125       NaN    NaN  \n",
      "136       NaN    NaN  \n"
     ]
    }
   ],
   "source": [
    "g1 = graph_publications_csv.query('publication_venue ==\"Applied Sciences\"') #we want all the publications that have \"Applied Sciencies\" as publication_venue.\n",
    "print(\"--this is the result of the first query\", g1)\n",
    "\n",
    "g2 = graph_publications_csv.query('title == \"Supply Chain Coordination Based On Web Service\"') #we want all the publications that have \"Supply Chain Coordination Based on Web Service\" as title.\n",
    "print(\"--this is the result of the second query\", g2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019.0"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_g = graph_publications_csv[\"publication_year\"].median() # here we want to compute the median of the publication years.\n",
    "median_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2021\n",
       "Name: publication_year, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_g = graph_publications_csv[\"publication_year\"].mode() # here we want to compute the mode of the publication years.\n",
    "mode_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_g = graph_publications_csv[\"publication_year\"].min() # we're also able to compute the minimum\n",
    "min_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_g = graph_publications_csv[\"publication_year\"].max() # same fo the maximum\n",
    "max_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--These are all the unique publishers ['crossref:78' 'crossref:297' 'crossref:286' 'crossref:1968' 'crossref:56'\n",
      " 'crossref:8722' 'crossref:301' 'crossref:311' 'crossref:140'\n",
      " 'crossref:98' 'crossref:7437' 'crossref:2432' 'crossref:2457'\n",
      " 'crossref:179' 'crossref:2780' 'crossref:530' 'crossref:3742' nan\n",
      " 'crossref:4340' 'crossref:735' 'crossref:263' 'crossref:22695'\n",
      " 'crossref:6225' 'crossref:1010' 'crossref:11929' 'crossref:2373'\n",
      " 'crossref:320' 'crossref:17207' 'crossref:265' 'crossref:316'\n",
      " 'crossref:4443' 'crossref:340' 'crossref:2560' 'crossref:237'\n",
      " 'crossref:2258' 'crossref:281' 'crossref:6951' 'crossref:374']\n",
      "--These are all the unique types ['journal-article' 'book-chapter']\n",
      "--These are all the unique venue types ['journal' 'book' nan]\n"
     ]
    }
   ],
   "source": [
    "unique_values_publisher = graph_publications_csv[\"publisher\"].unique()\n",
    "print(\"--These are all the unique publishers\", unique_values_publisher)\n",
    "unique_values_type = graph_publications_csv[\"type\"].unique()\n",
    "print(\"--These are all the unique types\", unique_values_type)\n",
    "unique_values_vtype = graph_publications_csv[\"venue_type\"].unique()\n",
    "print(\"--These are all the unique venue types\", unique_values_vtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ journal-article    407\n",
      "book-chapter        93\n",
      "Name: type, dtype: int64\n",
      "------ 1.0    79\n",
      "0.0    14\n",
      "Name: chapter, dtype: int64\n",
      "----- Applied Sciences                                                                    15\n",
      "Journal Of Biomedical Semantics                                                     12\n",
      "Nucleic Acids Research                                                              12\n",
      "Expert Systems                                                                       8\n",
      "The Semantic Web - Lecture Notes In Computer Science                                 7\n",
      "                                                                                    ..\n",
      "Journal Of Volcanology And Geothermal Research                                       1\n",
      "Cirp Journal Of Manufacturing Science And Technology                                 1\n",
      "Ecological Informatics                                                               1\n",
      "Computer Science Review                                                              1\n",
      "Lecture Notes In Computer Science - Ki 2020: Advances In Artificial Intelligence     1\n",
      "Name: publication_venue, Length: 299, dtype: int64\n",
      "---- 2021    124\n",
      "2020    110\n",
      "2019    103\n",
      "2018     79\n",
      "2017     36\n",
      "2016     19\n",
      "2014     11\n",
      "2015      9\n",
      "2022      7\n",
      "2011      1\n",
      "2013      1\n",
      "Name: publication_year, dtype: int64\n",
      "--- crossref:297      173\n",
      "crossref:78        80\n",
      "crossref:1968      57\n",
      "crossref:311       30\n",
      "crossref:286       27\n",
      "crossref:301       18\n",
      "crossref:56        13\n",
      "crossref:140       11\n",
      "crossref:2432       9\n",
      "crossref:179        8\n",
      "crossref:320        8\n",
      "crossref:340        7\n",
      "crossref:98         5\n",
      "crossref:2560       4\n",
      "crossref:7437       3\n",
      "crossref:530        3\n",
      "crossref:237        3\n",
      "crossref:263        3\n",
      "crossref:265        2\n",
      "crossref:281        2\n",
      "crossref:2258       2\n",
      "crossref:8722       2\n",
      "crossref:1010       2\n",
      "crossref:6951       1\n",
      "crossref:4443       1\n",
      "crossref:316        1\n",
      "crossref:735        1\n",
      "crossref:17207      1\n",
      "crossref:2373       1\n",
      "crossref:11929      1\n",
      "crossref:6225       1\n",
      "crossref:22695      1\n",
      "crossref:4340       1\n",
      "crossref:3742       1\n",
      "crossref:2780       1\n",
      "crossref:2457       1\n",
      "crossref:374        1\n",
      "Name: publisher, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "count_type = graph_publications_csv[\"type\"].value_counts()\n",
    "print(\"------\", count_type)\n",
    "count_chapter = graph_publications_csv[\"chapter\"].value_counts()\n",
    "print(\"------\", count_chapter)\n",
    "count_p_venue = graph_publications_csv[\"publication_venue\"].value_counts()\n",
    "print(\"-----\", count_p_venue)\n",
    "count_p_year = graph_publications_csv[\"publication_year\"].value_counts()\n",
    "print(\"----\", count_p_year)\n",
    "count_publisher = graph_publications_csv[\"publisher\"].value_counts()\n",
    "print(\"---\", count_publisher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(count_type.plot(kind=\"bar\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_p_year = graph_publications_csv[\"publication_year\"].value_counts()\n",
    "print(count_p_year.plot(kind=\"barh\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.260833,0.125;0.503333x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5FDNXA7iLiPaa+/ogPc5b8iIkKr6DiPYCeAGAreVORPQiEQVakdeh+ZQdEMs3ioj2QcT6r8y8J4i3/v0g9ul3jFUK5/2f7RUrgNQ/OGdmPv0RK0m+K2TFRgMoRi/2JepNrApM/QYPgOT8lcuqQz2Q85FzLkm+l/gazlHhBKwKTP0IJyTtziIEWOLZNb4b6gH0NCpx0ofOB4pV6N22wMGiRwhJjywLSzw7T1bsOABzQj2M3iCOGia/an/MKiDSt5mXvmJ1sKl1Ft2IJZ6d57yqbDNHzV10s7raKmHXt/lWqAdwPmKJZ+e5LNQD6G0etL02ZRIdORTqcVi0SatLkS16Fiva3hmyYgcCOIXz8EvHx2rBdM9zcbWIig31WCxaJT1/5bKCUA/ifOK8E4Gz5EKcp/fMTvqwD50rDgLWt20f5fJQD+B847wUgrPgolAPIJSkUOXMp+1PWwn0fZMrQj2A8w1LPDvHeS2eAPAFZdPCLyrrWhZbsQg9S9JXrHaGehDnE5Z4BktW7DAAI0M9jFBDBOVJ+x9GWRWY+hyRaKpWZtEL9BnxNAsc/yjU42iH897q9CMVmB6yKjD1PRaHegDnE31GPM22GV8P9TjawRLPACLIY1Vg6nvMCvUAzif6VKoSEf0W0vf8DQTU72Tm0P6TZsUSgBL043YbPcWL2hVrfqF9y5ou9g1qAMTnr1zWd/6pz2HOKOMWYqaYPx8O2MYw622GkExYwtkqN6ofzMk2JuWsNSZlhnosFogFMBbB1KO1OGv6lHgy84WhHkMbWEVn24AI9pfsv0qY43mm7BTiE0I9HgvMhiWevUKf8XkCABElEdGfieg/5u/jiejGUI8LVpS9XVTi5A+dD5y0KjD1CWaHegDnC31KPAG8BGmxMdj8/SCAu0I1mACsqjUdEE/1k1+1/9IqIBJ6JoZ6AOcLfU08BzHzmwAMADA7Z/aF9qrDQz2A/sAcdf9CqwJTyBkV6gGcL/Q18WwwG8kxABDRBZAIYqixxDNIrApMISclfcXqiFAP4nygr4nn3QDeAzCSiNYDeBnAHSEdUVZsPCSKaREERIh4y5HliEZDX/jSO1+xrM9eoE+Jp5nPuQjS0vd7ACYE2cmyJ7H8nZ3EYVVgCjWWePYCfUo8TWYBmAxgGoCvE1Goe7RYU/YuMJgqZj5lf8aqwBQaLPHsBfqUeBLRKwCeADAfwEzzMaPdg3oeSzy7yJXKxoVXK+utCky9z+COd7E4W/pUkjxEKMdzX1ozak3buwwRlN/anx212zviRD6nDAn1eM4jBoR6AOcDfcryBLAXQHKoB9ECy/I8CxRC3AeOBxutCky9iiWevUCfEE8i+jcRvQdgEID9RPQhEb3nf4R4eKkhvn6/J4I8Y991/HR7qMdxHmGJZy/QV6btT4R6AO3QV+5Rv2accmL+g7ZX1zxmVWDqDSzx7AX6hDAwczYAEFEkABczG0Q0BsA4AP8J6eAsuo2b1Q/mrDEm56wzMq0KTD2LJZ69QJ+YtgewBkAYEaUC+AjAtyHr3S3OAYhgX2VfmZCIqrJQj+UcJzLUA+gKRPQ4Ee0josdbbH+EiPYQ0S4i+oiIBpvb7zO37SKivUSkE9GAgONUItpJRO/3xHj7mngSMzcC+DKAPzDztbAKHZxTWBWYegW1O05CRN06Mw3ifHe2sT0GgAOiV0kAfgEAzPw4M08BcBWk8tlRZq40r5UPoBBAOiT10T+GNwIEN5+IdnX1/fQ58SSiOQC+CWC1ua2vjdHiLImn+smv2H+5PtTjOIcJWjyJ6HrTqttNRK8Q0UtE9DwRbQbwOBFNIaJN5j7vEFG8edydRLTf3P66uW1RgDDtJKJoIlpMRGvNwO9+0xp8nIi2msd+zzz2PYhAjoAUdA4kJeD5YJi530T0TVP89pjveSQRTTH3i4aIbiUALxENMrffBqAMYp0TgA+CvVct6RM+zwDuAvBjAO8w8z4iGgHgs9AOyeJsqFKUysMOe8kBh6PmgMPuOWK3Kydttqi5+yPqv2Zb/fs4jSM8DpWdhqHaNFWxkROkkKLDUBSfCruPlTBNRxjpKgyVDXYoMOywGyrZ2Qa7YSOQSlDsBEUFKyqRohKTQkQgBQCBiQAoxESAPAeICAox/PsRQSEiBQopRFCgkAKCQvKTQKSQIvuQ+btCIFJA/m1EINkBCgHmCyCFTl8GKlHAc/mpEKACpJIYCyoIMvSuEVQlMiKaAOBXEIHRIOI1HdIKp8Tc7U0APvOcGQAegwjQIwBOmvvYzZ+PQUSpAf73Id0h5qLp//gmABf4hwDgx0T0UStjexHA88y8zbx+vPlQ0FQsaDCk7oQXIpQE4Lhp4Q4AcA2AleaYfgAgC8A6AAkASgEMNZ93iT4lnmbgKDvg96No25S36AO4iVzH7LaTBxyOyjyHvfGww4ETNltYparEu4lSID6oZgGMSUeNnGlHLgxvGO9N25RY65t+pIQb4kdzbYIX9UYRHFUOJHsAe5iPGuLjjBpHAirs0QbITTFqAxMqya02wq14qZENYo+OqIZGjmloUCLdbkR63Yj0esmhg3QjDD6OYI3DSeMIGBwGlcNh152wsdNQjTBFgRMKO4mYiEFkKAprisIGERsKkUGAoRAZYDYIikEMJlYYTAyDGBrAmg5oDNbArDGgEcyfzLoC6ABrCmCoDF0BGypgKIBhAxs2gG2AYQPgAJgAVs1vBdUUY52IDAWqTkQ6QdEVUgzZrhgKFEMhRSf56V0eXBfibwCIgoinn0SIGEHGguGQurqAiOQ1EPGMgKTxVaPJ0k2F5GmXA6g1t41C89njLRARBkTsbAiYVrfBbcxcS0T3QDJz/Lngx83xlkMqsdUzcyURXW3+/qz5OqNJ6Aea+8eZ26/s4Npt0ifEk4h+x8x3EdG/YZajC4SZrwrBsCwA6IBeZLMVH3LYy/IcjoYDDrtWYLfbT6lqTKNCyQZRAsTfFFS1/eRKPvHgG0bK2vlTlZSy7Zwxxu36l7qQryz/A9t2T/COd07nhvRa2jUyFnWaixM8x6nOu1VJPVWJcE8013uijEQtDTE8jDWnAzSwgdXoClQnDFFK0+Nx1O5AlWFDveY0GjSnEub1IsbnQryvAQO0ekr2lQNcq/j4FGtqo6KpLsNn85BH9cKr+khjneAF4FFY8RArHoLqUcjmBTncpIZ5QFFucKQbHOlmDveAVHaQYoSpBCcpFKYATmLFqYCcim4L0zU1TNNs4YZmC9N1NVzT1DDoNid01Um64lR01a4Yit1mKDY7Kza7QaqDSXUwKWEAhTGzE2Q4AN0D1pihawZrms66D9B8LMKtgX0aWDMYWmOQH+8MiEAGEhbw3IHmGmGDiI4f1fx9aYA/kyBT5kQAo3FmnvQIiJgabYzpAIDXAFzs32AK5xAAt0L0wW+RzwXwHUjX3VQ0ifQFEPFOChjTtQD+CBHPqwEsh8RWupyZ0CfEE8Ar5s++nO95zlKuKuVH7PbSAw5HTZ7D4TvisCklNltkraIM0oDBIEoDkHa214lwc80TL+peT9gAVVfDxh+v3a+kHbqkdMG8//l+j0f5N46HI7Y07vQO2zFHmxFp54gB8dSYZCj5adG8Y2iaXlUdRVNrjlGEb59SGHaSFHeNqlZGad7KkRTpcvsSGmv18UoyOSMS4A0HPLYacFgj7HH1sEVXkyu8gYvDw5VTjqFGpSOeqpUoNPrC1FpXFNe5Ig2XO4w8brsygGsxWCnjZFs5UpRKSnRUqolcpSRRFUUodaypXrhVLzyqj+tUnetVpgpVNSpURa9UVVSrilqrKLZGIofqIZvDC4fDQ2qYGxTlgS/KBW+0C1p0I+vRLmhRbiDCzb5ID7xhXticPtgdGhx2HZqqQ1cYYFCEoTpUXXF4ddVp6LYwr66GeTU1zKfbnF5NDdc1W5iu2cJ1XXXWB/mRREKsSRVN03YVTbrgN2QiANRBpsji2zjzPLE4U2gXQRa+BOIwr+kJ2OYMeD4WEvM45d9ARH8CcEPA+TeZP5dABNX/BRBORD8A8FNIVbZiiGUdDWAgEaVAhPQxAAvM93zaNUJEOoAc89fjHRltfUI8mXm7+dOqwtMDNBI1HLXbTx502KvynA7XIbudCu22sCpFifcQDYY401v+kXcrisHa7/6oH3bomH5g+NJsEA32Gq6wid7BcUf3JuDy6Z/U3bz3cf0x77N2nveJmnt4q3fqoQW+urIB2pTjaeqUmDh7cWyR6h1cgL0Jkz157qvpZEki5tJe9SL3JrvNuct+IrLW9qGqkqPMxtGV0TSgZpQruiRBC/MwdDUMYRFDMNYZy5PsKnlsGrkdiuGx+RSKPK5wVKlBkWWsJFSi3mlHlTOOKu3xKLUNUPYr6Uq5PpDr3ZF6gyucPG47aW5VJZdukFs3FJdGCXqVmqRXqolcpQ5HFVKowpeCyvokqsIgqrHFUb09Au5wj1NzNoQjvFYFKm2qu1xV3eWqquWL8OpVqqLXKIpRryhGIynwEJFGsBmA06FpSrhH40hPoxbphhbtYl+0C54oF3zRLtajG2FEucFOL7zikgz+4zF/+n2lXohVlwbg5oD9/EJjQ9NUndAkslEB+xpo7nu9GcAJNPkr/edSAYQz81VExACOQqzPKyEuvG0ALjH381udE8xjn4VE3geY2z3M/AwRPQSxiKvR3ML1W8F+n6uO5pa3y4zeB0WfEE8iykEr03WYHwwzT+rlIQXS1vSiz6ABWqHNdvKgw16R53TUH3TY9QKb3VlmU2MaiZJYxHF0KMf4yCv6hrgGccSVJs2Ml63kq3QX1STVZWBw6T9xKHMs/XjfD/iK4szIh1OeTv5B5ucnL/zcpvPRRZSfONgbfaqOJp66XEmL5KjR4cfUqJT/4tTQqIY3w5fyofKRqC0Ow/SafXypsUWZPXCPUh6+NnZHhKJ/FB6mavVq2NAi6OllsTWxNaN94foQwCBqtGvsVXXEOOMp3paGWFskkz2M3Q7iRhsr9apPabTXGByx01Aiy8keUaWGR9TAFudCjS1WqbDFo0IdSKWUrJUgxchBpn29Hm33uO1OzWNTDDcbikt3k0trJJfuhsewkdeIsOneiIFGbXQC1UQlU2XDYKp0jaEKXwpVGImo9g2kWo6jeoqCS3XC57BB13wEe7WqokpR9ApV9VbEKt7yAaq3XFX1ClU1ClSFqhXV5iPo3wzuY2mA/H0Phfz/xZjbfQA2QKLYgEx/k819/P8PHgDhaD6NPgXxcdogghyoL7+HLHrxQlwDfmtTB3AqYBm2P9q+DkAuEUVARNx/DQNNU+08iNDOBVABsS6dkGk5QQTUf5zfOCDz+h6IuAZW3XcQ0V7z+SPM/EbLGxZInxBPAF8I9QDaoTzUAwCAU6p66rDdXprntNcdcDi8x+x2tdimRtcpyiAdSAHRUMg/QZ/jpv/q2aNPYhEAuB1xpZotQnJ3SfEerdsTNyfy6mGvHZyD7yc8VfzAhN9Wf6Asrs0tHJb439IVCatn22p+48iOu+ttPuWwLVG2G3W6Xr4bE6JHG0l1y8Nz80sMcpyIvSxpk9sxsrxxe+Q0elG/hh47dQtxsU8bWZbvvMTYpl+mbm1Iii6K3ZtYOSA7oqRue1hYeBUrSekl1JiZb1REVg50NeojdY8yHG5HBBptXnKrjQpxHcfYnEacPZri7EPVaMcg9tlVblR1xaZ6SVEaDAc1KElhVbZpEdvJEV6NyMgaV3hEDTnDG5xaDDkrlYGOMiXBKMZgVwkGe0uRrFVioKscw6OKDGek4UEsPIaDGvV6xaW5yaXpcOkaeQyFfIYXuuGFAc0Bn2MQao1EqtaTqVJPpgrfYKo0xlAFJ1G1MhC1tliqd5JMR4MhHzgd1Wc0WaB+QfEHjhTzdRtEWAGZemsBxwMiVv6psILmgSL/OWshomsEvH6slbHNh1ienhbnUQCAiJLNc6Sb2/0W5EA0BasCsxVUAEUB71OB+GXJbP1zgfn+3OZ5f0FE/2HmWrRBnxBPZi7wPzdvyizIm9zKzCVtHtg7FPbGReqIao857MUHHPbqPIfDfdhhR5HNFlGlqAO8hBQQJUI+7H7FhbuNLRfv5AX+308MWXIARKYjX9VKXMcmONnmSdEHbt+756LkR6fcM+H7GX8pOqYMPzy14PlZ/6r56fZ3bIXJ3/lGYjnKPk+4761PG2oGLnEfciTbcopWuxLt9qgrByxu8DbOittZeMydqJalfzfxnfKkwQcbDqUMt2crSz1/cn8z4tmSb/rUElftwOqKQRfV7NB/rGxyTVMO19WrnrgN48PqP4/wGDnOiphKZdvwpGo0ZBznoswCbhxUn6jZaARpjhilINIJl63C8Cj1qqGfgmLUUIwtQh3qiKV4R4ovzjGZ7LZIZ6NND69TPKhU6t2VVO+uJZfTUF0RqWE1cSMj93kiI9Y3RETWeMPD6moczkaf6vTG1YZFx1TEJSrFSKESDHYVY7BRhkStGvG+BkRpXjg0jwHUeQ0j361r5NJ9SqOmm0JL5DFs5DPCoRkxAHwFbXweQRJMipRfNHXI/6oOEUgbREA1NKU7taRlkCqwXbI/YPRzAGDmHCJqANAIsR4JwPvMXGLmbv4fgB8CGGMefwqSnrQUEiSab76uM3Ox6a9lNKVX+cc+HsATzHy/mSK5GxJUerqtG9AnxNMPEd0EcfZ+CrlJTxPRw8z8lxAOq1vE0wt4T9htJw86HBV5DnvDQYeDj9ttjnJVjXURpbAkH8d0eKJ+xJhCzrv1A2MCBVgOJUmzA96jojEMu0uv27lAyxj9Rm1VQkNl4oZfD7xrzF1jnxvQqNDWy46tXPBT45U1/yz8z9x/R0fsuvXOgWlLdn12/IaPPk0oTl168ujQxfaPyzbV27Xjzonx82hp9GKjsLBK3VFyzOam2vGXD9pYcHPqn2sah6px2elLqjdgQd1r9dfE/v3kFzxKqas+vLFx4AJ3jvPK6k2NP1X2GYNQoxxx2L3ZQ8J968aE0QFnWUo9lY9y+rZoY4r4WGY+V40tZCPKOxhsT0d99GCjOmoIlWiGw+cqI0PPcRtaqUJcHx5ri4ka5BikjXWmuOIcw32R9thIrZIi6hSPo1Kpr62gOm+R0lBfR27DA99A1d4YExNRy8mRx/R5ETs94RE1RlhYg2a3uzVV1QyNFFSFDTRKw5K14rjBejEG66VIoQoMstUgNsKFiFgNtgQQBds/Kh1N4gec6aKyB2wniCDaA/YnNAWb6gOO9/tDW2sEGIPmVicg1uV483mzQs5EdC1EpxLQ5GZYSkTfBPA4ZArvdwHUQaza+8zfH4MYHBqAeHNK/zlkms8QKzoSwDJI4CmJiC4FkAnAhQ4Ksfcp8YS86anMXAEApjm9AUAoxbMomJ0Y4BJVLT3ssJ/KczjqDjrsvqN2u73UpkbVK0qCDiSDKB1N04xzmoG1XPLzV/VYClhn7bFHl/nskaeLghCpGjNQ2HDQPSZ2xuB4jlyXu3/hBXPnvX7yQSWr8dHRP58FonUPH/32gnXGxJwX655Iv7Ch0Hnz+ETvDZMcg7/70SfHl6z/ZMiJIRfVHR12vbGrYaeyveL3RlrESL54wGLVaYumvJKi+r3lIyMa4Rk/Pq704CVpj5SFx1fE7RgzM/yTMZc0HubR3tU1qc7/Fi0ktcwNxetTpnsPu690bdR+ruxyplFRokGs7HY6j2fHh1duHBymvGW3p3mpdDhQSimVm09MKOCTmQXsHltCDhsNDvOEDzeqY6c11EUPdZSyLbzQXeMwGg7Vs1ZSZxgVmsre2Gh73IABjqTqVGeKO86R2hhpj1PtimNQQ703ulpp9FRQnVap1PuKqEFvII/ug2aAdA4LbzDCI2qMMREl+pTIPISH1ykOR2OY3e6JJjISiBDlZXuhuAM7JBIidOOZ+ZBpceVBRGgBxNd5M4D7zUDMLoiw6BDh2QwRtIsh0fZT5mMfJMgzHU1ur1uZ+bBpRTLEn0qQnE8DgL9Y9ipmfoOILgaQC+Bhc3+/tegzr1kG088Z8H6Wm2ObDLFUR5jv74T580YABRBNudN8/z5mfo2IPoA0m/wGZOquoyk3tFX6mnhWQD44P3XmtlBy2vKsUajmqN1efMDhqM5zODxHHHYqsqkR1ao60Cd+x2T0vWLOvY7Tyw2//aNepfLpZGgAQGHahXkgWtC0xWYAwNG6PWljYmdgoW980r9oq2P/vsV1EyZ+mnkdvbrh9dHfXsAKrfv08NS5Czy/L/3Q+cCp10+WLvgoInzH/ZcNGvnGQuj3vfWxvmjd/9KPD7247Oiw76sl2kGtsOjl8HDF5p484ELtush5w13kq9xZcazsQPXgZI2MMVFRFYdvS3uxYcDAQk9RXJrj47jLsBWzlTo9GhsqEjybiyazUumJhs4DxlDhiWXaporLXNuVO6hwiIP0YfVEdVvDw45+FhFevS3T6fxsqm20TpQKlCDcXVw7pmh9/qR8rs44xlpilRrN9tS42tiRddWxMxvrotLY44iMLzRq44+7SqqMum1u1soa2aitU8hIiLUPjIx3JnvSHclanHMoR9riDIcSxgaYaz0urbKmXqtQ6vQqqueT5LK7yBuhQR8IQpTN5ql2OFzHLl8S1Efl7wqbQ0SNaEp4j4SsPPqv+foTRPQoxJdoQKbBNgDz0GSVzoVYjWGQxHqGTKX9CfAfEFEGmlKhJkEESkfzdKOWSzO3Q4SYzH0Cg1Px5jYfRFhfRVNQyr+CCJAsgDcggaQdEJF1mGP3F6nJgEzz4yFW9FEAa9u7eX1CPInobvPpYQCbiehfkJt/NZoifiHhB0mDjm4JC8tzyWqZWFhtiNuFmI0n/6TvDfM182MBAE6mzGle7YdUAwBqfGUjDDZOJiBmdCScW6uqUmfW1iasuTL23YUHOCN758gZi6BgfdFBXDDd8/yAtxxZay9pPLZgXkFh/W3JCTt+cr1zwYgSHF7xj4/CLjz+8dCCoZcUHUu/JUnXiys2V3zs2Fz2/qD0qMxd0+PnR85Tx44uV+oObdePFh2oGziWCSnh4TUFy9I+wfKEv9g9ql1bn7jQ/XniRbbjGJbIGur3l8SVHjg5wvhtzTVDiDEsCZWnLlW3HrlC3+Jd0XA0KRyVY4iglKlK2brw8PzsiPDG3cOckXtGKJnsXwvORUZqedHxCQXZxZkF7BpTwhTdaE9wR6TFVceOLK+OnarVR6XWex0xUY3cGFmvl9YX1J90GzV7mfUqA+yCSirH2Afq8c5kLcGZYoxyJKuRtlinQwmLI1Csl7SaamoorWt0d+jyNJdmpqNpCq5ApsaEpqDQswGvOyFT7irIMkcvmqbeCoCHINNjH5r8oamQabV/On+T+XpggCoXLQwkc2mmPzr+OCTvqgZNUfKrIVbhv81z+l0JBkQ8B0L8r/5UqQiIEfQtSDwl1jxfOJryTTcDWAxgC2QG/DCkslubUF9oF0REP2vvdWb+eW+NpSWZqzJjId/IFkHw0Ot69uRjvKjldq89snLd3F/FIKCyjqf272tYL14IAJcMXr423pm84LhSvucjx+5JquqrnzP3jRoQD/4hnt9aQQmz1IL6jba8mpkE2O63vb72NvW92URwrAkP2/3DpIQ4jWjYoj3G1lv+awxSDWVowdBLNuUPuzxN5zrF1/jRMdaKpkfZ4iunDlx6NCV8xAQQBuQrp3bvsB1rqKKGKSBEORwNJalpuQcTE49G2+yeiQdo/KGPcdmp3Zia6EZ4Btx6iXqy8Yha4iKq10YRkBSFxtoLlV0Hr1Q31s9UDsTHoX4skazUOWq3HV8THl64NiLcl+twDKhTaBSIwv33IMrF1WMLuSAzn6vHFbKSUomBNs2RWB899FR13OjKmtiRen1kSqTXET2YocewXlZoaMWVhnbSy3p5GBv1AwFtqEo2I8Y+sHCAM6Uyxj7oi0ueu6vdQKtpSd4FscB0yFQ1AmIZngLwNmQppYGmdCQVskrnZvN3wzwOkP+RVDTld5ZDEs4vgwhtAWQmORFNvlQCcMR8XGJuuw7iBhgE4BfMvI2IfmuOFeb11jDzpUT0ZQD/NLf7I/+XQYp96BDB1wF8i5lfJ6L9kCn/UPO1aABlzJxMRGEQIU8BsAviZtjV7j3sC+LZ18lclXkKZ1FA4Hzhumx97Zc38ILWXjuavmxdfvoVzdYwe+veyja044sAYEzMjI1TBy6dAwCrnJ/v9ZE+MSHh2LZxGetmuBBW/338pdhLztHq8fpNttya6QTY5ys5OavsKxNV4iQ3keuOpEFbNoWFLVAN6Nd/Ymy4bDtPZlKiCoZeuil/2GVDDfiifa7P9xjevAwFFDc6Zvq2jLg5UU41fJIPeuN+tXDnXttxpwveqSCoNpu7cvDgA/uTkw87HM7GydUUV/s5Ljq4FouVUiSPB1Es1XqPqEWNheopdzjc+jgCYuzQvBco+w9cqWysnK/mhCejcoxCsqxRA7R9TseRzyPCT20ID6MjdnuSh2gEiE5XQlIM1oeUIX9iAZdOLGBfeglHxDUgzVDCI+pihh2vihtdXRMzwmiITI7x2aNSGTSIjapC1opLDa2o8q5XVl7W0WdFRB9CLK0yiKXmgYiJDhG6zyAieRTiirJDZqoDIeIaGPD5P4jQ+v2WBOBjiNBdBRHSKsj/UDhEhFVzv1xIytTl5rH/Ns8fKJ4amoJQlQD+AfFZ7of4Nf3W8jchQaoNEHH0R9avY+Y3iWgLZHoeBQkI2QGkM3OReU9+CxHTxzq6f0AfE08i+gytr20PzoPTQ2SuylwP8elYtMHsPGPH3e8Yk6gNV9C6OY9u8zrjmkUvvfX/yjZ8RxYBgFOJqLh66A8GEBHlqoWb1tsPXAAA06b/e31kZPW8EiQX3ounw5iUQWphwxbbvuopBDiSUHnqI+f9JbHUOAkANoU5996elBjuVWhkVCNX3fe2njPuBOYyKcgfdtmmgqGXDTOIEzX3li26e+tgQB8Z70g6PHXg0pODnGmTiSi2Ae7Snbb8vMNqcbJGxlgAUFVfXXLyob0pKQcRFl6XaZAStgvT936My2ryMD7NR46RMFhTKj15alFjuVLhiYfPyCAp9MGT6OjhZeqm4qXKTls6lQy3kXG6zFojUcO2MOeRzyPCq7aEh9mLbLYhmqzlbkZ0I1dmnOCCzGNcO66I1aQqJDh9GK7ZIhtqYoafqI4bVV0XlVbwjTdvv76jz4uI1kH+pse2CBZVQXyXaRBRu6NFsMgOEZ7AYFGS+TvQFCy6H7J88osQgfOvLnKY+/iDRd+H+DnvBfAbZr7XX1EJ4jt9Bk0RczckoPQDU/yXmucphAjzIgA/gQi22zz3LyEW8NcgU/J0iKgeBrDbrBkMM4XpBIAFZkGiDulr4jk94NcwAF8BoDHz/SEaEgAgc1XmC5BvVotWGFbKR379F30QteEP9tnCa9bOezwCRPbA7d761dmG78DpKf6Xh/0oz644xjHY+KvzswKDeLjN5q66YM4/NCIk7MS03U/gwQwQOZSihq32vdWTCHDaoPnecDyycbpyaCEAeAHP3UmDNmaHh88HkW1YKR/58Zt61YB6zDBI0fKHXb6xYOilww1SUnXv/q2aK9sJdk9Wyd6QEXvBzjGxMwbaFUcGAJRR7aHttiNFRUrlOCYJBpKiuRMT8vcMTs3zRUZWjSdCfDFSTnyCS45twvyIKsRPBFEYNKNBOeXOVU82NihV3hQYPJrMgMdQKi28QtlccJm61RhHxwc74RtB1JRfWakoFesjwo5lh4fX7wxzRpap6nBuqkl5GsVgLb0UxyYW8KmJ+eyLr+fNl63NXdHRZxYgPhrEYCmBTGcrIdbmbojl6UKTj9IOCSbVQCw7/3Q+AcBGyOoivy/zfoiIzQfwPkQgE9BUOg4wp9SQQI/NvOYvIYL8vPn6OjS3Il0Qv+VOSLm8iyBBHhXAFQC+C5n6+yBBoxSIKP4DEvCaDFn6qQC4kZlfMe/HYgArmdm/dLND+pR4tgYRbWHmWaEcQ+aqzO8gtOlSfZaYBq54/hm9wWa0vbopf+il64+OuGpey+2+ho+yde/e0+K5IOkrnw+OGLUYALbZjqzdZctfAACDB+duHDlq2xwA+Ce+uvZt+toCAFCKG7fZ91RNJLMS0F22t9b+UH17FpHk/W13OnNvTU5Q3YoyBgDm7zO23braiHfoGGmQ4ssfdsWmgqGXDGdFTTN8J/b7Gj+uYaN6FgA1IWzI/qkDllbGORKnE1E4g41jyqldO23HXKZ/1Ax+GdqgQSdyUlNz66JjyscScZIHzsZNmLv3U1zsPopRowxSJXfRo5erxa5DanGjRnW+4cRNxVbiUVt5ibrt8DJls2uqcnhQFFxjiNDsy6bAZitcGxF+Yk14mHe/0xFfoygjIX2/ArkjZ3nOMx19buYsbzFkut4IEZYhEDH9A8SXuBYiQi6IcDkggaHfoEl0bRDr0u97JPPxAoCvQqbfxyDLgythpgdBLNwYSDR9PkTMrjPTlPIAPArxPe6ACKNh/vSY17/HHI9frBkSXBoHEW0/BmR9fByAxWai/BsQgf4bM/+go3vV5j3sS+JJAf1HIDdkBoDfM3PL9IVeJXNV5jiIb8YiAJvGnj8+pR+I8qDd2gPrL3hkiydswBlfgL7Gz7N1z47T4pkaMXrX/KQvTwEAHYbnJednNUyyqmrmrLc3h4U1zAaAR5GVvZ8yFwGAUuLaYd9dOY7M5X+zaf/+1xyPxfunxT7A9+OEgRs+jIyYCyK7YrD2zc+MDcu28EQFGGCQ4juWvmzT8SEXi4jqVSf8wSUAkXbFWZMZt2D3iOjJqapiGynn1Br2q4W79tpOhJn+UdP/xxwXX7wvLW1/RWxsyQhF4SEAcBQjDn2My4u2Y2Z8A6Im+v2b1OA7rhY1FiilLhs16mMpoDxaODyNC5Q9B65UN9ZcoOTGDELNGKJmhTegA3quw3E0OyK8dH14mHHYYU/UiL6z44a9m9AOZqR9DeRL5ziaVgcNAfA/Zr7cXOM9FlLLcxDE+vNCRNMv2A0Q/+F6yNR8CJqCqy9AhBbM7CCiWwD8DGJ9+oM5PgDXQ6qq2SFV1eLN6/3CvNY2iBZ4IaJcBvHJDoW4FwKLmvwQUm90tPlaLURg7wbwO4gF6oLMkFIB3MXMHX7RtHkf+5h4HkOTz1OD+FweZuZ1IRsUgMxVmf4PbWBH+55PPPlHbX1aBc6wKAPRVGfdmvm/cUBWdzTD51q/TndvPh1EUqB4r0m/VzOLQSDbtj/7kK14EQA4HI2ls2b/M4wIsQYU/Q68sLOaBswAAOWUa5d9Z+Vof0L+IFSXfey8/2Q81U/2n3uvw3HoxpRErVFRMgAg0sU1d79j7JpYwHMJsIuIfmHT8SEXjWBFTWXDVeUPLgGcCACDI0btnjzgwoZoW/xMMl0QDXCX7rAdyzuslqToZPiXCAIAomNOHUhL218SH38yTVX1kQBQj6jqNbhwfzaWcBHSxjEp8jfFzFTtPaAWNZaqZe4oeI0MCihaoULXptPBg1eqG8sWKXucaVQ2UiFuGcT0AYhFVo2rvc+EiO6A1JPwQERPh9y7aIiQPQaZQtsgQhUHEaNjkOn9dDRZgjpEmO5Ek4UYBUk4/zHEukwF8DqASyH/12Hmvvshfspq/y0wx+f3eT4G+UKxQWZ+v4aUmfsEwOfM/CoRXQnJ4ayC1PWsh1i4H5jHHoNYt98xr+eE5J/aATiZucu9tPqaeIZDnLzzISK6FsBzzOxu98BeIHNV5vuQZVwWAO56R/98bh4v7mi/giFLNxwZ+eVWg22ae8sGzbWu2WtXDfn+tnBb9AwA8MBX84pzjQJCNAAMG7Zr3dBhOfMBoAERNd/Hnys0cowAAKXMvce+o2I4iQBAha695nhswwVK7umS6jqg/3TQgLXvRUXO8Yt5Whnn//hN/VRCLWYBgEGq9+jwL2w+PmTpSJA6mFnzBAaXACBMjSybHL94/9CojJEKqaen3qeo5uB229Hik+If9RfiBQBERFQdSxuy//jAgccTbDZtPAAYIGM/Ju7/GJeX52BysofCxp0+wGCvUu7OVYsaq5VKzyBoPI5a9CYaS8ePXaFuLrxE2U6jqGionfRSZNV06OIyxfPLkIDRIYj/cB5EWBoBfAiJN8RAxOooJKn97xAxvdfc1wGxPm8G8BQkkj4IYtUWQKxAF8QCzAEwDFI0+wQkh/QzSGvxEZD/96/7KxmRdNC8DSLwYZCp+lTIOngf5F4UQ4RyJEQ8a9DU72gopLBIhHmN/4OkRLF57HBmDqyo1Gn6mni+CbnRr5mbvgEgzh8RCyWZqzIfgtm173znyk3G+m9/ZrRrcfrZMPvnm93hg85ImAcAzb1rk+b6tJmDfvrAi7NHxUw7PZX/j31ndpFaefr32Re8udPh8EwFgCKkFtyP38fATERXyt059u0VwyigRsD31X+tv8/2xjQinM6tPGC3H70hJamhXlVOLxWdnWfs+MG/jWinJqX7RESv3Hx8yBJTRJkDg0vmYUZ61MTtmfELEK5GT/cXCTbA+jHl1K5dtmPu5v5RISysrig1NfdIQuKxWJvNm0nmtL8CA0s+xcWH1mOhowyJE0DUNE33GTVqqeuAerLRTTXeVDLOrNwfh7pf7Vp5XTDBoqsAvAWJTF8PEZNUyNT4B2ZOpAtiUV4Iyc3UICKXC4lUx0D8i49ClnJWmb8PgSxseRdiKWYz8xIiegVisb4HsXpHQazbcea5Gpg5KiBB/mOI+H2ApkLc+yEifRDil42EiKQbsjb+r+Z7eg4ydVcA/BliuQ4zj7sGkh3gA3CImf21QTtNXxPP/cw8vqNtoSBzVeaFkIIl5zWTjho5D71hjKHm1b9bRVccDdkLnlQCk8Kbve7Zt9XX+OHMwG3xjqTDl6TeMMr/ez3cxa871w8EyfK98PCa49NnvDeISKa0W3DBjt/j3kn+5Huq9Ox3bC1PDYz8z6ADua87Hom2kXHaSjQA49GB8WvfjI6a5R+fYrD+tTXGhqs38TiFJa/XINV7ZPhVm04MuXA0SE0BgJbBJQCItMUVTh2w5MjgiJHjiZTT02kftIZ9auGufWf4RwW73VU2ODUvLynpSITD4ZrkDxJpsHm3Y+be/+HSuoMYN0wje3qzm+fWStSTriNqcSNTvTaSxOL6Yv7KZf/q6HMhopchq3QcEMuwEuKLdAH4GyRB/lOIwGiQoM0AiDj9xjyNB1K9KBvS6TbC3PcjSO3O5yFCmg9JSdoPsRAVyFRfg1ij09E8WHS68RtJv/U5EHFlAHXMPNB8Dw9BXANJAK5m5veI6HNzXJPN7RUAHoBE50sgM9rp5liT/W2Ku0pfE89XATzDzJvM32cDuJ2ZO8xb62kyV2X6UzS6pSd2fyS5kk/87gU9XAmy6vyJ1MUbD42+dk5br+veQzt9Df+e2nL7ten3lSiknK4R8LZj87pKpf60b3TUqM3ZKYMPnrZGX8XyNf+hq05Pz6nKk+vYUp5MEnwAAAxATcVHzgcKBlHttMBrHbXbCq5PSaqqUdUp/m3hbq6961/GjilHeY7/S8Igm+fIiKs2n0i7cDRIERFtEVwCAILiGxUzddv4uLlhYWpEs/dWD3fxTtuxA4fVklSdjDOKU6uqtyYl5eC+5JRDalhYfab/CwIACpGW/z9cdnwL5kTWIDYTRM3KulGt95DtQM2ME/df3Gb9SeB0sOgzAK8y891mkPZJSNrSPWYy+V4ACcycREQPQ0RwMiRZfiSk9uU1kDzOI5D0olpI1P0myBT5BQAbmHmpGSz6ISRw8zkkwHQtMx8jIi/kf+pJyOflF87Hzf33QYT9Z5CA0kAALwKYBrFea5g52XxvxWhaYvoRgCuYmc32Gx9BrOsYAIuY+axbX/c18cyFRPiOm5uGQur7aQh9RXlkrspci447/Z2TRLi55o9P6eUOPbhGbwCwcdbPNroiEtsWT19+jq/+7cyW2y9N/c76OEfiabdABdUdecexZQRO50GyMWfuG7k2m+/0lCsLj605RGObBLTae8CxuWwQBQT5FBj6S/ZfrVuo5jRbPsoAPzEgbu3LMdHTAqfKKRV8/Mdv6ieTq0+3bTBF9OpNJ9IWj4Up8K0FlwAg1p5wdOrApYWJYUMnk9RFOM0pqjmw3Xa0pDX/KAAoitaYmHQkZ/DgA3pERM0EoiZL2oWw+o1YsO9TXOTNx/DRTGoygI0lF07pcCGH6e+cCJmOz2HmCiL6G0Qgfw+xOHdBpuE/gKwhXwcJ+PgrIP0Nkru5H7Jc84uQgM7bkKn+LojPtMh87XXI1D4MEsCJhawdfxMS4Glg5maZBER0iTme9QAWQqzf0RDL9yoAv4WsJCoBMI+Z883lmt+FWKvbIV8QL5u1M8ZB/nfvMc8xkZnPqktEXxPPYe29Hlg0ORRkrsq8C/KhnVcoBmvPP63viWvEtI73FnTF7spe8FujlTzE0xhaUa637o2MltvHxc5eP3nA4mY+1b87121pIM/pYEhkVMXhqVM/GErmdF6Hot2OF3PqKPa0tUc13kOOTWVx1GJp7Y3qBxt+Ynt1SqBlBwAnbLbCb6cklVbY1MDFGph2yNh9178MR5ivqUqUQTbP4ZFf3FSYuqhJRFsJLgGASjbXuNhZ28fGzoq3K85mPja/f3Sn7ZinWvyjZwQxiAzfoEEFe1LTchujoioyiJpb/gcx9sBWXPDsX5b8tM3CvU3nojsgOY5TITMIF8QnWIfmwaIyiJVpoKli+w0QK9MGse7+DyJu70ACdSrEwvsYUpc3MFhkQIJS8RA3wUGIH9JfgenrAdN2B2SqHQ/58jsJmWpfCkmKXwGJ6HsgQaPZEOH2p0uxeYwTUkHpcTSV36uAfAn8nZlv6Oh+tXsv+5J49nUyV2UOhUQRzysee0lbO6oYra5Zb4uilPmbDoz9erurNQzt1BFv3atnWLJhamTZVUNuH0REp1fcnFDKcz507G5mpY7LWJOdkFBw2oqsQ3TV7XixVifb6S9hqvUecWwsi6YWVfgn0+GDbzl+HmYn/Yzk/qfjYtf9MS4mEwHWIjEbX1nHG65Zb4xWuMlS1BWb+8iIL20uTF04DqQkATJFaiW4BAAY5EzNnTpwaXm8I3katfhi8UGr36ee2LXXdiLCDd+Ulv5RgY0BAwpzUtNya2JjT40iYn/x4GlLlxzZeeb+zQkIFk0IWJb5ESRdaC4ze81g0X3mssxsiDDOgAjVUIgQHoeI0D8B/JSZ1xPRO5BUp6GQKfY4s4anP1g0GeLf3AZZLrkRQEwrKUr+9KQnAPwPsnIoC2LB7kJTzyUbgLuZ+QVznA9D3AjvQ5aa/o+Znyei/wB4g5lfMsvifQIglc9S/Czx7CSZqzK3AJjZ4Y7nCDf/R8++eNeZVZI6YtPMn2xojExpdxpp6JUnvLUvnbGGGwC+MuzugzbF3ixv8mVndo6XtIBiyrp3ztw3ClRVP+0/LED6kQfxRAKITkfcqc53zLHhVDi1qLUai/rqj533H06k6jMqhheravG3BycVltpszT7rMC/X3/GesW3GIb6AAnqc64rdfXjkl7YUDV4wDqScFurWgksAYFecNRPi5u0eFT1lsKrYR6EF9XAX77AdPXBELW3VP3r6PcSW7E9N23/wq9eu+VJb+wTSj4JFhyGWYyTEyvwLJFL/e4i1uhcinqMgLoHfoGmGMRzic72bmXUiGg/gT+Z5GFLcud1yc8HQyjebRQf8s+Ndzg2W7DI2X7Sr9SpJ7WGQzdMYkTyxo/2I7C172ZymzF1Y3HLbLG1Us+RvZtWxb+8SL3NT+4hhyB95G546iAB/Fkfbh3vnJXqYmlcGr0FU3GzPM9M+0admt7xWiq6n/O/EyZk/rKxeT8xV/u1uB0U9fo26+M5b1fKTA7DRv101fGFjD725cNHau6NTi9Zkg41TAKDYh4x3xn53jiPmOyfJlpoNyYuEz/DE7qr8dOFbBU+OWlPyjz013vL1zOxvuIYohKUs1MYv/o7nwtFXemYcGKzHZxM39TI//R5qksfv37fkWFv3MRAzWHQZgD8zczhEeNZBptZ3MvPdEOGrYuZIiFDlQwTvfoj4XAQR0q9D6l5eCrEoH4EUDkmBJNSvZeYxkOrtGiRHOhySnjQeIryAVHJ/I8Dq/Jq5PLMRUl2JIOXhboUk1KeZ5xsLEfu/QYRxGkQ0kyCB3buYWQcAZt7PzPOYeTIzT+kO4QQs8ewK54V4jinkvO/9x5hIXfgbKUmasSfQ8msbW5vpTsfq90S33DZWHzxTZeVI4LaamuQJ1dXJzSp+z8eaGUvwcbNtHGUf5p2XpDM170nFUJQbffct+qlv+UZm1Le85k01tfP+d+KkL9WnNVvyWBpPaXd9zzbnF19Tchod2Offrhq+8LGH3li0aO3d0YOL1maDjTIAUNT4Ic7ory1yxt7mVRwZ2QCdFsJi19FJ/y3687x/HX+m9mjdnmyD9eOB10ri2LFX+KYt+o5nycDF3gnb44yI9WA0BuzSbovcAJZA/JNfIKKBZqqOAyKQMINaMQBKSCr+r4KIbTYkd/OfkIj1Q5CgzzZIsvuFEEHdCplS56GpzuclEN/qm5DKSwMha+pvgVi7XgBg5puYeRvEXzoRItbjIJbxX0lS0b5t7v82JHh0tfmeboOIabg5plhz3x7FmrZ3gcxVmbuB9tdz92cG1nDxM8/pUPn0ao1OsXnGg+sbolI7TKJn9rk91U+HtfaaQqr7mmH3wCxSe5rttqPrdtqONct4UBStcc7c18sVhZv5Lx/EE+sKaHizfalRK3SsKzWIzyxkMoGOHX7H8TObg7T01sb0akz0xl8PiBt9RnUjZv7iRt7wtTXGiJb3TFccjYdGfWXryZS54xGQ/9lWcMn/8tDIjB2T4hdpEbaYGRRQ69OPF1rdPvXE7jxbUd19P//xFa2NtyXmlP1SyLTb34jNX1czG9J64xOIX38QpA5nIiRItBxi2fn90K9A3FdDIT5RhkS4oyAC64CIWK75WjyaCoIkoymNzMfMYeb4AqftpRB3Qb05npPmdY6b5x4PmaZvYeYvmMe/Donu+8vm/ZCZ/xzMvekKluXZNf4R6gH0FE4vN/z2T3pNV4XTIMXXEDm4wym70LblabAe5jEa97fcPkVLn0XcvKWtYdgi8nIXntHr6mGsmBXB9c3auHCELc2zIMnOdGbgbx8PHzXd81x8MQ/Y2tqYvlVbN+fz40WU7vVtaPYCEb07V5m3/B41duM4ymY0WYWq4Y0Yd/DvixatvSdy8Mn12WCjXA6xOe3hcxc44+4cYYu4dAsobHfgGY835E5/v/D52e8XvlB6ouFANrNRGnhJB2zRU/Xh87/umd9uEZCAIU6ApPzUQSLtIyA9imoB3MTMV0JW7ZyATLWfgWn1QXI0v2hO9dPM7WkAHmXmARAh/DfE/zkKwE4z9ehGiL8zFyK8xZA18K+aw9KZOYyIXiSiGRBhfZWIjkKsyGxIIvwtENfAREi5uekQ8T4MYDwR7SKiOkgQah/Eur63J4UTsCzPLpG5KnMw5NuwT/SA6i6I2XjmD/rWhNoz+w8FS0nizG37x9/QbsvWQNxVT3rQxmqlmYMu+3xE9OTFLbevteV+fsB28oztU6auXhcdXdnM0qxGXNkd+KPHCFiDLhfWSpxrSl3EGH7mlZmfs/9uzWXK1oWBNTYDeSsqcvMjgwakG6d70DcxqIaLf/ymfjStHHMJzY/XFUfDwdHXbC1OnjMRpDSzYNsKLgEAgbSR0VO2TYif53AqEVPNTAQDwIi0lQs6zAAxU5SSISlC96GpGEgxpDf5h5AgzlUQ32Ocuf9wSJR7OsSaqwPwsnnau8wx1ECi4P7iHSeZeRkRvQWZofndAf5iIMP978+f32lanTshgZ48NC3JzINMxQ9CpufPQ5aEegG8x8zXmVkLa8xruSFT/2v9Ps+ewrI8u0DO8pyTOAd9nw+9bqw9G+EEgONDlnS2iIunrReO1u0Z3Nr22droaWDUtNyes+fiTGZqZpXGoTohCw82grmh2c5htmTPwuRIVtDMhyoQ3eb70aIfazdtYUarK3auqW+YveZ4kXOMx3vGSpXyWEq552bbvIe/oexvcCIn8DXV8EZmHPjb4kVr7wlPKd7wOdg4bTG3FVwCAAbbDtftvOBfx5+Z9t+iPxeUuI5la4bvnWCE02QmZPXP3RCR2gkR0GTIiqFMSLDlZYigfh1SEOTbkMh5jTkeglRkvw6S3sQQEf4PJOKdAmC06WqYZL5eCgngOCDT7iiIZXnj6fcnPs9nzSpHv4AYJ++aAZ6bzOPWQHynFRAXw3bz2AZIBactAG6HiHePCidgiefZ8FSoB9CdfP1zfe2k/M6nJAVikKLVRw3pZB0CalM8KzwnR7MZcAnEAVtMmjFwV8vtuu6IPXxo9hliMhKHx9yIF/ai5TQrTE30LEyOYYUOtXb91/Ulsy/zrqzwsK3VtgyxhhH3z5Ml8355qnybynxGj+99w5QJ37nblrlqqbJRJxQFviYi+trihevudSYXb8pGwDrr9oJLAFDrq0jPLnlz0T8LnnwVQdBiyr4EMg13QFKMHjKj7P4p++0QC/Q5yLR5BWTKPhKyQicWYrk+yszjIIEaf6BoNIB9AVF2/5R9DoCbiWgJRJRdAFz+KLs5ZfeP9TJIsCgfkq/ppxxSXDkHUnltAYD9RBRlLr+8DhI4W4Ygm9afLda0/SzIXJW5FZI83K+5INfY8aN32+4/FCylCdN27JtwY9CrkADAXfW7IsBIbev1y1Jv3BDrGHRGvmgD3KV/d66PA5055Z8x492N4RF1ZywL/QPuzF5Pi878gvDoFc41peVktF50OxKuuv86Htg/RClv0yqvI6q9JSVx916ns9XULrvG7ls/MDbN38czCM2LGgOApjrrD47+6vaSpNmZaF4UvK3gUgGAkfe88X6HFlYHU/bfQ6a53TVlL2LmL7QxZa+GWI0jAXgDpuw7INP0k5C8UH8L41OQoFEhxOL9FFKRKRVi6U6EfAnYzfEegiTV/+hs6nQGi2V5nh0dLofr6wwr5SM/etcYebbCCQAnhixt6HivlrRteQLA8Yb9ra4/jkRY0iCObjWws3v3paOYUdVy+/fx1KLBXLjhjAOc6kDPoqREVqnVbgENCI9e4P39rH/pc7IDc0oDiWaO+fvJ0gVPlpbtsDGfaPm6z0ZhT1+lLr7tdrUhPxHruEWjQ5vuiRqf98qihevusSeXbM5GQG5pG8Glp4MRzkCYeRUzT2TmyZD8zqeZ+a2A13ex9PBZBuAoyxiuAHAtJEhKAJ5g5pWQJZ6/h1ivP4eI3Ul/5Nvc9w5mTmLmcHPK7oQI3mnhNHkPwK+ZeQqA70HSogYDmAKpfjQFkjL1TYi4boIsybwcYvn6AGxm5gxmvqM3hBOwxPNseR3iz+mXxDZw+S9f0h3URuO2zsAgozZ66LiO92wJ+dp79Vjd3jNW3/hZ6BufCj6z26rPF55QkD/ljEg9ADyKe6eF8ZlRfDjUeM+i5MGs0r5WDgNA9EPfHYvu9t22nVvxt/q5uNE1bX1B4YDpLveaM9wEACpjKOn+G23zf/otNa8uDLtbvm7TPdHj815etHDdPbak0i2fg7m6aQRENueEWWFx3x/ujL3thbbG0HzUdD2kwMe9RPQGEb1ERH+FCOS3iWgKJFiUQERrSGqjfhtADUmf870QQXsAkhq009z+MmSqnw/Ju3wUwCIiUs2KSNMBvElEt5nj+ARicW9HG1/UAVP2q5i50XxoRDQcYikzxD2QCeAgMx81FxbUQqzoXsUSz7MgZ3mOF1JVpt9h09jzuxf0Yptxutf2WVE+cGJOYB5j0JDSrni69Lpk3fAdbu21ARw1PIrDtrT22okTmfM8nvBtLbc74At7HD8cqLB+5j+bXYn1LEoewjbKOeM1k3eMBTMv8j5e7WZ7q2MCgAjmyJdKTi38Q2lZjp05v7V9DgyhjBt/ZJv850uUTZqCMyxVm+6JnpC7avHCdfcqSaVbm4kogD/84I/Lzkjob4np6/wJpCDHLZBp9FWQVUIfQJLgX4YI4xxI+tJBiMU3DhJkqoCI1U5IsZBbIbmgTkhREA1S4ONpSLm5GyFT+REQF8BviOgAxB8KiItA8/s6zbSkH0BabvwHYkl+aqYfPW+uxT9ijqsREqj1AZhMRPuI6DHzWpVEtNY8bhcRnSSidzu6R2eDJZ5nz9PAmStT+jq//ou+LdKDM8rBdZXjQy5q0xprH7XDKVa552RRW68t0DLarNq0Z/elKa2tGhqAyqQHkVUF5jN7/diVGM+i5HS20Z4zXjM5wqnDpnueT843kja2tQ8ALHC5J20oKEya2+jKRhvlzz6crlxw/T1q4meTKJvFp9gMm+6OmZD70uKF6+5VEku3fQ42TiL4yl5LAPyDmcvNKXsGZIr8E4jIfQzp1JDNzLsgyx9PMPMXIcsoV0EEcDYzTzR/fxLiW7zELBH5CCTK/ZY5Zb8EUp1+ByQhvxSSqxkLCRLdx8xR/hVFzDwCwARI9P1JiDthqxllv9U81z8hRULKICJ/PySXcwqkDulvIK6ABeZxUyBFR94O8j51CUs8z5Kc5TllkBqG/YYfvaNnd9S4rTMwwDWxI7rY4VTpUDyP1e1pUyBTjQETHWxrVejc7ujUk0UZO1p7LQP7x38LL7VehcimRHsWJY9ku7Krres2IDxqsfe3c97UFn7elh8UAMKYw18oLVv0YsmpXIfBraRFAZqNnM8tUxfdcofqPpyCtYwzz2fT3TETc/+6ePGaH718+/NLzljj3gYzAdxERLuJ6BUiegkiPj+BBHcyASQR0R6zIlIMABDRnZDp8VRz361mpPw6iAg+AGCD6RJ4CmbxjxbpSQqk/9hwiGUZBSCciL7WcpDMXAp5z1+FFD2eZY7ji5D6n/sgwaN6iM/0CCR9ym4+EoGmbAaSpcFLIK1AegxLPLuHJyCrLvo8V24y1s/JO7uUpJZUDhi/11+OrbMQqR0GPQobD44PLJrRkgu00W3mlh49On2Bz+doVVwvx/tzZ/DmM4qCAABsSqRnUdIYdiitiq+f+7VbF9/hu2On0UqAKpDZbs+EjQUn0hY3NGajjYBGTRQlPHiDbcFD16uHaiLQmrDXKqz9ppXtZ9DP0pNSIKlHpRDB3ktSlPqnaOobFgdxJeRD/KaLIZboJxC3xHsBb/+LAD5h5nar6p8tVqpSN5G5KvM+yLd5n2XyEWPPg28aY4PpP9QZdkz+4Zrq+DELO97zTDw1L21go7LDCuhXD71jZ8u2Fn4YzC85Pz+ik9FqcCkiourYtOnvD6ZW0poY4B/h2c1llNx67VGd3c41JXvJa7SbkpZOxSc+cPy4MYK8HVrgu5yOvJuTE8mtKO3uu2SXseXGD41Eu4F0c9NPM/JyH+no/EC/SE/aY+5Ta147CrI6aB9EsOsgEXsVTf3i74K4BRZDpvk289g/Q4JZ081ruSCZBD26kKXfWZ5E9LjpKH68xfZrze1Gi281BxH9lYhyzOnL4hav/ZGIDhJRHhF9xdw+lIg+I6Kd5pQmmMILT8GsTtMXkZYSRmp3CycDXB03ss2IeIeQGlQrhOLGI21aEQSiKVp6m1kPjY3xw0+dGt7qGnACaCXuznSy+0CrB6sU5lmYnMlOpdW0KD/5nDJkuueFIYeNwWemQrVgisc7bkNB4YjL6huy0Y5F/ekUZdb196qpH02lbJ2QB/EJdoo+nJ50L4BRpt/0dXO/WZA2xkcg7XcaIOIaZl7zuwBOmVbuMIjb4ZeQVU1VzDwK0t9oDmSdfY9y1uJplorqNoI43y0AJjHzfS2274X0ol7TYvvNAMDMmZAP/zdktoiFlNbyfxiBNQZ/AuBNZp4K8fP8oaNx5yzP8UCWiPU5Il1c8/ifdZ8S0M+nu6iKG7sfpLa6jDI47EGJ59G6PYntvT5ZHzaLuO10lYMH5s7TdbVVgQyDJ3IlfhRFraxmAgCo5PQsTJ7MYerm9sbggjPiIu8Tc1/VlmYzo113hB2wP15Wsej1k6UFEYbRaloVAOgq2V+8TF30jfvVX2bk5QaVR9sf0pOY+aOAfEw3gHpmLgx4fQEzpzNzOiSm8BhELP9ERHEsAbgGyP/0cEgwy7y1YLSz7Le7CEo8ieh60wI77Xg20wg2A/g1EU0hok1+x7P5YYCI7iSi/eb2181tiwLSCXYSUTQRLTbTDN6D6Xg2Lcyt5rHfM499D+aH0dLxzMy5zNzaP8d4mC2DmfkUZBrht0y/C/nmAjMbzFzuPx2aen/HAs2L6LZFzvKc1yEJvH0GxWDtd3/Uj3SmcVtnOD704vKO92obIltQfqNyT+FYZj6jcpIfBYp9rJ56sO0zKLacnIuYGa36GxNxKvUBPFIC5tb/6RRyeBYkTTPC1Q4/359oNy661XfXHoOpzfH6meD1jt5QUDj2i3X12WBuy3e7hRV6paNzAX0uPUmB+DmnoHl6UjOfJ4DvoLmlONzUhmySuqJAU9X+RwCcJKJqSET/Y4gv15/u9VVI/KHbDYWWdCieAR/GEtP0/6H5Uhqk58ndMD8M0wTPgUTMAHE8TzW332puuxfSTngKxEnsTxeZBqm/53c81zDzTMiHeTMRDWfmq2B+GK05nttgN4CriMhmJttOBzCEiOLM1x8hoh1E9A9qqpCTBeBbRFQI+YO7o6P7FMD3gdb/QUPBL17WN8Z2onFbZ6mKG3N2okz2YPdU6rSq1qfWJrO0Ua0WDPFTV5s4rqoytc2Ws5nYk3kNXm97eq6Q3Ts/aYYRobabogQAHxqzpi7y/tbdwM5WVy0FogLqI+WVi/5ZVFIcrRstc0x1ALflLM8JNjjRZnoSMy9HN6YnMfPr7aUnMfOF/kG1SE/yFz7291/fhqYiIcUAhpqzvrshleKfNMeUBqmkFAHpV7SNmR8OfPPMvBhNmtKjBGN5nv4wAICbChj8g6U/SCzMD8PcvgoS5QNkmdVrRPQtNAnKegBPmukQcQGm+xZm9rcTuATA9US0C03Vp8/o4xL4IbTDXyBrY7dBzP8NkD9IG+TD2MDM0yB5YU+Yx3wdwEvMnAbx/7wSMNVvl5zlOTshU4yQc8t/9OzONm7rDNWxo3JZaVHqrdPYg45YnqjPbXcq7IAteqgxaFd7++zPXTjbMJQ221Z8CW/Nz+SdrUfgAUAhm3d+0iwj0tZh3+8TnJg6zfPC8DxjyLqO9gWAMT7f8HXHCydcV1u3Bsz+mqDP5izPaTfiHyTBTPmXQSrDT4OkJ9lMX+dNkADNeiLyryILPJ/f1+nPsXwLwG9biUtMJqKNZvzh30R0K2St+nIALxFRDiQQdQsAMPN2iP9zDMQabkRT7uY/zHECkqY0xLyGDTJb7NDqP1vOxufZax8GMw/nLvYdYWaNmX9knudqSHTuINr/MG6ElL4CM2+ETBmaVw9vn18AZy69602W7DI2L+1C/6HOUDD0orNemkpkb7VeZmscq88Z0dE+833jxoPb9nexYQvbv39RLbeyrNPP/Xh0wQAub9sCJVK98xLnGFG2DkXRA0fYZd5fzf+LdtmatlwGgSiA8lBF1cL3iorLkjXtI4hfvjN8CuBaIhooQ6UBCKgpysw1AKoCpsPfBpBtGgdDmPkzyJQ+FkAUEY1k5hxm/hUkPWkcWtQahfhPbyM6PY24FcAFrcQlXgSwwow/HIJYsFdBBNRpbr8UwPeIKJ2ks+doSCCLIQWXF5vnWgpJiwLEsl5uPr8GwKfcC2lEwYhnax/Gabrpw2hJsw+DiMZQO/2/24OIIvzHEtHFADSWhlDtfRjHzd9B0qo0DOL7CYqc5Tk+yIfZ7tLDnmLsCc7tav+hzlAZn5F+1ifphHg2aDWpOmvtNjuLgDMhgWNaXbLpp6oybXJdbcLatl5XwMqvcdc4O3vbXIIJIsU7N3GuEWNv8zyBPKxdv/BG3737DKag/o6G+7RhH584+XjO8pygVq/54xKQaW4ZgMNEVA6JWs8AkO6PS0AMhyfN51+GFNjYBTFwcsxjAPFnbjLjFkcgM8qbALxkXvNxItoKCU45AewgolpIXGJ9y7gEpBq830j6MsSQ+RgyU7vAtBovhETP/w2xYG8NmO0+ACDLHPe3Adxjbv8zgIEkHTfvhrgLe5wO/7mYeR8kqpZNRLvRerrEcgCPm29qCsQfoUJK6udAHM9PsazPvYuI9pr7+iDrWVvyIkTIdhDRXkh16TOi8IE+TyL6kumjnANgNRF9aO6WaJ4nF3LzAxtDtfVh3APxs+4G8HcAN3T2myxnec5uyH3rVQbWcHHWa3o8SU5fj1ETnX6QFVv6WZ+I7J0S+EpP8RnrwFuy0JcxBO2s+gGAvXuXTmWmNgOB4XBF/xL3OIiNthc/ECneCxLmG7GOlhkerfKpMW3yAs/vtDoOb6P4SDOeR1bN/4I5bytxiSsB/AsSvExm5gkQn6Q/LrEOwHrz+SAAk83no03rbx+kEvsUSCR7EmQ2FgmZEQ6BWHuBcYkESEGPGAANzDyJmd8A8HpAXGIbmirEPwXAbV5jtDnWYkg0/w5mzmTmacz8b//7ZOYCZl5onnspMx83t7uZ+VpmHsXMs5i51fqr3Y2VJN+DZK7KtEHW/baa3N3dOL3c8Ken9BNhvlat+W5lz4Sbs8sTppz1SiXNvXW95lob9FLR4VGZW2YlXDGro/3ecGzYVKe4Wk98N0lMPLp17Lj1M9vbZztm7noSD0wAtR/Zcmwpy1aqvEHdDwd8nn86frYlU8lvy62SB2A6smoa23i9GWQmxDPzQwHbXgLwGTOvMuMSOczSII+IRkJiFtOI6L+QZY/vQiq31xPRCgBfAvAagLeZuZAkP/pn/iAQNSXE+8cYC+B7zPwREdVz85xO/5jGQURzIER872TmgUQ0DxJovQHSD2ktgMt7SwS7Sr9Lku9P5CzP0SBWeY/nnBGz8eSf9H29IZwAUDFwQrdUYwLsZ3SGbI8TDQcymLlDd8gCbdwZrYtbcurUiJkNDbHtJrVPx9YpV+HtDtOTvLMSFukDnG0HmgL3hd15pfexBc9pV65lRsskeQ+ArwcrnB3Qp+ISzJzHzJcw83TIjM6/1v8bAP7LzD6WdML16AdFxi3x7GFylufkoClNq8cw+w91aJF1B3VRQ46wYu8weBMMRJ0TT4290V7D3WZSuZ/BxoAJTrZ3GLTbs/uSccxoN1f1a/jbgrG8v8OpuW/moEX6IOfnHe3n51fa1xdc71txQGcKDLzdg6yaXcGewySkcQnT93mIiH4XuAMRPWLmae8ioo+IKNPcrkDcDP8lIg2SU73EfO1mSNHjx4loOfowlnj2AjnLc15CD/Y8+sZn+pqz7T/UGQqGXlzY8V5BQo5Or1ArcR2rDma/C3yjO7RQNS1swLGj01vtYRTIT/CzebFcvb2j/XzTBy3WE8M+D2Z8ALDWmJQ5z/M0ajgiB8DfkFXzbLDH+mklLtFaybqziUu0ZlGejktAgjSFaMrv9vM4xG11E6Qf0fNEdBDiliiGrGH/CCLEUUSUB7GCHzXH+DMyF9z0RSzx7D3ugbnSqTuZk2tsv3oTd1hYozspH5h5FssxW0D2Tovn0brdQaWNjTZSZqisdCiMRUXj57jdke1G6BUY6uO4c5SNfe1G+wHAN3XgYj05PKgpPACUYEDSTM9znlMcd1Owx1CLVX+QKPU6yFLHCkhO873UVG6ugGUN+4uQvMlsAK8w83xItFwDcB0R7QTwoLmtBhIk2gXxQ+aSueoPwM3M/CCkZBwgvsrLgKZ2wsxcG5CLHQlgDzOPYVkIUwyp03kKEji6FrJe/q/M/DDLGvuP/efsi1ji2UuY/s+voumP7axJL+Ejd71rjOqO/kPBUh85+JihOs5YsNBViOxBLzHyU+Y+MY6bV1Zvk6na8KBqX+7ZfemQ9tprAEAkGmJ/gfsBmQa3i2/ygEV6SvjnwVwbQLkX9msTf14Q1MqYVqLrfXbVHxE9SkQnIFPxn5rbUiEBqedavLVUoFlV/UJzW5/EEs9eJGd5TgWAqxGcI79dYhu4/LFV3dN/qDMUDLn4ePeesfPiyWC1QasOqr3sJH3obGJqsxK9H48nMuXEiYltVo/3MwTHh/8Avz2MIPqC+yYNWKylRnze0aUBfCl/5bL8js4XQL9Z9cfMD5mpTa9BrFlArOIHuI3q+v0FSzx7GTOAdD3Q9gqXjrBr7O7O/kOdoSxhcpeKHrcJ2R1dOexEw4GgFiAoUGzj9NS2k90DKMifMt/ndbZeXT6AOVg//WL8t8PlmQCgTYxfrA2JbGsKzwBuyF+5LKjlm0HQp6LrLXgNwFfM5zMg+Z/5kBVBfyCpGn96maVJGoAOv/hChSWeISBnec7bAO7s6vG//ou+ozv7DwVLY3jicUN1dmsqFJGtS/VFj9btSQ9231nayBnooNK7fzS7d186gLnjwhI34MWFw/lwUKuLtPFxi7Rhkdkt2w0DuCd/5bLXgzlHC/rFqj8iCrRMr4YEimAKr7/c3FsAvs/M75rXuISI4s1A0SXmtj6JJZ4hImd5zjOQKGWnuPttPTu1Ar0aIPJTMPTibvPXNtE18azXqoYYrAflQrDDFjnMSAiq1oDLFTuspGRUu8EjP1l48IIorgvqvNq4uEX68Ki1AQL6q/yVy4Jt5NaMEK76SwZQR0RlaL7qzxGQklRI0kIYEPeAm4hckGDQ6XbJRHQZSdm6L0PaZvjdD49ABHwrgIcDXBJ9DmuFUYjJXJX5AICVwex71SZj/bc+M7qtcVtnyZ7/m/26LWx8d56T2ef2VD8d1vGeZ7I05VtrBoWlBtX+wwVv+WvOtVEgBHEtQ58z940DNpvW4XutRUzF7fiTyyBbUNWlbIdq16pH67YXrFz2o2D2bw9zyt1t5Q87Oh8R1QAYwC38vUQUw2a/INNvOp6ZbyWiuQBymbmKiC4HkMXMs0kaxR2EFDIuhAjl15m5w/zdvoRleYaYnOU5v4IZhWyPyUeMPd/8rP0+Oj2JK2xgUXcLp9A1yxMA8uv3Bn1sOByDEjmm3VYaTShqzp6L7cwdF3aJQe3An+PHLjCf0Ta4NbTRMTmeS1ODnnG0TEmivlmIPLBFSiRM65qZN5gpR4CsXfd/wcwCcJiZj7K0IXkdMq3vV1ji2QfIWZ7zCJq6BJ5BT/Uf6gwFQy4OKujSWYiI0MXlq8cbcse1tILaY6Fv/FB00B7DT339oNEV5UM77EcEACNwdPTN+ENuW73ZA3gWwA9KLpwS1HSvv6ckteBGNLkD+lVKUltY4tlHyFme838wW4IEEunimif+rGs90X+oM5xKmt6T1++SePoMT6zP8HRYqd1PHEcOi+HwoPyZAJCXN3+OYSit9lpvyWJ8OmshPmsvgPTLkgunBC2cJv09JQkAQEQXQsTzgU689z6PJZ59iJzlOQ+iqb3r6f5Ddh3dso68q7id8cWaGj6h565AXS6cUurO71QPpQW+jKDzYplVx769S1zcQXk7P9/Ds4vS+HjLFCYGcE/JhVMe7Mw4O6C/pCSBiCZBgk1Xc1MPqn6VktQWlnj2MXKW5/we0vbV9eiqnu0/FCzHhyw9BJle9xBdF8+jdXs6ZRGncPz4MLZ3mMvpp7o6ZWJNTVLQeZi/wH0zwrnRX6/TC+DbJRdO6XTLYJN+nZJEREMhnRq+zcyBzfm2AhhNRMOJyAHpUPteRzejr2GJZx8kZ3nO219ZZywcWXLmdCkUlCTNiuvZK1Cbvcs7otSVn9EiYNEhc3xjOrWyZd/eC6cbBnVYhBkA7NCcj+POBBv79gG4qOTCKa915lqBhCglaTSk8+ZJCihETkTXAggnIoMCOmAS0Tchlq3LTEl6Ck0pSX8FkA7gEyIqJaJt5vvSIFP7DwHkQtp8B1Mguk9hpSr1YXLHZaRBKoKHzPr0OGJPrZ/z6CAE2QCvK7irnjoAaGO7evwX0m7dEmmP7VQ5vpecnx3QyAj6moMGFezIGL8m2M8hxwv7VZcvycvvzJhaow+lI2VA3EkvALiXW2m8SFJy7l1mHmlayzsBTGfmMiJaBeBlZv6ku95LqLEszz5MRl5uISQq+maoxnAi7cK8nhROAAApXbY8AeBE44G2+p23yTRtRKd8peXlw6bV18cHM33/J4C5nRHOfpKOlMvM7bZ+hnSd9a+YGgHgEDP7ezb9DwG+0HMBSzz7OBl5uY0ZeblfA3A7mloe9BrFyRd0WJH97FGDTjdqjWN1e4Z29piJ+pDZxNSpuqQ5ey7O5OaFiwPxArhz6ZIj1yxdciSopm1A/0pHCoKvQSrEA8BhAGNJumDaIKuIer0WQ09iiWc/ISMv9w+QXkhBp9qcLV57VIXPHjWp56+knNW0tNZXkW6w3ikhVKDYxuupneqRo2nO2MOHZ7W2RPUQgLlLlxx5ujPnM+k36UjtQUSzATQy817z2CoAtwF4A1ILNB8ILse2v2CJZz8iIy/3IMSZ/3Og4x7gZ8uJtAv3Q5bS9ShEZ2d5AkCV91Sn193P0EbNAKNTa6dLisdc4HJFBfY0+jOAqUuXHOmwynwn6cvpSK1xHZqsTgAAM/+bmWcz8xxIO+ODrR7ZT7HEs5+RkZerZeTlZkFEtEf/GIuT50T05PmbsJ31F0FB/d5O1wW1Q41INxJyOnvc7t2XjmSmfQAuX7rkyE1Llxw5m/qs/SIdqT3MsXwVTf5O//ZE82c8pDvmi129Rl/EEs9+SkZerr+l8XM4i9qgbeGzRVZ5HTGTu/u8rUK2sx5/Qf3+sV0prjvPN24igihBF4Dh80b8Y93ab81ZuuTIfzt7vZb0djoSET0O8asmA9gRkI70GEkPoXAzKBUX6PMkopuJyID0HVpNRB8T0RZzzEfkrUirYCJ6jaRi0kEiqoK4Ela2yPXs91ipSucAueMyZkPy67qte+axYZevOzb8C/O763zt4al9fQ3rJ4OqjtQeXx5213674ux08ZJ/O7atKVVqgrn+HgC3ZmVlbez86FqnD6UiXQLgU2bWiOhXAMDMDwS8/hbkS3ozMz9BsmgikqXPux3SP+mHzLyJiK5Ak2j/DcAaZm7ZcqPfY1me5wAZebmbAVwA4DsASrrjnCcHz+u1IiTUDZYnAJS6CoLqV9SShb7xwzsoGHIKwPcATOuKcPaTVKSPAkQ3sAISSKq8HwOwL2B/ZmZ/VoHdfPirKX1gvs6QAGdQ5fr6G73WOMyiZ8nIy2UAL+WOy3gL0qnzHgBdSjPyqeE1Hkdc70zZAYDs3SKex+r2xKdFjun0cbEcMSSWIzbUUGPLItONkDa+v87KyurUKiY/AalIc5m53PRpPommVCTdnGLfwczZRPQwJBXpLkgq0nBm9hBRnHlKfyrSeiKKgnTLBCRoNJGZjxHRLTBTkYjICQkcfcTMVxFRvZnGBCJ6EcDzrUTUvwuJksO8xgOQ2pv3tnhvKoDtAEYBeJaZN7d43Q7x0f4Q5yCW5XmOkZGXW5+Rl/tzyB/005D8w05RlLpgL2TNce9A9m5ZN1/sOjY+wBrqFAt9GYGBGi/ElzwqKyvrJ10VTpN+lYpERA+Z1/IvK80C8NvW7isz66YQpwGYRUQTW+zyB8iUPah2Jf0Ny/I8R8nIyz0F4M7ccRm/giTY34Igy9qdHDy/V/8uCJ0OlLcKw7C79LpdEbaYmZ09NonjxkWyc30DebYA+E1WVlZPV/kJNhVpIYArATxERJnMvJKIVgO4AmJRXtrK+fypSJ3q/0NENwD4AoCl3BQMmQ3gGiL6NYA4AAYRuZn5Gf9xzFxNRJ9BeqzvNc/1MwAJEHfHOYllefYgpt9pnxnhDNz+CDX1fPmIiAab28cR0UYi8hBRyynSX4jolBkdDZqMvNyijLzcByGrO74HKcTQJprqrHc7B/TelB3oNssTAAobDnZlFVYxgIe+7pl/VVZW1t3dLJz9IhWJpO/Q/QCuYubT95CZF3BTs7bfAXiMmZ8hogS/K4GIwiHTen81pZsAXApprdGv2wu3x3llefZ2ZBNi7Z0R2QTwODP/n3mOOyGVt28FUAnpqvnFVs71EoBnIEv1Ok1GXq4LwB9zx2X8CTKt+5H5s5lwnUyZlwOiOV25RpchR7d9iR+rzxkyJjboFYXrIWk6b6StXHBW6+vbgpn3EZE/FUmHpBW1ZDmA54koAsBRSODPn4oUC/mMnjItvEdIigsbkADOfwDMAZBBRPsAfAAR23RIKlIsZMYRTkTNsjFMn+c7EP+mG7KU82MiijaPKQfwopl435IUAKtMv6cCqYz0vvna8wAKAGyUoDzeZuaHO3Xj+gHnXKoSEV0PcWwzxGekQ/4wpkL+WV6GfLgRkPy077I0qLoTImAagP3MfB0RLQLwe/PUDJlCTYd0+KuCfOtnQBq4LYa0yXiWmV8wI5vLIGuRf8nMb7Qx3h8DGMrMtwVsywJQz8xPtNg3HcD7zNzSt9QlcsdlZECCA1+Due544+ysTa7whAu64/zBorm3btBca7utI+i16fcVK6SktPFyEYBXAPw1beWCHss77EMpSO1WQ2olBemcaM7WG5xTlmd/imya1sj1AGoAXNizd6Z1MvJycwHclzsu434A8xjKV11hg5b1/kjs3eo+qvGWHYl3JgWKZwWkP/jfAaxNW7mgW6aSHX1RE1G3flGbQtjWF7U/BanZFzUz55pjbW38X4SkIAX6S083ZzP38Tdns8SzBeeUeKKVyKb5R9NeZPMf5nN/ZPNdAO+a2/yRzdcgU49C83wtI5uTiOga8/dYSGSz2VprZr6pxe8PQYIAP4YUhv0ZQoSZ5rQOwLrPbv30h5AgwRcBXAWxrHsUIke3rp/Pr9+rxDuTdkOmtP8BsCFt5YJurQXQn76o2xh/WylIrTVnm921u3Ruc66JZ1v0uchmAK9B/FQhE89Abn9+CUOSpDcBWPHsrZ8mQNbRzzcf04BuCo/7IXt3nO8AgM8BfH6wdtvnV/4pq1sWC7RDv/miboMsmClIrVmlFh1zronnpwDeIaInmbmitcgmEVUR0QIz9+yMyCYRrYNUiIkiooHMnAMgh4hmQnyc1S2u6Y9sfsrMPiIaA6CImdsUbCIazcyHzF9P93zpi9z+/JIyyD/4uwDw7K2fhkMskXmQ6WkGJKe063mhZO/M36EX4pPbCwmY7AWw6Z433u9psQyWvvxFHUirKUiQpPd+35ytNzinxLMXI5uBvIimyCYBKEMr0fIWU6mVRDTWPG8BzEK2RJQMYBuAGMgf810AxjNzLRH9HeLrGkREhQB+xsx/7sJtOituf36JC6aF59/27K2f2gAMBzASci+GAxgK+aeMMR+x5s9otEiRIxFPL4A681Fr/jwJsar8j6MAjt3zxvs9Xo4vCHrki9r0o8ZB2lD7faD+IOISABcR0TEAD0IKDhdB7nu4GW03AMxk5nar6zOzPzUqMED5DEnh4tFENNw893UAvtHVm3Quc85F2y36Ps/e+mkUZOqvQSr/+G5/fkmfLJTbXpSbiJYDuA8SKPJ/Ub/PzG+Zr09BU8DI/0XtgrSk8H9Rv2panU9DAocZkOn9csgX9b3M/AVT4BoAxEMsVv8X9VcgX2SjmDmCJJ/01wCeY+ZtRPQlyEqzBMisaRcz+61a//vIQkB2B0lhj99BjIq/MPOjXbh15zyWeFqc9/SH9LZ20teuAPANZv5Wd94TiyBgZuthPc7bB4AJEB/qIPP3AZAFCe8DUM1tewAsMp8/DOB35vOTAJzm8zjz578BzDOfR0FcY4shVuNwc/stAH5iPndCXDX+1+oDxvYigBnm8yxIK4s9AP4CIN7cfhckb/VDADsA3B/qe3q+PKzlmRbnO/2lcMdzEN/mFMhy0t+Y222QLIhvmj+/RERLu3QnLDqFJZ7nAdT2GvvHiSiPmupIxpnb7US0iohyiCjXzEX1HxNHRG+Zx+VSby/l7D0620OowAzYDETzHkJ/gPQ5mkZNdQwIwB0A1kCs0zrIUkgAABH9l4iqici/3BHMXMpSxcgA8Cc0Fb4uhFQuKmdZk/6BOR6LHsYSzxBgRjR783y3AJjEzPe12P4xJAF7EmTq6hfJayHT0UyIv+57JEtDAfHn/ZeZxwGYjA4KjYSKTtzjYAt3LDY3tVW4Ix7S6vd5bl64Yy2k1e9aiCvgp5ApdhaAMRCL8xHI8kk/j5vXCXw/gSumvgSzepF5rkwiijDf8yJYq4F6hXMqVakv0VEQgvrG0r3AjombAPiTrxlApPnPGA5JI6o1p7ALAdxgHu9FF+qFdhfddY8hPYS2mgLlMs/3P1Mw/ff4P0R0CHI/6gDsAhBBRC4Aw8whfQqghIjS0JTe9jaa0tsizXO/aF5/PCQ4VAYgzi+QzPyJee1JRDTDnLr/2ozeM8T3+T1z3yoiehIi1AzgA2ZefXZ31iIoQu10PRcf6CdBiBZj/jeAb5nP7ZBOiGXmNW4xt0+BtFV4CZKa8yKkj411j9u5xxBxPgGxFhPMbe8DmB+wzyctjlkMSXsK+d+z9Wj9YU3be4b+EoQA0Gr18FkQK24wJOH9HiIaARGUaZAcwqkQYVnRuVvTbfSbe8zMDzHzEMj9/cHZvnGLvoElnr1LZ4MQW80k7ZVoHoTwF8BtbeneFPMxnJtPy1uFmqqHf5OZ/Um/34D4NX3MfAoiLDMgwYlCbupV8xb6XnCiz93jAF6DJLUDsnrHWgbZj7HEs2fo19XDARyHWHYwz3EBgDxmLgFwgmRpKQAsReiCE/3lHgdapoF1DN6DWLFERBdAqiUVB/3uLUKOFTDqAbj/rLF/BuK7+1gOwSZmvhVilf3VTL0hAH9l5j3mKe6ATHkdAePudfrRPW61jgEkpegKyPr0RgTcRyJaCxHvKJI6Bjdy9xQDsehGrOWZFhYWFl3AmrZbWFhYdAFLPC0sLCy6gCWeFhYWFl3AEk8LCwuLLmCJp4WFhUUXsMTTwsLCogtY4mlhYWHRBSzxtLCwsOgClnhaWFhYdAFLPC0sLCy6gCWeFhYWFl3AEk8LCwuLLmCJp4WFhUUXsMTTwsLCogtY4mlhYWHRBSzxtLCwsOgC/w8ngarQhaeYjgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_publisher = graph_publications_csv[\"publisher\"].value_counts()\n",
    "print(count_publisher.plot(kind=\"pie\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crossref:297      173\n",
      "crossref:78        80\n",
      "crossref:1968      57\n",
      "crossref:311       30\n",
      "crossref:286       27\n",
      "crossref:301       18\n",
      "crossref:56        13\n",
      "crossref:140       11\n",
      "crossref:2432       9\n",
      "crossref:179        8\n",
      "crossref:320        8\n",
      "crossref:340        7\n",
      "crossref:98         5\n",
      "crossref:2560       4\n",
      "crossref:7437       3\n",
      "crossref:530        3\n",
      "crossref:237        3\n",
      "crossref:263        3\n",
      "crossref:265        2\n",
      "crossref:281        2\n",
      "crossref:2258       2\n",
      "crossref:8722       2\n",
      "crossref:1010       2\n",
      "crossref:6951       1\n",
      "crossref:4443       1\n",
      "crossref:316        1\n",
      "crossref:735        1\n",
      "crossref:17207      1\n",
      "crossref:2373       1\n",
      "crossref:11929      1\n",
      "crossref:6225       1\n",
      "crossref:22695      1\n",
      "crossref:4340       1\n",
      "crossref:3742       1\n",
      "crossref:2780       1\n",
      "crossref:2457       1\n",
      "crossref:374        1\n",
      "Name: publisher, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "best_publishers = graph_publications_csv[\"publisher\"].value_counts()[:50]\n",
    "print(best_publishers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crossref:297      173\n",
      "crossref:78        80\n",
      "crossref:1968      57\n",
      "crossref:311       30\n",
      "crossref:286       27\n",
      "crossref:301       18\n",
      "crossref:56        13\n",
      "crossref:140       11\n",
      "crossref:2432       9\n",
      "crossref:179        8\n",
      "crossref:320        8\n",
      "crossref:340        7\n",
      "crossref:98         5\n",
      "crossref:2560       4\n",
      "crossref:7437       3\n",
      "crossref:530        3\n",
      "crossref:237        3\n",
      "crossref:263        3\n",
      "crossref:265        2\n",
      "crossref:281        2\n",
      "crossref:2258       2\n",
      "crossref:8722       2\n",
      "crossref:1010       2\n",
      "crossref:6951       1\n",
      "crossref:4443       1\n",
      "crossref:316        1\n",
      "crossref:735        1\n",
      "crossref:17207      1\n",
      "crossref:2373       1\n",
      "crossref:11929      1\n",
      "crossref:6225       1\n",
      "crossref:22695      1\n",
      "crossref:4340       1\n",
      "crossref:3742       1\n",
      "crossref:2780       1\n",
      "crossref:2457       1\n",
      "crossref:374        1\n",
      "Name: publisher, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_publishers.plot(kind=\"bar\")\n",
    "print(best_publishers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crossref:1010       2\n",
      "crossref:11929      1\n",
      "crossref:140       11\n",
      "crossref:17207      1\n",
      "crossref:179        8\n",
      "crossref:1968      57\n",
      "crossref:2258       2\n",
      "crossref:22695      1\n",
      "crossref:237        3\n",
      "crossref:2373       1\n",
      "crossref:2432       9\n",
      "crossref:2457       1\n",
      "crossref:2560       4\n",
      "crossref:263        3\n",
      "crossref:265        2\n",
      "crossref:2780       1\n",
      "crossref:281        2\n",
      "crossref:286       27\n",
      "crossref:297      173\n",
      "crossref:301       18\n",
      "crossref:311       30\n",
      "crossref:316        1\n",
      "crossref:320        8\n",
      "crossref:340        7\n",
      "crossref:374        1\n",
      "crossref:3742       1\n",
      "crossref:4340       1\n",
      "crossref:4443       1\n",
      "crossref:530        3\n",
      "crossref:56        13\n",
      "crossref:6225       1\n",
      "crossref:6951       1\n",
      "crossref:735        1\n",
      "crossref:7437       3\n",
      "crossref:78        80\n",
      "crossref:8722       2\n",
      "crossref:98         5\n",
      "Name: publisher, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "best_publishers_sort = best_publishers.sort_index()\n",
    "print(best_publishers_sort)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bpplot = best_publishers_sort.plot(kind=\"bar\")\n",
    "print(bpplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUTHORS_DF                            doi_authors          family    given  \\\n",
      "0     doi:10.1016/j.websem.2021.100655  Espinoza-Arias    Paola   \n",
      "1     doi:10.1016/j.websem.2021.100655          Garijo   Daniel   \n",
      "2     doi:10.1016/j.websem.2021.100655          Corcho    Oscar   \n",
      "3        doi:10.1007/s10115-017-1100-y      Diefenbach   Dennis   \n",
      "4     doi:10.1016/j.websem.2014.03.003           Groth     Paul   \n",
      "...                                ...             ...      ...   \n",
      "1427               doi:10.1145/3309547            Wang    Xiang   \n",
      "1428  doi:10.1007/978-3-030-58285-2_27          Martin     Leon   \n",
      "1429  doi:10.1007/978-3-030-58285-2_27       Boockmann   Jan H.   \n",
      "1430  doi:10.1007/978-3-030-58285-2_27         Henrich  Andreas   \n",
      "1431    doi:10.1007/s11280-020-00842-7              Li      Lin   \n",
      "\n",
      "                    orcid  \n",
      "0     0000-0002-3938-2064  \n",
      "1     0000-0003-0454-7145  \n",
      "2     0000-0002-9260-0753  \n",
      "3     0000-0002-0046-2219  \n",
      "4     0000-0003-0183-6910  \n",
      "...                   ...  \n",
      "1427  0000-0002-6148-6329  \n",
      "1428  0000-0002-6747-5524  \n",
      "1429  0000-0001-6816-8393  \n",
      "1430  0000-0002-5074-3254  \n",
      "1431  0000-0001-7553-6916  \n",
      "\n",
      "[1432 rows x 4 columns]\n",
      "VENUES_ID_DF                         doi_venues_id           issn_isbn\n",
      "0    doi:10.1016/j.websem.2021.100655      issn:1570-8268\n",
      "1       doi:10.1007/s10115-017-1100-y      issn:0219-1377\n",
      "2       doi:10.1007/s10115-017-1100-y      issn:0219-3116\n",
      "3    doi:10.1016/j.websem.2014.03.003      issn:1570-8268\n",
      "4              doi:10.1093/nar/gkz997      issn:0305-1048\n",
      "..                                ...                 ...\n",
      "749               doi:10.1145/3309547      issn:1558-2868\n",
      "750  doi:10.1007/978-3-030-58285-2_27  isbn:9783030582845\n",
      "751  doi:10.1007/978-3-030-58285-2_27  isbn:9783030582852\n",
      "752    doi:10.1007/s11280-020-00842-7      issn:1386-145X\n",
      "753    doi:10.1007/s11280-020-00842-7      issn:1573-1413\n",
      "\n",
      "[754 rows x 2 columns]\n",
      "REFERENCES_DF                                  og_doi                           ref_doi\n",
      "0      doi:10.1016/j.websem.2021.100655     doi:10.1007/s10115-017-1100-y\n",
      "1      doi:10.1016/j.websem.2021.100655  doi:10.1016/j.websem.2014.03.003\n",
      "2      doi:10.1016/j.websem.2021.100655            doi:10.1093/nar/gkz997\n",
      "3      doi:10.1016/j.websem.2021.100655   doi:10.3390/publications7030050\n",
      "4      doi:10.1016/j.websem.2021.100655     doi:10.1017/s0269888920000065\n",
      "..                                  ...                               ...\n",
      "305    doi:10.1007/978-3-319-93417-4_10         doi:10.1038/sdata.2016.18\n",
      "306    doi:10.1007/978-3-030-04284-4_26         doi:10.1038/sdata.2016.18\n",
      "307  doi:10.12688/f1000research.12168.1         doi:10.1038/sdata.2016.18\n",
      "308           doi:10.1515/jib-2018-0023           doi:10.1093/nar/gkx1132\n",
      "309      doi:10.1007/s13218-020-00686-3  doi:10.1007/978-3-030-22102-7_21\n",
      "\n",
      "[310 rows x 2 columns]\n",
      "PUBLISHERS_DF           crossref     id_crossref  \\\n",
      "0     crossref:735    crossref:735   \n",
      "1    crossref:2780   crossref:2780   \n",
      "2    crossref:2373   crossref:2373   \n",
      "3    crossref:2560   crossref:2560   \n",
      "4     crossref:311    crossref:311   \n",
      "5     crossref:320    crossref:320   \n",
      "6      crossref:98     crossref:98   \n",
      "7    crossref:1968   crossref:1968   \n",
      "8   crossref:22695  crossref:22695   \n",
      "9    crossref:2258   crossref:2258   \n",
      "10   crossref:6951   crossref:6951   \n",
      "11    crossref:374    crossref:374   \n",
      "12  crossref:11929  crossref:11929   \n",
      "13   crossref:1010   crossref:1010   \n",
      "14    crossref:237    crossref:237   \n",
      "15     crossref:78     crossref:78   \n",
      "16   crossref:2432   crossref:2432   \n",
      "17   crossref:4340   crossref:4340   \n",
      "18    crossref:301    crossref:301   \n",
      "19    crossref:340    crossref:340   \n",
      "20  crossref:17207  crossref:17207   \n",
      "21    crossref:281    crossref:281   \n",
      "22   crossref:2457   crossref:2457   \n",
      "23   crossref:7437   crossref:7437   \n",
      "24    crossref:263    crossref:263   \n",
      "25     crossref:56     crossref:56   \n",
      "26    crossref:297    crossref:297   \n",
      "27   crossref:6225   crossref:6225   \n",
      "28    crossref:179    crossref:179   \n",
      "29    crossref:265    crossref:265   \n",
      "30    crossref:316    crossref:316   \n",
      "31    crossref:286    crossref:286   \n",
      "32    crossref:140    crossref:140   \n",
      "33   crossref:8722   crossref:8722   \n",
      "34    crossref:530    crossref:530   \n",
      "35   crossref:3742   crossref:3742   \n",
      "36   crossref:4443   crossref:4443   \n",
      "\n",
      "                                             name_pub  \n",
      "0                                 Thomas Telford Ltd.  \n",
      "1           The Korean Society of Medical Informatics  \n",
      "2                                      Editorial CSIC  \n",
      "3                                  F1000 Research Ltd  \n",
      "4                                               Wiley  \n",
      "5           Association for Computing Machinery (ACM)  \n",
      "6                                     Hindawi Limited  \n",
      "7                                             MDPI AG  \n",
      "8                      Polish Librarians' Association  \n",
      "9                                  Pensoft Publishers  \n",
      "10                                          Cogitatio  \n",
      "11                             Walter de Gruyter GmbH  \n",
      "12                Universidad Autonoma de Bucaramanga  \n",
      "13                             JMIR Publications Inc.  \n",
      "14                          The Company of Biologists  \n",
      "15                                        Elsevier BV  \n",
      "16                                         IGI Global  \n",
      "17                 Korean Home Management Association  \n",
      "18                                 Informa UK Limited  \n",
      "19                   Public Library of Science (PLoS)  \n",
      "20              Fundacion Universitaria San Pablo CEU  \n",
      "21                               MIT Press - Journals  \n",
      "22                      Trans Tech Publications, Ltd.  \n",
      "23                                          IOS Press  \n",
      "24  Institute of Electrical and Electronics Engine...  \n",
      "25                   Cambridge University Press (CUP)  \n",
      "26            Springer Science and Business Media LLC  \n",
      "27  National Academy of Sciences of Ukraine (Co. L...  \n",
      "28                                  SAGE Publications  \n",
      "29    Institution of Engineering and Technology (IET)  \n",
      "30                    American Chemical Society (ACS)  \n",
      "31                      Oxford University Press (OUP)  \n",
      "32                                            Emerald  \n",
      "33                                   The Open Journal  \n",
      "34                                FapUNIFESP (SciELO)  \n",
      "35            Sociedade Brasileira de Computacao - SB  \n",
      "36                                              PeerJ  \n"
     ]
    }
   ],
   "source": [
    "with open(\"testData/graph_other_data.json\", \"r\", encoding=\"utf-8\") as file:\n",
    "    jsondata = json.load(file)\n",
    "\n",
    "    # =============== AUTHORS DATAFRAME ===============\n",
    "    authors_df = pd.DataFrame({\n",
    "        \"doi_authors\": pd.Series(dtype=\"str\"),\n",
    "        \"family\": pd.Series(dtype=\"str\"),\n",
    "        \"given\": pd.Series(dtype=\"str\"),\n",
    "        \"orcid\": pd.Series(dtype=\"str\")\n",
    "    })\n",
    "\n",
    "    family = []\n",
    "    given = []\n",
    "    orcid = []\n",
    "    doi_authors = []\n",
    "\n",
    "    authors = jsondata['authors']\n",
    "    for key in authors:\n",
    "        for value in authors[key]:\n",
    "            doi_authors.append(key)\n",
    "            family.append(value['family'])\n",
    "            given.append(value['given'])\n",
    "            orcid.append(value['orcid'])\n",
    "\n",
    "    authors_df['doi_authors'] = doi_authors\n",
    "    authors_df['family'] = family\n",
    "    authors_df['given'] = given\n",
    "    authors_df['orcid'] = orcid\n",
    "\n",
    "    # =============== VENUES DATAFRAME ===============\n",
    "\n",
    "    venues_id_df = pd.DataFrame({\n",
    "        \"doi_venues_id\": pd.Series(dtype=\"str\"),\n",
    "        \"issn_isbn\": pd.Series(dtype=\"str\"),\n",
    "    })\n",
    "\n",
    "    doi_venues_id = []\n",
    "    issn_isbn = []\n",
    "\n",
    "    venues_id = jsondata[\"venues_id\"]\n",
    "    for key in venues_id:\n",
    "        for value in venues_id[key]:\n",
    "            doi_venues_id.append(key)\n",
    "            issn_isbn.append(value)\n",
    "\n",
    "    venues_id_df[\"doi_venues_id\"] = doi_venues_id\n",
    "    venues_id_df[\"issn_isbn\"] = pd.Series(issn_isbn)\n",
    "\n",
    "    # =============== REFERENCES DATAFRAME ===============\n",
    "\n",
    "    references_df = pd.DataFrame({\n",
    "        \"og_doi\": pd.Series(dtype=\"str\"),\n",
    "        \"ref_doi\": pd.Series(dtype=\"str\"),\n",
    "    })\n",
    "\n",
    "    og_doi = []\n",
    "    ref_doi = []\n",
    "\n",
    "    references = jsondata[\"references\"]\n",
    "    for key in references:\n",
    "        for value in references[key]:\n",
    "            og_doi.append(key)\n",
    "            ref_doi.append(value)\n",
    "\n",
    "    references_df[\"og_doi\"] = pd.Series(og_doi)\n",
    "    references_df[\"ref_doi\"] = pd.Series(ref_doi)\n",
    "\n",
    "    # =============== PUBLISHER DATAFRAME ===============\n",
    "\n",
    "    publishers_df = pd.DataFrame({\n",
    "        \"crossref\": pd.Series(dtype=\"str\"),\n",
    "        \"id_crossref\": pd.Series(dtype=\"str\"),\n",
    "        \"name_pub\": pd.Series(dtype=\"str\")\n",
    "    })\n",
    "\n",
    "    crossref = []\n",
    "    id_crossref = []\n",
    "    name_pub = []\n",
    "\n",
    "    publishers = jsondata[\"publishers\"]\n",
    "    for key in publishers:\n",
    "        crossref.append(key)\n",
    "        id_crossref.append(publishers[key][\"id\"])\n",
    "        name_pub.append(publishers[key][\"name\"])\n",
    "\n",
    "    publishers_df[\"crossref\"] = pd.Series(crossref)\n",
    "    publishers_df[\"id_crossref\"] = pd.Series(id_crossref)\n",
    "    publishers_df[\"name_pub\"] = pd.Series(name_pub)\n",
    "\n",
    "print(\"AUTHORS_DF\", authors_df)\n",
    "print(\"VENUES_ID_DF\", venues_id_df)\n",
    "print(\"REFERENCES_DF\", references_df)\n",
    "print(\"PUBLISHERS_DF\", publishers_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           doi_authors family  given                orcid\n",
      "count                             1432   1432   1432                 1432\n",
      "unique                             526   1094   1052                 1184\n",
      "top     doi:10.1016/j.xgen.2021.100029  Zhang  David  0000-0002-0736-9199\n",
      "freq                               160     15     12                   11\n",
      "                           doi_venues_id       issn_isbn\n",
      "count                                754             754\n",
      "unique                               486             488\n",
      "top     doi:10.1007/978-3-030-20485-3_11  issn:2076-3417\n",
      "freq                                   2              15\n",
      "                        og_doi                 ref_doi\n",
      "count                      310                     310\n",
      "unique                     169                      99\n",
      "top     doi:10.1093/nar/gkz997  doi:10.1093/nar/gkz997\n",
      "freq                        16                      39\n",
      "            crossref   id_crossref             name_pub\n",
      "count             37            37                   37\n",
      "unique            37            37                   37\n",
      "top     crossref:735  crossref:735  Thomas Telford Ltd.\n",
      "freq               1             1                    1\n"
     ]
    }
   ],
   "source": [
    "print(authors_df.describe(include=\"all\"))\n",
    "print(venues_id_df.describe(include=\"all\"))\n",
    "print(references_df.describe(include=\"all\"))\n",
    "print(publishers_df.describe(include=\"all\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value counts of og_doi doi:10.1093/nar/gkz997               16\n",
      "doi:10.1016/j.websem.2021.100655      7\n",
      "doi:10.1093/nar/gkaa1043              6\n",
      "doi:10.1093/jamiaopen/ooaa030         6\n",
      "doi:10.1371/journal.pcbi.1008376      6\n",
      "                                     ..\n",
      "doi:10.1016/j.aei.2018.10.009         1\n",
      "doi:10.1016/j.pmcj.2016.03.001        1\n",
      "doi:10.1080/15230406.2019.1647797     1\n",
      "doi:10.1007/s00778-019-00564-x        1\n",
      "doi:10.1007/s13218-020-00686-3        1\n",
      "Name: og_doi, Length: 169, dtype: int64\n",
      "value counts of ref_doi doi:10.1093/nar/gkz997              39\n",
      "doi:10.1038/sdata.2016.18           33\n",
      "doi:10.1007/s10115-017-1100-y       26\n",
      "doi:10.1093/nar/gky1105             14\n",
      "doi:10.1016/j.websem.2014.03.003    13\n",
      "                                    ..\n",
      "doi:10.3390/app9122553               1\n",
      "doi:10.1007/s00607-019-00701-y       1\n",
      "doi:10.1016/j.jbi.2018.06.008        1\n",
      "doi:10.1007/s00778-019-00564-x       1\n",
      "doi:10.1007/978-3-030-22102-7_21     1\n",
      "Name: ref_doi, Length: 99, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "og_count = references_df[\"og_doi\"].value_counts()\n",
    "ref_count = references_df[\"ref_doi\"].value_counts()\n",
    "print(\"value counts of og_doi\", og_count)\n",
    "print(\"value counts of ref_doi\", ref_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zhang              15\n",
      "Wang               13\n",
      "Robinson           11\n",
      "Li                 10\n",
      "Hoehndorf          10\n",
      "Valencia-García     7\n",
      "García-Sánchez      6\n",
      "Groth               6\n",
      "Slater              5\n",
      "Chen                5\n",
      "Haendel             5\n",
      "Smith               5\n",
      "Köhler              5\n",
      "Page                5\n",
      "Vasilevsky          5\n",
      "Pal                 5\n",
      "Lu                  4\n",
      "Liu                 4\n",
      "Zorn                4\n",
      "Janssen             4\n",
      "Name: family, dtype: int64\n",
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_authors = authors_df[\"family\"].value_counts()[:20]\n",
    "print(best_authors)\n",
    "print(best_authors.plot(kind=\"bar\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.260833,0.125;0.503333x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(best_authors.plot(kind=\"pie\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(best_authors.plot(kind=\"barh\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000-0002-0736-9199    11\n",
      "0000-0001-8149-5890    10\n",
      "0000-0003-2457-1791     8\n",
      "0000-0003-0183-6910     6\n",
      "0000-0003-2667-5359     6\n",
      "                       ..\n",
      "0000-0002-0072-8881     1\n",
      "0000-0001-9444-0848     1\n",
      "0000-0002-9652-5164     1\n",
      "0000-0003-1631-5931     1\n",
      "0000-0001-7553-6916     1\n",
      "Name: orcid, Length: 1184, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "authors_per_orcid = authors_df[\"orcid\"].value_counts()\n",
    "print(authors_per_orcid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000-0001-5012-0420    1\n",
      "0000-0001-5036-9365    1\n",
      "0000-0001-5044-2921    1\n",
      "0000-0001-5069-1370    1\n",
      "0000-0001-5087-8776    1\n",
      "                      ..\n",
      "0000-0003-4904-2511    1\n",
      "0000-0003-4905-0941    1\n",
      "0000-0003-4920-9553    1\n",
      "0000-0003-4953-1390    1\n",
      "0000-0003-4984-1532    1\n",
      "Name: orcid, Length: 1184, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "authors_per_orcid_sorted = authors_per_orcid.sort_index()\n",
    "print(authors_per_orcid_sorted)"
   ]
  }
 ],
 "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"
  },
  "vscode": {
   "interpreter": {
    "hash": "9e178ac2d01006df9e74389e28c68e3ef7cb95acb5e5b42727cfb2189b57440d"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}