{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import glob\n",
    "import re \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from lxml import etree\n",
    "import requests\n",
    "import time\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append(os.path.abspath(\"C:/Users/calvotello/Dropbox/MTB/Göttingen/research/\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from librarian_robot import downloading_datasets, map_classications_systems, tokenize, visualize\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "color = \"#669999\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(\"./../data/exemplars_secondary_literature.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medium</th>\n",
       "      <th>title</th>\n",
       "      <th>title_supplement</th>\n",
       "      <th>year</th>\n",
       "      <th>entry_first</th>\n",
       "      <th>author_first_name</th>\n",
       "      <th>author_last_name</th>\n",
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       "      <th>editor_first_name</th>\n",
       "      <th>editor_last_name</th>\n",
       "      <th>...</th>\n",
       "      <th>keyword_einzel_ppn</th>\n",
       "      <th>keyword_einzel</th>\n",
       "      <th>keyword_BDSL_a</th>\n",
       "      <th>keyword_BDSL_p</th>\n",
       "      <th>keyword_BDSL_t</th>\n",
       "      <th>keyword_BDSL_s</th>\n",
       "      <th>keyword_Fremddatenlieferanten_lieferanten</th>\n",
       "      <th>keyword_Fremddatenlieferanten</th>\n",
       "      <th>ppn</th>\n",
       "      <th>ILNs_libraries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1642067407</th>\n",
       "      <td>Aau</td>\n",
       "      <td>Camus</td>\n",
       "      <td>None</td>\n",
       "      <td>1987</td>\n",
       "      <td>HDBSRO:13-09-18</td>\n",
       "      <td>Morvan</td>\n",
       "      <td>Lebesque</td>\n",
       "      <td>077112679</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1642067407</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>23-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>31</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>448323834</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Avantgarde und Revolution</td>\n",
       "      <td>Mexikanische Lyrik von López Velarde bis Octav...</td>\n",
       "      <td>1987</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Klaus</td>\n",
       "      <td>Meyer-Minnemann</td>\n",
       "      <td>...</td>\n",
       "      <td>08708046X|085099805|08548833X|089277058|088575...</td>\n",
       "      <td>Paz, Octavio&amp;datl=1914-1998|Lyrik|Avantgarde|E...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>448323834</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448323834</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Avantgarde und Revolution</td>\n",
       "      <td>Mexikanische Lyrik von López Velarde bis Octav...</td>\n",
       "      <td>1987</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Klaus</td>\n",
       "      <td>Meyer-Minnemann</td>\n",
       "      <td>...</td>\n",
       "      <td>08708046X|085099805|08548833X|089277058|088575...</td>\n",
       "      <td>Paz, Octavio&amp;datl=1914-1998|Lyrik|Avantgarde|E...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>448323834</td>\n",
       "      <td>204-205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>204-205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041157 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           medium                                              title  \\\n",
       "ppn                                                                    \n",
       "1642067407    Aau                                              Camus   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "...           ...                                                ...   \n",
       "448323834     Oax                          Avantgarde und Revolution   \n",
       "448323834     Oax                          Avantgarde und Revolution   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "\n",
       "                                             title_supplement  year  \\\n",
       "ppn                                                                   \n",
       "1642067407                                               None  1987   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "...                                                       ...   ...   \n",
       "448323834   Mexikanische Lyrik von López Velarde bis Octav...  1987   \n",
       "448323834   Mexikanische Lyrik von López Velarde bis Octav...  1987   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "\n",
       "                entry_first author_first_name author_last_name author_gnd_id  \\\n",
       "ppn                                                                            \n",
       "1642067407  HDBSRO:13-09-18            Morvan         Lebesque     077112679   \n",
       "1678415065     EBP:08-10-19              None             None          None   \n",
       "1678415065     EBP:08-10-19              None             None          None   \n",
       "1678415065     EBP:08-10-19              None             None          None   \n",
       "1678415065     EBP:08-10-19              None             None          None   \n",
       "...                     ...               ...              ...           ...   \n",
       "448323834     6055:07-05-19              None             None          None   \n",
       "448323834     6055:07-05-19              None             None          None   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa          None   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa          None   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa          None   \n",
       "\n",
       "           editor_first_name editor_last_name  ...  \\\n",
       "ppn                                            ...   \n",
       "1642067407              None             None  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "...                      ...              ...  ...   \n",
       "448323834              Klaus  Meyer-Minnemann  ...   \n",
       "448323834              Klaus  Meyer-Minnemann  ...   \n",
       "44832184X               None             None  ...   \n",
       "44832184X               None             None  ...   \n",
       "44832184X               None             None  ...   \n",
       "\n",
       "                                           keyword_einzel_ppn  \\\n",
       "ppn                                                             \n",
       "1642067407                                               None   \n",
       "1678415065                                               None   \n",
       "1678415065                                               None   \n",
       "1678415065                                               None   \n",
       "1678415065                                               None   \n",
       "...                                                       ...   \n",
       "448323834   08708046X|085099805|08548833X|089277058|088575...   \n",
       "448323834   08708046X|085099805|08548833X|089277058|088575...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "\n",
       "                                               keyword_einzel keyword_BDSL_a  \\\n",
       "ppn                                                                            \n",
       "1642067407                                               None           None   \n",
       "1678415065                                               None           None   \n",
       "1678415065                                               None           None   \n",
       "1678415065                                               None           None   \n",
       "1678415065                                               None           None   \n",
       "...                                                       ...            ...   \n",
       "448323834   Paz, Octavio&datl=1914-1998|Lyrik|Avantgarde|E...           None   \n",
       "448323834   Paz, Octavio&datl=1914-1998|Lyrik|Avantgarde|E...           None   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...           None   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...           None   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...           None   \n",
       "\n",
       "           keyword_BDSL_p keyword_BDSL_t keyword_BDSL_s  \\\n",
       "ppn                                                       \n",
       "1642067407            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "...                   ...            ...            ...   \n",
       "448323834             NaN            NaN            NaN   \n",
       "448323834             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "\n",
       "           keyword_Fremddatenlieferanten_lieferanten  \\\n",
       "ppn                                                    \n",
       "1642067407                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "...                                              ...   \n",
       "448323834                                        NaN   \n",
       "448323834                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "\n",
       "           keyword_Fremddatenlieferanten         ppn ILNs_libraries  \n",
       "ppn                                                                  \n",
       "1642067407                          None  1642067407           2012  \n",
       "1678415065                          None  1678415065             11  \n",
       "1678415065                          None  1678415065             21  \n",
       "1678415065                          None  1678415065          23-24  \n",
       "1678415065                          None  1678415065             31  \n",
       "...                                  ...         ...            ...  \n",
       "448323834                           None   448323834             25  \n",
       "448323834                           None   448323834        204-205  \n",
       "44832184X                           None   44832184X             23  \n",
       "44832184X                           None   44832184X             25  \n",
       "44832184X                           None   44832184X        204-205  \n",
       "\n",
       "[1041157 rows x 77 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'medium, title, title_supplement, year, entry_first, author_first_name, author_last_name, author_gnd_id, editor_first_name, editor_last_name, editor_gnd_id, isbn, ILNs, content_type, publisher, language_text, language_original, pages, format, comment_isbn, place_publication, summary, title_continuing_resource, work_ppn, work_info, work_title, expression_ppn, expression_info, expression_title, DDC_notation, DDC_sachgruppe, DDC_grundnotation, BK_ppn, BK_notation, BK_j, RVK_ppn, RVK_notation, RVK_j, RVK_k, keyword_RSWK, keyword_K10plus, keyword_project, keyword_local, keyword_045D, keyword_LoC, lcc_notation, klassifikationssystem_system, klassifikationssystem_notation, uri_description, uri, GOK_ppn, GOK_notation, GOK_j, signatur_place, signatur, signatur_date, Abrufzeichen, query, year_publication, literary_texts, source, content_type_ppn, DDC_sachgruppe_a, DDC_sachgruppe_b, DDC_sachgruppe_c, keyword_fremd_ppn, keyword_fremd, keyword_einzel_ppn, keyword_einzel, keyword_BDSL_a, keyword_BDSL_p, keyword_BDSL_t, keyword_BDSL_s, keyword_Fremddatenlieferanten_lieferanten, keyword_Fremddatenlieferanten, ppn, ILNs_libraries'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\", \".join(df.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1041157, 77)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1980,\n",
       " 1981,\n",
       " 1982,\n",
       " 1983,\n",
       " 1984,\n",
       " 1985,\n",
       " 1986,\n",
       " 1987,\n",
       " 1988,\n",
       " 1989,\n",
       " 1990,\n",
       " 1991,\n",
       " 1992,\n",
       " 1993,\n",
       " 1994,\n",
       " 1995,\n",
       " 1996,\n",
       " 1997,\n",
       " 1998,\n",
       " 1999,\n",
       " 2000,\n",
       " 2001,\n",
       " 2002,\n",
       " 2003,\n",
       " 2004,\n",
       " 2005,\n",
       " 2006,\n",
       " 2007,\n",
       " 2008,\n",
       " 2009,\n",
       " 2010,\n",
       " 2011,\n",
       " 2012,\n",
       " 2013,\n",
       " 2014,\n",
       " 2015,\n",
       " 2016,\n",
       " 2017,\n",
       " 2018,\n",
       " 2019]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(df.year_publication.unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(value=np.nan, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>medium</th>\n",
       "      <th>title</th>\n",
       "      <th>title_supplement</th>\n",
       "      <th>year</th>\n",
       "      <th>entry_first</th>\n",
       "      <th>author_first_name</th>\n",
       "      <th>author_last_name</th>\n",
       "      <th>author_gnd_id</th>\n",
       "      <th>editor_first_name</th>\n",
       "      <th>editor_last_name</th>\n",
       "      <th>...</th>\n",
       "      <th>keyword_einzel_ppn</th>\n",
       "      <th>keyword_einzel</th>\n",
       "      <th>keyword_BDSL_a</th>\n",
       "      <th>keyword_BDSL_p</th>\n",
       "      <th>keyword_BDSL_t</th>\n",
       "      <th>keyword_BDSL_s</th>\n",
       "      <th>keyword_Fremddatenlieferanten_lieferanten</th>\n",
       "      <th>keyword_Fremddatenlieferanten</th>\n",
       "      <th>ppn</th>\n",
       "      <th>ILNs_libraries</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1642067407</th>\n",
       "      <td>Aau</td>\n",
       "      <td>Camus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987</td>\n",
       "      <td>HDBSRO:13-09-18</td>\n",
       "      <td>Morvan</td>\n",
       "      <td>Lebesque</td>\n",
       "      <td>077112679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1642067407</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>23-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678415065</th>\n",
       "      <td>Oau</td>\n",
       "      <td>Livre et société dans la France du XVIIIe siècle</td>\n",
       "      <td>[1]</td>\n",
       "      <td>2019</td>\n",
       "      <td>EBP:08-10-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geneviève</td>\n",
       "      <td>Bollème</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1678415065</td>\n",
       "      <td>31</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>448323834</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Avantgarde und Revolution</td>\n",
       "      <td>Mexikanische Lyrik von López Velarde bis Octav...</td>\n",
       "      <td>1987</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Klaus</td>\n",
       "      <td>Meyer-Minnemann</td>\n",
       "      <td>...</td>\n",
       "      <td>08708046X|085099805|08548833X|089277058|088575...</td>\n",
       "      <td>Paz, Octavio&amp;datl=1914-1998|Lyrik|Avantgarde|E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448323834</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448323834</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Avantgarde und Revolution</td>\n",
       "      <td>Mexikanische Lyrik von López Velarde bis Octav...</td>\n",
       "      <td>1987</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Klaus</td>\n",
       "      <td>Meyer-Minnemann</td>\n",
       "      <td>...</td>\n",
       "      <td>08708046X|085099805|08548833X|089277058|088575...</td>\n",
       "      <td>Paz, Octavio&amp;datl=1914-1998|Lyrik|Avantgarde|E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>448323834</td>\n",
       "      <td>204-205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44832184X</th>\n",
       "      <td>Oax</td>\n",
       "      <td>Die @Frau im spanischen Roman nach dem Bürgerk...</td>\n",
       "      <td>Camilo José Cela, Carmen Laforet, Ana María Ma...</td>\n",
       "      <td>1982</td>\n",
       "      <td>6055:07-05-19</td>\n",
       "      <td>Sylvia</td>\n",
       "      <td>Truxa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>087037629|087119080|087008505|087123223|085140...</td>\n",
       "      <td>Goytisolo, Juan&amp;datl=1931-2017|Laforet, Carmen...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44832184X</td>\n",
       "      <td>204-205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041157 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           medium                                              title  \\\n",
       "ppn                                                                    \n",
       "1642067407    Aau                                              Camus   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "1678415065    Oau   Livre et société dans la France du XVIIIe siècle   \n",
       "...           ...                                                ...   \n",
       "448323834     Oax                          Avantgarde und Revolution   \n",
       "448323834     Oax                          Avantgarde und Revolution   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "44832184X     Oax  Die @Frau im spanischen Roman nach dem Bürgerk...   \n",
       "\n",
       "                                             title_supplement  year  \\\n",
       "ppn                                                                   \n",
       "1642067407                                                NaN  1987   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "1678415065                                                [1]  2019   \n",
       "...                                                       ...   ...   \n",
       "448323834   Mexikanische Lyrik von López Velarde bis Octav...  1987   \n",
       "448323834   Mexikanische Lyrik von López Velarde bis Octav...  1987   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "44832184X   Camilo José Cela, Carmen Laforet, Ana María Ma...  1982   \n",
       "\n",
       "                entry_first author_first_name author_last_name author_gnd_id  \\\n",
       "ppn                                                                            \n",
       "1642067407  HDBSRO:13-09-18            Morvan         Lebesque     077112679   \n",
       "1678415065     EBP:08-10-19               NaN              NaN           NaN   \n",
       "1678415065     EBP:08-10-19               NaN              NaN           NaN   \n",
       "1678415065     EBP:08-10-19               NaN              NaN           NaN   \n",
       "1678415065     EBP:08-10-19               NaN              NaN           NaN   \n",
       "...                     ...               ...              ...           ...   \n",
       "448323834     6055:07-05-19               NaN              NaN           NaN   \n",
       "448323834     6055:07-05-19               NaN              NaN           NaN   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa           NaN   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa           NaN   \n",
       "44832184X     6055:07-05-19            Sylvia            Truxa           NaN   \n",
       "\n",
       "           editor_first_name editor_last_name  ...  \\\n",
       "ppn                                            ...   \n",
       "1642067407               NaN              NaN  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "1678415065         Geneviève          Bollème  ...   \n",
       "...                      ...              ...  ...   \n",
       "448323834              Klaus  Meyer-Minnemann  ...   \n",
       "448323834              Klaus  Meyer-Minnemann  ...   \n",
       "44832184X                NaN              NaN  ...   \n",
       "44832184X                NaN              NaN  ...   \n",
       "44832184X                NaN              NaN  ...   \n",
       "\n",
       "                                           keyword_einzel_ppn  \\\n",
       "ppn                                                             \n",
       "1642067407                                                NaN   \n",
       "1678415065                                                NaN   \n",
       "1678415065                                                NaN   \n",
       "1678415065                                                NaN   \n",
       "1678415065                                                NaN   \n",
       "...                                                       ...   \n",
       "448323834   08708046X|085099805|08548833X|089277058|088575...   \n",
       "448323834   08708046X|085099805|08548833X|089277058|088575...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "44832184X   087037629|087119080|087008505|087123223|085140...   \n",
       "\n",
       "                                               keyword_einzel keyword_BDSL_a  \\\n",
       "ppn                                                                            \n",
       "1642067407                                                NaN            NaN   \n",
       "1678415065                                                NaN            NaN   \n",
       "1678415065                                                NaN            NaN   \n",
       "1678415065                                                NaN            NaN   \n",
       "1678415065                                                NaN            NaN   \n",
       "...                                                       ...            ...   \n",
       "448323834   Paz, Octavio&datl=1914-1998|Lyrik|Avantgarde|E...            NaN   \n",
       "448323834   Paz, Octavio&datl=1914-1998|Lyrik|Avantgarde|E...            NaN   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...            NaN   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...            NaN   \n",
       "44832184X   Goytisolo, Juan&datl=1931-2017|Laforet, Carmen...            NaN   \n",
       "\n",
       "           keyword_BDSL_p keyword_BDSL_t keyword_BDSL_s  \\\n",
       "ppn                                                       \n",
       "1642067407            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "1678415065            NaN            NaN            NaN   \n",
       "...                   ...            ...            ...   \n",
       "448323834             NaN            NaN            NaN   \n",
       "448323834             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "44832184X             NaN            NaN            NaN   \n",
       "\n",
       "           keyword_Fremddatenlieferanten_lieferanten  \\\n",
       "ppn                                                    \n",
       "1642067407                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "1678415065                                       NaN   \n",
       "...                                              ...   \n",
       "448323834                                        NaN   \n",
       "448323834                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "44832184X                                        NaN   \n",
       "\n",
       "           keyword_Fremddatenlieferanten         ppn ILNs_libraries  \n",
       "ppn                                                                  \n",
       "1642067407                           NaN  1642067407           2012  \n",
       "1678415065                           NaN  1678415065             11  \n",
       "1678415065                           NaN  1678415065             21  \n",
       "1678415065                           NaN  1678415065          23-24  \n",
       "1678415065                           NaN  1678415065             31  \n",
       "...                                  ...         ...            ...  \n",
       "448323834                            NaN   448323834             25  \n",
       "448323834                            NaN   448323834        204-205  \n",
       "44832184X                            NaN   44832184X             23  \n",
       "44832184X                            NaN   44832184X             25  \n",
       "44832184X                            NaN   44832184X        204-205  \n",
       "\n",
       "[1041157 rows x 77 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Describe Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1041157, 77)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[\"source\"]\n",
    "df[\"ppn\"]\n",
    "df[\"source_ppn\"] = df[\"source\"] + \"_\" + df[\"ppn\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(334221, 78)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"source_ppn\").head(1).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\calvotello\\Dropbox\\MTB\\Göttingen\\research\\librarian_robot\\visualize.py:120: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df[\"source\"].value_counts()\n",
    "visualize.plot_bar_counter(df, \"source\", percentage=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bar_stacked_counter(df, column_to_count, head = 99999999, percentage = True, cmap = \"tab10_r\", title = \"\", figsize = (10,5), xlabel = \"\", ylabel = \"\", wdir = \"./../visualizations/\", figure_name = \"bar_counter\"):\n",
    "    \"\"\"\n",
    "        bar_counter(df.loc[(df[\"medium_1_letter_standard\"].isin(media_lt) )], \"medium_1_letter_standard\", title = \"Percentage of medium of entries\", xlabel= \"percentage\", ylabel = \"medium\", figure_name = \"\")\n",
    "    \"\"\"\n",
    "\n",
    "    #pd.DataFrame(df.groupby([column_to_count]).count().iloc[:,0]).T.plot.barh(stacked=True)\n",
    "\n",
    "    if percentage == True:\n",
    "        new_df = pd.DataFrame(df['source'].value_counts(normalize=True)*100)\n",
    "    else:\n",
    "        new_df = pd.DataFrame(df['source'].value_counts()).T.plot.barh(stacked=True)\n",
    "\n",
    "\n",
    "    ax = new_df.T.plot.barh(stacked=True, cmap = cmap, grid=True, figsize = figsize, fontsize=12, title = title)\n",
    "    ax.set_axisbelow(True)\n",
    "    ax.set_xlabel(xlabel, fontsize=12)\n",
    "    ax.set_ylabel(ylabel, fontsize=12)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    fig = ax.get_figure()\n",
    "    fig.savefig( wdir + figure_name + \".png\", dpi=300, format=\"png\")\n",
    "    fig.show()\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\calvotello\\Dropbox\\MTB\\Göttingen\\research\\librarian_robot\\visualize.py:145: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df = visualize.plot_bar_stacked_counter(df, \"source\",  cmap=\"tab10\",title = \"Percentage of analyzed records in the databases of K10plus and HeBIS\", xlabel = \"percentage\", figure_name = \"figure_02_percentage_consortia\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "k10plus    843558\n",
       "hebis      197599\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"source\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>k10plus</th>\n",
       "      <td>81.02121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hebis</th>\n",
       "      <td>18.97879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           source\n",
       "k10plus  81.02121\n",
       "hebis    18.97879"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Completeness of the Fields\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "literary_texts     0.0\n",
       "uri                0.0\n",
       "keyword_BDSL_t     0.0\n",
       "keyword_BDSL_p     0.0\n",
       "expression_info    0.0\n",
       "                  ... \n",
       "ILNs               1.0\n",
       "entry_first        1.0\n",
       "year               1.0\n",
       "source             1.0\n",
       "source_ppn         1.0\n",
       "Length: 78, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna().sum().div(df.shape[0]).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "(df.notna().sum().div(df.shape[0])*100).sort_values().plot.barh(figsize=(5,15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = ['ppn',\n",
    " 'medium',\n",
    " 'title',\n",
    " 'title_supplement',\n",
    " 'year',\n",
    " 'entry_first',\n",
    " 'author_last_name',\n",
    " 'editor_last_name',\n",
    " 'isbn',\n",
    " 'ILNs',\n",
    " 'content_type',\n",
    " 'publisher',\n",
    " 'language_text',\n",
    " 'language_original',\n",
    " 'pages',\n",
    " 'format',\n",
    " 'comment_isbn',\n",
    " 'place_publication',\n",
    " 'summary',\n",
    " 'title_continuing_resource',\n",
    " 'work_title',\n",
    " 'expression_title',\n",
    " 'DDC_notation',\n",
    " 'BK_notation',\n",
    " 'RVK_notation',\n",
    " 'keyword_RSWK',\n",
    " 'keyword_K10plus',\n",
    " 'keyword_project',\n",
    " 'keyword_local',\n",
    " 'keyword_045D',\n",
    " 'keyword_LoC',\n",
    " 'lcc_notation',\n",
    " 'uri',\n",
    " 'GOK_notation'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function librarian_robot.visualize.plot_bar_not_nas(df, figsize=(5, 10), title='', color='#669999', xlabel='', ylabel='', outdir='./../visualizations/', figure_name='bat_plot_not_na')>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualize.plot_bar_not_nas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\calvotello\\Dropbox\\MTB\\Göttingen\\research\\librarian_robot\\visualize.py:90: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.plot_bar_not_nas(df[selected_columns], title = \"Percentage of records with\\ninformation in field\", xlabel = \"percentage of titles with information\", ylabel = \"fields\", figure_name = \"percentage_titles_fields\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chronological Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.plot_line_year_value_counts(df[\"year_publication\"], title = \"Number of records classified as romance studies per year\", xlabel= \"year of publication\", ylabel= \"number of records in K10plus\", figure_name = \"number_records_years\", ylim = (0,36000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_source_df = df.groupby([\"source\", \"year_publication\"])[\"source\"].count().unstack().T.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>source</th>\n",
       "      <th>hebis</th>\n",
       "      <th>k10plus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_publication</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>3627</td>\n",
       "      <td>13703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>3340</td>\n",
       "      <td>13167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3677</td>\n",
       "      <td>13171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>4053</td>\n",
       "      <td>14270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>4386</td>\n",
       "      <td>15535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>4355</td>\n",
       "      <td>15722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>5089</td>\n",
       "      <td>17565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>5355</td>\n",
       "      <td>17320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5908</td>\n",
       "      <td>19579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>5926</td>\n",
       "      <td>20427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6169</td>\n",
       "      <td>22648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6111</td>\n",
       "      <td>23354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6027</td>\n",
       "      <td>22816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>5755</td>\n",
       "      <td>24086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>5779</td>\n",
       "      <td>26483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>5834</td>\n",
       "      <td>25681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>5872</td>\n",
       "      <td>25501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>5691</td>\n",
       "      <td>24563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>5745</td>\n",
       "      <td>25077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>5593</td>\n",
       "      <td>25542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>5823</td>\n",
       "      <td>25814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>5585</td>\n",
       "      <td>25400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>5531</td>\n",
       "      <td>23693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>5371</td>\n",
       "      <td>24350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>5275</td>\n",
       "      <td>23975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>5520</td>\n",
       "      <td>24592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>5028</td>\n",
       "      <td>25104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>5105</td>\n",
       "      <td>25819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4745</td>\n",
       "      <td>24460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4602</td>\n",
       "      <td>22301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4316</td>\n",
       "      <td>23566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4346</td>\n",
       "      <td>24670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4114</td>\n",
       "      <td>22811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>4065</td>\n",
       "      <td>20066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>4241</td>\n",
       "      <td>19451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3957</td>\n",
       "      <td>19112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4451</td>\n",
       "      <td>18345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3870</td>\n",
       "      <td>16094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>3903</td>\n",
       "      <td>14548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>3459</td>\n",
       "      <td>13177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "source            hebis  k10plus\n",
       "year_publication                \n",
       "1980               3627    13703\n",
       "1981               3340    13167\n",
       "1982               3677    13171\n",
       "1983               4053    14270\n",
       "1984               4386    15535\n",
       "1985               4355    15722\n",
       "1986               5089    17565\n",
       "1987               5355    17320\n",
       "1988               5908    19579\n",
       "1989               5926    20427\n",
       "1990               6169    22648\n",
       "1991               6111    23354\n",
       "1992               6027    22816\n",
       "1993               5755    24086\n",
       "1994               5779    26483\n",
       "1995               5834    25681\n",
       "1996               5872    25501\n",
       "1997               5691    24563\n",
       "1998               5745    25077\n",
       "1999               5593    25542\n",
       "2000               5823    25814\n",
       "2001               5585    25400\n",
       "2002               5531    23693\n",
       "2003               5371    24350\n",
       "2004               5275    23975\n",
       "2005               5520    24592\n",
       "2006               5028    25104\n",
       "2007               5105    25819\n",
       "2008               4745    24460\n",
       "2009               4602    22301\n",
       "2010               4316    23566\n",
       "2011               4346    24670\n",
       "2012               4114    22811\n",
       "2013               4065    20066\n",
       "2014               4241    19451\n",
       "2015               3957    19112\n",
       "2016               4451    18345\n",
       "2017               3870    16094\n",
       "2018               3903    14548\n",
       "2019               3459    13177"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_source_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_source_rel_df = years_source_df.loc[:].div(df.groupby(\"year_publication\")[\"ppn\"].count(), axis=\"index\")*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>source</th>\n",
       "      <th>hebis</th>\n",
       "      <th>k10plus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_publication</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>20.929025</td>\n",
       "      <td>79.070975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>20.233840</td>\n",
       "      <td>79.766160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>21.824549</td>\n",
       "      <td>78.175451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>22.119740</td>\n",
       "      <td>77.880260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>22.016967</td>\n",
       "      <td>77.983033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>21.691488</td>\n",
       "      <td>78.308512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>22.464024</td>\n",
       "      <td>77.535976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>23.616318</td>\n",
       "      <td>76.383682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>23.180445</td>\n",
       "      <td>76.819555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>22.487003</td>\n",
       "      <td>77.512997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>21.407503</td>\n",
       "      <td>78.592497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>20.739861</td>\n",
       "      <td>79.260139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>20.895885</td>\n",
       "      <td>79.104115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>19.285547</td>\n",
       "      <td>80.714453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>17.912715</td>\n",
       "      <td>82.087285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>18.511820</td>\n",
       "      <td>81.488180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>18.716731</td>\n",
       "      <td>81.283269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>18.810736</td>\n",
       "      <td>81.189264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>18.639284</td>\n",
       "      <td>81.360716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>17.963706</td>\n",
       "      <td>82.036294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18.405664</td>\n",
       "      <td>81.594336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>18.024851</td>\n",
       "      <td>81.975149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>18.926225</td>\n",
       "      <td>81.073775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>18.071397</td>\n",
       "      <td>81.928603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>18.034188</td>\n",
       "      <td>81.965812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>18.331562</td>\n",
       "      <td>81.668438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>16.686579</td>\n",
       "      <td>83.313421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>16.508214</td>\n",
       "      <td>83.491786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>16.247218</td>\n",
       "      <td>83.752782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>17.105899</td>\n",
       "      <td>82.894101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15.479521</td>\n",
       "      <td>84.520479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>14.977943</td>\n",
       "      <td>85.022057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>15.279480</td>\n",
       "      <td>84.720520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>16.845551</td>\n",
       "      <td>83.154449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>17.900557</td>\n",
       "      <td>82.099443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>17.152889</td>\n",
       "      <td>82.847111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>19.525355</td>\n",
       "      <td>80.474645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>19.384893</td>\n",
       "      <td>80.615107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>21.153325</td>\n",
       "      <td>78.846675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>20.792258</td>\n",
       "      <td>79.207742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "source                hebis    k10plus\n",
       "year_publication                      \n",
       "1980              20.929025  79.070975\n",
       "1981              20.233840  79.766160\n",
       "1982              21.824549  78.175451\n",
       "1983              22.119740  77.880260\n",
       "1984              22.016967  77.983033\n",
       "1985              21.691488  78.308512\n",
       "1986              22.464024  77.535976\n",
       "1987              23.616318  76.383682\n",
       "1988              23.180445  76.819555\n",
       "1989              22.487003  77.512997\n",
       "1990              21.407503  78.592497\n",
       "1991              20.739861  79.260139\n",
       "1992              20.895885  79.104115\n",
       "1993              19.285547  80.714453\n",
       "1994              17.912715  82.087285\n",
       "1995              18.511820  81.488180\n",
       "1996              18.716731  81.283269\n",
       "1997              18.810736  81.189264\n",
       "1998              18.639284  81.360716\n",
       "1999              17.963706  82.036294\n",
       "2000              18.405664  81.594336\n",
       "2001              18.024851  81.975149\n",
       "2002              18.926225  81.073775\n",
       "2003              18.071397  81.928603\n",
       "2004              18.034188  81.965812\n",
       "2005              18.331562  81.668438\n",
       "2006              16.686579  83.313421\n",
       "2007              16.508214  83.491786\n",
       "2008              16.247218  83.752782\n",
       "2009              17.105899  82.894101\n",
       "2010              15.479521  84.520479\n",
       "2011              14.977943  85.022057\n",
       "2012              15.279480  84.720520\n",
       "2013              16.845551  83.154449\n",
       "2014              17.900557  82.099443\n",
       "2015              17.152889  82.847111\n",
       "2016              19.525355  80.474645\n",
       "2017              19.384893  80.615107\n",
       "2018              21.153325  78.846675\n",
       "2019              20.792258  79.207742"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years_source_rel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SS2Z2JTDE3a9p5f2MAB5x971acz8CUY/5GODYJnP3RKTA6UKUIiLSLliYne04wvT4nYq7353vOkhuRRkKsxoaLSKFRUMERUQk78zsIcKhatc0yewoIiLSoWiIoIiIiIiISI6oB0tERERERCRHCm4OVv/+/X3w4MF52/+WLVvo1q1p8jfZGWrL3FA75o7aMnfUlrmjtswNtWPuqC1zZ1facuLEievdfbccV0kowABr8ODBTJgwIW/7Hz16NCNGjMjb/jsTtWVuqB1zR22ZO2rL3FFb5obaMXfUlrmzK21pZrp+WyvREEEREREREZEcUYAlIiIiIiKSIwqwREREREREckQBloiIiIiISI4owBIREREREckRBVgiIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLBERERERkRxRgCUiIiIiIpIjCrBERERERERyRAGWiBS8ZCrgyQ+XMnvV5nxXRURERDq4RL4rICKSbze/Ood7xi0C4MDdu3PZJwfz+WP2pEdpUZ5rJiIiIh2NerBEpKA9P2UF94xbRNwMAxasq+Sn/57Bcb8eyQ8fm8SHizfi7vmupoiIiHQQ6sESkYI1e9Vmrn96GkVxoz4VBVHRr8CdF6et4sVpqxjYq5RLTxzMBUP3YrceJfmrsIiIiLR76sESkYK0qaqO7/xzIil3aKaHKhU03l+zuYbfvzqH43/3Bpfc9z5vzVlDMnMBERERkYh6sESk4KQC5+onprC8rIpELEZdsO0hgJlPj523nrHz1tOrSxEXDd+bi4fvyz79urZyjQtLTX2KQMMyRUSkg1KAJSIF55aRcxn70ToScaNuB3qiMgOtytp67hqzkLvGLOTovXtx+Un7UZpUULAzapMpJi3ZxLsL1vPO/PVMWbaJkjict3Ea5x0ziOH79SUes3xXU0REJCsKsESkoLw6YxV3jFpAPGYkUzsfEKXjspjBtGXl/OiJKQCUjP4PvboU0adrMf26F9OnWzF9uoaPe3fNvB/+7tO1mB6lCWIFFECkAmfGinLeWbCe9xZs4INFG6lNhg0ajxlmUJuCJz5cxhMfLqNXlyI+d/RAzj9mEMft06eg2kpERDoeBVgiUjDmrang2qembp3UYhdl9mrFLUyOsXFLHesra5m7Jiw3GnJnNMsMepQk6NOtmAuOG8SVpx5AaVE8J/VrD9yd+WsreWf+et5dsIH3Fm6goiYJQCJmBO6UJGLUJgNSzQzXrKxN8sj4pTwyfil9uhZx3jGDOO+YPTlm796YKdgSEZH2RQGWiBSEzTX1XPnPidS3YnKKlEOqmcCtpeAqETPSnTHVdSkqa6u4ZeQ8Hnx3Cb/7wpF89vABHTaAWF5WxbsLNvDu/PW8s2AD6ypqgfCYUxkBVTIKqNI9WM3JDLo21yR58N3FPPjuYvp3L+YLxw7ivGMGcfiePTtsW4mISOeiAEtEOr0gcK59cgpLNmwJk1q0kwyAyRaSa5RtqeO7j0zkqL168ecLj+agAT3auGbZq02mWLy+inlrK5i3ppL56yqZvrycpRurgHDIX7qHqi7LgGpbMoOtsqo67hm3iHvGLWJAzxK+eOxefP6YPTl0jx4KtkREJG8UYIlIp/e3t+bzxuy1JGI7ltQiX9IhxLTl5Zz5l7FcOGwvfvpfQ+jVpShvdaqqS7Jw3ZbGQGptJR+trWDphqqth0nGjCBwiuMxUkHjkL+dDai2JfNPub6ijjvHLODOMQvYs3cpXxm2D98dsT8lic4z1FJERDoGBVgi0qm9OXsNt77xUZjUYjvp2NurpyYs58Wpq7jh7EP5+gn7tmpGvcraJB+tqWD+mkrmra0IA6k1lazYVN2wjAGxmJEKnKK4UWRQl3Scxh6mtg5kU1FadwNWl9fwlzc+4qVpK3nuqpPoXqKPunxYvH4Lj32wlBemrOSC4wZx3WcPUc+iiBQEfeqISKe1aP0WrnlyCon4rmUMzKd0rWuTKX75wkzuHbeQP114NMfv3y8323dnwbotjJqzljfnrOHDxWUNQVI6kEr3SJmFPVGZgVSukoXkitN43eh5ayv59J/H8PLVJ9Ove0le61UokqmAt+as5Z/jlzBu3nosyvByx+gFfLS2kru+PlQp90Wk01OAJSKd0pbaJFc+PIGa+tR2s/h1BOnOt+Vl1Xzl7vGMOGQ3fveFI9mzd5cd3lZtMsUHizby5uy1vDVnbcN8qUTMwJ3iaL5UPnukcmX15hpO+/NoXrn6FPbqowtCt5a1FTU8+cEyHv1gKavLa0jEjETMcHdS0TIjZ63hi39/hye/c2KnypIpItKUAiwR6XTcnf9+eirz11ZSFG8/SS1yIR0ojp67jlP+MIrvnLo/V59x0HZPWNdW1DB6zjrenLOGcfPWU1WXashg2DQBRaoV5kvl0+bqJJ++ZQzPff+THDawV76r02m4O+MXbuSR8Ut4deZqUoETj1lDhsjmTF1ezmduGcOLPzyZ3l2L27jGIiJtQwGWiHQ6d41ZyCvTV3eYpBY7y935++gFPPb+Un5z/hH811EDG+a4BIEzY2U5b81Zy1uz1zJtRTnw8etOQeskoGhvauoDPve3d3jkW8dzQo6GVxaqzTX1PDtxOY+MX8r8dZUNPVVFMaM+8GavZZZpWVk1I/44mhd/eDJ791Wvooh0PgqwRKRTGTdvHX98bQ6JDpzUIlvpwyuvqecHj0/mzjELuOLk/Ri/cANvzVnH+srw2lPxmFEUD0+CdzVNekeWDJyL7hnPHRcfxzlHDsx3dTqcGSvKefT9JTw3eQU19QGJmFEcN+qieXiBZ///tqm6nk/fMoanv3ciRw7q3Uo1FhHJj1YLsMysFBgLlET7edrdf2lm+wFPAP2AicA33L3OzEqAh4GhwAbgK+6+ONrWT4ArgBRwtbu/FpWfBfwViAP3uvvvW+t4RKT9W7axih88Npl4zJq94G9nlT6vnblyM9c+NZW4GU5jL1UqaJwHU/Acvv/oJG4673AuOXFwvmvTZtydwMM5dYGHt1TgBEEYGKWisiAIMzIGQeMyk5du4pHxS5i8bFPDsNLihFGX3LX/sdpkwPm3v8s9lw7l9EMH5OAoRUTah9bswaoFTnf3SjMrAt42s/8A1wJ/cfcnzOwuwsDpzuh3mbsfaGZfBW4GvmJmQ4CvAocDewJvmNnB0T7uAD4DLAc+NLMX3H1WKx6TiLRT1XUprvznBLbUJokZFF7/TKN0yvJC7KXaHifMjviL52eyrqKWH595SL6r1CqWbqji/ncW8czE5VTUJuHVV3Zpe4mYURT1CjvscnCVFrhz+YMT+N0XjuDi4/fNyTZFRPKt1QIsd3egMnpYFN0cOB24OCp/CLiRMMA6L7oP8DRwu4WTCc4DnnD3WmCRmc0HhkfLzXf3hQBm9kS0rAIskQLj7vzk2WnMXlVBcSdLaiG5lw4N/vbWfNZW1PL7Lx7Zaa7PNHHJRu4Zu4jXZ61uKEtYmG6fjGO0qBEcwoiTzIKt7zq02mUO0lv93+dmsKKsmv8+69BW2Y+ISFtq1TlYZhYnHAZ4IGFv0wJgk7sno0WWA4Oi+4OAZQDunjSzcsJhhIOA8RmbzVxnWZPy41uox5XAlQADBgxg9OjRu3Rcu6KysjKv++9M1Ja50dHbsTblPD+/nlcW1ROzjptOXPLjyQ+XMXfxSq4+roRYBw2yUoEzaW2KVxfVs6A8CIfxRZFLEN1IOe39YgV3jF7ApLmL+fZR7fNv0dHfK9sTtWXuqC3bp1YNsNw9BRxjZr2B54C8fDXl7ncDdwMMGzbMR4wYkY9qADB69Gjyuf/ORG2ZGx21HWuTKR5/fym3vz2f9ZX14byrTp7UQlrHlHUpbp9dzONXnkBJYtevz7RsYxXvLdjArFWbOWSPHhy/X1/2698t571klbVJnvpwGQ+8s4hlZbUUxa0hk19H9d6qFN6llIcvP57iRCzf1dlKR32vbI/Ulrmjtmyf2iSLoLtvMrNRwIlAbzNLRL1YewErosVWAHsDy80sAfQiTHaRLk/LXKelchHppJKpgGcmLeevb85j5abwgqbbuu6OSDYmLd3EOX8dx7+vOokepUU7tO7azTW8t3AD787fwDsL1rO8rBpgqwtc9+pSxIkH9OOkA/oxfL9+HLR793DY3k5YVV7Ng+8u5rH3l1JRk+x0/wPjF27knNvG8dz3P7nDfwsRkfagNbMI7gbUR8FVF8JkFDcDo4AvEWYSvBR4Plrlhejxe9Hzb7m7m9kLwGNmdgthkouDgA8IP7sOirISriBMhJGe2yUibaA2meKd+etJxGKcsH+/Vv3GOQicF6et5C8jP2LxhqqtTio7ezp2aRsL1m3hjD+P4eWrT2a3HqUtLrepqo7xCzfy3oL1vLtgA/PWhtON4xZeY6w4ESOVCkhPWzJgS22SV2es5tUZ4dyo7iUJjt+vL588sD/H79eXwwb2JL6dgGvGinLuHbeQF6etIgicmHXe/4H5ays5/U9jeOmHJzGgV5d8V0dEZIe0Zg/WQOChaB5WDHjK3V8ys1nAE2b2G2AycF+0/H3AP6MkFhsJAybcfaaZPUWYvCIJXBUNPcTMfgC8Rpim/X53n9mKxyMiQF0y4O3563hp6ipen7WGytpwSmWXojhnHLY7nz96T049eDdKi3Z9qBWECSxen7WGW16fy9w1lRTFjeJEjLpOeFIp+be2opbT/zyGl394Mvv06waEwdEHizfy3oINvDt/PTNXbsahMWV5PEbgja/HuiY9SQ5bvVYNqKlP8eactbw5Zy0Q/v8MG9yHkw/sz/H79+PwPXtSFI8RBM6ouWu5Z9xCxi/cGAZh7sRj4TZTOcrm1x6tqwz/Fv++6iQOGtAj39UREclaa2YRnAYc20z5QhqzAGaW1wAXtrCt3wK/bab8FWDXcs+KyHbVpwLenr+el6et4rWZq6moSRKPhReuTX+DXp8KeGnaKl6atorieIxPHbwbnz9mT047dHe6l+z4W427M27eev70+lymLS8PA6uGi5p23pNKyb+KmiSf+ctYLjtpMB8uLmPKsk2kAscMDCMRDyOr+qiLakcTqzQNuMJtBYybt55x89YDUJyIcdw+vVlXUcuCdVsoihuJaJ6hpzdSALbUpTj7r+P45xXDOfGA/vmujohIVtpkDpaIdDz1qYD3Fmzg5Wmr+M/MVWyu/nhQBY3XWso8YQzcGTl7DSNnryERMz55QD/OO2YQnz5sAL26bn9OxYeLN/LH1+bywaKN4cT9uDWczIq0hdpkwF1jFkbD9rwh/b/jBK1w1eamAZcHzvsLN4JBSdyoLeDXfypwvnbv+zz5nRP5xOC++a6OiMh2KcASkQbJVMD7izby0rRV/GfGKjZV1W8zqGpxOxkni44zdt56xs5bT8xg2OC+nH/MIM48fAD9u5dstd605Zv40+sfMfajdY0Z0Qr4xFLyL52ZMtXGl65uyAToFHRwBWFnnTt87Z73Gfs/I9ijA87Jqk2meGrCco7YsyfH7tMn39URkVamAEukwAXuvLdgAy9NW8l/Zqxm45a6hrklOxJUtSRz9JQZfLBoIx8s2shPn5vOUXv14gvHDmLInr247+2FvDYz7PFKKLASkSbqUgHn/u1t3r7+9JzN8WwLo+as5VcvzmTxhiriZjz6reGcoOGOIp2aAiyRAjZh8Ub+36hqyuvG5zSoaknTYGva8nKmLi8HaAisGuaYiIg0sb6yjq/84z3+fdVJOb+uWK4t2bCFm16cxZtz1m41h/Rr933Aiz84iSF79sp3FUWklbSvq/iJSJtZs7mG7z4ykS31TkncCBwCz31Q1ZLAG+fpxy3sSUsquBKR7Zi6vJwbnp2W72q0qKouyR9fm8OnbxnD6I/WNfTI10W98qnA+cLf32XZxi15rqmItBYFWCIFqD4VcNWjkyirqgfyP8cjFQV3IiLZePLD5Tz07uJ8V2Mr7s6LU1dy+p/HcMeoBQQOhjd7OYnaZMDZf32bjZW1eaipiLQ2BVgiBeh3r8xmwpIyYgad+DI6ItKJ3fjCTMYv3JDvagAwZ/VmLrpnPD98fDIbKmspjsdIBc62BgRU1ib57K3j2BJdS1BEOg8FWCIF5vkpK3jgncXElUhCRDowB75x3/usLKvKWx3Kq+q58YWZ/Ndf32bC4rKGzKfZXhttXWUt/3XbOGqTrZD7X0TyRgGWSAGZu7qC65+ZRlHcGtJPi4h0VPUp53O3v0NNfdsGKEHgPPHBUkb8aVQ4VNEcd29Mr78DFm+o4sK73tN7skgnogBLpEBsrqnnO/+cEH6I63NcRDqJDVvq+NJd7+LeNm9sk5eWcf4d73DDs9OpqElSnDBSQTiXdGdNW17O5Q9+2GbHICKtSwGWSAFwd657aipLNlZh2E59yyoi0l7NWLGZ6/7VupkFy2ud6/41lS/8/V1mr95MUdxIBk5tjiayjvloHT/+19ScbEtE8kvXwRIpAHeNWcjrs8KL+GY7N0BEpCN5ZtJyDt+zJ5efvF9Ot1tZm+Shdxfzt7FV1AVVxA2Sqda5pMSzk1bQv3sJ/3vOYa2wdRFpKwqwRDq5d+av54+vzSERs2bTBYuIdBY3vTSLw/boyYkH9tvlbVXVJXn4vSXcNWYBm6rqiRltkhzo7rEL6d+9hCtP3b9V9yMirUdDBEU6sZWbqvnBY5NIxGIECq5EpABc8sD7LN+FzII19SnuHbeQU24exe//M4eKmiQl8RiB02aZV3/3ymyem7y8TfYlIrmnAEukk6pNpvjeo5OoqEkCjgYGikghqE85n/vb21Tt4PWlaupTPPjOIk79wyh+8/JsyqvrKUmE17OqzcPQ6mufnMqYuevafL8isusUYIl0Ur9+aRZTl20iZkadrnclIgWkrKqeL931XlY993XJgEfGL2HEn0Zz44uz2LiljpJELEpgkb+vphy47MEPmLpsU97qICI7RwGWSCf09MTlPDJ+KfEYSmohIgVp1qrNXPvUlBafr08FPPnhUk7702h+9u8ZrK+obReBVabA4Ut3vcvCdZX5roqI7AAFWCKdzMyV5fz0uekkYuG1WURECtW/p6zknrELtypLpgKenricM/48huufmc6azTXtLrDKVJ9yzv3b26wpr8l3VUQkSwqwRDqR8qp6vvvPiQTu6GrCIiLw21dmM+6jdaQC5/kpK/jMX8Zy3b+msmpTdbsOrDJV1aU4669jKa+uy3dVRCQLStMu0kkEgXPNk5NZsamaoliMWmUNFBEB4LIHP2Rw/27MX1tJUdwoTsSoSwbhGLwOoqyqnnP++jajrhtBcULfj4u0Z/oPFekkbh81n1Fz1xEzy0vGKxGR9ioZOAvXVVKciFGf8jC46oBWbKrme49MzHc1RGQ7FGCJdAKj567lLyM/Iq6LCYuINCtwOmxglenNOWv5z/RV+a6GiGyDAiyRDm7Zxip+9MQUEnHTxYRFRArAj56YQkV1fb6rISItUIAl0oHV1Kf43iMT2VKbxFBaCxGRQlCXCrj0gQ/yXQ0RaYECLJEOanlZFdc8MYUZKzfrYsIiIgVm0tJNPPb+knxXQ0SaoSyCIh3MtOWbuGfcIl6ZtorAnXjMdDFhEZEC9PPnZ/KZIQPYrUdpvqsiIhkUYIl0AEHgvDlnLfeMW8gHizYSj4UDAuMxlNRCRKRApQLnonve541rP5XvqohIBgVYIu1YTX2KZyYt595xi1i0fgtFcaMoZtSngyrFViIiBW3+2kr+9tY8fnj6QfmuiohEFGCJtEPrK2t5+L0l/PO9xZRV1ZOIGSVxo1bzrEREpIlbXv+Izx21J4P7d8t3VUQEBVgi7cr8tZXc9/ZCnpm4grpUQDwGJYkYtcmAZL4rJyIi7ZIDX71nPO/dcDpmlu/qiBQ8BVgieebuvLdwA/eOW8Rbc9YSiz4bi+JGfcpJBUpgISIi27a6vIabXpzFLz9/eL6rIlLwFGCJ5EkqcF6atpJ7xi5kxsrNJGJGPGa4O4FDoOGAIiKyAx54dzFfHLoXRw7qle+qiBQ0XQdLJE9+/5/Z/OiJKcxdU0Fx3EgGTioIgysREZGd8Y173yelDxKRvFKAJZIH46MhgfFYOAxQFwkWEZFc2FRdz/97ckq+qyFS0FotwDKzvc1slJnNMrOZZvajqPxGM1thZlOi2zkZ6/zEzOab2Vwz+2xG+VlR2XwzuyGjfD8zez8qf9LMilvreERypbI2yXX/mkpRPEagbxlFRCTHXpi6krfnrct3NUQKVmv2YCWBH7v7EOAE4CozGxI99xd3Pya6vQIQPfdV4HDgLODvZhY3szhwB3A2MAS4KGM7N0fbOhAoA65oxeMRyYnfvDSLFWXVgC5jJSIirePbD0+kpl75Z0XyodUCLHdf5e6TovsVwGxg0DZWOQ94wt1r3X0RMB8YHt3mu/tCd68DngDOszAP6enA09H6DwHnt8rBiOTIW3PW8MSHy4jFoC6l7IAiItI6qutTXPnwxHxXQ6QgtUkWQTMbDBwLvA+cBPzAzC4BJhD2cpURBl/jM1ZbTmNAtqxJ+fFAP2CTuyebWb7p/q8ErgQYMGAAo0eP3vWD2kmVlZV53X9n0tHasrLO+enbVSQMkoqtRESklY2dt56bHx/J8QOL8l2VrXS0z+/2TG3ZPrV6gGVm3YFngGvcfbOZ3Qn8mnB01K+BPwOXt2Yd3P1u4G6AYcOG+YgRI1pzd9s0evRo8rn/zqSjteUPHptERX0VRXEjmdTgQBERaX33zqjnu+ePoFeX9hNkdbTP7/ZMbdk+tWoWQTMrIgyuHnX3ZwHcfY27p9w9AO4hHAIIsALYO2P1vaKylso3AL3NLNGkXKTdeXHqSl6atoqYGbUKrkREpI3Up5xv3Pd+vqshUlBaM4ugAfcBs939lozygRmLfQGYEd1/AfiqmZWY2X7AQcAHwIfAQVHGwGLCRBgvuLsDo4AvRetfCjzfWscjsrPWbq7hZ/+eEfZcKWugiIi0sWnLy3novcX5roZIwWjNIYInAd8AppvZlKjsfwmzAB5DOERwMfAdAHefaWZPAbMIMxBe5e4pADP7AfAaEAfud/eZ0fauB54ws98AkwkDOpF2w925/plpVNYmiVu+ayMiIoXqVy/M5LND9mCPXqX5ropIp9dqAZa7vw00d0r5yjbW+S3w22bKX2luPXdfSOMQQ5F258kPlzFq7joSMdPFhEVEJG8Ch4vuHs9b132KcJCRiLSWVp2DJVLIlm2s4qaXZpGIaWigiIjk36INW/jxv6YyY0U5SV0qRKTVtEmadpFCEwTOdf+aSl0yIKYvCkVEpJ14dtIKnp20gpJEjCMH9eLEA/oxdN8+HLtPn3aVaVCkI1OAJdIK7n9nEe8v2khRXEMDRUSk/QkCZ+KSMiYsKQPCOR379O3KiQf0Y9jgvgzbtw/79uuq4YQiO0EBlkiOzVtTwR9enUsiZtQruBIRkXaovsnQ9XjMWF5WxRMfVvHEh8sA6FmaYNjgvhy/X1+G7tuHIwb1orQono/qinQoCrBEcqg+FXDtU1NwnDBRpoiISPvXdK6wGVTVpXhrzlremrMWCIOwIQN7cs6Re3D2EQMZ3L9bPqoq0u4pwBLJoTtGzWf6is0Ux2Mf+3ZQRESko3CHpG/9ORYDZqwsZ/qKcm5+dS779uvKF44ZxNlHDuTgAd01nFAkogBLJEemLy/nb2/NJx4z6pSdSUREOpnMLw6NMFvurW/O49Y35zGwVynnHzOIc44cyBGDeirYkoKmAEskB2rqU1z71BRiFk4cFhER6cycsJcLwmBrzeZa7hyzgDvHLKB/92I+f/Se/NdRAzl27z7ElE5XCowCLJEc+PPrc5m3tpLieIyUK8ASEZHCEQZbjZ99ZVV13P/OYu5/ZzG9uhTxX0cO5NyjBjJ8v74k4roEq3R+CrBEdtH7Czdw77hFGhooIiICZH4UVtbW89gHS3nsg6V0L0nw2cMHsFsqSb/l5Qzu35Uepbr2lnQ+CrBEdkFlbZLr/jWVoriRVEp2ERGRrWQGWzX1KZ6ZtAKAu6a+DYSp4Af378aBu3VncP9u4a1fV/bt100XPpYOSwGWyC747cuzWVZWTXE8RqC07CIiIi1qmgq+KG5U16eYvqKcacvLP7Z8j9IE+/bryoG7dWe//t0Z3D8MvPbrr+BL2jcFWCI7adTctTz+wVLiMTQ0UEREZAfVb2PkRyJm1NSnmLlyMzNWbN7quZjBTecdwddP2Le1qyiyUxRgSUF6edoq/vrmR9QlA1LuBIGTCiBwJ3AnFTiBE/123GlYLv3YCb9929YHhIiIiOy4pr1dmRz4+b9ncMpB/dm3ny52LO2PAiwpOHNWb+bap6aQDLwh65ERpZA1yEwmaw0/QjGDWPraHmaYhgWKiIi0qXTCwovuGc8715+ua25Ju6NcmVJQttQm+f6jkwjcMcJeqiDqnUpFPVfJjFt94NSnGm91mbdkQG1SAZaIiEg+rNxUw//9Z3a+qyHyMQqwpGC4Oz//9wwWrtuCYSQ1bUpERKRDu2fsIj5aU5HvaohsRQGWFIx/TVzOs5NXKCmFiIhIJ+HA1+59n2Abc7ZE2poCLCkIH62p4Bf/nkFR3FBsJSIi0nmsq6jlF8/PyHc1RBpsN8Ays25mFovuH2xmnzczXXxAOoyquiRXPTqJVDr1n4iIiHQqj7y/lGnLNuW7GiJAdj1YY4FSMxsEvA58A3iwNSslkku/fH4m89ZWYhj1GkIgIiLSKV1y/wek9Dkv7UA2AZa5exXwReDv7n4hcHjrVkskN56dtJx/TVyueVciIiKd3Kbqev77X1PzXQ2R7AIsMzsR+BrwclQWb70qieTG/LWV/PS5GRTFNO9KRESkEDw7eQUfLNqQ72pIgcsmwLoG+AnwnLvPNLP9gVGtWiuRXVRTn+KqxyaRDIKtrxwsIiIindrlD06gLpnKdzWkgG03wHL3Me7+eXe/OXq80N2vbv2qiey8X704k7mrK8J5VymNxxYRESkUlbVJrn58cr6rIQUs0dITZvYi28i55u6fb5Uaieyi56es4PEPlmnelYiISIF6deYaRs9dy4hDds93VaQAbasH60/An4FFQDVwT3SrBBa0ftVEdtzCdZX85Nnput6ViIhIgfveI5OoqUvmuxpSgFoMsKKhgWOAk9z9K+7+YnS7GDil7aookp30vKv6VKBpVyIiIgWuuj7Flf+cmO9qSAHKJslFtyixBQBmth/QrfWqJLJzfvPyLGavCudd1WnelYiISMEbO289r0xfle9qSIFpcQ5WhmuA0Wa2kDAf277Ala1ZKZEd9dK0lTwyfqnmXYmIiMhWrnlyCqce1J/upUX5rooUiG32YJlZDOgFHAT8CLgaOMTdX2+DuolkZcmGLVz/zDTNuxIREZGPqUsGXP7gh/muhhSQbQZY7h4A/+Pute4+NbrVtlHdRLarNpniqkcnUZfUvCsRERFp3geLy3h64rJ8V0MKRDZzsN4ws+vMbG8z65u+tXrNRLLwf6/MYcbKzZp3JSIiItt0wzPT2VRVl+9qSAHIJsD6CnAVMBaYGN0mtGalRLIxYXWSB99dTDxmmnclIiIi25QMnK/f936+qyEFYLsBlrvv18xt/+2tF/V4jTKzWWY208x+FJX3NbORZjYv+t0nKjczu83M5pvZNDM7LmNbl0bLzzOzSzPKh5rZ9Gid28xMo8QKxLKNVdw3ozaad6WeKxEREdm+GSs28/B7i/NdDenktptF0MyKgO8Bp0ZFo4F/uHv9dlZNAj9290lm1gOYaGYjgW8Cb7r7783sBuAG4HrgbMJkGgcBxwN3AsdHwxF/CQwDPNrOC+5eFi3zbeB94BXgLOA/WR67tGNB4Gyqrmfjllo2VNZRVlXHhi11bKwMf4+bt466FMSz6YMVERERifzqhVmcfcQe7NajNN9VkU4qmzTtdwJFwN+jx9+Iyr61rZXcfRWwKrpfYWazgUHAecCIaLGHCAO266Pyh93dgfFm1tvMBkbLjnT3jQBRkHaWmY0Gerr7+Kj8YeB8FGC1e+sra3ln/no2bqlj45YwYCqLfq+vDAOqipp6WuqYill4vQADzbsSERGRHZJy58v/GM+r15xCSSKe7+pIJ5RNgPUJdz864/FbZjZ1R3ZiZoOBYwl7mgZEwRfAamBAdH8QkJneZXlUtq3y5c2UN7f/K4mu3TVgwABGjx69I9XPqcrKyrzuP98WbEpx26QayjPmmKbHdaZDpZiFY1fjMXCHwCFzhpVGBIqIiMiuWLR+Cyf99jX+9/hS+pZ23OEwhX5e2V5lE2ClzOwAd18AYGb7A6lsd2Bm3YFngGvcfXPmNCl3dzNr9dNld78buBtg2LBhPmLEiNbeZYtGjx5NPvefT89PWcHNI6cBRnG8sfep6QugIaBSICUiIiKtZH2185O3a7n7kqF86uDd812dnVLI55XtWTYB1n8Do8xsIWFnw77AZdlsPJq/9QzwqLs/GxWvMbOB7r4qGgK4NipfAeydsfpeUdkKGocUpstHR+V7NbO8tDNB4Nwy8iNuHzWforjh7iSV9E9ERETyrC4ZcOn9H/LD0w/k2s8cTKHnS5s4ceLuiUTiXuAIsss2XqgCYEYymfzW0KFD1zZ9crsBlru/aWYHAYdERXOzudhwlNHvPmC2u9+S8dQLwKXA76Pfz2eU/8DMniBMclEeBWGvAb9LZxsEzgR+4u4bzWyzmZ1AOPTwEuBv26uXtK0ttUmufWoKr81cQzxm1GvOlIiIiLQT6bOSv701nw8Xb+S+Sz9Bt5Js+h86p0Qice8ee+xx2G677VYWi8V00taCIAhs3bp1Q1avXn0v8Pmmz283MjWzq4Au7j7N3acBXc3s+1ns+yTChBinm9mU6HYOYWD1GTObB3w6egxhFsCFwHzgHuD7AFFyi18DH0a3m9IJL6Jl7o3WWYASXLQrKzZV86W73uX1mWtIxJROXURERNqv8Qs38qk/jmLB2op8VyWfjthtt902K7jatlgs5rvttls5YU/fx2QTon/b3e9IP3D3MjP7No1ZBZvl7m/TmL+gqTOaWd4JL2jc3LbuB+5vpnwCLRyY5NfEJWV8558T2FRVT1E8pgsBi4iISLu3vrKOM28dx61fOYbPHb1nvquTDzEFV9mJ2qnZzqpsxlbGMy/ga2ZxoDhHdZNO6NlJy/nq3e+xubqeWAwFVyIiItJhBIHzw8cn89Pnpmv0jeyUbAKsV4EnzewMMzsDeDwqE9lKKnB+/585XPvUVJzwDaouqTcmERER6TjSZy6Pvr+Uz9/+NmVb6ra5vGTn+uuv3+PAAw88/OCDDx5y6KGHDnnrrbe65btOrSWbIYLXA98Bvhc9Hkk470mkQWVtkmuemMwbs9cSjxlJJbMQERGRDm7mys2c+sdRPPqt4zlqr975rk6H9cYbb3R77bXXek+fPn1Wly5dfNWqVYna2tpdStlYX19PUVFRrqqYU9vtwXL3AHgQ+Km7f8nd/+HuWV8HSzq/ZRuruODOd3lzzlolsxAREZFOpaImyXl3vMM/31uc76p0WCtWrCjq27dvskuXLg4wcODA5ODBg+uff/75HocddtiQgw8+eMiFF144uLq62gAGDRp05KpVqxIAY8eO7Tp8+PBDAK699to9zz///P2OO+64Q7/4xS/ut2zZssRnPvOZAw455JAhhxxyyJCRI0d2A/j73//e98gjjzzs0EMPHXLxxRfvm0wm2/R4s8ki+HlgCtGwQDM7xsxeaOV6SQfx4eKNnHfHOyxYW0lRLEZSwZWIiIh0Ng4/f34mVz06iTpdzHOHnX/++ZtXrlxZPHjw4CO+/vWv7/Pyyy93r6qqsu985zv7Pfnkkws++uijWclkkj/+8Y+7bW9b8+bNKx07duzcF198cdF3v/vdfU455ZSKuXPnzpo5c+as4447rmbSpEmlTz/9dN8JEybMmTNnzqxYLOZ33XVXv7Y4zrRshgj+EhhOeHFf3H2Kme3XmpWSjuFfE5bxk2enYwYxUzILERER6ZzSXx+/PH0VM1aU89R3T2RAz9K81qkj6dWrVzBjxoxZr776ao8333yzx6WXXnrAj370o1V77bVX7VFHHVUL8M1vfnPDHXfcsTvwsQv3ZjrrrLM2de/e3QHefffdHk8//fQigEQiQb9+/VL/+Mc/+s6YMaPr0UcffRhATU1NbPfdd2/TLqxsAqx6dy9vcmVrdVMUsDCZxWzuGbeIRMwIAkdTrkRERKQQLNlYxaf+MIp/XnE8n9ivb76r02EkEgnOPffcinPPPbfiqKOOqr7rrrta7K2Kx+MeBOEX99XV1VuNuOvWrds2v9F3d7vwwgs33HHHHStyUvGdkE0WwZlmdjFhuvaDzOxvwLutXC9pp8qr6/n2wxO4Z9yiMJmFgisREREpMLXJgK/c/R5vzFqd76p0CFOnTi2ZPn16Sfrx5MmTuwwePLh2xYoVxTNmzCgBePjhh/udcsopFQB77bVX3TvvvNMV4KmnnurT0nZPOumkivSwwmQyyYYNG+JnnXXW5pdeeqnPihUrEgBr1qyJf/TRR216ialsAqwfAocDtcBjQDlwTSvWSdqpGSvK+dzf3mb0XCWzEBERkcLlgDt8++GJPDc5bx0lHcbmzZvjl1xyyX4HHHDA4QcffPCQOXPmdLn11ltX3HXXXYsvvPDCAw4++OAhsViM6667bh3AL37xi5X/8z//s88RRxxxWDweb/GE884771w6ZsyYHgcffPCQI444YsjkyZNLhw4dWvOzn/1sxRlnnHHwwQcfPOT0008/eNmyZW2abtDcWz5Jji4q/Ia7n9Z2VWpdw4YN8wkTJuRt/6NHj2bEiBF52//OcHee/HAZv3h+Jo5jmOZbiYiISMEzwmDr1+cdzjdOHNzm+9+V80ozm+juwzLLpk6duvjoo49en4u6FYKpU6f2P/roowc3Ld/mHCx3T5lZYGa93L281Won7VZ1XYqfPz+DpycuJxEzDKdOPVciIiIiDUkJfv78TDZXJ7nq9APzWh9pH7JJclEJTDezkcCWdKG7X91qtZJ2YeG6Sr736CTmrq4gHkMp2EVERERa8MfX57K5pp6fnHNYvqsieZZNgPVsdJMC8sr0Vfz301OpSwYUx2MaEigiIiKyHf8Yu5DN1fX87otH0iQDtxSQ7QZY7v5QW1RE2of6VMDv/zOH+95eRFHcMHR9KxEREZFsPf7hMjbXJLn94mMVZBWobLIISoFYVV7NV+8ez31vhynY61NOnXKwi4iIiOyQl6ev4tIHPlDG5QKlAKsNrauoJdhG1sZ8envees756zimLttEUVwp2EVERER2xdiP1nPhXe9Rl9RIoELT4hBBM/unu3/DzH7k7n9ty0p1Vt9/dCILV1fzvcRCvvyJvelZ2qYp+ZsVBM7to+bzl5EfkYgbMUO9ViIiIiI5MGlpGZ+//W3+fdVJlBbF812dHXbYuOlHlyVT2eRsyEqfRDw5+5Qjp25rmblz5xafe+65B82bN29mNtu84IILBp977rnll112WVlm+dixY7vef//9/R588MFlu1LnnbGtHqyhZrYncLmZ9TGzvpm3tqpgZ+HuXDR8HzbUOL95eTZDfz2S65+Zxvy1lXmrU9mWOi578ENuGfkRsZiR0pBAERERkZyas7qCz946loqa+nxXZYflMrhqje1ty6mnnlqVj+AKth1g3QW8CRwKTGxyy9+VejsoM+PsIwYSi+Y6BkF48d5P3zKGC+58lzdnryFow2F5k5eWcc5t4xg3bx2JWDgkUB3YIiIiIrm3ZEMVn75lDGVb6vJdlQ4hlUrx1a9+dd8DDzzw8JNOOumgyspKmzlzZskpp5xy0OGHH37Y0KFDD5k8eXJpevmRI0f2OOKIIw4bPHjwEY8//ngvgJdeeqnHaaeddiDAyy+/3P3QQw8dcuihhw457LDDhpSVlbXqNKkWN+7ut7n7YcD97r6/u++Xcdu/NStVCNIdRWYwcUkZVzw0geP/703uGbuQ8urW+4bD3Xno3cVceNd7bKispShuur6ViIiISCtbs7mW0/88mjXl1fmuSru3dOnS0quvvnrt/PnzZ/bq1Sv18MMP9/nWt76179///velM2fOnP3HP/5x+fe+97190ssvW7asZOrUqbNffPHFeddcc82+VVVVW6Vv/POf/7zHbbfdtmTOnDmzxo8fP6d79+6t2q+QTZr275nZ0cApUdFYd5/WmpUqJJk5LzZU1vLbV2bzh9fm8IVjB3Hlqftz4O49dmn76ytrmbOqgjmrNzN3dQXTV5QzZ3VFQ6+VRgSKiIiItI2yqnrOuGUsr1x9Mvv065bv6rRbgwYNqv3kJz9ZDXDsscdWLV68uGTy5MndL7zwwgPSy9TV1TUEURdccMHGeDzOkUceWbv33nvXTpkypTRzeyeccELlddddt/eXv/zljRdddFHZAQcckN8Ay8yuBq6k8WLDj5rZ3e7+t9asWCFKdyQFgfPUhOU8NWE5x+3Tm++POJDTDt2deKzlaynU1KeYt6aS2VEgNXd1BbNWbWZjRld03IzAnaKYUa9eKxEREZE2V1mb5Mxbx/LCVSdx8B49812ddqm4uLjhRDUej/uaNWsSPXr0SM6ZM2dWc8s3vd5Y08e/+93vVp9//vnlzz//fK9TTjnl0JdffnnescceW9MadYcsAizgW8Dx7r4FwMxuBt4DFGC1kszhg5OWbuJbD0+gf/dirjx1f748bG/Kq+uZs7qCOasqmLtmM7NWbWbphqqGAM0MYhgBTkkiRhA49YGTirrLFFyJiIiI5E9NfcB//e1t3r7+dAb0LN3+CgWuZ8+ewV577VV3//3397n88svLgiDg/fff73LiiSdWAzz77LN9fvCDH2yYM2dOybJly0qOPvromrfeeqt7ev2ZM2eWDB8+vHr48OHVEydO7DpjxozSfAdYBqQyHqeiMmllmcMHN26p43evzOF3r8zZapl4NNSvKG4UmVGbDHCHFOHKtbr2goiIiEi7tGZzTbsOsPok4slcp2nf2XUff/zxhd/+9rf3vfnmmwcmk0n7whe+sDEdYA0aNKju6KOPPqyysjJ+6623LunatetWvQl/+MMfdn/33Xd7mpkfcsgh1V/60pfKd/VYtiWbBnsAeN/Mnosenw/c12o1kmalO53iMUjEYiRTASmn4YLA9SkH1DMlIiIi0hGUFsVp74OKtnfNqtZwyCGH1GVeA+umm25ak74/bty4eU2Xf+aZZxY3t51zzz234txzz60AeOihh9o0XXs2SS5uMbPRwMlR0WXuPrlVayUtSgWQCtQrJSIiIiLSHmXV5efuk4BJrVwXERERERGRDq1VL7IlIiIiIiJSSBRgiYiIiIiI5Mg2Aywzi5vZqLaqjIiIiIiISEe2zQDL3VNAYGa92qg+IiIiIiIiHVY2SS4qgelmNhLYki5096tbrVYiIiIiIpJXx9z0+tGbqupzdh2s3l2LklN+ceY2U7/PnTu3+Nxzzz0oM1X76tWr4+edd94B06dP7/alL31pw8MPP7w0/dy4ceO6XnHFFYNrampip59+evn999+/LBZruQ/ptttu6zdhwoRumdvItWzmYD0L/BwYC0zMuImIiIiISCeVy+BqV7bXtWtXv+mmm1beeOONy5s+9/3vf3/fO++8c8nixYtnLFy4sPTpp5/uues13TXbDbDc/SHgKWC8uz+UvrV+1UREREREpFDNmjWr+LDDDhsyefLk0s9+9rOVpaWlW10MdsmSJUWVlZWxM844Y0ssFuNrX/vahn//+999AIYPH37IZZddtvehhx465KCDDjp81KhRXZtu/4ILLhj8wAMP9Ek/7tq167Hp7Q4bNuyQ9Lqvvvpq9x2p93YDLDP7HDAFeDV6fIyZvZDFeveb2Vozm5FRdqOZrTCzKdHtnIznfmJm881srpl9NqP8rKhsvpndkFG+n5m9H5U/aWbFWR+1iIiIiIi0W1OnTi254IILDrz//vsXfepTn6pqbpklS5YUDRw4sD79eN99961btWpVUfpxdXV1bM6cObNuu+22JVdeeeV+2e77/vvv73vGGWeUz5kzZ9bs2bNnHn/88c3uvyXZDBG8ERgObAJw9ynA/lms9yBwVjPlf3H3Y6LbKwBmNgT4KnB4tM7fowyGceAO4GxgCHBRtCzAzdG2DgTKgCuyqJOIiIiIiLRjGzduTJx//vkHPvLIIwtPPPHE6p3dzsUXX7wR4Oyzz66srKyMrV+/Pp7NeieccMKWxx9/vP+111675wcffNClT58+wfbXapRNgFXv7uVNyra7E3cfC2zMsh7nAU+4e627LwLmEwZ1w4H57r7Q3euAJ4DzzMyA04Gno/UfAs7Pcl8iIiIiItJO9ejRI7XnnnvWjRo1aptD8/bdd9/6zB6rJUuWFGf2aIUhQ6OmjxOJhKdSKQBSqRT19fUGYUA2duzYuYMGDaq7/PLL97v99tv77Uj9s5loNtPMLgbiZnYQcDXw7o7spIkfmNklwATgx+5eBgwCxmcsszwqA1jWpPx4oB+wyd2TzSz/MWZ2JXAlwIABAxg9evQuVH/n1aYccMC2t6iIiIiIdGLJZJJJEyeyaUFWnSrNqqyszNt5bWsqKiry//znPwtOO+20g7p37x5897vfbbbTZt99963v3r178Oabb3Y77bTTtjz66KP9rrrqqrXp5x9//PE+n/vc5ypee+217j169Ej169cv1WT9uokTJ3b91re+VfbYY4/1TiaTBvDRRx8V77///nU//vGP19fW1tqkSZO6AhuyrX82AdYPgZ8CtcDjwGvAr7PdQRN3Rut69PvPwOU7ua2sufvdwN0Aw4YN8xEjRrT2LptVXZeCN17Ny75FREREpP1IJBIcN3Qox+zde6e3MXr0aFrzvLZ316JkrtO0Z7tsz549g9dee23+iBEjDu7Ro0fqf/7nf/aprKyM19fX22uvvdb7lVde+Wjo0KE1d9xxx5Irrrhiv5qaGjvttNM2X3jhhQ0j70pLS/2www4bkkwm7e67717UdB8//OEP15177rkHHnLIIUNOP/308i5dugQAr732Wo/bbrttj0Qi4V27dk09+uijH1t3W8zds1vQrCfg7l6R9cbNBgMvufsR23rOzH5CuPH/i557jXDuF8CN7v7ZqPwnUdnvgXXAHu6eNLMTM5fblmHDhvmECROyPYScqq5LcfgvXyXIrslFREREpJPqUZrgn1ccn7cAy8wmuvuwzLKpU6cuPvroo9fvdIXakeHDhx/ypz/9admpp566QwkqdsTUqVP7H3300YOblmeTRfATZjYdmEZ4weGpZjZ0ZyphZgMzHn4BSGcYfAH4qpmVmNl+wEHAB8CHwEFRxsBiwkQYL3gYFY4CvhStfynw/M7USUREREREJFey6fK7D/i+u48DMLOTgQeAo7a1kpk9DowA+pvZcuCXwAgzO4ZwiOBi4DsA7j7TzJ4CZgFJ4Cp3T0Xb+QHhsMQ4cL+7p6/qfD3whJn9Bpgc1VNERERERArcBx98MDdf+84mwEqlgysAd3/bzLY7ftLdL2qmuMUgyN1/C/y2mfJXgFeaKV9ImGVQRERERER2XRAEgcViMU1o2Y4gCIwWMqu3GGCZ2XHR3TFm9g/CBBcOfAUYneM6ioiIiIhIfs1Yt27dkN12261cQVbLgiCwdevW9aJxutNWttWD9ecmj3+ZcV8NLiIiIiLSiSSTyW+tXr363tWrVx9BdtfLLVQBMCOZTH6ruSdbDLDc/bRWq5KIiIiIiLQrQ4cOXQt8Pt/16Oi2OwfLzHoDlwCDM5d396tbrVYiIiIiIiIdUDZJLl4BxgPTaWEil4iIiIiIiGQXYJW6+7WtXhMREREREZEOLpvJa/80s2+b2UAz65u+tXrNREREREREOphserDqgD8CP6Uxe6AD+7dWpURERERERDqibAKsHwMHuvv61q6MiIiIiIhIR5bNEMH5QFVrV0RERERERKSjy6YHawswxcxGAbXpQqVpFxERERER2Vo2Ada/o5uIiIiIiIhsw3YDLHd/qC0qIiIiIiIi0tFtN8Ays0U0Zg9s4O7KIigiIiIiIpIhmyGCwzLulwIXAroOloiIiIiISBPbzSLo7hsybivc/Vbgv1q/aiIiIiIiIh1LNkMEj8t4GCPs0cqm50tERERERKSgZBMo/TnjfhJYDHy5VWojIiIiIiLSgWWTRfC0tqiIiIiIiIhIR5fNEMES4AJgcOby7n5T61VLRERERESk48lmiODzQDkwEaht3eqIiIiIiIh0XNkEWHu5+1mtXhMREREREZEObrtp2oF3zezIVq+JiIiIiIhIB5dND9bJwDfNbBHhEEED3N2PatWaiYiIiIiIdDDZBFhnt3otREREREREOoFs0rQvaYuKiIiIiIiIdHTZzMESERERERGRLCjAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHFGAJSIiIiIikiMKsERERERERHKk1QIsM7vfzNaa2YyMsr5mNtLM5kW/+0TlZma3mdl8M5tmZsdlrHNptPw8M7s0o3yomU2P1rnNzKy1jkVERERERCQbrdmD9SBwVpOyG4A33f0g4M3oMcDZwEHR7UrgTggDMuCXwPHAcOCX6aAsWubbGes13ZeIiIiIiEibarUAy93HAhubFJ8HPBTdfwg4P6P8YQ+NB3qb2UDgs8BId9/o7mXASOCs6Lme7j7e3R14OGNbIiIiIiIieZFo4/0NcPdV0f3VwIDo/iBgWcZyy6OybZUvb6a8WWZ2JWHPGAMGDGD06NE7fwS7oDblgAMazSgiIiJSyJLJJJMmTmTTgvhOb6OysjJv57XSsrYOsBq4u5uZt9G+7gbuBhg2bJiPGDGiLXb7MdV1KXjj1bzsW0RERETaj0QiwXFDh3LM3r13ehujR48mX+e10rK2ziK4JhreR/R7bVS+Atg7Y7m9orJtle/VTLmIiIiIiEjetHWA9QKQzgR4KfB8RvklUTbBE4DyaCjha8CZZtYnSm5xJvBa9NxmMzshyh54Sca2RERERERE8qLVhgia2ePACKC/mS0nzAb4e+ApM7sCWAJ8OVr8FeAcYD5QBVwG4O4bzezXwIfRcje5ezpxxvcJMxV2Af4T3URERERERPKm1QIsd7+ohafOaGZZB65qYTv3A/c3Uz4BOGJX6igiIiIiIpJLbT1EUEREREREpNNSgCUiIiIiIpIjCrBERERERERyRAGWiIiIiIhIjijAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHFGAJSIiIiIikiMKsERERERERHJEAZaIiIiIiEiOKMASERERERHJEQVYIiIiIiIiOaIAS0REREREJEcUYImIiIiIiOSIAiwREREREZEcUYAlIiIiIiKSIwqwREREREREckQBloiIiIiISI4owBIREREREckRBVgiIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLBERERERkRxRgCVS4DzfFRARERHpRBL5roCItC6PgZcm8C7x6JbY6jclcQg8ugGBYw2PHVKOOZDKKAsci5YlFS2fiu6nAkim7zcpT2VsT0RERKQTUoAl0sG5sXXgVJrAu8bx0rCM0vjWKwSO1aSgKkl8XX0Y9MQMjOhmeCz8TczC7ccMimIQN4hFjzNvcduxSqcDsmTQEIRZdRKrSmFVSWJVSWxLEuoCdnDLIiIiInmlAEukjXkMgr4lBP1LCXoWhYFMRnAD0X1ofI4wkNqqLL18whrXgzCAqg0DqNiGGmL1YQ+TxyAoiuHFcegSx3sXk0pkOUrYPRxLmNHTZUmHIIDA8a16vcA8uu8Qc6JxiOluqzBoIwriPGZ43PAexQS7x8OALS0ZYNVh0GVR0JUOwKjNb/DlEB5DRqDp6UC1yW83azjeht/pwDVzeTOoD7DaFFYX/qYuKLgePzfAUXAtIiIdkgIskTYQdImHAdVupQR9iyEeg1SAba4PgwgnDGKgMQ5JByXR41jmc3jDWaglAywVBW6JGF4cgy4JvFcx3i9GkIsDsIxAILN6GbyF+1nvPxXAlmQYXCQDYqmod644hndLEOxWunXwlQoaerysKonVR3uKWbj/dMBiTe5nHI83XaZpsJQRQDV9vFVdWltdCqvNCLhqo8d1qTDQjAIy6rcOOpsPArf9GCPsUUxvr5nt7iqPW9TDGvW8pu+XhjdK42GgXpMMA+zqVNjDWdN4P98BtoiISEsUYIm0AreMXqr+JXj3IgCsKkl8RRWxuoBUFHRREleiCQiDzh4xvEdR2FmW+VzKoSo6wU46sVSAm+FFMbxrImzHpsMU071uDb+blof3LeN+5jw0kkHYKxf13MUytxVAFMZhnhlQOmbW+NjCzTWUWfTDvDHIa/IYNyzqGYxFjeAxwxPh8dK7mKAk3vywzKjeOQ8C3bcOtuqCMKCtS9+PAr90WeANwdLHAqguiXC4aZN6W20KqlPEymqxeofoeL0kHvb0Fjcz1LWZwMuqU+EQ2JqUAjAREckLBVgiOeKlcVL9S6JeqhJIxCDlxMpqia+uxmJGsk8xqb26kWrL3o/OIG7QvQjv3kzwFThEJ9Y4LfdcbasXK2Zb99Kle3fa7gi3kt7vx3r/okCH6mTYexUN/7QgDBQ9HcBlBJeWsUFLbz0j4Asgo40yfqe3m149PZQzHdQWx8JAKZvXcjIIg6CqFLHN1WGvq0NghhdZOGy1NB4G131LPn7MtQGU1YbrJZ1Y4GGAnQjXDXYrCpO1NNVSYpYWHhOwVYIXS3rYe1gXtGqPXq553Bp7B0tixCqTWHldwQ01FRHJFwVYIjvJDYI+xQ1D/xp6qaqTxFZVEa9JkeqaIOhXGvawSOuIGXQrwrsV5bsmrc8s7MkpjuOEx5uXc+ZUGHhQWR/2WqUzRnrY62ZRYBPEjSBhWGnYixX0L9nxXjWzcMhgabyh43GrwNMd6gLYVIfVpbDAiaW8YcRtZuCYGUx7OllLIgoSP5a8hbBXtTlBY4+e1QWNQVhDr140ZLMuwJJR9syk5ywo8xgfywaa+ZviZuqdDIiV1RHbWEtsYy22ub5dB4kiIh2ZAiyRbXAD7xqduHRNZNyiE5lY+E1/rKyW+JpqzCDZu4RgUDcC9VJJZxU36JKALk2mDNL8nLtWDQLNwt6rkhYCsG1pmrwlFfVaeZS8JdU4xxEPLzVgNCYoSffo0bO4sUdvW5IZwWgyvJyBZWbTTHo4tzDZWBbOV2t8z2m4tEKmVJQZtDpJvKIai3rYgrjhiRgkLOwx7JYgeUivcJ36gFhZGGzFNtRilUkFXCIiOZKXAMvMFgMVQApIuvswM+sLPAkMBhYDX3b3MjMz4K/AOUAV8E13nxRt51LgZ9Fmf+PuD7XlcUjn4HFrDJq6JsKTmK4Jgq7RZPvMDH3JIEyoUJEktqE2nEtVGifoV0LQT71UIh1KlslbthkgetQ7VRfglfVhEJYkCsg8zKhJRg9alDmTeBSgFcega5wgEV0GIW5bv+fA1plB19eHmUHdMxLbRJlB+5SQ2tYlE9xhcx2x6iRmRqp7EcHuXcLn6lLENmb0cG1RwCUisrPy2YN1mruvz3h8A/Cmu//ezG6IHl8PnA0cFN2OB+4Ejo8Csl8Cwwg//yaa2QvuXtaWByEdT1AaJzWoK0G/Erxr4uPfBtdF12IqqyNWl8ICGlOcl0ZB2IAwvXoqP4cgIu2FGRRFPVndEtsPyJoKMnqu6gI83YPlUY9aAJ6w3GQGNQt723oWN+57Uy2x2iCcj9eriGCPKOCqTTX0bsW2JBuCQuJRgJiwhnl5xJs8l74fT187z8IEJBX1xCrqw+GJVUnNCRORTqs9DRE8DxgR3X8IGE0YYJ0HPOzuDow3s95mNjBadqS7bwQws5HAWcDjbVtt6Qg8ZgQDSqPAqjT8dnlTHbF1NcTSw2liEBRF3wR3DU9mgqbfJIuI5FLMoNiA2MeGWza93yr77l3SGLClHNtUS6w2Fb5n9i0hGNh1+9tJhQFiGBB69Dga5lgbDsP0rnGCfbs3JvhJOVZZT2xzfWPgVVEfDpUUEeng8hVgOfC6mTnwD3e/Gxjg7qui51cDA6L7g4BlGesuj8paKv8YM7sSuBJgwIABjB49OkeHsWNqU+nvNnXS3hYc8F5FpAZ1I7VHFyiKhWnSF2zG3EkN7ErQp1turhMlItLRxQ3vXdLYM58MsLLasLcpytyYHlLZcA25pj1Y8RgURY9j0UW2IeyRqwpT6ceia9YFJXFSA7rA3t0alrGqFFZRt1XgpWueSWeVTCaZNHEimxY0kwU1S5WVlXk7r5WW5SvAOtndV5jZ7sBIM5uT+aS7exR85UQUwN0NMGzYMB8xYkSuNr1DqutS8Maredl3IfHiGKmBXUnt1TXM7JcKiK2pIVFRR33fUlL79Wjbi8SKiHREiRjepwTvU7L9ZbfHLBxC2S2x9dDquhRWVoPVBsSCcCh20LOYYI+uWy9TGV5MvCFNfjpjY/p++rn0hdtFOoBEIsFxQ4dyzN69d3obo0ePJl/ntdKyvARY7r4i+r3WzJ4DhgNrzGygu6+KhgCujRZfAeydsfpeUdkKGocUpstHt3LVpZ1yg6B/NARwt9JwzP+mOhIfleMlYcBVt2cWQ11ERKTtFMfx4vjW2R9TDuV1xGpTYcr9mBEUx/BuCYLeWVx/rUkA1pBSvyYZJu+oii5IrUBMRFpJmwdYZtYNiLl7RXT/TOAm4AXgUuD30e/no1VeAH5gZk8QJrkoj4Kw14DfmVmfaLkzgZ+04aFIOxB0S5Aa1JXUnl3DZBW1KeJLtxCrT5HcvQvJg3p+PCOXiIi0X3GDXsUEZARdUdIP6oMWrr8WXmk7fa0zz8zS2D1BUBQLr3mWFngYaFU1Bl2xLeF96jQksT1zA+9ehG2pDy8WLtIO5aMHawDwXJh9nQTwmLu/amYfAk+Z2RXAEuDL0fKvEKZon0+Ypv0yAHffaGa/Bj6MlrspnfBC2h9PGN69iKB7Au9RhJfEwwxW0bQ0S1+PJvO6NBmPG54PorKYkRrQBe9dHF6Han0N8bI6kj2LSO3dbdupikVEpGOxKHNhIpb19dfCJ6JrjtWksLowMLNUeNHnoMjwLuHF4Mn8zEgGjT1dW5LE0kFYTTS4sWEOGo2p/pv89lhzzwH1HvakqQcta27gPYvCpCv9Sgh6l4R/r1QQZrlcW0N8XU04RFSknWjzAMvdFwJHN1O+ATijmXIHrmphW/cD9+e6jrLzPAberQjvEQVT3YsIehSF15NKSwZbfVBhhNn6WvrAamEoiFXWk5i/GeJGco8ujddzERERgShLYzwcikgzWRkDh+pUGPTUhz1hbuFc3qBXMezRhVRrjYKoTYWBVnUYvFlNk8fJ/EdgDlAUw4tjeEksbMeiWFjHKGlJrgNFh/AcIh1Q9Slu6H20inriK7YQS3k4gqVHeC23JGDldcTX1hBbV41V6Dpukl/tKU27dCBuhKnMuycIuocBlXcvwrtmXJg35diW+vBaKrXht3Wp6BtD75YIv4GKequsSY8V7g29Ve6Eb+AevYtHz3lxjOQBPTQEUEREdk6sMfnGVvPAIOz5qqjHasNgJxYl0HDPyAVsGUGbgWM4jllGBsX0k9GXiOYebiceC6+v2LOYYPf41r1oEM4dq8kIuKpT4QWnUx4GhgHRZ2f0eRn41p+nQcZyGZt1gIThJfFwCGX020viEP3OfLzN+W6Bh/Vq6PGrx6pSxKqSYa9hFn8Ch3B+Xb+SMKjqUxLuF8JziFVVxGtSpIrjeJ/icJRKum2D9KUFAoLSOMkDe8BBPaE6SXxdDbG1NeGFs/Mfq0qBUYAl2+XxaHhfz6Kwm75nEd6tqPHDwKOx7JX1xNbVEA+cVMIISuLQo4hgYJftXk9qe+99em8UEZE2lYhBz+KGnq9WG4CWDKA62RDIWSoK5GKGJ2LhMMY+JWFyj52VDrachos/N7tMXYDVpcKsjluSkAwa57fFIIiHdTIjrCdGkIjhXeIE/YohnlHHVBD2cjUz3NIT1hhQ9S0J51BDmMZ/XTXx6iRBIkbQu4RgUDeCloK8WMalBdwh2gdxC+dn79M9PIb1tWHAta4mnL8n0soUYMlWPGFh13zPIoKexXjPorC3KR0g1aWwzfXEy7YQSwV4zEgVR4FUlL0vte1diIiISFoiBt1j4SiQ5p4PHKtN4ZX1YTbEzGtqRr1hBg33HcPw9NPh7/RnuIXbi0UXdHazMGiKWxjApXuwuhc1BGHNfaY3O9SyJoVV1zX09nnM8KJYeE6xe2nzQV1tKhzlsiUJcUj1Ci9u3WJAtS1m0L2IoHtR47bLarDASfUuJtijS9hGm+rCOVvldWGDNJ0vFz1umGcX+/g8O4/iyNiWJLGyuqx766RwKMAqYF4Ua+yV6lGE9ywOg6mI1aSwzdEbUeCkimN41yK8VzGp/qUKpERERFpbLBxaT5dE8/PIdkLOP79jBl0T4dQBmvT2pRyqomGO9QEx94aLRyejL2eDgbvQO9eSkng4kiaqg5XVEquPhhIe3GvXt+/eOFSxJkWsrJbYpjpim+qwinoNSyxwCrDa0KLq2vDbnDZOAetE86V6NCadCHoWQZeMYKo6iZXXE19bHc2VioXzq/qWKHmEiIiI7Jx42LOU7qHLywC9uOF9MoYSVtQRq0iGPW+ZE+rMG0bseOaJWroXK5pLZ2YEsXAOXKw+7K0LehcTDIyut5kMiJXXYZvqiJVFQVdKEVchUYDVhv7fR8uoPm1gOHF1S5P0r+nrcezCP6ADFMfC3qjuRXiPKAFF90TjuGj3cALqpjpiq6oxPJw4mv4WSenNRUREpLMygx7FBD2Kd3oTmZcJaOgNTDlsipJ6YQRd4gT79wh7udyxivqGHq70sELpvBRgtaEfD96Db46cGWYXKo4T9C0mGNR164WqmwRdLVx1viHxRNQrlU6LTnFGOvTaVJTStCoaDx0jVRKDbkUEA7rs3BhnEREREdla3KB3ydYXx46+SI+lnKAkTmrPKPEGQE2K8s111AVKutEZKcBqQyf37QEDu9AwFbI+gPK6MHNQKiDm0WTTknjYzVzUzFXna1NhmvOuGX+6ZIBVJsN0pHUpzCxMh95N86VERERE2lxG0o2GEKo2hZXXhMMK4zHqi2JU5GfQpLQyBVj5VBSDXs2kgE1PAK2sbcgY1HDV+eJ4eDG9NdVh4okiw7uEwwC9V9ftpkMXERERkTwoieMl8YYvvbvHjD59NM+9M1KA1R6ZQWkcL403mwrVexRpeJ+IiIhIB2b6UrzTUoDV0SiwEhERERFpt1rhwgMiIiIiIiKFSQGWiIiIiIhIjijAEhERERERyREFWCIiIiIiIjmiAEtERERERCRHFGCJiIiIiIjkiAIsERERERGRHFGAJSIiIiIikiMKsERERERERHJEAZaIiIiIiEiOKMASERERERHJEQVYIiIiIiIiOaIAS0REREREJEcUYImIiIiIiOSIAiwREREREZEcUYAlIiIiIiKSIwqwREREREREckQBloiIiIiISI4owBIREREREckRBVgiIiIiIiI5ogBLREREREQkRxRgiYiIiIiI5IgCLBERERERkRzp8AGWmZ1lZnPNbL6Z3ZDv+oiIiIiISOHq0AGWmcWBO4CzgSHARWY2JL+1EhERERGRQtWhAyxgODDf3Re6ex3wBHBenuskIiIiIrJNQb4rIK0mke8K7KJBwLKMx8uB45suZGZXAldGDyvNbG4b1K1Z8X32O9ISCc/X/jsTd4+bWSrf9ejo1I65o7bMHbVl7qgtc0PtmDtqy9AGdx+6fMlH1NfX78Jm+gPrd3LdfXdhv7INHT3Ayoq73w3cne96AJjZBHcflu96dAZqy9xQO+aO2jJ31Ja5o7bMDbVj7qgtc0dt2T519CGCK4C9Mx7vFZWJiIiIiIi0uY4eYH0IHGRm+5lZMfBV4IU810lERERERApUhx4i6O5JM/sB8BoQB+5395l5rtb2tIuhip2E2jI31I65o7bMHbVl7qgtc0PtmDtqy9xRW7ZD5q58CyIiIiIiIrnQ0YcIioiIiIiItBsKsERERERERHJEAdYuMrP7zWytmc3IKDvazN4zs+lm9qKZ9YzKi8zsoah8tpn9JGOds8xsrpnNN7Mb8nEs+ZbDtlwclU8xswn5OJZ828G2LDazB6LyqWY2ImOdoVH5fDO7zcys7Y8mv3LYlqOj//Ep0W33tj+a/DGzvc1slJnNMrOZZvajqLyvmY00s3nR7z5RuUWvuflmNs3MjsvY1qXR8vPM7NJ8HVO+5LgtUxmvyYJKErUT7Xho9H9fa2bXNdlWQX+G57gtC/ozfCfa8mvR//V0M3vXzI7O2FZBvy7zyt1124UbcCpwHDAjo+xD4FPR/cuBX0f3LwaeiO53BRYDgwkTdCwA9geKganAkHwfW0dsy+jxYqB/vo+nA7XlVcAD0f3dgYlALHr8AXACYMB/gLPzfWwduC1HA8PyfTx5bMeBwHHR/R7AR8AQ4A/ADVH5DcDN0f1zotecRa/B96PyvsDC6Hef6H6ffB9fR2zL6LnKfB9PB2rH3YFPAL8FrsvYTsF/hueqLaPnFlPAn+E70ZafTL8HAmdnvFcW/Osynzf1YO0idx8LbGxSfDAwNro/ErggvTjQzcwSQBegDtgMDAfmu/tCd68DngDOa+26tzc5akthh9tyCPBWtN5aYBMwzMwGAj3dfbyH79YPA+e3bs3bn1y0ZevXsv1z91XuPim6XwHMBgYRvtc9FC32EI2vsfOAhz00HugdvSY/C4x0943uXkbY/me13ZHkXw7bsqDtaDu6+1p3/xCob7Kpgv8Mz2FbFrydaMt3o/dCgPGE14QFvS7zSgFW65hJ44v4Qhovhvw0sAVYBSwF/uTuGwn/cZZlrL88KpMdb0sIg6/XzWyimV3ZlpVt51pqy6nA580sYWb7AUOj5wYRvhbT9LpstKNtmfZANOzl52aFN9wyzcwGA8cC7wMD3H1V9NRqYEB0v6X3Rb1fZtjFtgQoNbMJZjbezM5v/Rq3T1m2Y0v0msywi20J+gxvsBNteQVhbzXodZlXCrBax+XA981sImH3bl1UPhxIAXsC+wE/NrP981PFDmNn2vJkdz+OsKv8KjM7tY3r3F611Jb3E77xTgBuBd4lbFtp2c605dfc/UjglOj2jbascHthZt2BZ4Br3H2rXueop1TXDslSjtpyX3cfRjjs+lYzOyD3NW3f9JrMnRy1pT7D2fG2NLPTCAOs69usktIiBVitwN3nuPuZ7j4UeJxwDCyEH2Cvunt9NHzoHcLhQyvY+lvuvaKygrcTbYm7r4h+rwWeIwzGCl5LbenuSXf/f+5+jLufB/QmHPO9gsahBqDXZYOdaMvM12UF8BgF+Lo0syLCE4ZH3f3ZqHhNerha9HttVN7S+6LeL8lZW2a+LhcSzhM8ttUr347sYDu2RK9JctaW+gxnx9vSzI4C7gXOc/cNUbFel3mkAKsVWJQdzMxiwM+Au6KnlgKnR891I5xsPIdwwvxBZrafmRUDXwUKKptTS3a0Lc2sm5n1yCg/E5jRdLuFqKW2NLOuUVthZp8Bku4+KxqKsNnMToiGs10CPJ+f2rcvO9qW0ZDB/lF5EXAuBfa6jF5D9wGz3f2WjKdeANKZAC+l8TX2AnCJhU4AyqPX5GvAmWbWJ8qidWZUVjBy1ZZRG5ZE2+wPnATMapODaAd2oh1bUvCf4blqS32G73hbmtk+wLPAN9z9o4zlC/51mVfeRtk0OuuN8NvrVYQTNZcTds/+iPBb64+A3wMWLdsd+Bfh/I1ZwH9nbOecaPkFwE/zfVwdtS0Js+VMjW4z1ZZZteVgYC7hRNo3CIcMpbczjPDDbQFwe3qdQrrloi2BboQZBadFr8u/AvF8H1sbt+PJhENapgFTots5QD/gTWBe1GZ9o+UNuCN67U0nIwMj4RDN+dHtsnwfW0dtS8LsY9Oj98vpwBX5PrZ23o57RO8BmwkT2CwnTAQEBf4Znqu2RJ/hO9OW9wJlGctOyNhWQb8u83lLnxSIiIiIiIjILtIQQRERERERkRxRgCUiIiIiIpIjCrBERERERERyRAGWiIiIiIhIjijAEhERERERyREFWCIislPM7FAzm2Jmk83sgF3c1jfN7PYWnquMfu9pZk/vwvb3zHh8r5kN2bnaioiItEwBloiIbMXM4lkuej7wtLsf6+4LWrFKALj7Snf/0k6u/k2gIcBy92+5e8FcVFdERNqOAiwRkQ7KzG4ys2syHv/WzH4U3f9vM/vQzKaZ2a8ylvm3mU00s5lmdmVGeaWZ/dnMpgInNtnPMWY2PtrWc2bWx8zOAa4Bvmdmo5qpW6WZ/SXaz5tmtltUPtrMhkX3+5vZ4ozV9o6en2dmv2xmm4PNbEZ0P25mfzKzGVG9fhiV/yI67hlmdreFvkR40exHox63Lk3qcZGZTY/WubnJMfzWzKZGxz8gyz+NiIgUMAVYIiId1/3AJQBmFgO+CjxiZmcCBwHDgWOAoWZ2arTO5e4+lDDguNrM+kXl3YD33f1od3+7yX4eBq5396OA6cAv3f0V4C7gL+5+WjN16wZMcPfDgTHAxwKmZgwHLgCOAi5MB0AtuBIYDBwT1evRqPx2d/+Eux8BdAHOdfengQnA19z9GHevTm8kGjZ4M3A6YVt9wszOzziG8e5+NDAW+HYWxyAiIgVOAZaISAfl7ouBDWZ2LHAmMNndN0T3zwQmA5OAQwkDLgiDqqnAeGDvjPIU8EzTfZhZL6C3u4+Jih4CTm26XDMC4Mno/iPAyVmsM9LdN0QB0LPbWefTwD/cPQng7huj8tPM7H0zm04YNB2+nX1+Ahjt7uuibT1K4/HVAS9F9ycSBnQiIiLblMh3BUREZJfcSzi/aA/CHi0AA/7P3f+RuaCZjSAMTE509yozGw2URk/XuHuqFevp0e8kjV/ulbawTEuPt8nMSoG/A8PcfZmZ3djMPnZEvbun65BCn5kiIpIF9WCJiHRszwFnEfbEvBaVvQZcbmbdAcxskJntDvQCyqLg6lDghO1t3N3LgTIzOyUq+gbhkL/tiQHphBQXA+lhh4uBodH9pgkrPmNmfc2sC2ECjXe2sf2RwHfMLAFgZn1pDKbWR8eeuf0KoEcz2/kA+FQ0HywOXER2xyciItIsfRsnItKBuXtdlGRiU7oHyt1fN7PDgPfMDKAS+DrwKvBdM5sNzCUcJpiNS4G7zKwrsBC4LIt1tgDDzexnwFrgK1H5n4CnogQbLzdZ5wPCYYp7AY+4+4RtbP9e4GBgmpnVA/e4++1mdg8wA1gNfJix/IPRMVSTkcTD3VeZ2Q3AKMKev5fd/fksjk9ERKRZ1jj6QUREOpooucUk4EJ3n5fv+qSZWaW7d893PURERNqahgiKiHRQ0YVy5wNvtqfgSkREpJCpB0tERERERCRH1IMlIiIiIiKSIwqwREREREREckQBloiIiIiISI4owBIREREREckRBVgiIiIiIiI58v8B+6SijI+rzCMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.plot_area(years_source_df, title = \"Number of records over time by source (K10plus and HeBIS)\", cmap=\"tab10_r\", legend_title = \"Source\", figure_name = \"figure_03_area_source_develope\", xlabel=\"year of publication\", ylabel=\"number of records\", grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>source</th>\n",
       "      <th>hebis</th>\n",
       "      <th>k10plus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_publication</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>23.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>17.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>17.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>16.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>17.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>17.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>17.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "source            hebis  k10plus\n",
       "year_publication                \n",
       "1980               21.0     79.0\n",
       "1981               20.0     80.0\n",
       "1982               22.0     78.0\n",
       "1983               22.0     78.0\n",
       "1984               22.0     78.0\n",
       "1985               22.0     78.0\n",
       "1986               22.0     78.0\n",
       "1987               24.0     76.0\n",
       "1988               23.0     77.0\n",
       "1989               22.0     78.0\n",
       "1990               21.0     79.0\n",
       "1991               21.0     79.0\n",
       "1992               21.0     79.0\n",
       "1993               19.0     81.0\n",
       "1994               18.0     82.0\n",
       "1995               19.0     81.0\n",
       "1996               19.0     81.0\n",
       "1997               19.0     81.0\n",
       "1998               19.0     81.0\n",
       "1999               18.0     82.0\n",
       "2000               18.0     82.0\n",
       "2001               18.0     82.0\n",
       "2002               19.0     81.0\n",
       "2003               18.0     82.0\n",
       "2004               18.0     82.0\n",
       "2005               18.0     82.0\n",
       "2006               17.0     83.0\n",
       "2007               17.0     83.0\n",
       "2008               16.0     84.0\n",
       "2009               17.0     83.0\n",
       "2010               15.0     85.0\n",
       "2011               15.0     85.0\n",
       "2012               15.0     85.0\n",
       "2013               17.0     83.0\n",
       "2014               18.0     82.0\n",
       "2015               17.0     83.0\n",
       "2016               20.0     80.0\n",
       "2017               19.0     81.0\n",
       "2018               21.0     79.0\n",
       "2019               21.0     79.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "years_source_df.div(years_source_df.sum(axis=1), axis=0).round(2)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 35000.0)"
      ]
     },
     "execution_count": 32,
     "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": [
    "ax = df.loc[df[\"source\"]==\"k10plus\"][\"year_publication\"].value_counts().sort_index().plot.line(linewidth = 5, grid= True)\n",
    "ax.set_ylim(0,35000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 35000.0)"
      ]
     },
     "execution_count": 33,
     "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": [
    "ax = df.loc[df[\"source\"]==\"hebis\"][\"year_publication\"].value_counts().sort_index().plot.line(linewidth = 5, grid= True)\n",
    "ax.set_ylim(0,35000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 35000.0)"
      ]
     },
     "execution_count": 34,
     "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": [
    "ax = df[\"year_publication\"].value_counts().sort_index().plot.line(linewidth = 5, grid= True)\n",
    "ax.set_ylim(0,35000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Year of Publication against Year of Entry in the Catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~df[\"entry_first\"].str.contains(\"\\-00\\-\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.loc[df[\"entry_first_date\"].astype(int) < 2023]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df[\"entry_first_date\"] = pd.to_datetime(df[\"entry_first\"].str[-8:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df[\"entry_first_date_year\"] = df[\"entry_first_date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df[\"entry_first_date_year\"] < 2023]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.plot_line_year_value_counts(df[\"entry_first_date_year\"], title = \"Number of records classified as romance studies per year of entry\", xlabel= \"year of entry\", ylabel= \"number of records in K10plus\", figure_name = \"number_records_years_entry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.sample(10000, random_state=2021).plot.scatter(\"year_publication\", \"entry_first_date_year\", figsize=(10,10), grid=True, color =color, title =\"Year of publication and year of entry in the catalog (sample)\")\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "ax.set_xlabel(\"year of publication\")\n",
    "ax.set_ylabel(\"year of first record in K10plus\")\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"./../visualizations/scatter_year_publication_year_entry_catalog.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3125"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = df.sample(10000, random_state=2021).groupby(\"year_publication\")[\"entry_first_date_year\"].quantile(0.25)\n",
    "Q3 = df.sample(10000, random_state=2021).groupby(\"year_publication\")[\"entry_first_date_year\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "IQR.mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_publication\n",
       "1980     7.25\n",
       "1981    12.50\n",
       "1982    14.00\n",
       "1983    11.00\n",
       "1984    10.00\n",
       "1985    11.00\n",
       "1986     7.00\n",
       "1987     6.00\n",
       "1988     5.75\n",
       "1989     5.00\n",
       "1990     3.00\n",
       "1991     3.00\n",
       "1992     2.00\n",
       "1993     2.00\n",
       "1994     2.00\n",
       "1995     2.00\n",
       "1996     2.00\n",
       "1997     2.00\n",
       "1998     2.00\n",
       "1999     1.00\n",
       "2000     1.00\n",
       "2001     1.00\n",
       "2002     1.00\n",
       "2003     1.00\n",
       "2004     1.00\n",
       "2005     1.00\n",
       "2006     1.00\n",
       "2007     1.00\n",
       "2008     1.00\n",
       "2009     1.00\n",
       "2010     1.00\n",
       "2011     3.00\n",
       "2012     1.00\n",
       "2013     1.00\n",
       "2014     1.00\n",
       "2015     1.00\n",
       "2016     1.00\n",
       "2017     1.00\n",
       "2018     1.00\n",
       "2019     1.00\n",
       "Name: entry_first_date_year, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3125"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_first_date_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_publication</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  entry_first_date_year\n",
       "year_publication                       \n",
       "1980                               25.0\n",
       "1981                               26.0\n",
       "1982                               27.0\n",
       "1983                               25.0\n",
       "1984                               26.0\n",
       "1985                               24.0\n",
       "1986                               21.0\n",
       "1987                               18.0\n",
       "1988                               18.0\n",
       "1989                               17.0\n",
       "1990                               16.0\n",
       "1991                               16.0\n",
       "1992                               14.0\n",
       "1993                               14.0\n",
       "1994                               14.0\n",
       "1995                               13.0\n",
       "1996                               13.0\n",
       "1997                               12.0\n",
       "1998                               11.0\n",
       "1999                               12.0\n",
       "2000                               11.0\n",
       "2001                               11.0\n",
       "2002                               12.0\n",
       "2003                               11.0\n",
       "2004                               11.0\n",
       "2005                               10.0\n",
       "2006                               10.0\n",
       "2007                                8.0\n",
       "2008                                9.0\n",
       "2009                                8.0\n",
       "2010                                8.0\n",
       "2011                                6.0\n",
       "2012                                6.0\n",
       "2013                                5.0\n",
       "2014                                5.0\n",
       "2015                                5.0\n",
       "2016                                4.0\n",
       "2017                                4.0\n",
       "2018                                4.0\n",
       "2019                                2.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q05 = df.groupby(\"year_publication\")[\"entry_first_date_year\"].quantile(0.05)\n",
    "Q95 = df.groupby(\"year_publication\")[\"entry_first_date_year\"].quantile(0.95)\n",
    "\n",
    "iqr90 = Q95 - Q05\n",
    "iqr90_df = pd.DataFrame(iqr90)\n",
    "\n",
    "iqr90_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entry_first_date_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_publication</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  entry_first_date_year\n",
       "year_publication                       \n",
       "1980                               25.0\n",
       "1981                               26.0\n",
       "1982                               27.0\n",
       "1983                               25.0\n",
       "1984                               26.0\n",
       "1985                               24.0\n",
       "1986                               21.0\n",
       "1987                               18.0\n",
       "1988                               18.0\n",
       "1989                               17.0\n",
       "1990                               16.0\n",
       "1991                               16.0\n",
       "1992                               14.0\n",
       "1993                               14.0\n",
       "1994                               14.0\n",
       "1995                               13.0\n",
       "1996                               13.0\n",
       "1997                               12.0\n",
       "1998                               11.0\n",
       "1999                               12.0\n",
       "2000                               11.0\n",
       "2001                               11.0\n",
       "2002                               12.0\n",
       "2003                               11.0\n",
       "2004                               11.0\n",
       "2005                               10.0\n",
       "2006                               10.0\n",
       "2007                                8.0\n",
       "2008                                9.0\n",
       "2009                                8.0\n",
       "2010                                8.0\n",
       "2011                                6.0\n",
       "2012                                6.0\n",
       "2013                                5.0\n",
       "2014                                5.0\n",
       "2015                                5.0\n",
       "2016                                4.0\n",
       "2017                                4.0\n",
       "2018                                4.0\n",
       "2019                                2.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr90_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "entry_first_date_year    12.8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr90_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "entry_first_date_year    7.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr90_df.iloc[25:35].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'How long did it take for the 90% of the publications to get into the catalogue?\\n (95th Quantile - 5th Quantile of year of entry)'}, xlabel='year_publication'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iqr90_df.plot.bar(figsize=(10,5), title =\"How long did it take for the 90% of the publications to get into the catalogue?\\n (95th Quantile - 5th Quantile of year of entry)\", color = color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'AxesSubplot' object has no attribute 'rowNum'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-68-8a1be5402f2a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2021\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhexbin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"year_publication\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"entry_first_date_year\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mhexbin\u001b[1;34m(self, x, y, C, reduce_C_function, gridsize, **kwargs)\u001b[0m\n\u001b[0;32m   1586\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"gridsize\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1588\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"hexbin\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1589\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    790\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 792\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    793\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    264\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 266\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_adorn_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_adorn_subplots\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    466\u001b[0m                 \u001b[0mncols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mncols\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    467\u001b[0m                 \u001b[0msharex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msharex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 468\u001b[1;33m                 \u001b[0msharey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msharey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    469\u001b[0m             )\n\u001b[0;32m    470\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\calvotello\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\tools.py\u001b[0m in \u001b[0;36m_handle_shared_axes\u001b[1;34m(axarr, nplots, naxes, nrows, ncols, sharex, sharey)\u001b[0m\n\u001b[0;32m    296\u001b[0m                 \u001b[0mlayout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncols\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxarr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 298\u001b[1;33m                     \u001b[0mlayout\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrowNum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolNum\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    300\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxarr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'AxesSubplot' object has no attribute 'rowNum'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sample(10000, random_state=2021).plot.hexbin(\"year_publication\", \"entry_first_date_year\", figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='entry_first_date_year', ylabel='year_publication'>"
      ]
     },
     "execution_count": 69,
     "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": [
    "sns.kdeplot(df.sample(10000, random_state=2021)['entry_first_date_year'], df.sample(10000)['year_publication'], shade=True, cmap='PuBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize.plot_boxplots_by(df.sample(10000, random_state=2021), column_to_plot = \"entry_first_date_year\", column_by = \"year_publication\", xlabel= \"year of publication\", ylabel= \"year of first record in K10plus\",\n",
    "title = \"Boxplot of year of first entry by year of publication\",\n",
    "figure_name = \"boxplot_year_publication_year_entry_catalog\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.6 ('base')",
   "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": "3.7.6"
  },
  "orig_nbformat": 2,
  "vscode": {
   "interpreter": {
    "hash": "f47a9bd213414a99a19c677dbc3fe06cd2e784c4236a07176834505119fd1ea8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}