{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee929161-1b4b-47f1-8b13-6640ad6f48e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries for json import \n",
    "import json\n",
    "import urllib.request, json \n",
    "\n",
    "# import json for visualiziation\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "# import Matpltlib for data visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import Numpy for data handling\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5b0d72-50ab-47fd-8209-99356d96af44",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Get Metadata from DataCite REST API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fc9af36-be55-46a4-b6e9-2a7584e06923",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# connect to DataCite REST API and get items with own search query, e.g. query=Max%20Planck&page[size]=1000'\n",
    "with urllib.request.urlopen('https://api.datacite.org/dois?query=Ilia%20State%20University&page[size]=5000') as url:\n",
    "    \n",
    "# create a json file out of API call\n",
    "    DataCite = json.load(url)\n",
    "\n",
    "# create and open a new json file    \n",
    "with open('DataCite_Raw_Metadata.json', 'w') as f:\n",
    "    # write results in json file\n",
    "    json.dump(DataCite, f)\n",
    "\n",
    "# load to normalize json file\n",
    "data = json.load(open('DataCite_Raw_Metadata.json'))\n",
    "\n",
    "# load to normalize json file\n",
    "data_normalized = pd.json_normalize(data,'data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ada51f1-1459-4ce6-abe6-df9f75702fe0",
   "metadata": {},
   "source": [
    "## Create Pandas Dataframe as Preview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "713d9258-494f-416c-aaed-9f63cd9624f7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>attributes.doi</th>\n",
       "      <th>attributes.identifiers</th>\n",
       "      <th>attributes.creators</th>\n",
       "      <th>attributes.titles</th>\n",
       "      <th>attributes.publisher</th>\n",
       "      <th>attributes.publicationYear</th>\n",
       "      <th>attributes.subjects</th>\n",
       "      <th>attributes.contributors</th>\n",
       "      <th>...</th>\n",
       "      <th>attributes.versionOfCount</th>\n",
       "      <th>attributes.created</th>\n",
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       "      <th>attributes.container.identifierType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.5281/zenodo.7223794</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.5281/zenodo.7223794</td>\n",
       "      <td>[{'identifier': 'https://zenodo.org/record/722...</td>\n",
       "      <td>[{'name': 'Grossmann, Yves Vincent', 'givenNam...</td>\n",
       "      <td>[{'title': 'Research Data Policies – A Short O...</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>2022</td>\n",
       "      <td>[{'subject': 'ISUBII22'}, {'subject': 'Ilia St...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-26T12:15:33Z</td>\n",
       "      <td>2022-10-26T12:15:34Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-26T12:15:34Z</td>\n",
       "      <td>cern.zenodo</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.5281/zenodo.7223795</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.5281/zenodo.7223795</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'name': 'Grossmann, Yves Vincent', 'givenNam...</td>\n",
       "      <td>[{'title': 'Research Data Policies – A Short O...</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>2022</td>\n",
       "      <td>[{'subject': 'ISUBII22'}, {'subject': 'Ilia St...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-26T12:15:32Z</td>\n",
       "      <td>2022-10-26T12:15:33Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-26T12:15:33Z</td>\n",
       "      <td>cern.zenodo</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.5281/zenodo.7243699</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.5281/zenodo.7243699</td>\n",
       "      <td>[{'identifier': 'https://zenodo.org/record/724...</td>\n",
       "      <td>[{'name': 'Grossmann, Yves Vincent', 'givenNam...</td>\n",
       "      <td>[{'title': 'DataCite ROR Parser'}]</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>2022</td>\n",
       "      <td>[{'subject': 'ISUBII22'}, {'subject': 'Python'...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-24T08:08:18Z</td>\n",
       "      <td>2022-10-24T08:08:19Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-26T06:40:27Z</td>\n",
       "      <td>cern.zenodo</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.5281/zenodo.7243700</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.5281/zenodo.7243700</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'name': 'Grossmann, Yves Vincent', 'givenNam...</td>\n",
       "      <td>[{'title': 'DataCite ROR Parser'}]</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>2022</td>\n",
       "      <td>[{'subject': 'ISUBII22'}, {'subject': 'Python'...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-24T08:08:17Z</td>\n",
       "      <td>2022-10-24T08:08:18Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-26T06:40:26Z</td>\n",
       "      <td>cern.zenodo</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5281/zenodo.7225465</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.5281/zenodo.7225465</td>\n",
       "      <td>[{'identifier': 'https://zenodo.org/record/722...</td>\n",
       "      <td>[{'name': 'Grossmann, Yves Vincent', 'givenNam...</td>\n",
       "      <td>[{'title': 'Research Data Management – A Short...</td>\n",
       "      <td>Zenodo</td>\n",
       "      <td>2022</td>\n",
       "      <td>[{'subject': 'ISUBII22'}, {'subject': 'Ilia St...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-25T07:15:22Z</td>\n",
       "      <td>2022-10-25T07:15:22Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-10-25T07:15:22Z</td>\n",
       "      <td>cern.zenodo</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>97</th>\n",
       "      <td>10.13140/rg.2.1.3170.7766</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.13140/rg.2.1.3170.7766</td>\n",
       "      <td>[{'identifier': 'https://doi.org/10.13140/rg.2...</td>\n",
       "      <td>[{'name': 'Tumanishvili, George G.', 'nameType...</td>\n",
       "      <td>[{'title': 'ხელშეკრულების შედგენის ტექნიკა და ...</td>\n",
       "      <td>ILIA STATE UNIVERSITY PRESS</td>\n",
       "      <td>2012</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-26T21:47:27Z</td>\n",
       "      <td>2016-06-26T21:47:28Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-06-29T13:28:40Z</td>\n",
       "      <td>rg.rg</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>10.13140/rg.2.1.1126.8720</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.13140/rg.2.1.1126.8720</td>\n",
       "      <td>[{'identifier': 'https://doi.org/10.13140/rg.2...</td>\n",
       "      <td>[{'name': 'Tumanishvili, George G.', 'nameType...</td>\n",
       "      <td>[{'title': 'სამართლის პროფესიები'}]</td>\n",
       "      <td>ILIA STATE UNIVERSITY PRESS</td>\n",
       "      <td>2009</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-26T17:15:55Z</td>\n",
       "      <td>2016-06-26T17:15:56Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-06-29T13:28:40Z</td>\n",
       "      <td>rg.rg</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>10.13140/rg.2.1.3944.9206</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.13140/rg.2.1.3944.9206</td>\n",
       "      <td>[{'identifier': 'https://doi.org/10.13140/rg.2...</td>\n",
       "      <td>[{'name': 'Godoladze, Karlo', 'nameType': 'Per...</td>\n",
       "      <td>[{'title': 'საკონსტიტუციო ცვლილებები საქართველ...</td>\n",
       "      <td>ილიას სახელმწიფო უნივერსიტეტის გამომცემლობა, I...</td>\n",
       "      <td>2013</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-26T16:37:37Z</td>\n",
       "      <td>2016-06-26T16:37:38Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-06-29T13:28:40Z</td>\n",
       "      <td>rg.rg</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10.13140/rg.2.1.5071.3209</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.13140/rg.2.1.5071.3209</td>\n",
       "      <td>[{'identifier': 'https://doi.org/10.13140/rg.2...</td>\n",
       "      <td>[{'name': 'Tumanishvili, George G.', 'nameType...</td>\n",
       "      <td>[{'title': 'საკუთრება, როგორც მოთხოვნის უზრუნვ...</td>\n",
       "      <td>ILIA STATE UNIVERSITY PRESS</td>\n",
       "      <td>2012</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-06-26T21:41:26Z</td>\n",
       "      <td>2016-06-26T21:41:27Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-06-29T13:28:40Z</td>\n",
       "      <td>rg.rg</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>10.15468/dl.77nmev</td>\n",
       "      <td>dois</td>\n",
       "      <td>10.15468/dl.77nmev</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'name': 'Occdownload Gbif.Org', 'nameType': ...</td>\n",
       "      <td>[{'title': 'Occurrence Download'}]</td>\n",
       "      <td>The Global Biodiversity Information Facility</td>\n",
       "      <td>2020</td>\n",
       "      <td>[{'lang': 'eng', 'subject': 'GBIF'}, {'lang': ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-06-11T20:24:17Z</td>\n",
       "      <td>2020-06-11T20:24:17Z</td>\n",
       "      <td>None</td>\n",
       "      <td>2020-06-11T20:24:18Z</td>\n",
       "      <td>gbif.gbif</td>\n",
       "      <td>clients</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id  type             attributes.doi  \\\n",
       "0       10.5281/zenodo.7223794  dois     10.5281/zenodo.7223794   \n",
       "1       10.5281/zenodo.7223795  dois     10.5281/zenodo.7223795   \n",
       "2       10.5281/zenodo.7243699  dois     10.5281/zenodo.7243699   \n",
       "3       10.5281/zenodo.7243700  dois     10.5281/zenodo.7243700   \n",
       "4       10.5281/zenodo.7225465  dois     10.5281/zenodo.7225465   \n",
       "..                         ...   ...                        ...   \n",
       "97   10.13140/rg.2.1.3170.7766  dois  10.13140/rg.2.1.3170.7766   \n",
       "98   10.13140/rg.2.1.1126.8720  dois  10.13140/rg.2.1.1126.8720   \n",
       "99   10.13140/rg.2.1.3944.9206  dois  10.13140/rg.2.1.3944.9206   \n",
       "100  10.13140/rg.2.1.5071.3209  dois  10.13140/rg.2.1.5071.3209   \n",
       "101         10.15468/dl.77nmev  dois         10.15468/dl.77nmev   \n",
       "\n",
       "                                attributes.identifiers  \\\n",
       "0    [{'identifier': 'https://zenodo.org/record/722...   \n",
       "1                                                   []   \n",
       "2    [{'identifier': 'https://zenodo.org/record/724...   \n",
       "3                                                   []   \n",
       "4    [{'identifier': 'https://zenodo.org/record/722...   \n",
       "..                                                 ...   \n",
       "97   [{'identifier': 'https://doi.org/10.13140/rg.2...   \n",
       "98   [{'identifier': 'https://doi.org/10.13140/rg.2...   \n",
       "99   [{'identifier': 'https://doi.org/10.13140/rg.2...   \n",
       "100  [{'identifier': 'https://doi.org/10.13140/rg.2...   \n",
       "101                                                 []   \n",
       "\n",
       "                                   attributes.creators  \\\n",
       "0    [{'name': 'Grossmann, Yves Vincent', 'givenNam...   \n",
       "1    [{'name': 'Grossmann, Yves Vincent', 'givenNam...   \n",
       "2    [{'name': 'Grossmann, Yves Vincent', 'givenNam...   \n",
       "3    [{'name': 'Grossmann, Yves Vincent', 'givenNam...   \n",
       "4    [{'name': 'Grossmann, Yves Vincent', 'givenNam...   \n",
       "..                                                 ...   \n",
       "97   [{'name': 'Tumanishvili, George G.', 'nameType...   \n",
       "98   [{'name': 'Tumanishvili, George G.', 'nameType...   \n",
       "99   [{'name': 'Godoladze, Karlo', 'nameType': 'Per...   \n",
       "100  [{'name': 'Tumanishvili, George G.', 'nameType...   \n",
       "101  [{'name': 'Occdownload Gbif.Org', 'nameType': ...   \n",
       "\n",
       "                                     attributes.titles  \\\n",
       "0    [{'title': 'Research Data Policies – A Short O...   \n",
       "1    [{'title': 'Research Data Policies – A Short O...   \n",
       "2                   [{'title': 'DataCite ROR Parser'}]   \n",
       "3                   [{'title': 'DataCite ROR Parser'}]   \n",
       "4    [{'title': 'Research Data Management – A Short...   \n",
       "..                                                 ...   \n",
       "97   [{'title': 'ხელშეკრულების შედგენის ტექნიკა და ...   \n",
       "98                 [{'title': 'სამართლის პროფესიები'}]   \n",
       "99   [{'title': 'საკონსტიტუციო ცვლილებები საქართველ...   \n",
       "100  [{'title': 'საკუთრება, როგორც მოთხოვნის უზრუნვ...   \n",
       "101                 [{'title': 'Occurrence Download'}]   \n",
       "\n",
       "                                  attributes.publisher  \\\n",
       "0                                               Zenodo   \n",
       "1                                               Zenodo   \n",
       "2                                               Zenodo   \n",
       "3                                               Zenodo   \n",
       "4                                               Zenodo   \n",
       "..                                                 ...   \n",
       "97                         ILIA STATE UNIVERSITY PRESS   \n",
       "98                         ILIA STATE UNIVERSITY PRESS   \n",
       "99   ილიას სახელმწიფო უნივერსიტეტის გამომცემლობა, I...   \n",
       "100                        ILIA STATE UNIVERSITY PRESS   \n",
       "101       The Global Biodiversity Information Facility   \n",
       "\n",
       "     attributes.publicationYear  \\\n",
       "0                          2022   \n",
       "1                          2022   \n",
       "2                          2022   \n",
       "3                          2022   \n",
       "4                          2022   \n",
       "..                          ...   \n",
       "97                         2012   \n",
       "98                         2009   \n",
       "99                         2013   \n",
       "100                        2012   \n",
       "101                        2020   \n",
       "\n",
       "                                   attributes.subjects  \\\n",
       "0    [{'subject': 'ISUBII22'}, {'subject': 'Ilia St...   \n",
       "1    [{'subject': 'ISUBII22'}, {'subject': 'Ilia St...   \n",
       "2    [{'subject': 'ISUBII22'}, {'subject': 'Python'...   \n",
       "3    [{'subject': 'ISUBII22'}, {'subject': 'Python'...   \n",
       "4    [{'subject': 'ISUBII22'}, {'subject': 'Ilia St...   \n",
       "..                                                 ...   \n",
       "97                                                  []   \n",
       "98                                                  []   \n",
       "99                                                  []   \n",
       "100                                                 []   \n",
       "101  [{'lang': 'eng', 'subject': 'GBIF'}, {'lang': ...   \n",
       "\n",
       "    attributes.contributors  ... attributes.versionOfCount  \\\n",
       "0                        []  ...                         0   \n",
       "1                        []  ...                         0   \n",
       "2                        []  ...                         0   \n",
       "3                        []  ...                         0   \n",
       "4                        []  ...                         0   \n",
       "..                      ...  ...                       ...   \n",
       "97                       []  ...                         0   \n",
       "98                       []  ...                         0   \n",
       "99                       []  ...                         0   \n",
       "100                      []  ...                         0   \n",
       "101                      []  ...                         0   \n",
       "\n",
       "       attributes.created attributes.registered attributes.published  \\\n",
       "0    2022-10-26T12:15:33Z  2022-10-26T12:15:34Z                 None   \n",
       "1    2022-10-26T12:15:32Z  2022-10-26T12:15:33Z                 None   \n",
       "2    2022-10-24T08:08:18Z  2022-10-24T08:08:19Z                 None   \n",
       "3    2022-10-24T08:08:17Z  2022-10-24T08:08:18Z                 None   \n",
       "4    2022-10-25T07:15:22Z  2022-10-25T07:15:22Z                 None   \n",
       "..                    ...                   ...                  ...   \n",
       "97   2016-06-26T21:47:27Z  2016-06-26T21:47:28Z                 None   \n",
       "98   2016-06-26T17:15:55Z  2016-06-26T17:15:56Z                 None   \n",
       "99   2016-06-26T16:37:37Z  2016-06-26T16:37:38Z                 None   \n",
       "100  2016-06-26T21:41:26Z  2016-06-26T21:41:27Z                 None   \n",
       "101  2020-06-11T20:24:17Z  2020-06-11T20:24:17Z                 None   \n",
       "\n",
       "       attributes.updated relationships.client.data.id  \\\n",
       "0    2022-10-26T12:15:34Z                  cern.zenodo   \n",
       "1    2022-10-26T12:15:33Z                  cern.zenodo   \n",
       "2    2022-10-26T06:40:27Z                  cern.zenodo   \n",
       "3    2022-10-26T06:40:26Z                  cern.zenodo   \n",
       "4    2022-10-25T07:15:22Z                  cern.zenodo   \n",
       "..                    ...                          ...   \n",
       "97   2020-06-29T13:28:40Z                        rg.rg   \n",
       "98   2020-06-29T13:28:40Z                        rg.rg   \n",
       "99   2020-06-29T13:28:40Z                        rg.rg   \n",
       "100  2020-06-29T13:28:40Z                        rg.rg   \n",
       "101  2020-06-11T20:24:18Z                    gbif.gbif   \n",
       "\n",
       "    relationships.client.data.type attributes.container.type  \\\n",
       "0                          clients                       NaN   \n",
       "1                          clients                       NaN   \n",
       "2                          clients                       NaN   \n",
       "3                          clients                       NaN   \n",
       "4                          clients                       NaN   \n",
       "..                             ...                       ...   \n",
       "97                         clients                       NaN   \n",
       "98                         clients                       NaN   \n",
       "99                         clients                       NaN   \n",
       "100                        clients                       NaN   \n",
       "101                        clients                       NaN   \n",
       "\n",
       "    attributes.container.identifier attributes.container.identifierType  \n",
       "0                               NaN                                 NaN  \n",
       "1                               NaN                                 NaN  \n",
       "2                               NaN                                 NaN  \n",
       "3                               NaN                                 NaN  \n",
       "4                               NaN                                 NaN  \n",
       "..                              ...                                 ...  \n",
       "97                              NaN                                 NaN  \n",
       "98                              NaN                                 NaN  \n",
       "99                              NaN                                 NaN  \n",
       "100                             NaN                                 NaN  \n",
       "101                             NaN                                 NaN  \n",
       "\n",
       "[102 rows x 51 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create Pandas dataframe\n",
    "df = pd.DataFrame(data_normalized)\n",
    "\n",
    "# display Pandas dataframe as table\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39a9bf2-1cb7-49e1-ab27-1ce06f4f027f",
   "metadata": {},
   "source": [
    "## Create csv File for Raw Data Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a36d7640-743f-43a7-b0a7-3e07d5e06747",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create csv file\n",
    "df.to_csv('DataCite_Raw_Metadata.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87913507-4eb4-459f-a6e5-49c0b954615c",
   "metadata": {},
   "source": [
    "# Count Attributes in Pandas Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b38915-969f-4e19-af21-5045ecc54862",
   "metadata": {},
   "source": [
    "## Count all attributes in Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "683392be-bd5f-49ea-a68e-9e78f78c30bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                      102\n",
      "type                                    102\n",
      "attributes.doi                          102\n",
      "attributes.identifiers                  102\n",
      "attributes.creators                     102\n",
      "attributes.titles                       102\n",
      "attributes.publisher                    102\n",
      "attributes.publicationYear              102\n",
      "attributes.subjects                     102\n",
      "attributes.contributors                 102\n",
      "attributes.dates                        102\n",
      "attributes.language                      26\n",
      "attributes.types.ris                    102\n",
      "attributes.types.bibtex                 102\n",
      "attributes.types.citeproc               102\n",
      "attributes.types.schemaOrg              102\n",
      "attributes.types.resourceType            51\n",
      "attributes.types.resourceTypeGeneral    102\n",
      "attributes.relatedIdentifiers           102\n",
      "attributes.sizes                        102\n",
      "attributes.formats                      102\n",
      "attributes.version                        4\n",
      "attributes.rightsList                   102\n",
      "attributes.descriptions                 102\n",
      "attributes.geoLocations                 102\n",
      "attributes.fundingReferences            102\n",
      "attributes.url                          102\n",
      "attributes.contentUrl                     0\n",
      "attributes.metadataVersion              102\n",
      "attributes.schemaVersion                 90\n",
      "attributes.source                        93\n",
      "attributes.isActive                     102\n",
      "attributes.state                        102\n",
      "attributes.reason                         0\n",
      "attributes.viewCount                    102\n",
      "attributes.downloadCount                102\n",
      "attributes.referenceCount               102\n",
      "attributes.citationCount                102\n",
      "attributes.partCount                    102\n",
      "attributes.partOfCount                  102\n",
      "attributes.versionCount                 102\n",
      "attributes.versionOfCount               102\n",
      "attributes.created                      102\n",
      "attributes.registered                   102\n",
      "attributes.published                      0\n",
      "attributes.updated                      102\n",
      "relationships.client.data.id            102\n",
      "relationships.client.data.type          102\n",
      "attributes.container.type                29\n",
      "attributes.container.identifier          29\n",
      "attributes.container.identifierType      29\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count all attributes in Pandas dataframe\n",
    "print(df.count())\n",
    "\n",
    "# create csv file of the count\n",
    "df.to_csv('DataCite-Data_Count_All_Attributes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b5e48a-f7c8-478b-b3cd-f270a470cf41",
   "metadata": {},
   "source": [
    "## Give List of DOIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32d77370-df22-4605-bab4-ebbf4f1cd8fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         10.5281/zenodo.7223794\n",
      "1         10.5281/zenodo.7223795\n",
      "2         10.5281/zenodo.7243699\n",
      "3         10.5281/zenodo.7243700\n",
      "4         10.5281/zenodo.7225465\n",
      "                 ...            \n",
      "97     10.13140/rg.2.1.3170.7766\n",
      "98     10.13140/rg.2.1.1126.8720\n",
      "99     10.13140/rg.2.1.3944.9206\n",
      "100    10.13140/rg.2.1.5071.3209\n",
      "101           10.15468/dl.77nmev\n",
      "Name: attributes.doi, Length: 102, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# create values with all DOIs\n",
    "df_DOI = df['attributes.doi']\n",
    "\n",
    "# give results\n",
    "print(df_DOI)\n",
    "\n",
    "# create csv file of the count\n",
    "df_DOI.to_csv('DataCite-Data_DOI-List.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b929fa3-786c-4a84-80d0-9fbfdffc0aa3",
   "metadata": {},
   "source": [
    "## Count the Publication Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d54182da-0aea-4468-b54f-0367f8584f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count_Publication_Types\n",
      "CreativeWork           1\n",
      "SoftwareSourceCode     2\n",
      "Book                   9\n",
      "ScholarlyArticle      43\n",
      "Dataset               47\n",
      "Name: attributes.types.schemaOrg, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count values in the column \"attributes.types.schemaOrg\" and list it\n",
    "df_pub_types = df['attributes.types.schemaOrg'].value_counts(dropna=False).rename_axis('Count_Publication_Types').sort_values(ascending=True)\n",
    "\n",
    "# give results\n",
    "print(df_pub_types)\n",
    "\n",
    "# create csv file of the count\n",
    "df_pub_types.to_csv('DataCite-Data_Publication-Types.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccd6fe8-477d-4ffb-bcf2-07fd21a4359a",
   "metadata": {},
   "source": [
    "## Count the Relationship Clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2c1d542-adc8-4745-a7b8-1456140734d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count_Relationship_Clients\n",
      "tib.wdcrsat         2\n",
      "gdcc.harvard-dv     5\n",
      "rg.rg              14\n",
      "gbif.gbif          40\n",
      "cern.zenodo        41\n",
      "Name: relationships.client.data.id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count values in the column \"relationships.client.data.id\" and list it\n",
    "df_relationship = df['relationships.client.data.id'].value_counts(dropna=False).rename_axis('Count_Relationship_Clients').sort_values(ascending=True)\n",
    "print(df_relationship)\n",
    "\n",
    "# create csv file of the count\n",
    "df_relationship.to_csv('DataCite-Data_Relationship-Clients.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21cc7315-8245-4dd7-b58e-c7df1a6f3636",
   "metadata": {},
   "source": [
    "## Count by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "543a782e-811a-42c6-a0f0-5a892b9b44ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Publication_Year\n",
      "2008     2\n",
      "2009     2\n",
      "2010     4\n",
      "2011     8\n",
      "2012     4\n",
      "2013     2\n",
      "2014     2\n",
      "2015     2\n",
      "2016     1\n",
      "2017     6\n",
      "2019     2\n",
      "2020    12\n",
      "2021    28\n",
      "2022    27\n",
      "Name: attributes.publicationYear, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count values in the column \"attributes.publisher\" and list it\n",
    "df_year = df['attributes.publicationYear'].value_counts(dropna=False).rename_axis('Publication_Year')\n",
    "\n",
    "# sort values by year\n",
    "df_year = df_year.sort_index(ascending=True)\n",
    "\n",
    "# display result\n",
    "print(df_year)\n",
    "\n",
    "# create csv file of the count\n",
    "df_year.to_csv('DataCite-Data_Count_by_Year.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e27801-9291-42d0-9ac7-301035328238",
   "metadata": {},
   "source": [
    "## Count Publisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2acdf56a-8123-443b-ae01-397fcb56aa53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count_Publisher\n",
      "National Herbarium of Georgia TBI, Botanical Institute of Ilia State University                   1\n",
      "Ilia State University Institute of Addictology, Alternative Georgia Addiction Research Center     1\n",
      "ილიას სახელმწიფო უნივერსიტეტის გამომცემლობა, ILIA STATE UNIVERSITY PRESS                          1\n",
      "Ilia State University                                                                             2\n",
      "Unpublished                                                                                       2\n",
      "World Data Center for Remote Sensing of the Atmosphere (WDC-RSAT)                                 2\n",
      "Harvard Dataverse                                                                                 5\n",
      "ILIA STATE UNIVERSITY PRESS                                                                       8\n",
      "The Global Biodiversity Information Facility                                                     39\n",
      "Zenodo                                                                                           41\n",
      "Name: attributes.publisher, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# create list of counted values in the column \"attributes.publisher\"\n",
    "df_publisher = df['attributes.publisher'].value_counts(dropna=False).rename_axis('Count_Publisher').sort_values(ascending=True)\n",
    "\n",
    "# create csv file of this list\n",
    "df_publisher.to_csv('DataCite-Data_Publisher.csv')\n",
    "\n",
    "# print list of counted publisher\n",
    "print(df_publisher)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495d85f9-da9b-460f-8084-73d05c974047",
   "metadata": {},
   "source": [
    "## Pivot by Publisher and Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5166162-3052-4d08-a2e2-b56d64ce9b18",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create table with publishers by years\n",
    "df_publisher_y = df[['attributes.publicationYear','attributes.publisher']].copy()\n",
    "\n",
    "# count the published datasets by publisher and year\n",
    "df_publisher_y['Count.Year'] = df_publisher_y.groupby(['attributes.publisher'])['attributes.publisher'].transform('count')\n",
    "\n",
    "# create Pivot table with pandas\n",
    "df_pivot = df_publisher_y.pivot_table(index='attributes.publisher', columns='attributes.publicationYear', values = 'Count.Year', aggfunc='count', fill_value = 0)\n",
    "\n",
    "# create file with pivot values\n",
    "df_pivot.to_csv('DataCite-Data_Publisher-Year-Pivot.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab21afd-82f9-4d72-a668-90206588e6be",
   "metadata": {},
   "source": [
    "# Visualisations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95904fe6-099d-48e9-b6fe-aefe96b67518",
   "metadata": {},
   "source": [
    "## Graph on Publications by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38d13501-a769-46d6-af30-ff5e77fcede4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgYxOLuqrarao+WVXXVNXVVfWKYflOVfXxqvrq8Pl+kxoDAADAfDfJI3VrkryytbYsyeOT/F5V7Znk+CQXttZ2T3Lh8DUAAABbYGJR11r7dmvtsuHy6iTXJHlQkqcnOXO42plJnjGpMQAAAMx3c/KcuqpamuQxSb6Q5AGttW8no/BLsvMGbnNMVa2sqpU33njjXAwTAACgOxOPuqq6T5IPJDm2tXbzbG/XWjuttbaitbZi8eLFkxsgAABAxyYadVW1XUZB957W2geHxd+pqiXD+iVJvjvJMQAAAMxnk3z1y0pyepJrWmt/PmPVR5K8YLj8giQfntQYAAAA5rtFE9z2ryZ5XpIvV9UVw7I/TnJikvdV1dFJ/m+S/zrBMQAAAMxrE4u61to/JakNrH7ipPYLAACwkMzJq18CAAAwGaIOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY5uMuhrZbS4GAwAAwObZZNS11lqSv5v8UAAAANhcsz398vNV9diJjgQAAIDNtmiW1zs4yUuralWSW5NURgfxHjWpgQEAALBps426wyY6CgAAALbIrE6/bK19M8luSZ4wXL5ttrcFAABgcmYVZlX12iR/mOSPhkXbJTl7UoMCAABgdmZ7tO2ZSX4zo+fTpbX2rSTbT2pQAAAAzM5so+5nw1sbtCSpqntPbkgAAADM1myj7n1V9VdJ7ltVv5PkE0neOblhAQAAMBuzfaGUtyR5f5IPJPmVJP+rtfa2jd2mqt5VVd+tqqtmLHtdVd1QVVcMH0/ZmsEDAAAsdLN9S4O01j6e5OObse0zkrw9yV+vs/zkIRIBAADYSrN99cvDq+qrVfWjqrq5qlZX1c0bu01r7eIkPxjLKAEAAFiv2T6n7s1JfrO1tmNrbYfW2vattR22cJ8vr6orh9Mz77ehK1XVMVW1sqpW3njjjVu4KwAAgPlttlH3ndbaNWPY3zuSPDzJ8iTfTnLShq7YWjuttbaitbZi8eLFY9g1AADA/LPR59RV1eHDxZVV9d4kf5fkp2vXt9Y+uDk7a619Z8a235nkY5tzewAAAO5qUy+U8hszLt+W5NAZX7ckmxV1VbWktfbt4ctnJrlqY9cHAABg4zYada21397SDVfVOUkOSnL/qro+yWuTHFRVyzMKwlVJXrKl2wcAAGCWb2lQVW9O8qdJfpzkgiSPTnJsa+3sDd2mtXbkehafviWDBAAAYP1m+0Iph7bWbk7ytCTXJ3lEkldPbFQAAADMymyjbrvh81OSnNNa8/5zAAAA24BZnX6Z5KNV9ZWMTr98WVUtTvKTyQ0LAACA2ZjVkbrW2vFJ9k+yorV2e5Jbkzx9kgMDAABg0zb1PnVPaK1dNOP96lJVM6+yWW9pAAAAwHht6vTLX09yUe76fnVrbfb71AEAADBem3qfutcOn7f4/eoAAACYnE2dfnncxta31v58vMMBAABgc2zq9Mvt52QUAAAAbJFNnX55wlwNBAAAgM03q7c0qKqHVdVHq+rGqvpuVX24qh426cEBAACwcbOKuiR/k+R9SZYk2SXJ3yY5Z1KDAgAAYHZmG3XVWjurtbZm+Dg7o7c0AAAAYIo29eqXOw0XP1lVxyc5N6OYe3aS8yY8NgAAADZhU69+eWlGEVfD1y+Zsa4l+ZNJDAoAAIDZ2dSrXz50rgYCAADA5tvUkbokSVU9f33LW2t/Pd7hAAAAsDlmFXVJHjvj8j2TPDHJZUlEHQAAwBTNKupaa78/8+uq2jHJWRMZEQAAALM227c0WNdtSXYf50AAAADYfLN9Tt1H8x/vS/cLSfbM6M3IAQAAmKLZPqfuLTMur0nyzdba9RMYDwAAAJthU28+fs8kL03yy0m+nOT01tqauRgYAAAAm7ap59SdmWRFRkF3WJKTJj4iAAAAZm1Tp1/u2Vp7ZJJU1elJvjj5IQEAADBbmzpSd/vaC067BAAA2PZs6kjdo6vq5uFyJfnF4etK0lprO0x0dAAAAGzURqOutXa3uRoIAAAAm29L33wcAACAbYCoAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwCALfSiF70oO++8c/bee+87l7361a/OHnvskUc96lF55jOfmZtuuml6A2RBEHUAALCFXvjCF+aCCy64y7JDDjkkV111Va688so84hGPyBvf+MYpjY6FQtQBAMAWOvDAA7PTTjvdZdmhhx6aRYsWJUke//jH5/rrr5/G0FhARB0AAEzIu971rhx22GHTHgbznKgDAIAJeP3rX59FixblqKOOmvZQmOcWTXsAAAAw35x55pn52Mc+lgsvvDBVNe3hMM+JOgAAGKMLLrggb3rTm/LpT38697rXvaY9HBYAp18CAMAWOvLII7P//vvn2muvza677prTTz89L3/5y7N69eoccsghWb58eV760pdOe5jMc47UAQDAFjrnnHP+07Kjjz56CiNhIRN1AACwHkuPP2/O97nqxKfO+T7pn9MvAQAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOjaxqKuqd1XVd6vqqhnLdqqqj1fVV4fP95vU/gEAABaCSR6pOyPJk9dZdnySC1truye5cPgaAACALTSxqGutXZzkB+ssfnqSM4fLZyZ5xqT2DwAAsBDM9XPqHtBa+3aSDJ933tAVq+qYqlpZVStvvPHGORsgAABAT7bZF0pprZ3WWlvRWluxePHiaQ8HAABgmzTXUfedqlqSJMPn787x/gEAAOaVuY66jyR5wXD5BUk+PMf7BwAAmFcm+ZYG5yT5XJJfqarrq+roJCcmOaSqvprkkOFrAAAAttCiSW24tXbkBlY9cVL7BAAAWGi22RdKAQAAYNNEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHQAAQMdEHcxzJ598cvbaa6/svffeOfLII/OTn/xk2kMCAGCMRB3MYzfccEPe+ta3ZuXKlbnqqqtyxx135Nxzz532sAAAGCNRB/PcmjVr8uMf/zhr1qzJbbfdll122WXaQwIAYIxEHcxjD3rQg/KqV70qD37wg7NkyZLsuOOOOfTQQ6c9LAAAxkjUwTz2wx/+MB/+8Ifzb//2b/nWt76VW2+9NWefffa0hwUAwBiJOpjHPvGJT+ShD31oFi9enO222y6HH354PvvZz057WAAAjJGog3nswQ9+cD7/+c/ntttuS2stF154YZYtWzbtYQEAMEaiDuax/fbbL8961rOyzz775JGPfGR+/vOf55hjjpn2sAAAGKNF0x4AMFknnHBCTjjhhGkPAwCACRF10Imlx583lf2uOvGpU9kvAACz4/RLAACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AIA5dNNNN+VZz3pW9thjjyxbtiyf+9znpj0koHOLpj0AAICF5BWveEWe/OQn5/3vf39+9rOf5bbbbpv2kIDOiToAgDly88035+KLL84ZZ5yRJLn73e+eu9/97tMdFNA9p18CAMyRb3zjG1m8eHF++7d/O495zGPy4he/OLfeeuu0hwV0TtQBAMyRNWvW5LLLLsvv/u7v5vLLL8+9733vnHjiidMeFtA5UQcAMEd23XXX7Lrrrtlvv/2SJM961rNy2WWXTXlUQO9EHQDAHHngAx+Y3XbbLddee22S5MILL8yee+455VEBvfNCKQAAc+htb3tbjjrqqPzsZz/Lwx72sLz73e+e9pCAzok6AIA5tHz58qxcuXLawwDmEVEHALCFlh5/3lT2u+rEp05lv8C2yXPqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOrZoGjutqlVJVie5I8ma1tqKaYwDAACgd1OJusHBrbXvTXH/AAAA3XP6JQAAQMemFXUtyT9W1aVVdcz6rlBVx1TVyqpaeeONN87x8AAAAPowraj71dbaPkkOS/J7VXXguldorZ3WWlvRWluxePHiuR8hAABAB6YSda21bw2fv5vkQ0keN41xAAAA9G7Oo66q7l1V26+9nOTQJFfN9TgAAADmg2m8+uUDknyoqtbu/29aaxdMYRwAAADdm/Ooa619I8mj53q/AAAA85G3NAAAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqGNBuuOOO/KYxzwmT3va06Y9FMbMfQsALDSijgXp1FNPzbJly6Y9DCbAfQsALDSijgXn+uuvz3nnnZcXv/jF0x4KY+a+BQAWIlHHgnPsscfmzW9+c37hF/z4zzfuWwBgIfLIhwXlYx/7WHbeeefsu+++0x4KY+a+BQAWKlHHgnLJJZfkIx/5SJYuXZrnPOc5ueiii/Lc5z532sNiDNy3AMBCJepYUN74xjfm+uuvz6pVq3LuuefmCU94Qs4+++xpD4sxcN8CAAuVqAMAAOjYomkPAKbloIMOykEHHTTtYTAB7lsAYCERdXRr6fHnTWW/q0586lT2u9C4fwEAZsfplwAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0TdQAAAB0Tdeu47rrrcvDBB2fZsmXZa6+9cuqpp057SBO10OYL88VC+9013/k9X+YvP8swNxZNewDbmkWLFuWkk07KPvvsk9WrV2fffffNIYcckj333HPaQ5uIhTZfmC8W2u+u+c7v+TJ/+VmGueFI3TqWLFmSffbZJ0my/fbbZ9myZbnhhhumPKrJWWjzhfliof3umu/8ni/zl59lmBuibiNWrVqVyy+/PPvtt9+0hzInFtp8Yb5YaL+75gt98rMMkyPqNuCWW27JEUcckVNOOSU77LDDtIczcQttvjBfLLTfXfOFPvlZhskSdetx++2354gjjshRRx2Vww8/fNrDmbiFNl+YLxba7675Qp/8LMPkibp1tNZy9NFHZ9myZTnuuOOmPZyJW2jzhfliof3umi/0yc8yzA1Rt45LLrkkZ511Vi666KIsX748y5cvz/nnnz/tYU3MQpsvzBcL7XfXfOf3fJm//CzD3PCWBus44IAD0lqb9jDmzEKbL8wXC+1313yhT36WYW7M+6hbevx5U9nvqhOfOpX9LrT5wnyx0H53F9J8F9Jcmf/8PMO2yemXAAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AMBUvehFL8rOO++cvffee9pDAeiSqAMApuqFL3xhLrjggmkPA6Bbog4AmKoDDzwwO+2007SHAdAtUQcAANAxUQcAANAxUQcAANAxUQcAANAxUQcATNWRRx6Z/fffP9dee2123XXXnH766dMeEkBXFk17AADAwnbOOedMewgAXRN1AMDYLD3+vKnsd9WJT53KfgG2BU6/BAAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6JioAwAA6NhUoq6qnlxV11bV16rq+GmMAQAAYD6Y86irqrsl+YskhyXZM8mRVbXnXI8DAABgPpjGkbrHJflaa+0brbWfJTk3ydOnMA4AAIDuVWttbndY9awkT26tvXj4+nlJ9mutvXyd6x2T5Jjhy19Jcu2cDnTk/km+N4X9TstCmu9CmmtivvOd+c5fC2muifnOdwtpvgtpron5zpWHtNYWr2/ForkeSZJaz7L/VJattdOSnDb54WxYVa1sra2Y5hjm0kKa70Kaa2K+8535zl8Laa6J+c53C2m+C2muifluC6Zx+uX1SXab8fWuSb41hXEAAAB0bxpR989Jdq+qh1bV3ZM8J8lHpjAOAACA7s356ZettTVV9fIk/5Dkbkne1Vq7eq7HMUtTPf1zChbSfBfSXBPzne/Md/5aSHNNzHe+W0jzXUhzTcx36ub8hVIAAAAYn6m8+TgAAADjIeoAAAA6tqCirqp2q6pPVtU1VXV1Vb1iWL5TVX28qr46fL7fjNv8UVV9raquraonzVh+ZFV9uaqurKoLqur+05jTxox5vs8e5np1Vb15GvPZmM2da1X90nD9W6rq7etsa9/hvv1aVb21qtb3NhxTNeb5vr6qrquqW6Yxl9kY13yr6l5VdV5VfWXYzonTmtPGjPn+vaCqvjRs5y+r6m7TmNPGjHO+M7b5kaq6ai7nMRtjvm8/NfxbfcXwsfM05rQxY57v3avqtKr61+F3+IhpzGljxvhv1fYz7tcrqup7VXXKlKa1QWO+f+fb44xDqurSGj2euLSqnjBjW/PxccbG5jsfH2esd741zccZrbUF85FkSZJ9hsvbJ/nXJHsmeXOS44flxyd503B5zyRfSnKPJA9N8vWMXtxlUZLvJrn/cL03J3ndtOc3wfn+UpL/m2TxcL0zkzxx2vPbyrneO8kBSV6a5O3rbOuLSfbP6D0V/z7JYdOe34Tn+/hhe7dMe16Tnm+SeyU5eLh89ySfWQD37w7D50rygSTPmfb8JjnfYf3hSf4myVXTntuE79tPJVkx7TnN4XxPSPKnw+VfyPB/8Lb0Me6f5RnbvTTJgdOe36Tmm/n5OOMxSXYZLu+d5IYZ25qPjzM2Nt/5+DhjvfPNFB9nTP2bOOU78MNJDklybZIlM+7Ua4fLf5Tkj2Zc/x+GX8LtktyY5CHDL+RfJjlm2vOZ4Hwfm+QTM5Y/L8n/nvZ8tmauM673wtz1P5YlSb4y4+sjk/zVtOczqfmus26b/cd2EvMd1p+a5HemPZ85un+3S/LRJM+e9nwmOd8k90nyTxn9Z7zNRd2Y5/qpbONRN+b5Xpfk3tOew1zNd8a63Ye517TnM6n5Zh4/zhiWV5LvZ/RH83n9OGPd+a6zfN49ztjYfId1c/Y4Y0GdfjlTVS3NqLK/kOQBrbVvJ8nwee0pLA/K6B/Sta5P8qDW2u1JfjfJlzN64/Q9k5w+NyPfMlsz3yRfS7JHVS2tqkVJnpG7voH8NmWWc92QB2U077XWfg+2WVs53+6Ma75Vdd8kv5HkwvGPcnzGMd+q+oeMzi5YneT9kxnpeIxhvn+S5KQkt01qjOMypp/ldw+n5/3PbfEUrpm2Zr7D72uS/ElVXVZVf1tVD5jgcLfaGP9tPjLJe9vwCHFbtZXzne+PM45Icnlr7adZGI8zZs63O+Oa71w/zliQUVdV98noNKRjW2s3b+yq61nWqmq7jKLuMUl2SXJlRke5tklbO9/W2g8zmu97MzqMvCrJmnGPcxw2Y64b3MR6lm2z/5GOYb5dGdd8hwcN5yR5a2vtG+Ma37iNa76ttSdl9BfGeyR5wiauPjVbO9+qWp7kl1trHxr32MZtTPftUa21Ryb5teHjeeMa37iNYb6Lkuya5JLW2j5JPpfkLWMc4liN+d/m52T079U2a2vnO58fZ1TVXknelOQlaxet52rz5nHGeubblXHNdxqPMxZc1A1B9oEk72mtfXBY/J2qWjKsX5LRX7ST0V9PZv6laNeMjswtT5LW2teHv5y9L8l/mfzoN9+Y5pvW2kdba/u11vbP6FD0V+di/JtjM+e6IddnNO+17vwebGvGNN9ujHm+pyX5amvtlLEPdEzGff+21n6S5CNJnj7usY7DmOa7f5J9q2pVRqdgPqKqPjWZEW+5cd23rbUbhs+rM3oO4eMmM+KtM6b5fj+jo69rg/1vk+wzgeFutXH+7lbVo5Msaq1dOpHBjsEYf57n3eOMqto1o5/Z57fWvj4snrePMzYw326Meb5z/jhjQUXdcGrK6Umuaa39+YxVH0nyguHyCzI6j3bt8udU1T2q6qEZndf+xSQ3JNmzqhYP1zskyTWTHv/mGuN8U8Orqg2v+vOyJP9n8jOYvS2Y63oNh9ZXV9Xjh20+f1O3mYZxzbcX45xvVf1pkh2THDvmYY7NuOZbVfeZ8Z/RoiRPSfKV8Y9464zx9/cdrbVdWmtLM3oxhn9trR00/hFvuTHet4tqeNXl4YHI05Jsi6/2Oa77tmX0nNCDhkVPTPIvYx3sGEzg3+Yjsw0fpRvzv83z6nHGcOrdeRm9VsEla688Xx9nbGi+vRjnfKf2OGNznoDX+0dG/8m3jE6XvGL4eEpGr7p0YUZ/FbowyU4zbvPfM3oVyGsz49VrMnrlpmuGbX00yS9Ne34Tnu85Gf0H+i/ZNl89b0vmuirJD5LcktFfzvYclq/I6MHR15O8Pdvgk9PHPN83D1//fPj8umnPb1Lzzegvom343V27nRdPe34TnO8DkvzzsJ2rk7wto7/6T32Ok/p5nrF+abbBF0oZ431774xeEXHtfXtqkrtNe36TvG8zenGyi4dtXZjkwdOe36R/lpN8I8ke057XHN2/8+pxRpL/keTWGde9IsnOw7p59zhjE/Odd48zNjTfTPFxRg0DAwAAoEML6vRLAACA+UbUAQAAdEzUAQAAdEzUAQAAdEzUAQAAdEzUATARVfXAqjq3qr5eVf9SVedX1SPGvI+Dquq/bOZt7lFVn6iqK6rq2eusO6Oq/m1Yd1lV7b+Jbb2uql61nuVLq+qq4fKKqnrr5oxxxnaOrap7zfj6/OH9kQDgToumPQAA5p/hjVw/lOTM1tpzhmXLM3rvvH8d464Oyuj9rj67Gbd5TJLtWmvLN7D+1a2191fVoUn+KsmjtmaArbWVSVZu4c2PTXJ2ktuGbT1la8YCwPzkSB0Ak3Bwkttba3+5dkFr7YrW2mdq5M+q6qqq+vLao2XDUbePrb1+Vb29ql44XF5VVScMR8++XFV7VNXSJC9N8t+GI2u/NnMAVbVTVf1dVV1ZVZ+vqkdV1c4ZRdLy4TYP38gcLk7yyzP2f//h8oqq+tSM6z26qi6qqq9W1e+su5GZ86qq+1TVu4c5XFlVRwzL31FVK6vq6qo6YVj2B0l2SfLJqvrkesZx3PA9vKqqjh2WLa2qa6rqncO2/rGqfnHt9oYjpldW1bkbmTcAnXGkDoBJ2DvJpRtYd3iS5UkeneT+Sf65qi6exTa/11rbp6peluRVrbUXV9VfJrmltfaW9Vz/hCSXt9aeUVVPSPLXrbXlVfXi4fZP28T+fiPJl2cxrkcleXySeye5vKrO28h1/2eSH7XWHpkkVXW/Yfl/b639oKruluTCqnpUa+2tVXVckoNba9+buZGq2jfJbyfZL0kl+UJVfTrJD5PsnuTI1trvVNX7khyRUcgen+ShrbWfOoUTYH5xpA6AuXZAknNaa3e01r6T5NNJHjuL231w+HxpkqWz3M9ZSdJauyjJL1XVjrO43Z9V1RVJjkly9Cyu/+HW2o+H8Ppkksdt5Lr/T5K/WPtFa+2Hw8XfqqrLklyeZK8ke25inwck+VBr7dbW2i0ZfW/WHqn8t9baFcPlmd+rK5O8p6qem2TNLOYFQCdEHQCTcHWSfTewrjawfE3u+v/SPddZ/9Ph8x2Z3Zkm69tPm8XtXt1aW95aO6S1dtV6xrbuuNbd5sb2Ueuur6qHJnlVkie21h6V5Lz17GN929mQn864PPN79dSMgnLfJJdWlbN1AOYJUQfAJFyU5B4zn2NWVY+tql/P6Llqz66qu1XV4iQHJvlikm8m2XN4dcodkzxxFvtZnWT7Day7OMlRw74Pyuj0zZu3cD6r8h+ResQ6655eVfesql/K6IVb/nkj2/nHJC9f+8Vw+uUOSW5N8qOqekCSw2Zcf0PzuzjJM6rqXlV17yTPTPKZDe20qn4hyW6ttU8meU2S+ya5z0bGCUBHRB0AY9daaxmFxiE1ekuDq5O8Lsm3MnpVzCuTfCmj+HtNa+3fW2vXJXnfsO49GZ2KuCkfTfLM9b1QyrC/FVV1ZZITk7xgK6Z0QpJTq+ozGR39mumLGR1d+3ySP2mtfWsj2/nTJPcbXtzkSxk9X+5LGc316iTvSnLJjOufluTv175QylqttcuSnDHs+wtJ/k9rbWPfr7slObuqvjzs6+TW2k0buT4AHanR/7sAAAD0yJE6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjok6AACAjv3/8ZrMRK1XmJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot with bar  \n",
    "ax = df_year.plot(x='attributes.publicationYear', y='Publication_Year', kind='bar', figsize = (15, 15), legend=False, rot=0)\n",
    "\n",
    "# set plot title\n",
    "plt.title('Publications by Year')\n",
    "\n",
    "# set x axis title\n",
    "plt.xlabel('Count of Publications')\n",
    "\n",
    "# set y axis title\n",
    "plt.ylabel('Publisher')\n",
    "\n",
    "# set values at the bars\n",
    "ax.bar_label(ax.containers[0], label_type='edge')\n",
    "\n",
    "# create image file of it\n",
    "plt.savefig('Publications_by_Year.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c66925-9273-48cd-9aa2-33f548576475",
   "metadata": {},
   "source": [
    "## Graph on Relationship Clients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "149d9f7d-aee6-4a27-b650-a854c033f065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot with crossbar\n",
    "ax = df_relationship.plot(x='relationship.client.data.id', y='Count_Relationship_Clients', kind='barh', figsize = (15, 15), legend=False, rot=0)\n",
    "\n",
    "# set plot title\n",
    "plt.title('Counts of Relationship Clients')\n",
    "\n",
    "# set x axis title\n",
    "plt.xlabel('Count of Publications')\n",
    "\n",
    "# set y axis title\n",
    "plt.ylabel('Relationship Clients')\n",
    "\n",
    "# set values at the bars\n",
    "ax.bar_label(ax.containers[0], label_type='edge')\n",
    "\n",
    "# create image file of it\n",
    "plt.savefig('Relationship_Clients.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1816ed1c-5efc-4c02-950a-6a2467e3a411",
   "metadata": {},
   "source": [
    "## Graph on Publication Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c358482-71db-4c84-913e-fb996998fc88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot with pie chart\n",
    "df_pub_types.plot.pie(autopct='%.2f', figsize = (10, 10))\n",
    "\n",
    "# set plot title\n",
    "plt.title('Count of Publication Types')\n",
    "\n",
    "# create image file of it\n",
    "plt.savefig('Publication_Types.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077794ff-59fa-4779-bca1-096c5f70c1e2",
   "metadata": {},
   "source": [
    "## Graph on Publications by Publisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9373e0ce-bbad-44ef-8926-7328e8142d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot with crossbar\n",
    "ax = df_publisher.plot(x='attributes.publisher', y='Count_Publisher', kind='barh', figsize = (15,15), legend=False, rot=0)\n",
    "\n",
    "# set plot title\n",
    "plt.title('Total Publications by Publishers')\n",
    "\n",
    "# set x axis title\n",
    "plt.xlabel('Count of Publications')\n",
    "\n",
    "# set y axis title\n",
    "plt.ylabel('Publisher')\n",
    "\n",
    "# set values at the bars\n",
    "ax.bar_label(ax.containers[0], label_type='edge')\n",
    "\n",
    "# create image file of it\n",
    "plt.savefig('Publications_by_Publisher.jpg', bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.10.5"
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}