CCLAMP_Network_Community_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2b238ef",
   "metadata": {},
   "source": [
    "# CCLAMP Network: Community detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "74c6c86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "from pyvis.network import Network\n",
    "import community.community_louvain as community_louvain\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5bc064d",
   "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>Target</th>\n",
       "      <th>Source</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Target_DOB</th>\n",
       "      <th>Source_DOB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>3</td>\n",
       "      <td>17 september 1892</td>\n",
       "      <td>7 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>12</td>\n",
       "      <td>10 juli 1900</td>\n",
       "      <td>20 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>3</td>\n",
       "      <td>10 juli 1900</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>3</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>20 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>2</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>37</td>\n",
       "      <td>17 februari 1904</td>\n",
       "      <td>20 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>3</td>\n",
       "      <td>17 februari 1904</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albe</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>25</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>20 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albe</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>2</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>2</td>\n",
       "      <td>14 maart 1878</td>\n",
       "      <td>7 juni 1900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Target              Source  Weight         Target_DOB  \\\n",
       "0  Hendrik Andriessen       Albert Helman       3  17 september 1892   \n",
       "1       Henri Bruning  Henk Kuitenbrouwer      12       10 juli 1900   \n",
       "2       Henri Bruning           Jan Bruna       3       10 juli 1900   \n",
       "3        Joep Nicolas  Henk Kuitenbrouwer       3     6 oktober 1897   \n",
       "4        Joep Nicolas           Jan Bruna       2     6 oktober 1897   \n",
       "5        Albert Kuyle  Henk Kuitenbrouwer      37   17 februari 1904   \n",
       "6        Albert Kuyle           Jan Bruna       3   17 februari 1904   \n",
       "7                Albe  Henk Kuitenbrouwer      25        8 juni 1902   \n",
       "8                Albe           Jan Bruna       2        8 juni 1902   \n",
       "9      Carel Scharten        Jan Engelman       2      14 maart 1878   \n",
       "\n",
       "         Source_DOB  \n",
       "0   7 november 1903  \n",
       "1  20 november 1903  \n",
       "2  17 augustus 1876  \n",
       "3  20 november 1903  \n",
       "4  17 augustus 1876  \n",
       "5  20 november 1903  \n",
       "6  17 augustus 1876  \n",
       "7  20 november 1903  \n",
       "8  17 augustus 1876  \n",
       "9       7 juni 1900  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the network\n",
    "network = pd.read_csv(\"CCLAMP_Directed_Network.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "network.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20359be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiGraph with 7477 nodes and 158330 edges\n"
     ]
    }
   ],
   "source": [
    "# Create a weighted graph\n",
    "G = nx.from_pandas_edgelist(network, \n",
    "                            source = 'Source', \n",
    "                            target = 'Target', \n",
    "                            edge_attr = 'Weight',\n",
    "                            create_using = nx.DiGraph())\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51dbf06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "CCLAMP.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000px\"\n",
       "            height=\"700px\"\n",
       "            src=\"CCLAMP.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x23aca66ab90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Network(notebook = True, width=\"1000px\", height=\"700px\", bgcolor=\"#222222\", font_color=\"white\")\n",
    "\n",
    "node_degree = dict(G.degree)\n",
    "      \n",
    "# setting up node attribute\n",
    "nx.set_node_attributes(G, node_degree, 'size')\n",
    "\n",
    "net.from_nx(G)\n",
    "net.show('CCLAMP.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b671be6e",
   "metadata": {},
   "source": [
    "# Detect communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14078b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Albert Helman': 0,\n",
       " 'Hendrik Andriessen': 0,\n",
       " 'Henk Kuitenbrouwer': 0,\n",
       " 'Henri Bruning': 0,\n",
       " 'Jan Bruna': 0,\n",
       " 'Joep Nicolas': 38,\n",
       " 'Albert Kuyle': 0,\n",
       " 'Albe': 2,\n",
       " 'Jan Engelman': 0,\n",
       " 'Carel Scharten': 0,\n",
       " 'Willem Maas': 0,\n",
       " 'Frans Coenen': 0,\n",
       " 'Herman Robbers': 0,\n",
       " 'Roel Houwink': 0,\n",
       " 'Dirk Coster': 0,\n",
       " 'F. Bordewijk': 0,\n",
       " 'Albert Plasschaert': 38,\n",
       " 'Erich Wichman': 0,\n",
       " 'L.J.M. Feber': 38,\n",
       " 'Willem Pijper': 10,\n",
       " 'Frederik van Eeden': 0,\n",
       " 'M. Bruna': 0,\n",
       " 'Karel van den Oever': 38,\n",
       " 'Just Havelaar': 0,\n",
       " 'Gerard Bruning': 0,\n",
       " 'Gerard Brom': 0,\n",
       " 'Frans Erens': 0,\n",
       " 'H. Marsman': 0,\n",
       " 'Herman Menagé Challa': 0,\n",
       " 'C.J. Kelk': 0,\n",
       " 'H.W.J.M. Keuls': 0,\n",
       " 'P.C. Boutens': 0,\n",
       " 'Bernard Verhoeven': 38,\n",
       " 'Multatuli': 0,\n",
       " 'Albert Verwey': 0,\n",
       " 'Marcellus Emants': 0,\n",
       " 'Hélène Swarth': 0,\n",
       " 'Herman Gorter': 0,\n",
       " 'Wouter Hutschenruyter': 0,\n",
       " 'J.A. Lambert-van der Kolf': 0,\n",
       " 'Marie Koenen': 38,\n",
       " 'Marie Gijsen': 0,\n",
       " 'Ed. Hoornik': 10,\n",
       " 'Rie Cramer': 0,\n",
       " 'Johan de Meester': 0,\n",
       " 'Pieter van der Meer de Walcheren': 38,\n",
       " 'Pol de Mont': 38,\n",
       " 'Karel van de Woestijne': 38,\n",
       " 'Cyriel Verschaeve': 38,\n",
       " 'Max Brod': 10,\n",
       " 'H. Mol': 4,\n",
       " 'Willem Kloos': 0,\n",
       " 'Daan van der Zee': 0,\n",
       " 'Ina Boudier-Bakker': 0,\n",
       " 'Edward Vermeulen': 38,\n",
       " 'Maria Viola': 0,\n",
       " 'Sybold van Ravesteyn': 0,\n",
       " 'Nico Rost': 10,\n",
       " 'Huib Luns': 0,\n",
       " 'Arthur van Schendel': 0,\n",
       " 'Wies Moens': 38,\n",
       " 'A. Roland Holst': 10,\n",
       " 'Geerten Gossaert': 0,\n",
       " 'Charley Toorop': 0,\n",
       " 'Piet Visser': 0,\n",
       " 'J. Eigenhuis': 0,\n",
       " 'Herman Teirlinck': 38,\n",
       " 'Jan Campert': 0,\n",
       " 'Henriette Roland Holst-van der Schalk': 0,\n",
       " 'Marnix Gijsen': 38,\n",
       " 'Richard Minne': 38,\n",
       " 'J.C. Bloem': 0,\n",
       " 'Jacob Israël de Haan': 0,\n",
       " 'Daan Boens': 38,\n",
       " 'Aart van der Leeuw': 0,\n",
       " 'Jan Stuyt': 38,\n",
       " 'Otto van Rees': 38,\n",
       " 'Gerard van Eckeren': 0,\n",
       " 'Maurice Roelants': 38,\n",
       " 'Jan van Nijlen': 38,\n",
       " 'Job Steynen': 0,\n",
       " 'Kees Meekel': 0,\n",
       " 'G.P.M. Knuvelder': 4,\n",
       " 'Paul Claudel': 38,\n",
       " 'B.H. Molkenboer': 4,\n",
       " 'Antoon Derkinderen': 0,\n",
       " 'C.A. Terburg': 0,\n",
       " 'P.A. Kerstens': 0,\n",
       " 'Jop Pollmann': 0,\n",
       " 'Matthijs Vermeulen': 10,\n",
       " 'Jacques Schreurs': 10,\n",
       " 'Menno ter Braak': 0,\n",
       " 'Wim Snitker': 38,\n",
       " 'Gérard de Nerval': 10,\n",
       " 'Sigmund Freud': 10,\n",
       " 'Robert Guiette': 38,\n",
       " 'Wouter Lutkie': 0,\n",
       " 'Vincent Cleerdin': 38,\n",
       " 'Jozef Cantré': 38,\n",
       " 'Mary Robbers': 38,\n",
       " 'Constant van Wessem': 0,\n",
       " 'Willem Nieuwenhuis': 0,\n",
       " 'J.A. Alberdingk Thijm': 2,\n",
       " 'J.F.M. Sterck': 4,\n",
       " 'Emile Erens': 0,\n",
       " 'Israël Querido': 0,\n",
       " 'Gerben Colmjon': 0,\n",
       " 'Charles Nypels': 0,\n",
       " 'Michel de Ghelderode': 38,\n",
       " 'Willem Doevenspeck': 38,\n",
       " 'Willem Putman': 38,\n",
       " 'Anton Van de Velde': 38,\n",
       " 'Herman de Man': 38,\n",
       " 'J. Cuypers': 38,\n",
       " 'Charles Eyck': 0,\n",
       " 'J.J. ten Berge S.J.': 2,\n",
       " 'J.H. Abendanon': 2,\n",
       " 'A. den Doolaard': 10,\n",
       " 'Herman van den Bergh': 0,\n",
       " 'Anton van Duinkerken': 0,\n",
       " 'A.A.M. Stols': 0,\n",
       " 'M. Stol': 0,\n",
       " 'Blaise Cendrars': 0,\n",
       " 'Top Naeff': 0,\n",
       " 'Frits Hopman': 0,\n",
       " 'Felix Timmermans': 38,\n",
       " 'A.M. de Jong': 0,\n",
       " 'Guido Gezelle': 38,\n",
       " 'Caesar Gezelle': 38,\n",
       " 'J.C. van Schagen': 0,\n",
       " 'Julien Benda': 0,\n",
       " 'Miguel de Unamuno': 0,\n",
       " 'William Blake': 10,\n",
       " 'H.W.E. Moller': 0,\n",
       " 'Dop Bles': 0,\n",
       " 'Selma Lagerlöf': 38,\n",
       " 'Willem ten Berge': 0,\n",
       " 'Jan Greshoff': 0,\n",
       " 'D.A.M. Binnendijk': 10,\n",
       " 'J. Wille': 4,\n",
       " 'J.C. Hol': 0,\n",
       " 'Alphons Diepenbrock': 0,\n",
       " 'J. Slauerhoff': 0,\n",
       " 'Jozef Muls': 38,\n",
       " 'Joris Iven': 10,\n",
       " 'Joris Ivens': 10,\n",
       " 'H.G. Cannegieter': 38,\n",
       " 'Jacob van Lennep': 2,\n",
       " 'Jean Cocteau': 38,\n",
       " 'Mathias Kemp': 0,\n",
       " 'Pierre Kemp': 0,\n",
       " 'Lodewijk van Deyssel': 0,\n",
       " 'Theo Bogaerts': 38,\n",
       " 'Joris Eeckhout': 38,\n",
       " 'Ernest Michel': 0,\n",
       " 'Cornelis Veth': 0,\n",
       " 'Theo Vonck': 38,\n",
       " 'Gerard Wijdeveld': 0,\n",
       " 'Herman Poort': 0,\n",
       " 'W.L.M.E. van Leeuwen': 0,\n",
       " 'Novalis': 0,\n",
       " 'Hendrik de Vries': 10,\n",
       " 'Antoon Coolen': 38,\n",
       " 'Stijn Streuvels': 38,\n",
       " 'Herluf van Merlet': 5,\n",
       " 'Mien Proost': 0,\n",
       " 'Bernard Shaw': 0,\n",
       " 'Theo Schlichting': 0,\n",
       " 'N. van Wijk': 0,\n",
       " 'Hendrik Wiegersma': 0,\n",
       " 'Melati van Java': 2,\n",
       " 'Henri Morel': 0,\n",
       " 'Henri Borel': 0,\n",
       " 'Ellen Russe': 38,\n",
       " 'K.F. Proost': 0,\n",
       " 'Prosper van Langendonck': 38,\n",
       " 'Achilles Mussche': 38,\n",
       " 'Pieter G. Buckinx': 38,\n",
       " 'Jan Vercammen': 38,\n",
       " 'Urbain van de Voorde': 38,\n",
       " 'André Demedts': 38,\n",
       " 'Paul Rogghé': 38,\n",
       " 'Eduard Verkade': 0,\n",
       " 'Sigrid Undset': 38,\n",
       " 'Hieronymus van Alphen': 4,\n",
       " 'Herman Heijermans': 10,\n",
       " 'Louis Knuvelder': 0,\n",
       " 'P. Geyl': 0,\n",
       " 'Stefan George': 0,\n",
       " 'L. Stolte': 0,\n",
       " 'P.N. van Eyck': 0,\n",
       " 'Allard Pierson': 2,\n",
       " 'Nicolaas Beets': 2,\n",
       " 'Martinus des Amorie van der Hoeven': 2,\n",
       " 'Abraham Kuyper': 0,\n",
       " 'J.J. van Geuns': 0,\n",
       " 'J. van Geuns': 2,\n",
       " 'Luc Tournier': 0,\n",
       " 'Wilhelmus Nicolaï': 38,\n",
       " 'Jan Toorop': 0,\n",
       " 'Jan van Gelder': 0,\n",
       " 'Chris de Graaff': 0,\n",
       " 'A.C. Willink': 4,\n",
       " 'Oscar Leyendekkers': 0,\n",
       " 'Anton Reichling': 0,\n",
       " 'Halbo C. Kool': 10,\n",
       " 'Jan H. Eekhout': 10,\n",
       " 'Willem Brandt': 10,\n",
       " 'A.J.D. van Oosten': 0,\n",
       " 'Jan H. de Groot': 10,\n",
       " 'Insider': 10,\n",
       " 'Ad. Sassen': 0,\n",
       " 'Emile Seipgens': 2,\n",
       " 'S.H. de Roos': 0,\n",
       " 'Alfons Olterdissen': 4,\n",
       " 'E. Jaspar': 0,\n",
       " 'J.B. Stolte': 0,\n",
       " 'Constance Teichmann': 38,\n",
       " 'G.J. Hoogewerff': 0,\n",
       " 'A. van Domburg': 0,\n",
       " 'G. Kalff': 0,\n",
       " 'M.J. Brusse': 38,\n",
       " 'Annie Salomons': 0,\n",
       " 'Louis Saalborn': 10,\n",
       " 'Ilja Ehrenburg': 0,\n",
       " 'Giovanni Papini': 38,\n",
       " 'Gabriël Smit': 10,\n",
       " 'J.C. Noordstar': 10,\n",
       " 'Rainer Maria Rilke': 10,\n",
       " 'J. Dhont': 0,\n",
       " 'Giacomo Leopardi': 10,\n",
       " 'J.H. Leopold': 5,\n",
       " 'Herman Oosterwijk': 4,\n",
       " 'Gerard Walschap': 38,\n",
       " 'Felix Rutten': 38,\n",
       " 'Leo van Breen': 10,\n",
       " 'Martin Leopold': 0,\n",
       " 'Ernest Claes': 38,\n",
       " 'Bergman': 38,\n",
       " 'J.H. Groenewegen': 2,\n",
       " 'Hilda Ram': 38,\n",
       " 'Alie Smeding': 0,\n",
       " 'Albrecht Rodenbach': 38,\n",
       " 'E.J. Potgieter': 2,\n",
       " 'Hendrik Conscience': 38,\n",
       " 'Beb Vuyk': 0,\n",
       " 'Louis de Bourbon': 4,\n",
       " 'Berend Kuitert Cz': 0,\n",
       " 'J.J.P. Oud': 0,\n",
       " 'A.C. Ramselaar': 0,\n",
       " 'Freek van Leeuwen': 10,\n",
       " 'Koos van Doorne': 10,\n",
       " 'Eric van der Steen': 10,\n",
       " 'Wally Moes': 0,\n",
       " 'Jef Last': 10,\n",
       " 'Aug. Cuypers': 0,\n",
       " 'E. du Perron': 0,\n",
       " 'Mark Prager Lindo': 2,\n",
       " 'W.A.P. Smit': 4,\n",
       " 'Jan Derks': 38,\n",
       " 'M. Revis': 5,\n",
       " 'Paul Vlemminx': 0,\n",
       " 'Heleen Hoekstra': 5,\n",
       " 'Han G. Hoekstra': 10,\n",
       " 'Henrik Scholte': 0,\n",
       " 'A. Kluyver': 0,\n",
       " 'Gerlach Royen': 0,\n",
       " 'A.P.Th. Sassen': 38,\n",
       " 'Jos. van Schaveren': 38,\n",
       " 'C.J. Blaauw': 0,\n",
       " 'Maurits Dekker': 10,\n",
       " 'H.J. Elias': 4,\n",
       " 'J. de Jong': 5,\n",
       " 'J.H. Scholte': 2,\n",
       " 'F.A. Vercammen': 4,\n",
       " 'Cor van der Lugt Melsert': 0,\n",
       " 'D.H. Lawrence': 10,\n",
       " 'E.H.M. Beekman': 0,\n",
       " 'Peter van Gestel': 4,\n",
       " 'Theun de Vries': 0,\n",
       " 'Alfred de Vigny': 0,\n",
       " 'Titus Brandsma': 5,\n",
       " 'G.A. van Klinkenberg': 0,\n",
       " 'F. Schmidt-Degener': 10,\n",
       " 'A.A. Verdenius': 4,\n",
       " 'Ben Onie': 10,\n",
       " 'Constantinus': 38,\n",
       " 'Carole Vos': 0,\n",
       " 'Mimosa': 38,\n",
       " 'Frans van Cauwelaert': 38,\n",
       " 'Jan Boon': 38,\n",
       " 'J.W.F. Werumeus Buning': 0,\n",
       " 'Frans van Oldenburg Ermke': 0,\n",
       " 'C.R. de Klerk': 0,\n",
       " 'Martin Bruijns': 0,\n",
       " 'Joannes Reddingius': 0,\n",
       " 'Johan Fabricius': 10,\n",
       " 'Maurice Gilliams': 38,\n",
       " 'Piet Oomes': 0,\n",
       " 'René Verbeeck': 38,\n",
       " 'J.N. Bakhuizen van den Brink': 0,\n",
       " 'D. Plooij': 4,\n",
       " 'J.A. Goris': 38,\n",
       " 'August van Cauwelaert': 38,\n",
       " 'Wouter Paap': 0,\n",
       " 'Marie-Sophie Nathusius': 4,\n",
       " 'G. Vrijmoed': 0,\n",
       " 'David de Kok': 0,\n",
       " 'Pablo Picasso': 10,\n",
       " 'J.A.A. Mekel': 0,\n",
       " 'A. Plas': 38,\n",
       " 'Gerard den Brabander': 10,\n",
       " 'W.P.J. Pompe': 38,\n",
       " 'L. van Hulle': 38,\n",
       " 'L.S.J. Hanekroot': 38,\n",
       " 'E. Moll': 2,\n",
       " 'Maurits Uyldert': 0,\n",
       " 'Elisabeth Zernike': 10,\n",
       " 'P.A. de Génestet': 2,\n",
       " 'Willem Bilderdijk': 4,\n",
       " 'Jan van Beers': 4,\n",
       " 'Johan van der Woude': 38,\n",
       " 'C.C. Berg': 5,\n",
       " 'H. Kraemer': 0,\n",
       " 'J. Bierens de Haan': 0,\n",
       " 'Johan Brouwer': 10,\n",
       " 'Wilma': 5,\n",
       " 'Karel Vertommen': 38,\n",
       " 'Simon Koster': 38,\n",
       " 'H. Colijn': 0,\n",
       " 'Martinus Nijhoff': 0,\n",
       " 'Jan Fabricius': 0,\n",
       " 'Franz de Backer': 38,\n",
       " 'C.P.M. Romme': 0,\n",
       " 'Ruth Schaumann': 38,\n",
       " 'W.C. Braat': 0,\n",
       " 'G.W. van der Does': 2,\n",
       " 'C.H. Telders': 2,\n",
       " 'Robert Fruin': 2,\n",
       " 'Jan Veth': 0,\n",
       " 'Hubert Cuypers jr.': 0,\n",
       " 'W. Kramer': 0,\n",
       " 'Martien Beversluis': 0,\n",
       " 'Christine Mohrmann': 0,\n",
       " 'Jos. Panhuijsen': 0,\n",
       " 'A.M. Hammacher': 0,\n",
       " 'W.H. Beuken': 4,\n",
       " 'Dick Ouwendijk': 10,\n",
       " 'A.G.W. Severijnen': 0,\n",
       " 'Ernest van der Hallen': 38,\n",
       " 'Henri van Booven': 0,\n",
       " 'Louis Couperus': 5,\n",
       " 'Yge Foppema': 0,\n",
       " 'Anna de Noailles': 0,\n",
       " 'B.C.J. Loder': 0,\n",
       " 'Willem de Mérode': 10,\n",
       " 'H.M. van Randwijk': 10,\n",
       " 'Johan Schmidt': 0,\n",
       " 'Maurits Wagenvoort': 0,\n",
       " 'August Heyting': 0,\n",
       " 'Anton Koolhaas': 10,\n",
       " 'P. Morlion': 38,\n",
       " 'Pol Heyns': 38,\n",
       " 'K.H.R. de Josselin de Jong': 0,\n",
       " 'R. de Josselin de Jong': 0,\n",
       " 'Lode Geysen': 38,\n",
       " 'C. van Vollenhoven': 0,\n",
       " 'Leo Boekraad': 10,\n",
       " 'Ernst Wiechert': 10,\n",
       " 'Jan Kalf': 0,\n",
       " 'Bertus van Lier': 10,\n",
       " 'A. Goslinga': 0,\n",
       " 'Stéphane Mallarmé': 38,\n",
       " 'F.W. Grosheide': 10,\n",
       " 'N. Donker': 2,\n",
       " 'C.G.N. de Vooys': 0,\n",
       " 'Ben van Eysselsteijn': 0,\n",
       " 'Paul de Vree': 38,\n",
       " 'J.A. van Lieshout': 0,\n",
       " 'Maurits Mok': 10,\n",
       " 'P.J.H. Vermeeren': 4,\n",
       " 'Jan Romein': 0,\n",
       " 'Max Kijzer': 0,\n",
       " 'Luc van Hoek': 10,\n",
       " 'Ab Visser': 10,\n",
       " 'Bertus Aafjes': 10,\n",
       " 'Charles van Lerberghe': 0,\n",
       " 'W.C.L. van der Grinten': 0,\n",
       " 'Garmt Stuiveling': 4,\n",
       " 'K. Steur': 38,\n",
       " 'H. Robbers': 38,\n",
       " 'Gerth Schreiner': 0,\n",
       " 'Jo Manders': 0,\n",
       " 'H. Wagenvoort': 0,\n",
       " 'A.J.M. Cornelissen': 38,\n",
       " 'Maxim Gorki': 38,\n",
       " 'A. Sassen': 5,\n",
       " 'Paul Haimon': 10,\n",
       " 'A.C. Josephus Jitta': 0,\n",
       " 'Barend de Goede': 10,\n",
       " 'Bert Bakker': 10,\n",
       " 'Juliaan Haest': 38,\n",
       " 'Julia Tulkens': 38,\n",
       " 'Maurits Peeters': 38,\n",
       " 'W.J.C. Buitendijk': 4,\n",
       " 'L. Peeters': 4,\n",
       " 'Alice Nahon': 38,\n",
       " 'Bert Decorte': 38,\n",
       " 'Marcel Coole': 38,\n",
       " 'Simon Vestdijk': 10,\n",
       " 'Johan de Molenaar': 0,\n",
       " 'Lode Zielens': 38,\n",
       " 'Joris Vriamont': 38,\n",
       " 'Willem Elsschot': 38,\n",
       " 'Jo Kalmijn-Spierenburg': 10,\n",
       " 'J. Karsemeijer': 4,\n",
       " 'Kees Klap': 10,\n",
       " 'P. Minderaa': 4,\n",
       " 'Vic Reinders': 0,\n",
       " 'L.G. Kortenhorst': 0,\n",
       " 'A.A. Smijers': 0,\n",
       " 'Victor J. Brunclair': 10,\n",
       " 'Jan van Gilse': 2,\n",
       " 'Paul Verbruggen': 38,\n",
       " 'Gabriëlle Demedts': 38,\n",
       " 'Gaston Burssens': 38,\n",
       " 'Herwig Hensen': 38,\n",
       " 'Thomas Mann': 10,\n",
       " 'Kasper Niehaus': 0,\n",
       " 'Marcel Matthijs': 38,\n",
       " 'René Berghen': 38,\n",
       " 'Staf Weyts': 38,\n",
       " 'W.H. Staverman': 2,\n",
       " 'Johan Huizinga': 0,\n",
       " 'Wiel van der Randen': 0,\n",
       " 'Dick Elffers': 10,\n",
       " 'C.J. Vos': 2,\n",
       " 'Bert Verbist': 38,\n",
       " 'A. van der Wey': 0,\n",
       " 'Charles Roelofsz': 38,\n",
       " 'G.D. Franquinet': 2,\n",
       " 'Robert Franquinet': 10,\n",
       " 'August Vermeylen': 38,\n",
       " 'Jacques Benoit': 38,\n",
       " 'Adrien Jans': 0,\n",
       " 'Albert Westerlinck': 4,\n",
       " 'A.G. Christiaens': 38,\n",
       " 'Hans Keilson': 38,\n",
       " 'Gustav Delory': 38,\n",
       " 'W.J.W. Koster': 0,\n",
       " 'Jan Prins': 0,\n",
       " 'J.D. Meerwaldt': 0,\n",
       " 'W.E.J. Kuiper': 0,\n",
       " 'Willem de Geus': 0,\n",
       " 'Jacob Hendrik van Duinen': 0,\n",
       " 'Joh. Hartog': 38,\n",
       " 'Johan Daisne': 38,\n",
       " 'Frans Buyle': 38,\n",
       " 'Herman van Snick': 38,\n",
       " 'O. Praem': 38,\n",
       " 'Eugenie Boeye': 38,\n",
       " 'Luc van Brabant': 38,\n",
       " 'Bert Peleman': 38,\n",
       " 'Bernard Delfgaauw': 0,\n",
       " 'Elisabeth Reitsma': 0,\n",
       " 'J.A. Worp': 4,\n",
       " 'E.F. Kossmann': 4,\n",
       " 'Roel Verlaan': 38,\n",
       " 'Albert C.A. Voortman': 2,\n",
       " 'Chris van der Weye': 0,\n",
       " 'A.G.M. van de Wijnpersse': 4,\n",
       " 'J.F. Willems': 38,\n",
       " 'Karel Jonckheere': 38,\n",
       " 'Nico van Suchtelen': 0,\n",
       " 'Amado Nervo': 0,\n",
       " 'G. Engelberg': 38,\n",
       " 'A.W. Grauls': 38,\n",
       " 'Frank Valkenier': 38,\n",
       " 'Jozef Simons': 38,\n",
       " 'Sjoerd Leiker': 10,\n",
       " 'Gery Helderenberg': 38,\n",
       " 'Marc Staels': 0,\n",
       " 'K.H.E. de Jong': 0,\n",
       " 'Frans Mijnssen': 0,\n",
       " 'Jeronimo de Vries': 2,\n",
       " 'Petrus Conradi': 2,\n",
       " 'P.J. Risseeuw': 10,\n",
       " 'Franz Zonneberg': 10,\n",
       " 'K.H. de Raaf': 0,\n",
       " 'N.A. Donkersloot': 0,\n",
       " 'J.A. Rispens': 10,\n",
       " 'Charles Baudelaire': 38,\n",
       " 'Frans Kusters': 38,\n",
       " 'D.A. de Graaf': 0,\n",
       " 'Jeanne Liedmeier': 2,\n",
       " 'Max Prick van Wely': 10,\n",
       " 'K.H. Miskotte': 0,\n",
       " 'L.J. Rogier': 4,\n",
       " 'Montanus': 5,\n",
       " 'P.C. Boeren': 4,\n",
       " 'G. Vos de Wael': 2,\n",
       " 'Frederik de Graaf': 38,\n",
       " 'Albert van Hoogenbemt': 38,\n",
       " 'Johan Berger': 38,\n",
       " 'Frans de Wilde': 0,\n",
       " 'Arnold Sauwen': 38,\n",
       " 'K.J. Hahn': 10,\n",
       " 'Victor E. van Vriesland': 10,\n",
       " 'Pierre H. Dubois': 10,\n",
       " 'Max Nord': 10,\n",
       " 'Gerrit Achterberg': 10,\n",
       " 'M. Rutten': 4,\n",
       " 'Frank Baur': 4,\n",
       " 'Alfred de Musset': 2,\n",
       " 'William Wordsworth': 38,\n",
       " 'F.J.J. Buytendijk': 0,\n",
       " 'James Joyce': 5,\n",
       " 'H.J.M.F. Lodewick': 4,\n",
       " 'R.C. Bakhuizen van den Brink': 2,\n",
       " 'J. van Delden': 10,\n",
       " 'William Butler Yeats': 10,\n",
       " 'D.C. Tinbergen': 4,\n",
       " 'J.W. Muller': 4,\n",
       " 'J. Prins': 0,\n",
       " 'A. Blok': 38,\n",
       " 'A. Marja': 10,\n",
       " 'H. de Vries': 2,\n",
       " 'Hein de Bruin': 10,\n",
       " 'W.J.C. van Hasselt': 2,\n",
       " 'Willem van den Berg': 4,\n",
       " 'A.C.W. Staring': 4,\n",
       " 'L.A. te Winkel': 5,\n",
       " 'J. Ackersdijck': 2,\n",
       " 'J.P. Arend': 2,\n",
       " 'F.A.W. Miquel': 2,\n",
       " 'Montanus de Haan Hettema': 2,\n",
       " 'H. Halbertsma': 2,\n",
       " 'Jacob Geel': 2,\n",
       " 'Wopke Eekhoff': 2,\n",
       " 'J.J.A. Goeverneur': 2,\n",
       " 'Henricus Bernardus Martini van Geffen': 0,\n",
       " 'Petrus de Raadt': 2,\n",
       " 'Vincent Loosjes': 0,\n",
       " 'Herman Pol': 2,\n",
       " 'W.J.A. Jonckbloet': 2,\n",
       " 'Lanceloot': 38,\n",
       " 'Johan Rudolph Thorbecke': 2,\n",
       " 'J.R. Thorbecke': 2,\n",
       " 'A.W. Engelen': 2,\n",
       " 'Joost Hiddes Halbertsma': 4,\n",
       " 'Willem de Clercq': 4,\n",
       " 'R.P.A. Dozy': 2,\n",
       " 'P.J. Veth': 2,\n",
       " 'S.J. van den Bergh': 2,\n",
       " 'P.T. Helvetius van den Bergh': 4,\n",
       " 'A. Kramer': 2,\n",
       " 'Johan de Wal': 2,\n",
       " 'J. Tideman': 2,\n",
       " 'J.I.D. Nepveu': 2,\n",
       " 'Gerrit de Clercq': 2,\n",
       " 'J. van de Velde': 38,\n",
       " 'H. Roose': 5,\n",
       " 'Hendrik Chistiaan Millies': 2,\n",
       " 'Friedrich Kaiser': 2,\n",
       " 'J.J. van Toorenenbergen': 2,\n",
       " 'J. van Tooren': 2,\n",
       " 'S. Vissering': 2,\n",
       " 'Dionys Burger': 2,\n",
       " 'L. Ali Cohen': 2,\n",
       " 'Isaäc da Costa': 4,\n",
       " 'W.R. van Hoëvell': 2,\n",
       " 'Henry Riehm': 2,\n",
       " 'C. Bakker': 2,\n",
       " 'Joannes Jacobus de Hollander': 2,\n",
       " 'P. Halbertsma': 2,\n",
       " 'Willem Gerard Brill': 2,\n",
       " 'E.W. Beth': 0,\n",
       " 'C.W. Opzoomer': 2,\n",
       " 'L.Ph.C. van den Bergh': 2,\n",
       " 'Salomon Müller': 2,\n",
       " 'G.E. Voorhelm Schneevoogt': 2,\n",
       " 'Ch. Lucas': 2,\n",
       " 'Willem Richard Boer': 2,\n",
       " 'Adam Smith': 2,\n",
       " 'J.J. Rovère van Breugel': 2,\n",
       " 'H. Engel': 0,\n",
       " 'Dirk Harting': 2,\n",
       " 'J. van Vloten': 2,\n",
       " 'Willem Ernst Johan Berg': 2,\n",
       " 'E.B. Swalue': 2,\n",
       " 'E. Piaget': 2,\n",
       " 'Jan Heemskerk Bzn.': 2,\n",
       " 'A.L. Lesturgeon': 2,\n",
       " 'A.V. Michiels': 2,\n",
       " 'Johannes Kneppelhout': 2,\n",
       " 'Christiaan Lodewijk Schuller tot Peursum': 2,\n",
       " 'P.W. Korthals': 2,\n",
       " 'A.C. Oudemans': 2,\n",
       " 'Carel Adriaan Engelbregt': 2,\n",
       " 'W. Koch': 38,\n",
       " 'Antonie Niermeyer': 2,\n",
       " 'J.J.L. ten Kate': 2,\n",
       " 'Jan Pieter Heije': 4,\n",
       " 'David Bierens de Haan': 2,\n",
       " 'Willem Jan Knoop': 2,\n",
       " 'Jan Heemskerk Azn.': 2,\n",
       " 'Aug. Philips': 2,\n",
       " 'H.J. Schimmel': 2,\n",
       " 'B. van Meer': 2,\n",
       " 'Johannes Zeeman': 2,\n",
       " 'J.H. van den Broek': 2,\n",
       " 'Lodewijk Willem van Deventer': 2,\n",
       " 'W.C. Mees': 2,\n",
       " 'Jakob Karel Willem Quarles van Ufford': 2,\n",
       " 'C.C. Huysmans': 2,\n",
       " 'H.J. van der Helm': 4,\n",
       " 'J. de Wit': 4,\n",
       " 'T.W.J. Juynboll': 2,\n",
       " 'H. van Herwerden': 2,\n",
       " 'D.J. Coster': 2,\n",
       " 'Jacob Leonard de Bruyn Kops': 2,\n",
       " 'W.J. Hofdijk': 2,\n",
       " 'Ernst Julius Kiehl': 2,\n",
       " 'John Murray': 2,\n",
       " 'Johan Carl Zimmerman': 2,\n",
       " 'Jozef Israëls': 0,\n",
       " 'P.A.S. van Limburg Brouwer': 2,\n",
       " 'C.A.J.A. Oudemans': 2,\n",
       " 'W. de Sitter': 2,\n",
       " 'F.M. Cowan': 2,\n",
       " 'A.J. de Bull': 2,\n",
       " 'Henriette Frederika Bosscha': 2,\n",
       " 'Hendrik Jacob van der Heim': 2,\n",
       " 'C.L. van Woelderen': 2,\n",
       " 'Eugen Mehler': 2,\n",
       " 'A. Jager': 2,\n",
       " 'J.C.G. Evers': 2,\n",
       " 'George Willem Vreede': 2,\n",
       " 'N.G. van Kampen': 2,\n",
       " 'W.H. Stenfert Kroese': 2,\n",
       " 'L.M. Heemskerk': 5,\n",
       " 'Marinus Lodewijk van Deventer': 2,\n",
       " 'A. Rientjes': 10,\n",
       " 'David Koning': 10,\n",
       " 'J.J. Viotta': 0,\n",
       " 'J. van Wijk': 2,\n",
       " 'C.W. van der Pot': 0,\n",
       " 'S.J. Hoekstra': 2,\n",
       " 'Arie de Jager': 2,\n",
       " 'L.A.J.W. Sloet': 2,\n",
       " 'C.H.D. Buys Ballot': 2,\n",
       " 'Johannes Bosscha': 2,\n",
       " 'Winand Carel Hugo Staring': 2,\n",
       " 'Pieter Arend Leupe': 4,\n",
       " 'M.M. von Baumhauer': 2,\n",
       " 'K.A. Rombach': 2,\n",
       " 'W.M. Perk': 2,\n",
       " 'Jan Willem van Sypesteyn': 2,\n",
       " 'F.C. Donders': 2,\n",
       " 'J. Pijnappel': 2,\n",
       " 'J. van Kuyk': 10,\n",
       " 'Theo Hermans': 5,\n",
       " 'F.J. Swartwout': 2,\n",
       " 'A. Wassenbergh': 0,\n",
       " 'Tjalling': 10,\n",
       " 'A. van Hengel': 2,\n",
       " 'Cornelis Hooyer': 2,\n",
       " 'H. Vos': 5,\n",
       " 'L.J. Egeling': 2,\n",
       " 'H.C. van Hall': 2,\n",
       " 'Sytze Hoekstra Bzn.': 2,\n",
       " 'J.G.A. Faber': 2,\n",
       " 'L.A.J. Burgersdijk': 2,\n",
       " 'J. Burger': 2,\n",
       " 'A. Kool': 2,\n",
       " 'L.R. Beynen': 2,\n",
       " 'A.W.M. van Hasselt': 2,\n",
       " 'Daniel Koorders': 2,\n",
       " 'Carl A.X.G.F. Sicherer': 2,\n",
       " 'C. Nicolaï': 2,\n",
       " 'Johannes Aletta Marinus Mensinga': 2,\n",
       " 'Jacobus Izaäk Doedes': 2,\n",
       " 'J.J.P. Valeton': 2,\n",
       " 'Adriaan Loosjes': 4,\n",
       " 'C.P. Tiele': 2,\n",
       " 'H. van Cappelle': 2,\n",
       " 'Jan Herman Herman de Ridder': 2,\n",
       " 'D.D. Büchler': 2,\n",
       " 'C. Dumortier': 38,\n",
       " 'R.T.H.P.L.A. van Boneval Faure': 2,\n",
       " 'B.M. Perk': 2,\n",
       " 'Pieter Nicolaas Muller': 2,\n",
       " 'Conradus Leemans': 2,\n",
       " 'J.J. de Gelder': 2,\n",
       " 'Joannes Tideman': 2,\n",
       " 'Gozewinus Acker Stratingh': 2,\n",
       " 'Samuel Adrianus Naber': 2,\n",
       " 'B.J. van Eyk': 0,\n",
       " 'Willem Doorenbos': 2,\n",
       " 'Guillaume Henri Marie Delprat': 2,\n",
       " 'J. Brugman': 0,\n",
       " 'J.T. Buys': 2,\n",
       " 'Elise van Calcar': 2,\n",
       " 'P.J. Hollman': 2,\n",
       " 'J.E. Banck': 2,\n",
       " 'C. de Groot': 5,\n",
       " 'A. Smits': 38,\n",
       " 'W.C.A. Staring': 2,\n",
       " 'Nicolaus Meursinge': 2,\n",
       " 'Mari Johan Mees': 2,\n",
       " 'W.C.D. Olivier': 2,\n",
       " 'August von Platen': 2,\n",
       " 'Lodewijk Willem Ernst Rauwenhoff': 2,\n",
       " 'B. van Geuns': 2,\n",
       " 'Lodewijk Mulder': 2,\n",
       " 'N.W.P. Rauwenhoff': 2,\n",
       " 'J.C. Reepmaker': 2,\n",
       " 'H.A. Wijnne': 2,\n",
       " 'Jan Jacob de Gelder': 2,\n",
       " 'Johan Adam Wijnne': 2,\n",
       " 'Jan Pieter Nicolaas Land': 2,\n",
       " 'Jan van Rossem': 2,\n",
       " 'Abraham Kuenen': 2,\n",
       " 'E. van der Ven': 2,\n",
       " 'Herman Franke': 2,\n",
       " 'Johannes Pols': 2,\n",
       " 'Adrianus Bruinvisch Maatjes': 2,\n",
       " 'Rhoda': 2,\n",
       " 'Albert Hendrik Arnoldus Ekker': 2,\n",
       " 'Ben Sloot': 2,\n",
       " 'H.C. Rogge': 2,\n",
       " 'R. Vos': 4,\n",
       " 'Jan Wit': 10,\n",
       " 'H. Burger': 2,\n",
       " 'Antony Winkler Prins': 2,\n",
       " 'Taco Roorda': 5,\n",
       " 'L.W.C. Keuchenius': 2,\n",
       " 'Simke Heerts Rinkes': 2,\n",
       " 'Prosper Mérimée': 2,\n",
       " 'C.P. Burger': 2,\n",
       " 'T.K.M. von Baumhauer': 2,\n",
       " 'W.M. Logeman': 2,\n",
       " 'Nicolas Charles Sieburgh': 2,\n",
       " 'H. Wouters': 2,\n",
       " 'H.P. van Karnebeek': 2,\n",
       " 'G.H. Betz': 2,\n",
       " 'M.H. Jansen': 2,\n",
       " 'H. Jansen': 2,\n",
       " 'B.W. Wttewaall van Wickenburgh': 2,\n",
       " 'John Keats': 38,\n",
       " 'Leigh Hunt': 0,\n",
       " 'Samuel Johnson': 0,\n",
       " 'A.S. Carpentier Alting': 0,\n",
       " 'Henry Wadsworth Longfellow': 0,\n",
       " 'Edgar Allan Poe': 10,\n",
       " 'W. Ruys': 2,\n",
       " 'D.W. Rost van Tonningen': 2,\n",
       " 'P.J.B.C. Robidé van der Aa': 2,\n",
       " 'J. Wttewaall': 2,\n",
       " 'Cd. Busken Huet': 2,\n",
       " 'J.P. Hasebroek': 2,\n",
       " 'H. Hardenberg': 0,\n",
       " 'C. Wolterbeek': 0,\n",
       " 'G.F. de Bruyn Kops': 2,\n",
       " 'T.T.H. Jorissen': 2,\n",
       " 'E.J.P. Jorissen': 2,\n",
       " 'Hendrik de Veer': 2,\n",
       " 'W.N. du Rieu': 2,\n",
       " 'S.J. Hingst': 2,\n",
       " 'C. Winkler': 2,\n",
       " 'Johan Willem Tydeman': 2,\n",
       " 'A. Viruly': 10,\n",
       " 'Anton Reinhard Falck': 2,\n",
       " 'Abraham Dirk Loman': 2,\n",
       " 'A.L.G. Bosboom-Toussaint': 2,\n",
       " 'Samuël Senior Coronel': 2,\n",
       " 'Johannes Margadant': 2,\n",
       " 'Hippoliet Bauduin': 2,\n",
       " 'H. van Lennep': 10,\n",
       " 'Tobias Michael Carel Asser': 2,\n",
       " 'P.A. Tiele': 2,\n",
       " 'T.J. Halbertsma': 2,\n",
       " 'Steven Blaupot ten Cate': 2,\n",
       " 'Johannes Philippus Heukelom': 2,\n",
       " 'Nikolaas Gerard Pierson': 2,\n",
       " 'Gerard Keller': 2,\n",
       " 'H.P.G. Quack': 2,\n",
       " 'J. ten Brink': 2,\n",
       " 'Julius Christiaan van Deventer': 2,\n",
       " 'Eelco Verwijs': 4,\n",
       " 'Firmin van der Poorten': 38,\n",
       " 'Johannes Tauler': 0,\n",
       " 'N.J. den Tex': 2,\n",
       " 'A.T. Reitsma': 2,\n",
       " 'Herman Agatho des Amorie van der Hoeven': 2,\n",
       " 'Hermannus Bouman': 2,\n",
       " 'David Jacob van Lennep': 4,\n",
       " 'J.F.B. Baert': 2,\n",
       " 'J. Beckering Vinckers': 2,\n",
       " 'F.P.J. Sibmacher Zìjnen': 2,\n",
       " 'Jan de Jong': 5,\n",
       " 'Samuel van Houten': 2,\n",
       " 'Pieter Rutger Feith': 2,\n",
       " 'R. Feith': 2,\n",
       " 'Gerrit Kuyper Hzn': 2,\n",
       " 'Raul': 2,\n",
       " 'George Eliot': 2,\n",
       " 'Thomas Kroon JHz.': 2,\n",
       " 'F.G. Steck': 2,\n",
       " 'J. Bogaerts': 2,\n",
       " 'H. de Lange': 2,\n",
       " 'Henri Ernest Moltzer': 2,\n",
       " 'D.E.W. Wolff': 2,\n",
       " 'R.S. Tjaden Modderman': 2,\n",
       " 'Nicolaas Jacob Bernard Kappeyne van de Coppello': 2,\n",
       " 'Isaäc Dornseiffen': 2,\n",
       " 'W.A. van Rees': 2,\n",
       " 'Tiberius Cornelius Winkler': 2,\n",
       " 'Izaak Jan Blaupot ten Cate': 2,\n",
       " 'Frans de Cort': 38,\n",
       " 'Nicolaas Bernhardus Donkersloot': 38,\n",
       " 'S.E. Harthoorn': 2,\n",
       " 'J.J. Meinsma': 2,\n",
       " 'G.H.J. Elliot Boswel': 2,\n",
       " 'W.P. Sautijn Kluit': 2,\n",
       " 'H.G. van de Sande Bakhuyzen': 2,\n",
       " 'A.H.G.P. van den Es': 2,\n",
       " 'Jan ten Brink': 2,\n",
       " 'G. Colmjon': 4,\n",
       " 'A. Kleijn': 0,\n",
       " 'Joan Karel Hendrik de Roo van Alderwerelt': 2,\n",
       " 'F.A. Snellaert': 5,\n",
       " 'H.Q. Janssen': 2,\n",
       " 'Michiel Johannes de Goeje': 2,\n",
       " 'H. Oort': 2,\n",
       " 'G.J. Grashuis': 2,\n",
       " 'J.B. Kan': 38,\n",
       " 'B.H.C.K. van der Wijck': 2,\n",
       " 'J.H. Swildens': 2,\n",
       " 'J.A. van Dijk': 2,\n",
       " 'John Bake': 5,\n",
       " 'N.P. van den Berg': 2,\n",
       " 'Folkertinus Nicolaas Sickenga': 2,\n",
       " 'W.A. Reiger': 2,\n",
       " 'C.A. Crommelin': 0,\n",
       " 'H. Neubronner van der Tuuk': 2,\n",
       " 'J.C. de Vries': 2,\n",
       " 'H. van der Hoeven': 0,\n",
       " 'T.J. Stieltjes': 2,\n",
       " 'A.J.A. Gerlach': 2,\n",
       " 'H.T. Karsten': 2,\n",
       " 'A. Quak': 5,\n",
       " 'Carel Vosmaer': 2,\n",
       " 'L. de Hartog': 2,\n",
       " 'A.E. Croockewit': 2,\n",
       " 'Meinardus Siderius Pols': 2,\n",
       " 'Piet van Os': 2,\n",
       " 'M.T.H. Perelaer': 2,\n",
       " 'Daniël Veegens': 2,\n",
       " 'A.C. de Jong': 4,\n",
       " 'Henricus Oort': 2,\n",
       " 'Abraham Seyne Kok': 2,\n",
       " 'Wilhelm Martin': 2,\n",
       " 'P.J. de Roode': 2,\n",
       " 'C.J.F. Mirandolle': 2,\n",
       " 'Simon Gorter': 2,\n",
       " 'W.C. van Manen': 2,\n",
       " 'Charles Boissevain': 2,\n",
       " 'Elizabeth Barrett Browning': 38,\n",
       " 'Johan Gram': 2,\n",
       " 'A.G.A. van Rappard': 2,\n",
       " 'Willem Johannes Franciscus Nuyens': 2,\n",
       " 'T.C. van der Kulk': 4,\n",
       " 'Jacob Gijsbert de Hoop Scheffer': 2,\n",
       " 'Dirk van Delft': 0,\n",
       " 'Joan Bohl': 2,\n",
       " 'Michaël Wilhelm Scheltema': 5,\n",
       " 'J. Gerritsen': 5,\n",
       " 'D. Huizinga': 2,\n",
       " 'A.C. Loffelt': 2,\n",
       " 'H.F.R. Hubrecht': 2,\n",
       " \"J. l' Ange Huet\": 2,\n",
       " 'François de Casembroot': 2,\n",
       " 'Isaac Arnold Lamping': 2,\n",
       " 'P. de Boer': 10,\n",
       " 'K.W. van Gorkom': 2,\n",
       " 'J.M. de Jong': 2,\n",
       " 'J.H. Holwerda': 2,\n",
       " 'Jan Persyn': 38,\n",
       " 'Anton Gillis Cornelis van Duyl': 5,\n",
       " 'H. Was': 4,\n",
       " 'W.F.R. Suringar': 2,\n",
       " 'Jérome Alexandre Sillem': 2,\n",
       " 'Tobias van Westrheene Wzn': 0,\n",
       " 'Jan Six': 2,\n",
       " 'C.M. Vos': 2,\n",
       " 'Cornelius Marius Kan': 2,\n",
       " 'J. Peters': 0,\n",
       " 'P.J.W. Teding van Berkhout': 2,\n",
       " 'Victor de Stuers': 2,\n",
       " 'Pertinax': 0,\n",
       " 'J.S.G. Gramberg': 2,\n",
       " 'Adriaan van Eck': 2,\n",
       " 'F.S. van Nierop': 2,\n",
       " 'Jan van der Hoeven': 10,\n",
       " 'Willem Pieter Wolters': 5,\n",
       " 'Francis Bacon': 10,\n",
       " 'Arthur Hugh Clough': 2,\n",
       " 'H. Kern': 2,\n",
       " 'S.J. Fockema Andreae': 0,\n",
       " 'Sadi de Gorter': 4,\n",
       " 'J. de Groot': 4,\n",
       " 'P. van Geer': 2,\n",
       " 'Martinus Leopold': 38,\n",
       " 'M.H. du Croo': 2,\n",
       " 'P.J. Cosijn': 2,\n",
       " 'D.J. Mom Visch': 2,\n",
       " 'G. de Bosch Kemper': 2,\n",
       " 'W. Scheffer': 2,\n",
       " 'Cornelis Bellaar Spruyt': 2,\n",
       " 'Pieter Lodewijk Muller': 2,\n",
       " 'Balthazar Heldring': 2,\n",
       " 'E.B. Kielstra': 2,\n",
       " 'Pieter Marinus Netscher': 2,\n",
       " 'Virginie Loveling': 38,\n",
       " 'Jean Baptist van Hugenpoth tot den Beerenclauw': 2,\n",
       " 'Hendrik Wolfgang van der Meij': 2,\n",
       " 'Th.H. MacGillavry': 2,\n",
       " 'N.G. Cnoop Koopmans': 2,\n",
       " 'J.N. van Hall': 0,\n",
       " 'A.J.W. Farncombe Sanders': 2,\n",
       " 'Johannes Bodel Nijenhuis': 2,\n",
       " 'Johan Hermann Christian Heyse': 2,\n",
       " 'D. de Loos': 2,\n",
       " 'Herman François Waller': 2,\n",
       " 'Hugo Beyerman': 2,\n",
       " 'Bruno Jz. Tideman': 2,\n",
       " 'H.J. van Ankum': 2,\n",
       " 'Pieter Heering': 2,\n",
       " 'Gerrit Mulder': 10,\n",
       " 'W.A. Verschuer': 2,\n",
       " 'W.C. Hojel': 2,\n",
       " 'C. Honigh': 2,\n",
       " 'J. Groot': 38,\n",
       " 'H. Baudet': 0,\n",
       " 'J. van Baal': 2,\n",
       " 'E. Land': 0,\n",
       " 'G. Burger': 2,\n",
       " 'Albert Jan ten Brink': 2,\n",
       " 'Wilhelm Scherer': 2,\n",
       " 'Jan Willem Gerbrandt van Oordt': 2,\n",
       " 'W. Burger': 10,\n",
       " 'L.M.J. Glasius': 2,\n",
       " 'H. van de Stadt': 2,\n",
       " 'A.J.C. Geerts': 5,\n",
       " 'Jacob Paulus Amersfoordt': 2,\n",
       " 'M.C.L. Lotsij': 0,\n",
       " 'R.A. Mees': 2,\n",
       " 'Jan Herman': 5,\n",
       " 'M.A.N. Rovers': 2,\n",
       " 'Pieter Antonie van der Lith': 2,\n",
       " 'Jan Hartog': 2,\n",
       " 'Jeronimo de Bosch Kemper': 2,\n",
       " 'G.A. van Hamel': 2,\n",
       " 'Floris der Kinderen Fzn.': 2,\n",
       " 'W. Heineken': 2,\n",
       " 'Adriaan Huet': 2,\n",
       " 'Barthold Jacob Lintelo de Geer van Jutphaas': 2,\n",
       " 'Daniël Hartevelt': 2,\n",
       " 'Elise Adelaïde Haighton': 2,\n",
       " 'Jan Ploeger': 4,\n",
       " 'R.P. Mees Azn.': 2,\n",
       " 'Samuel Muller': 2,\n",
       " 'Gerardus Petrus Booms': 2,\n",
       " 'Pierre Joseph Henry Baudet': 2,\n",
       " 'J.L. Hoorweg': 2,\n",
       " 'J.E. Enklaar': 38,\n",
       " 'Johan Winkler': 4,\n",
       " 'P. Koster': 2,\n",
       " 'Taco H. de Beer': 0,\n",
       " 'Max Rooses': 38,\n",
       " 'Frederik Christiaan de Brieder': 2,\n",
       " 'Hugo de Vries': 0,\n",
       " 'W.K. van Dedem': 2,\n",
       " 'A.S.C. Wallis': 0,\n",
       " 'F. de Witt Huberts': 2,\n",
       " 'Rolande': 2,\n",
       " 'Willem Bernard Johannes van Eyk': 0,\n",
       " 'J.C. de Marez Oyens': 2,\n",
       " 'Ewaldus Daniël Pijzel': 2,\n",
       " 'W. Koster Gzn.': 2,\n",
       " 'Henrick Witte': 2,\n",
       " 'M.C. Piepers': 2,\n",
       " 'J.A.F.L. van Heeckeren': 2,\n",
       " 'F.H. Boogaard': 0,\n",
       " 'Marie Henriëtte Koorders-Boeke': 2,\n",
       " 'Willem Hendrik de Beaufort': 2,\n",
       " 'H.J.A.M. Schaepman': 2,\n",
       " 'Willem Degenhardt': 2,\n",
       " 'Johan Hendrik Gallée': 2,\n",
       " ...}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_undirected = G.to_undirected()\n",
    "communities = community_louvain.best_partition(G_undirected)\n",
    "communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "882a424a",
   "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>Author</th>\n",
       "      <th>Community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albe</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Author  Community\n",
       "0       Albert Helman          0\n",
       "1  Hendrik Andriessen          0\n",
       "2  Henk Kuitenbrouwer          0\n",
       "3       Henri Bruning          0\n",
       "4           Jan Bruna          0\n",
       "5        Joep Nicolas         38\n",
       "6        Albert Kuyle          0\n",
       "7                Albe          2\n",
       "8        Jan Engelman          0\n",
       "9      Carel Scharten          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_df = pd.DataFrame.from_dict(communities, orient='index')\n",
    "communities_df = communities_df.rename_axis('Author').reset_index()\n",
    "communities_df.columns = ['Author', 'Community']\n",
    "\n",
    "communities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83b363ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save communities\n",
    "communities_df.to_csv(\"CCLAMP_Network_Communities.txt\", index=False, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086194a6",
   "metadata": {},
   "source": [
    "# Create list of authors and their DOB, DOD, POB and POD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36bff963",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read metadata file with the authors\n",
    "metadata_df = pd.read_csv(\"C-CLAMP_metadata_gender.txt\", sep=\"\\t\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2390460c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\548935938.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>DOB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>7 juni 1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Willem Maas</td>\n",
       "      <td>28 april 1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>7 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>19 november 1879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>28 januari 1898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>27 december 1895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>20 november 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>17 februari 1904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L.J.M. Feber</td>\n",
       "      <td>24 april 1885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>G.P.M. Knuvelder</td>\n",
       "      <td>25 januari 1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M. Bruna</td>\n",
       "      <td>30 januari 1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bernard Verhoeven</td>\n",
       "      <td>29 april 1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Adr. E. Maas</td>\n",
       "      <td>6 januari 1817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Marnix Gijsen</td>\n",
       "      <td>20 oktober 1899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ed. Hoornik</td>\n",
       "      <td>9 maart 1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Gerard Bruning</td>\n",
       "      <td>18 februari 1898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Author               DOB\n",
       "0                   NaN               NaN\n",
       "1          Jan Engelman       7 juni 1900\n",
       "2           Willem Maas     28 april 1897\n",
       "3          Joep Nicolas    6 oktober 1897\n",
       "4         Albert Helman   7 november 1903\n",
       "5    Willem Nieuwenhuis              1886\n",
       "6   Karel van den Oever  19 november 1879\n",
       "7            Wies Moens   28 januari 1898\n",
       "8   Eduard A. Serrarens  27 december 1895\n",
       "9    Henk Kuitenbrouwer  20 november 1903\n",
       "10            Jan Bruna  17 augustus 1876\n",
       "11         Albert Kuyle  17 februari 1904\n",
       "12         L.J.M. Feber     24 april 1885\n",
       "13     G.P.M. Knuvelder   25 januari 1902\n",
       "14             M. Bruna   30 januari 1903\n",
       "15    Bernard Verhoeven     29 april 1897\n",
       "16         Adr. E. Maas    6 januari 1817\n",
       "17        Marnix Gijsen   20 oktober 1899\n",
       "18          Ed. Hoornik      9 maart 1910\n",
       "19       Gerard Bruning  18 februari 1898"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make list of all authors and DOB\n",
    "\n",
    "# Select Author and DOB\n",
    "metadata = metadata_df[['Author', 'DOB']]\n",
    "\n",
    "# Flatten the dataframe\n",
    "flattened_data = []\n",
    "\n",
    "for index, row in metadata.iterrows():\n",
    "    authors = str(row['Author']).split('; ')\n",
    "    dobs = str(row['DOB']).split('; ')\n",
    "    \n",
    "    for author, dob in zip(authors, dobs):\n",
    "        flattened_data.append({'Author': author, 'DOB': dob})\n",
    "\n",
    "flattened_df = pd.DataFrame(flattened_data)\n",
    "\n",
    "# Replace 'NA' or 'nan' with NaN\n",
    "flattened_df.replace(['NA', 'nan'], np.nan, inplace=True)\n",
    "\n",
    "# Remove duplicate rows\n",
    "authors_dob_df = flattened_df.drop_duplicates()\n",
    "\n",
    "# Remove duplicate authors where the dob is nan\n",
    "# Loop over the DataFrame\n",
    "for author in authors_dob_df['Author'].unique():\n",
    "    author_entries = authors_dob_df[authors_dob_df['Author'] == author]\n",
    "    \n",
    "    # Check if there are multiple entries for the author\n",
    "    if len(author_entries) > 1:\n",
    "        # Keep the entry where DOB is not NaN\n",
    "        filtered_entry = author_entries.dropna(subset=['DOB']).iloc[0]\n",
    "        \n",
    "        # Drop other entries for the same author\n",
    "        authors_dob_df = authors_dob_df.drop(authors_dob_df[authors_dob_df['Author'] == author].index)\n",
    "        \n",
    "        # Append the filtered entry back to the DataFrame\n",
    "        authors_dob_df = authors_dob_df.append(filtered_entry, ignore_index=True)\n",
    "        \n",
    "# Display the flattened dataframe\n",
    "authors_dob_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4ad6f919",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DOB list\n",
    "authors_dob_df.to_csv(\"author_DOB_list.txt\", index=False, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e89a9246",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3313332636.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>POB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willem Maas</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>Antwerpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>Sint-Gillis-Bij-Dendermonde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>Maastricht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>L.J.M. Feber</td>\n",
       "      <td>Bergen op Zoom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G.P.M. Knuvelder</td>\n",
       "      <td>Arnhem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bernard Verhoeven</td>\n",
       "      <td>Arnhem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Adr. E. Maas</td>\n",
       "      <td>Rotterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M. Bruna</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ed. Hoornik</td>\n",
       "      <td>Den Haag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gerard Bruning</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sybold van Ravesteyn</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Jos. van der Kolk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>C.A. Terburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>J.A. Dom Gregorius de Wit</td>\n",
       "      <td>Hilversum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jean Cocteau</td>\n",
       "      <td>Maisons-Laffitte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Author                          POB\n",
       "0                         NaN                          NaN\n",
       "1                 Willem Maas                      Utrecht\n",
       "2                Joep Nicolas                          NaN\n",
       "3          Willem Nieuwenhuis                          NaN\n",
       "4         Karel van den Oever                    Antwerpen\n",
       "5                  Wies Moens  Sint-Gillis-Bij-Dendermonde\n",
       "6         Eduard A. Serrarens                   Maastricht\n",
       "7                L.J.M. Feber               Bergen op Zoom\n",
       "8            G.P.M. Knuvelder                       Arnhem\n",
       "9                   Jan Bruna                          NaN\n",
       "10          Bernard Verhoeven                       Arnhem\n",
       "11               Adr. E. Maas                    Rotterdam\n",
       "12                   M. Bruna                      Utrecht\n",
       "13                Ed. Hoornik                     Den Haag\n",
       "14             Gerard Bruning                    Amsterdam\n",
       "15       Sybold van Ravesteyn                          NaN\n",
       "16          Jos. van der Kolk                          NaN\n",
       "17               C.A. Terburg                          NaN\n",
       "18  J.A. Dom Gregorius de Wit                    Hilversum\n",
       "19               Jean Cocteau             Maisons-Laffitte"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make list of all authors and POB\n",
    "\n",
    "# Select Author and POB\n",
    "metadata = metadata_df[['Author', 'POB']]\n",
    "\n",
    "# Flatten the dataframe\n",
    "flattened_data = []\n",
    "\n",
    "for index, row in metadata.iterrows():\n",
    "    authors = str(row['Author']).split('; ')\n",
    "    pobs = str(row['POB']).split('; ')\n",
    "    \n",
    "    for author, pob in zip(authors, pobs):\n",
    "        flattened_data.append({'Author': author, 'POB': pob})\n",
    "\n",
    "flattened_df = pd.DataFrame(flattened_data)\n",
    "\n",
    "# Replace 'NA' or 'nan' with NaN\n",
    "flattened_df.replace(['NA', 'nan'], np.nan, inplace=True)\n",
    "\n",
    "# Remove duplicate rows\n",
    "authors_pob_df = flattened_df.drop_duplicates()\n",
    "\n",
    "# Remove duplicate authors where the pob is nan\n",
    "# Loop over the DataFrame\n",
    "for author in authors_pob_df['Author'].unique():\n",
    "    author_entries = authors_pob_df[authors_pob_df['Author'] == author]\n",
    "    \n",
    "    # Check if there are multiple entries for the author\n",
    "    if len(author_entries) > 1:\n",
    "        # Keep the entry where POB is not NaN\n",
    "        filtered_entry = author_entries.dropna(subset=['POB']).iloc[0]\n",
    "        \n",
    "        # Drop other entries for the same author\n",
    "        authors_pob_df = authors_pob_df.drop(authors_pob_df[authors_pob_df['Author'] == author].index)\n",
    "        \n",
    "        # Append the filtered entry back to the DataFrame\n",
    "        authors_pob_df = authors_pob_df.append(filtered_entry, ignore_index=True)\n",
    "        \n",
    "# Display the flattened dataframe\n",
    "authors_pob_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5bbb1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save POB list\n",
    "authors_pob_df.to_csv(\"author_POB_list.txt\", index=False, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac157ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\2728974229.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>POD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willem Maas</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>Antwerpen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>Geleen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>Roermond</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>L.J.M. Feber</td>\n",
       "      <td>Den Haag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>G.P.M. Knuvelder</td>\n",
       "      <td>Eindhoven</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bernard Verhoeven</td>\n",
       "      <td>Arnhem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adr. E. Maas</td>\n",
       "      <td>Scheveningen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M. Bruna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ed. Hoornik</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gerard Bruning</td>\n",
       "      <td>Nijmegen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Piet Visser</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jos. van der Kolk</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>C.A. Terburg</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>J.A. Dom Gregorius de Wit</td>\n",
       "      <td>Visp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Jean Cocteau</td>\n",
       "      <td>Milly-la-Forêt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wim Snitker</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Author             POD\n",
       "0                         NaN             NaN\n",
       "1                 Willem Maas         Utrecht\n",
       "2          Willem Nieuwenhuis             NaN\n",
       "3         Karel van den Oever       Antwerpen\n",
       "4                  Wies Moens          Geleen\n",
       "5         Eduard A. Serrarens        Roermond\n",
       "6                L.J.M. Feber        Den Haag\n",
       "7            G.P.M. Knuvelder       Eindhoven\n",
       "8                   Jan Bruna             NaN\n",
       "9           Bernard Verhoeven          Arnhem\n",
       "10               Adr. E. Maas    Scheveningen\n",
       "11                   M. Bruna             NaN\n",
       "12                Ed. Hoornik       Amsterdam\n",
       "13             Gerard Bruning        Nijmegen\n",
       "14                Piet Visser             NaN\n",
       "15          Jos. van der Kolk             NaN\n",
       "16               C.A. Terburg             NaN\n",
       "17  J.A. Dom Gregorius de Wit            Visp\n",
       "18               Jean Cocteau  Milly-la-Forêt\n",
       "19                Wim Snitker             NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make list of all authors and their POD\n",
    "\n",
    "# Select Author and POD\n",
    "metadata = metadata_df[['Author', 'POD']]\n",
    "\n",
    "# Flatten the dataframe\n",
    "flattened_data = []\n",
    "\n",
    "for index, row in metadata.iterrows():\n",
    "    authors = str(row['Author']).split('; ')\n",
    "    pods = str(row['POD']).split('; ')\n",
    "    \n",
    "    for author, pod in zip(authors, pods):\n",
    "        flattened_data.append({'Author': author, 'POD': pod})\n",
    "\n",
    "flattened_df = pd.DataFrame(flattened_data)\n",
    "\n",
    "# Replace 'NA' or 'nan' with NaN\n",
    "flattened_df.replace(['NA', 'nan'], np.nan, inplace=True)\n",
    "\n",
    "# Remove duplicate rows\n",
    "authors_pod_df = flattened_df.drop_duplicates()\n",
    "\n",
    "# Remove duplicate authors where the pod is nan\n",
    "# Loop over the DataFrame\n",
    "for author in authors_pod_df['Author'].unique():\n",
    "    author_entries = authors_pod_df[authors_pod_df['Author'] == author]\n",
    "    \n",
    "    # Check if there are multiple entries for the author\n",
    "    if len(author_entries) > 1:\n",
    "        # Keep the entry where pod is not NaN\n",
    "        filtered_entry = author_entries.dropna(subset=['POD']).iloc[0]\n",
    "        \n",
    "        # Drop other entries for the same author\n",
    "        authors_pod_df = authors_pod_df.drop(authors_pod_df[authors_pod_df['Author'] == author].index)\n",
    "        \n",
    "        # Append the filtered entry back to the DataFrame\n",
    "        authors_pod_df = authors_pod_df.append(filtered_entry, ignore_index=True)\n",
    "\n",
    "# Display the flattened dataframe\n",
    "authors_pod_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "86bddf46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save POD list\n",
    "authors_pod_df.to_csv(\"author_POD_list.txt\", index=False, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b75da5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
      "C:\\Users\\u0149275\\AppData\\Local\\Temp\\ipykernel_4992\\3036065502.py:38: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>DOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>20 maart 1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Willem Maas</td>\n",
       "      <td>6 mei 1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>25 juli 1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>10 juli 1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Willem Nieuwenhuis</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Karel van den Oever</td>\n",
       "      <td>6 oktober 1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wies Moens</td>\n",
       "      <td>5 februari 1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Eduard A. Serrarens</td>\n",
       "      <td>25 november 1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>11 maart 1931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>4 maart 1958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>L.J.M. Feber</td>\n",
       "      <td>3 juli 1964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>G.P.M. Knuvelder</td>\n",
       "      <td>29 juni 1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M. Bruna</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bernard Verhoeven</td>\n",
       "      <td>4 juni 1965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Adr. E. Maas</td>\n",
       "      <td>3 juli 1886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ed. Hoornik</td>\n",
       "      <td>1 maart 1970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gerard Bruning</td>\n",
       "      <td>8 oktober 1926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Piet Visser</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>J. Eigenhuis</td>\n",
       "      <td>16 oktober 1944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Author               DOD\n",
       "0                   NaN               NaN\n",
       "1          Jan Engelman     20 maart 1972\n",
       "2           Willem Maas        6 mei 1950\n",
       "3          Joep Nicolas      25 juli 1972\n",
       "4         Albert Helman      10 juli 1996\n",
       "5    Willem Nieuwenhuis              1935\n",
       "6   Karel van den Oever    6 oktober 1926\n",
       "7            Wies Moens   5 februari 1982\n",
       "8   Eduard A. Serrarens  25 november 1955\n",
       "9             Jan Bruna     11 maart 1931\n",
       "10         Albert Kuyle      4 maart 1958\n",
       "11         L.J.M. Feber       3 juli 1964\n",
       "12     G.P.M. Knuvelder      29 juni 1982\n",
       "13             M. Bruna               NaN\n",
       "14    Bernard Verhoeven       4 juni 1965\n",
       "15         Adr. E. Maas       3 juli 1886\n",
       "16          Ed. Hoornik      1 maart 1970\n",
       "17       Gerard Bruning    8 oktober 1926\n",
       "18          Piet Visser               NaN\n",
       "19         J. Eigenhuis   16 oktober 1944"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make list of all authors and DOD\n",
    "\n",
    "# Select Author and DOD\n",
    "metadata = metadata_df[['Author', 'DOD']]\n",
    "\n",
    "# Flatten the dataframe\n",
    "flattened_data = []\n",
    "\n",
    "for index, row in metadata.iterrows():\n",
    "    authors = str(row['Author']).split('; ')\n",
    "    dods = str(row['DOD']).split('; ')\n",
    "    \n",
    "    for author, dod in zip(authors, dods):\n",
    "        flattened_data.append({'Author': author, 'DOD': dod})\n",
    "\n",
    "flattened_df = pd.DataFrame(flattened_data)\n",
    "\n",
    "# Replace 'NA' or 'nan' with NaN\n",
    "flattened_df.replace(['NA', 'nan'], np.nan, inplace=True)\n",
    "\n",
    "# Remove duplicate rows\n",
    "authors_dod_df = flattened_df.drop_duplicates()\n",
    "\n",
    "# Remove duplicate authors where the dod is nan\n",
    "# Loop over the DataFrame\n",
    "for author in authors_dod_df['Author'].unique():\n",
    "    author_entries = authors_dod_df[authors_dod_df['Author'] == author]\n",
    "    \n",
    "    # Check if there are multiple entries for the author\n",
    "    if len(author_entries) > 1:\n",
    "        # Keep the entry where DOD is not NaN\n",
    "        filtered_entry = author_entries.dropna(subset=['DOD']).iloc[0]\n",
    "        \n",
    "        # Drop other entries for the same author\n",
    "        authors_dod_df = authors_dod_df.drop(authors_dod_df[authors_dod_df['Author'] == author].index)\n",
    "        \n",
    "        # Append the filtered entry back to the DataFrame\n",
    "        authors_dod_df = authors_dod_df.append(filtered_entry, ignore_index=True)\n",
    "        \n",
    "# Display the flattened dataframe\n",
    "authors_dod_df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0fc87993",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DOD list\n",
    "authors_dod_df.to_csv(\"author_DOD_list.txt\", index=False, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a0d3bc",
   "metadata": {},
   "source": [
    "# Merge communities with DOB, DOD, POB & POD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "77ad3d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Community</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POB</th>\n",
       "      <th>POD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>0</td>\n",
       "      <td>17 september 1892</td>\n",
       "      <td>12 april 1981</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>Haarlem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>0</td>\n",
       "      <td>20 november 1903</td>\n",
       "      <td>3 augustus 1974</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>0</td>\n",
       "      <td>10 juli 1900</td>\n",
       "      <td>17 januari 1983</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Nijmegen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>0</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "      <td>11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>38</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Steyl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>0</td>\n",
       "      <td>17 februari 1904</td>\n",
       "      <td>4 maart 1958</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Albe</td>\n",
       "      <td>2</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>10 oktober 1973</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Brussel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 juni 1900</td>\n",
       "      <td>20 maart 1972</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>0</td>\n",
       "      <td>14 maart 1878</td>\n",
       "      <td>30 oktober 1950</td>\n",
       "      <td>Middelburg</td>\n",
       "      <td>Florence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Author  Community                DOB              DOD  \\\n",
       "0       Albert Helman          0    7 november 1903     10 juli 1996   \n",
       "1  Hendrik Andriessen          0  17 september 1892    12 april 1981   \n",
       "2  Henk Kuitenbrouwer          0   20 november 1903  3 augustus 1974   \n",
       "3       Henri Bruning          0       10 juli 1900  17 januari 1983   \n",
       "4           Jan Bruna          0   17 augustus 1876    11 maart 1931   \n",
       "5        Joep Nicolas         38     6 oktober 1897     25 juli 1972   \n",
       "6        Albert Kuyle          0   17 februari 1904     4 maart 1958   \n",
       "7                Albe          2        8 juni 1902  10 oktober 1973   \n",
       "8        Jan Engelman          0        7 juni 1900    20 maart 1972   \n",
       "9      Carel Scharten          0      14 maart 1878  30 oktober 1950   \n",
       "\n",
       "          POB        POD  \n",
       "0  Paramaribo  Amsterdam  \n",
       "1     Haarlem    Haarlem  \n",
       "2     Utrecht    Utrecht  \n",
       "3   Amsterdam   Nijmegen  \n",
       "4         NaN        NaN  \n",
       "5         NaN      Steyl  \n",
       "6     Utrecht    Utrecht  \n",
       "7    Mechelen    Brussel  \n",
       "8     Utrecht  Amsterdam  \n",
       "9  Middelburg   Florence  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_df = pd.merge(communities_df, authors_dob_df, on='Author', how='left')\n",
    "communities_df = pd.merge(communities_df, authors_dod_df, on='Author', how='left')\n",
    "communities_df = pd.merge(communities_df, authors_pob_df, on='Author', how='left')\n",
    "communities_df = pd.merge(communities_df, authors_pod_df, on='Author', how='left')\n",
    "\n",
    "communities_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fa06bb",
   "metadata": {},
   "source": [
    "# Save communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7ae0bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities_df.to_csv(\"CCLAMP_Network_Communities.txt\", index=True, sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11c2d02",
   "metadata": {},
   "source": [
    "# How many members does each community have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8cd93d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities_df = pd.read_csv(\"CCLAMP_Network_Communities.txt\", sep=\"\\t\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9b9805b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>DOB</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>0</td>\n",
       "      <td>17 september 1892</td>\n",
       "      <td>12 april 1981</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>Haarlem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>0</td>\n",
       "      <td>20 november 1903</td>\n",
       "      <td>3 augustus 1974</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>0</td>\n",
       "      <td>10 juli 1900</td>\n",
       "      <td>17 januari 1983</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Nijmegen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>0</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "      <td>11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>38</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Steyl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>0</td>\n",
       "      <td>17 februari 1904</td>\n",
       "      <td>4 maart 1958</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Albe</td>\n",
       "      <td>2</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>10 oktober 1973</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Brussel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 juni 1900</td>\n",
       "      <td>20 maart 1972</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Amsterdam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>0</td>\n",
       "      <td>14 maart 1878</td>\n",
       "      <td>30 oktober 1950</td>\n",
       "      <td>Middelburg</td>\n",
       "      <td>Florence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0              Author  Community                DOB  \\\n",
       "0           0       Albert Helman          0    7 november 1903   \n",
       "1           1  Hendrik Andriessen          0  17 september 1892   \n",
       "2           2  Henk Kuitenbrouwer          0   20 november 1903   \n",
       "3           3       Henri Bruning          0       10 juli 1900   \n",
       "4           4           Jan Bruna          0   17 augustus 1876   \n",
       "5           5        Joep Nicolas         38     6 oktober 1897   \n",
       "6           6        Albert Kuyle          0   17 februari 1904   \n",
       "7           7                Albe          2        8 juni 1902   \n",
       "8           8        Jan Engelman          0        7 juni 1900   \n",
       "9           9      Carel Scharten          0      14 maart 1878   \n",
       "\n",
       "               DOD         POB        POD  \n",
       "0     10 juli 1996  Paramaribo  Amsterdam  \n",
       "1    12 april 1981     Haarlem    Haarlem  \n",
       "2  3 augustus 1974     Utrecht    Utrecht  \n",
       "3  17 januari 1983   Amsterdam   Nijmegen  \n",
       "4    11 maart 1931         NaN        NaN  \n",
       "5     25 juli 1972         NaN      Steyl  \n",
       "6     4 maart 1958     Utrecht    Utrecht  \n",
       "7  10 oktober 1973    Mechelen    Brussel  \n",
       "8    20 maart 1972     Utrecht  Amsterdam  \n",
       "9  30 oktober 1950  Middelburg   Florence  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7b33e6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = communities_df['Community'].value_counts()\n",
    "\n",
    "ax = x.plot(kind=\"bar\", figsize=(8, 10), color='#86bf91', zorder=2, width=0.85)\n",
    "\n",
    "# Despine\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Labels and title\n",
    "ax.set_xlabel(\"Community\", labelpad=20, weight='bold', size=12)\n",
    "ax.set_ylabel(\"Number of authors\", labelpad=20, weight='bold', size=12)\n",
    "ax.set_title(\"Number of authors per community\", size=12, weight='bold')\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "ax.bar_label(ax.containers[0])  # Add labels to the bars\n",
    "plt.tight_layout()  # Adjust layout\n",
    "ax.legend().set_visible(False)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb40236",
   "metadata": {},
   "source": [
    "# What is the most frequent POB per community?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b08b7571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Most Frequent PlaceOfBirth per Community:\n",
      "                      POB_1                 POB_2            POB_3  \\\n",
      "Community                                                            \n",
      "0          [Amsterdam, 132]       [Rotterdam, 51]   [Den Haag, 45]   \n",
      "1                                                                    \n",
      "2           [Amsterdam, 96]        [Den Haag, 48]  [Rotterdam, 31]   \n",
      "3           [Maastricht, 1]         [Portugal, 1]                    \n",
      "4           [Amsterdam, 40]       [Rotterdam, 22]   [Den Haag, 12]   \n",
      "5           [Amsterdam, 45]        [Den Haag, 23]  [Rotterdam, 19]   \n",
      "6           [Leeuwarden, 1]                                          \n",
      "7            [Amsterdam, 1]                                          \n",
      "8               [Brugge, 1]                                          \n",
      "9                                                                    \n",
      "10         [Amsterdam, 100]       [Rotterdam, 42]   [Den Haag, 38]   \n",
      "11                                                                   \n",
      "12           [Rotterdam, 1]   [St. Petersburg, 1]                    \n",
      "13                [Rome, 2]          [Vicenza, 2]       [Genua, 1]   \n",
      "14                                                                   \n",
      "15                                                                   \n",
      "16              [Goënga, 1]       [Den Helder, 1]                    \n",
      "17            [Portugal, 2]  [Altweerterheide, 1]    [Lissabon, 1]   \n",
      "18                                                                   \n",
      "19                                                                   \n",
      "20                                                                   \n",
      "21                                                                   \n",
      "22           [Boekarest, 1]                                          \n",
      "23                                                                   \n",
      "24                                                                   \n",
      "25                                                                   \n",
      "26                                                                   \n",
      "27                                                                   \n",
      "28           [Rotterdam, 1]                                          \n",
      "29               [Tunis, 1]          [Haarlem, 1]      [Servië, 1]   \n",
      "30             [Utrecht, 1]                                          \n",
      "31                                                                   \n",
      "32                                                                   \n",
      "33          [20ste eeuw, 1]           [Kielce, 1]                    \n",
      "34                                                                   \n",
      "35                                                                   \n",
      "36                                                                   \n",
      "37          [20ste eeuw, 2]                                          \n",
      "38          [Antwerpen, 59]            [Gent, 21]  [19de eeuw, 20]   \n",
      "39           [Roeselare, 1]                                          \n",
      "40             [Beersel, 1]         [Maldegem, 1]                    \n",
      "41          [Gyobingauk, 1]                                          \n",
      "42                                                                   \n",
      "43           [Amsterdam, 1]                                          \n",
      "44            [Den Haag, 1]        [Oudewater, 1]                    \n",
      "\n",
      "                      POB_4            POB_5  \n",
      "Community                                     \n",
      "0             [Utrecht, 38]  [Groningen, 24]  \n",
      "1                                             \n",
      "2             [Utrecht, 26]    [Haarlem, 26]  \n",
      "3                                             \n",
      "4          [Maastricht, 12]   [Antwerpen, 8]  \n",
      "5              [Leiden, 11]  [Paramaribo, 9]  \n",
      "6                                             \n",
      "7                                             \n",
      "8                                             \n",
      "9                                             \n",
      "10            [Utrecht, 22]  [Groningen, 19]  \n",
      "11                                            \n",
      "12                                            \n",
      "13                [Lviv, 1]        [Lwów, 1]  \n",
      "14                                            \n",
      "15                                            \n",
      "16                                            \n",
      "17                                            \n",
      "18                                            \n",
      "19                                            \n",
      "20                                            \n",
      "21                                            \n",
      "22                                            \n",
      "23                                            \n",
      "24                                            \n",
      "25                                            \n",
      "26                                            \n",
      "27                                            \n",
      "28                                            \n",
      "29               [Ukkel, 1]  [Paramaribo, 1]  \n",
      "30                                            \n",
      "31                                            \n",
      "32                                            \n",
      "33                                            \n",
      "34                                            \n",
      "35                                            \n",
      "36                                            \n",
      "37                                            \n",
      "38             [Brugge, 16]  [Amsterdam, 11]  \n",
      "39                                            \n",
      "40                                            \n",
      "41                                            \n",
      "42                                            \n",
      "43                                            \n",
      "44                                            \n"
     ]
    }
   ],
   "source": [
    "# Find the top 5 most frequent PlaceOfBirth values per community\n",
    "top_pob_per_community = communities_df.groupby('Community')['POB'].apply(lambda x: x.value_counts().head(5).reset_index(name='Frequency').values.tolist() if not x.empty else [])\n",
    "\n",
    "# Create DataFrames to display the results\n",
    "top_pob_per_community_df = pd.DataFrame(top_pob_per_community.tolist(), index=top_pob_per_community.index).fillna('')\n",
    "top_pob_per_community_df.columns = [f'POB_{i+1}' for i in range(5)]\n",
    "\n",
    "# Display the results\n",
    "print(\"Top 5 Most Frequent PlaceOfBirth per Community:\")\n",
    "print(top_pob_per_community_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4df9316",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Find the top 5 most frequent PlaceOfBirth values per community\n",
    "top_pob_per_community = communities_df.groupby('Community')['POB'].apply(lambda x: x.value_counts().head(5).reset_index(name='Frequency').values.tolist() if not x.empty else [])\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "top_pob_per_community_df = pd.DataFrame(top_pob_per_community.tolist(), index=top_pob_per_community.index).fillna('')\n",
    "top_pob_per_community_df.columns = [f'POB {i+1}' for i in range(5)]\n",
    "\n",
    "# Plotting the bar plot for Place of Birth\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for i in range(5):\n",
    "    plt.bar(np.arange(len(top_pob_per_community_df.index)) + i * 0.15, top_pob_per_community_df[f'POB {i+1}'].apply(lambda x: x[1] if isinstance(x, list) and len(x) > 1 else 0), width=0.15, label=f'POB {i+1}')\n",
    "\n",
    "plt.title('Top 5 Most Frequent Place of Birth per Community')\n",
    "plt.xticks(np.arange(len(top_pob_per_community_df.index)), top_pob_per_community_df.index)\n",
    "plt.legend()\n",
    "\n",
    "# Add labels to the bars\n",
    "for i, community in enumerate(top_pob_per_community_df.index):\n",
    "    for j in range(5):\n",
    "        label = top_pob_per_community_df.at[community, f'POB {j+1}']\n",
    "        if isinstance(label, list) and len(label) > 1:\n",
    "            plt.text(i + j * 0.15, label[1] + 0.05, label[0], ha='center', va='bottom', rotation=45, fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252c8900",
   "metadata": {},
   "source": [
    "# What is the most frequent POD per community?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bf913520",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 5 Most Frequent PlaceOfDeath per Community:\n",
      "                             POD_1             POD_2            POD_3  \\\n",
      "Community                                                               \n",
      "0                  [Den Haag, 109]  [Amsterdam, 101]     [Leiden, 47]   \n",
      "1                                                                       \n",
      "2                  [Den Haag, 102]   [Amsterdam, 59]     [Leiden, 41]   \n",
      "3                                                                       \n",
      "4                  [Amsterdam, 23]      [Leiden, 16]   [Den Haag, 14]   \n",
      "5                  [Amsterdam, 37]      [Leiden, 13]   [Den Haag, 10]   \n",
      "6                       [Breda, 1]                                      \n",
      "7                     [Utrecht, 1]                                      \n",
      "8          [Sint-Pieters-Jette, 1]                                      \n",
      "9                                                                       \n",
      "10                [Amsterdam, 100]    [Den Haag, 33]  [Rotterdam, 11]   \n",
      "11                                                                      \n",
      "12                  [Amsterdam, 1]                                      \n",
      "13                       [Rome, 4]     [Warschau, 4]      [Londen, 2]   \n",
      "14                                                                      \n",
      "15                     [Bogotá, 1]                                      \n",
      "16                   [Den Haag, 2]                                      \n",
      "17                   [Lissabon, 1]                                      \n",
      "18                  [Amsterdam, 1]                                      \n",
      "19                                                                      \n",
      "20                     [Athene, 1]                                      \n",
      "21                                                                      \n",
      "22                                                                      \n",
      "23                  [Stockholm, 1]                                      \n",
      "24                                                                      \n",
      "25                                                                      \n",
      "26                                                                      \n",
      "27                                                                      \n",
      "28                                                                      \n",
      "29           [Loenen a/d Vecht, 1]                                      \n",
      "30                    [Haarlem, 1]                                      \n",
      "31                                                                      \n",
      "32                                                                      \n",
      "33                     [Napels, 1]                                      \n",
      "34                                                                      \n",
      "35                                                                      \n",
      "36                                                                      \n",
      "37                                                                      \n",
      "38                 [Antwerpen, 41]     [Brussel, 23]       [Gent, 22]   \n",
      "39                                                                      \n",
      "40                                                                      \n",
      "41                    [Rangoon, 1]                                      \n",
      "42                                                                      \n",
      "43                                                                      \n",
      "44                   [Den Haag, 1]         [Deil, 1]                    \n",
      "\n",
      "                     POD_4                  POD_5  \n",
      "Community                                          \n",
      "0            [Utrecht, 35]          [Haarlem, 26]  \n",
      "1                                                  \n",
      "2            [Utrecht, 39]        [Groningen, 15]  \n",
      "3                                                  \n",
      "4            [Utrecht, 13]          [Nijmegen, 8]  \n",
      "5             [Utrecht, 7]         [Groningen, 6]  \n",
      "6                                                  \n",
      "7                                                  \n",
      "8                                                  \n",
      "9                                                  \n",
      "10         [Antwerpen, 10]            [Leiden, 9]  \n",
      "11                                                 \n",
      "12                                                 \n",
      "13             [Turijn, 2]  [Lido di Camaiore, 1]  \n",
      "14                                                 \n",
      "15                                                 \n",
      "16                                                 \n",
      "17                                                 \n",
      "18                                                 \n",
      "19                                                 \n",
      "20                                                 \n",
      "21                                                 \n",
      "22                                                 \n",
      "23                                                 \n",
      "24                                                 \n",
      "25                                                 \n",
      "26                                                 \n",
      "27                                                 \n",
      "28                                                 \n",
      "29                                                 \n",
      "30                                                 \n",
      "31                                                 \n",
      "32                                                 \n",
      "33                                                 \n",
      "34                                                 \n",
      "35                                                 \n",
      "36                                                 \n",
      "37                                                 \n",
      "38          [Den Haag, 13]        [Amsterdam, 12]  \n",
      "39                                                 \n",
      "40                                                 \n",
      "41                                                 \n",
      "42                                                 \n",
      "43                                                 \n",
      "44                                                 \n"
     ]
    }
   ],
   "source": [
    "# Find the top 5 most frequent PlaceOfDeath values per community\n",
    "top_pod_per_community = communities_df.groupby('Community')['POD'].apply(lambda x: x.value_counts().head(5).reset_index(name='Frequency').values.tolist() if not x.empty else [])\n",
    "\n",
    "# Create DataFrames to display the results\n",
    "top_pod_per_community_df = pd.DataFrame(top_pod_per_community.tolist(), index=top_pod_per_community.index).fillna('')\n",
    "top_pod_per_community_df.columns = [f'POD_{i+1}' for i in range(5)]\n",
    "\n",
    "# Display the results\n",
    "print(\"\\nTop 5 Most Frequent PlaceOfDeath per Community:\")\n",
    "print(top_pod_per_community_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c9e7f21b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Find the top 5 most frequent PlaceOfDeath values per community\n",
    "top_pod_per_community = communities_df.groupby('Community')['POD'].apply(lambda x: x.value_counts().head(5).reset_index(name='Frequency').values.tolist() if not x.empty else [])\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "top_pod_per_community_df = pd.DataFrame(top_pod_per_community.tolist(), index=top_pod_per_community.index).fillna('')\n",
    "top_pod_per_community_df.columns = [f'POD {i+1}' for i in range(5)]\n",
    "\n",
    "# Plotting the bar plot for Place of Death\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for i in range(5):\n",
    "    plt.bar(np.arange(len(top_pod_per_community_df.index)) + i * 0.15, top_pod_per_community_df[f'POD {i+1}'].apply(lambda x: x[1] if isinstance(x, list) and len(x) > 1 else 0), width=0.15, label=f'POD {i+1}')\n",
    "\n",
    "plt.title('Top 5 Most Frequent Place of Death per Community')\n",
    "plt.xticks(np.arange(len(top_pod_per_community_df.index)), top_pod_per_community_df.index)\n",
    "plt.legend()\n",
    "\n",
    "# Add labels to the bars\n",
    "for i, community in enumerate(top_pod_per_community_df.index):\n",
    "    for j in range(5):\n",
    "        label = top_pod_per_community_df.at[community, f'POD {j+1}']\n",
    "        if isinstance(label, list) and len(label) > 1:\n",
    "            plt.text(i + j * 0.15, label[1] + 0.05, label[0], ha='center', va='bottom', rotation=45, fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6dbabd0",
   "metadata": {},
   "source": [
    "# Which authors are the most important (according to centrality measures)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7c4b8bee",
   "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>Author</th>\n",
       "      <th>Harmonic</th>\n",
       "      <th>Closeness</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Betweenness</th>\n",
       "      <th>In-degree</th>\n",
       "      <th>Out-degree</th>\n",
       "      <th>Eigenvector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elisabeth Augustin</td>\n",
       "      <td>1971.128571</td>\n",
       "      <td>0.246153</td>\n",
       "      <td>0.004414</td>\n",
       "      <td>4.326378e-05</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>4.047689e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frank Ligtvoet</td>\n",
       "      <td>1799.169048</td>\n",
       "      <td>0.222343</td>\n",
       "      <td>0.006688</td>\n",
       "      <td>4.116129e-05</td>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>3.256413e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Gommeren</td>\n",
       "      <td>1396.711111</td>\n",
       "      <td>0.176384</td>\n",
       "      <td>0.000669</td>\n",
       "      <td>3.153200e-08</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>1.231055e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Willem Bijsterbosch</td>\n",
       "      <td>1590.119048</td>\n",
       "      <td>0.202468</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.754394e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pierre-Henri Simon</td>\n",
       "      <td>1338.673413</td>\n",
       "      <td>0.172600</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>1.127559e-04</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>2.377946e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F. Collin</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>3.552026e-97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G.A.J. Hazeu</td>\n",
       "      <td>1788.859524</td>\n",
       "      <td>0.226192</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>5.190570e-06</td>\n",
       "      <td>0.001739</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>1.222224e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>R. de Josselin de Jong</td>\n",
       "      <td>2123.488095</td>\n",
       "      <td>0.266325</td>\n",
       "      <td>0.003879</td>\n",
       "      <td>4.927031e-05</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>1.062600e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B.H. Kazemier</td>\n",
       "      <td>1580.168651</td>\n",
       "      <td>0.201666</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>2.046270e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Clarissa Jacobi</td>\n",
       "      <td>1820.133333</td>\n",
       "      <td>0.233108</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.067012e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Author     Harmonic  Closeness    Degree   Betweenness  \\\n",
       "0      Elisabeth Augustin  1971.128571   0.246153  0.004414  4.326378e-05   \n",
       "1          Frank Ligtvoet  1799.169048   0.222343  0.006688  4.116129e-05   \n",
       "2          Mario Gommeren  1396.711111   0.176384  0.000669  3.153200e-08   \n",
       "3     Willem Bijsterbosch  1590.119048   0.202468  0.000401  0.000000e+00   \n",
       "4      Pierre-Henri Simon  1338.673413   0.172600  0.000268  1.127559e-04   \n",
       "5               F. Collin     0.000000   0.000000  0.000134  0.000000e+00   \n",
       "6            G.A.J. Hazeu  1788.859524   0.226192  0.002541  5.190570e-06   \n",
       "7  R. de Josselin de Jong  2123.488095   0.266325  0.003879  4.927031e-05   \n",
       "8           B.H. Kazemier  1580.168651   0.201666  0.000268  0.000000e+00   \n",
       "9         Clarissa Jacobi  1820.133333   0.233108  0.000535  0.000000e+00   \n",
       "\n",
       "   In-degree  Out-degree   Eigenvector  \n",
       "0   0.001873    0.002541  4.047689e-03  \n",
       "1   0.003478    0.003210  3.256413e-03  \n",
       "2   0.000134    0.000535  1.231055e-04  \n",
       "3   0.000401    0.000000  2.754394e-04  \n",
       "4   0.000134    0.000134  2.377946e-05  \n",
       "5   0.000000    0.000134  3.552026e-97  \n",
       "6   0.001739    0.000803  1.222224e-03  \n",
       "7   0.003344    0.000535  1.062600e-02  \n",
       "8   0.000134    0.000134  2.046270e-04  \n",
       "9   0.000535    0.000000  1.067012e-03  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centrality_df = pd.read_csv(\"CCLAMP_Network_Centralities.txt\", sep=\"\\t\", encoding='utf-8')\n",
    "centrality_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dabb0872",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Author</th>\n",
       "      <th>Community</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POB</th>\n",
       "      <th>POD</th>\n",
       "      <th>Harmonic</th>\n",
       "      <th>Closeness</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Betweenness</th>\n",
       "      <th>In-degree</th>\n",
       "      <th>Out-degree</th>\n",
       "      <th>Eigenvector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Albert Helman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 november 1903</td>\n",
       "      <td>10 juli 1996</td>\n",
       "      <td>Paramaribo</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>2530.069048</td>\n",
       "      <td>0.307316</td>\n",
       "      <td>0.039593</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.027020</td>\n",
       "      <td>0.012574</td>\n",
       "      <td>0.050024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hendrik Andriessen</td>\n",
       "      <td>0</td>\n",
       "      <td>17 september 1892</td>\n",
       "      <td>12 april 1981</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>2112.869048</td>\n",
       "      <td>0.260140</td>\n",
       "      <td>0.008828</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>0.006956</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.011359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Henk Kuitenbrouwer</td>\n",
       "      <td>0</td>\n",
       "      <td>20 november 1903</td>\n",
       "      <td>3 augustus 1974</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>2116.452381</td>\n",
       "      <td>0.261323</td>\n",
       "      <td>0.030498</td>\n",
       "      <td>0.001064</td>\n",
       "      <td>0.005886</td>\n",
       "      <td>0.024612</td>\n",
       "      <td>0.015769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Henri Bruning</td>\n",
       "      <td>0</td>\n",
       "      <td>10 juli 1900</td>\n",
       "      <td>17 januari 1983</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Nijmegen</td>\n",
       "      <td>2421.095238</td>\n",
       "      <td>0.297748</td>\n",
       "      <td>0.013109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Jan Bruna</td>\n",
       "      <td>0</td>\n",
       "      <td>17 augustus 1876</td>\n",
       "      <td>11 maart 1931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1975.728571</td>\n",
       "      <td>0.247870</td>\n",
       "      <td>0.008159</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.006287</td>\n",
       "      <td>0.007280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Joep Nicolas</td>\n",
       "      <td>38</td>\n",
       "      <td>6 oktober 1897</td>\n",
       "      <td>25 juli 1972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Steyl</td>\n",
       "      <td>2132.319048</td>\n",
       "      <td>0.261310</td>\n",
       "      <td>0.009497</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>0.008026</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>0.024807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Albert Kuyle</td>\n",
       "      <td>0</td>\n",
       "      <td>17 februari 1904</td>\n",
       "      <td>4 maart 1958</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>2364.728571</td>\n",
       "      <td>0.286814</td>\n",
       "      <td>0.038523</td>\n",
       "      <td>0.001377</td>\n",
       "      <td>0.019663</td>\n",
       "      <td>0.018860</td>\n",
       "      <td>0.040630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Albe</td>\n",
       "      <td>2</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>10 oktober 1973</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>4004.716667</td>\n",
       "      <td>0.452943</td>\n",
       "      <td>0.300696</td>\n",
       "      <td>0.060445</td>\n",
       "      <td>0.297485</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.284444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Jan Engelman</td>\n",
       "      <td>0</td>\n",
       "      <td>7 juni 1900</td>\n",
       "      <td>20 maart 1972</td>\n",
       "      <td>Utrecht</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>2613.111905</td>\n",
       "      <td>0.317101</td>\n",
       "      <td>0.064874</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.031300</td>\n",
       "      <td>0.033574</td>\n",
       "      <td>0.067639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Carel Scharten</td>\n",
       "      <td>0</td>\n",
       "      <td>14 maart 1878</td>\n",
       "      <td>30 oktober 1950</td>\n",
       "      <td>Middelburg</td>\n",
       "      <td>Florence</td>\n",
       "      <td>2464.935714</td>\n",
       "      <td>0.303819</td>\n",
       "      <td>0.038390</td>\n",
       "      <td>0.003423</td>\n",
       "      <td>0.014312</td>\n",
       "      <td>0.024077</td>\n",
       "      <td>0.041210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0              Author  Community                DOB  \\\n",
       "0           0       Albert Helman          0    7 november 1903   \n",
       "1           1  Hendrik Andriessen          0  17 september 1892   \n",
       "2           2  Henk Kuitenbrouwer          0   20 november 1903   \n",
       "3           3       Henri Bruning          0       10 juli 1900   \n",
       "4           4           Jan Bruna          0   17 augustus 1876   \n",
       "5           5        Joep Nicolas         38     6 oktober 1897   \n",
       "6           6        Albert Kuyle          0   17 februari 1904   \n",
       "7           7                Albe          2        8 juni 1902   \n",
       "8           8        Jan Engelman          0        7 juni 1900   \n",
       "9           9      Carel Scharten          0      14 maart 1878   \n",
       "\n",
       "               DOD         POB        POD     Harmonic  Closeness    Degree  \\\n",
       "0     10 juli 1996  Paramaribo  Amsterdam  2530.069048   0.307316  0.039593   \n",
       "1    12 april 1981     Haarlem    Haarlem  2112.869048   0.260140  0.008828   \n",
       "2  3 augustus 1974     Utrecht    Utrecht  2116.452381   0.261323  0.030498   \n",
       "3  17 januari 1983   Amsterdam   Nijmegen  2421.095238   0.297748  0.013109   \n",
       "4    11 maart 1931         NaN        NaN  1975.728571   0.247870  0.008159   \n",
       "5     25 juli 1972         NaN      Steyl  2132.319048   0.261310  0.009497   \n",
       "6     4 maart 1958     Utrecht    Utrecht  2364.728571   0.286814  0.038523   \n",
       "7  10 oktober 1973    Mechelen    Brussel  4004.716667   0.452943  0.300696   \n",
       "8    20 maart 1972     Utrecht  Amsterdam  2613.111905   0.317101  0.064874   \n",
       "9  30 oktober 1950  Middelburg   Florence  2464.935714   0.303819  0.038390   \n",
       "\n",
       "   Betweenness  In-degree  Out-degree  Eigenvector  \n",
       "0     0.002856   0.027020    0.012574     0.050024  \n",
       "1     0.000422   0.006956    0.001873     0.011359  \n",
       "2     0.001064   0.005886    0.024612     0.015769  \n",
       "3     0.000000   0.013109    0.000000     0.032193  \n",
       "4     0.000123   0.001873    0.006287     0.007280  \n",
       "5     0.000656   0.008026    0.001471     0.024807  \n",
       "6     0.001377   0.019663    0.018860     0.040630  \n",
       "7     0.060445   0.297485    0.003210     0.284444  \n",
       "8     0.009542   0.031300    0.033574     0.067639  \n",
       "9     0.003423   0.014312    0.024077     0.041210  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.merge(communities_df, centrality_df, on='Author', how='left')\n",
    "merged_df.to_csv(\"CCLAMP_Network_Communities_Centrality.txt\", index=True, sep='\\t', encoding='utf-8')\n",
    "merged_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b1d3532",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Author</th>\n",
       "      <th>Community</th>\n",
       "      <th>DOB</th>\n",
       "      <th>DOD</th>\n",
       "      <th>POB</th>\n",
       "      <th>POD</th>\n",
       "      <th>Harmonic</th>\n",
       "      <th>Closeness</th>\n",
       "      <th>Degree</th>\n",
       "      <th>Betweenness</th>\n",
       "      <th>In-degree</th>\n",
       "      <th>Out-degree</th>\n",
       "      <th>Eigenvector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Albe</td>\n",
       "      <td>2</td>\n",
       "      <td>8 juni 1902</td>\n",
       "      <td>10 oktober 1973</td>\n",
       "      <td>Mechelen</td>\n",
       "      <td>Brussel</td>\n",
       "      <td>4004.716667</td>\n",
       "      <td>0.452943</td>\n",
       "      <td>0.300696</td>\n",
       "      <td>0.060445</td>\n",
       "      <td>0.297485</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.284444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>Multatuli</td>\n",
       "      <td>0</td>\n",
       "      <td>2 maart 1820</td>\n",
       "      <td>19 februari 1887</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Nieder-Ingelheim</td>\n",
       "      <td>3233.202381</td>\n",
       "      <td>0.375051</td>\n",
       "      <td>0.133494</td>\n",
       "      <td>0.013521</td>\n",
       "      <td>0.132156</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.165608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>330</td>\n",
       "      <td>Martinus Nijhoff</td>\n",
       "      <td>0</td>\n",
       "      <td>20 april 1894</td>\n",
       "      <td>26 januari 1953</td>\n",
       "      <td>Den Haag</td>\n",
       "      <td>Den Haag</td>\n",
       "      <td>3143.335714</td>\n",
       "      <td>0.364776</td>\n",
       "      <td>0.144329</td>\n",
       "      <td>0.033983</td>\n",
       "      <td>0.118914</td>\n",
       "      <td>0.025415</td>\n",
       "      <td>0.136109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>Guido Gezelle</td>\n",
       "      <td>38</td>\n",
       "      <td>1 mei 1830</td>\n",
       "      <td>27 november 1899</td>\n",
       "      <td>Brugge</td>\n",
       "      <td>Brugge</td>\n",
       "      <td>2883.135714</td>\n",
       "      <td>0.339832</td>\n",
       "      <td>0.077849</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>0.077047</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.136622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Albert Verwey</td>\n",
       "      <td>0</td>\n",
       "      <td>15 mei 1865</td>\n",
       "      <td>8 maart 1937</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Noordwijk</td>\n",
       "      <td>2832.685714</td>\n",
       "      <td>0.336064</td>\n",
       "      <td>0.070894</td>\n",
       "      <td>0.003619</td>\n",
       "      <td>0.065811</td>\n",
       "      <td>0.005083</td>\n",
       "      <td>0.109928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>A. Roland Holst</td>\n",
       "      <td>10</td>\n",
       "      <td>23 mei 1888</td>\n",
       "      <td>6 augustus 1976</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Bergen</td>\n",
       "      <td>2810.835714</td>\n",
       "      <td>0.334833</td>\n",
       "      <td>0.073970</td>\n",
       "      <td>0.007870</td>\n",
       "      <td>0.060861</td>\n",
       "      <td>0.013109</td>\n",
       "      <td>0.111440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>427</td>\n",
       "      <td>Thomas Mann</td>\n",
       "      <td>10</td>\n",
       "      <td>6 juni 1875</td>\n",
       "      <td>12 augustus 1955</td>\n",
       "      <td>Lübeck</td>\n",
       "      <td>Zürich</td>\n",
       "      <td>2749.669048</td>\n",
       "      <td>0.329342</td>\n",
       "      <td>0.053371</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>0.053237</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.070986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>Marnix Gijsen</td>\n",
       "      <td>38</td>\n",
       "      <td>20 oktober 1899</td>\n",
       "      <td>29 september 1984</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Leuven</td>\n",
       "      <td>2749.335714</td>\n",
       "      <td>0.327790</td>\n",
       "      <td>0.086811</td>\n",
       "      <td>0.009279</td>\n",
       "      <td>0.056447</td>\n",
       "      <td>0.030364</td>\n",
       "      <td>0.113986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>Herman Gorter</td>\n",
       "      <td>0</td>\n",
       "      <td>26 november 1864</td>\n",
       "      <td>15 september 1927</td>\n",
       "      <td>Wormerveer</td>\n",
       "      <td>St-Joost-Ten-Noode, Brussel</td>\n",
       "      <td>2742.185714</td>\n",
       "      <td>0.327964</td>\n",
       "      <td>0.051632</td>\n",
       "      <td>0.001945</td>\n",
       "      <td>0.051097</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.092512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>Willem Kloos</td>\n",
       "      <td>0</td>\n",
       "      <td>6 mei 1859</td>\n",
       "      <td>31 maart 1938</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Den Haag</td>\n",
       "      <td>2741.969048</td>\n",
       "      <td>0.329474</td>\n",
       "      <td>0.056314</td>\n",
       "      <td>0.005775</td>\n",
       "      <td>0.048422</td>\n",
       "      <td>0.007892</td>\n",
       "      <td>0.097182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>119</td>\n",
       "      <td>Anton van Duinkerken</td>\n",
       "      <td>0</td>\n",
       "      <td>2 januari 1903</td>\n",
       "      <td>27 juli 1968</td>\n",
       "      <td>Bergen op Zoom</td>\n",
       "      <td>Nijmegen</td>\n",
       "      <td>2740.861905</td>\n",
       "      <td>0.324602</td>\n",
       "      <td>0.135902</td>\n",
       "      <td>0.033313</td>\n",
       "      <td>0.059524</td>\n",
       "      <td>0.076378</td>\n",
       "      <td>0.104881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Frederik van Eeden</td>\n",
       "      <td>0</td>\n",
       "      <td>3 april 1860</td>\n",
       "      <td>16 juni 1932</td>\n",
       "      <td>Haarlem</td>\n",
       "      <td>Bussum</td>\n",
       "      <td>2736.435714</td>\n",
       "      <td>0.326685</td>\n",
       "      <td>0.054708</td>\n",
       "      <td>0.002194</td>\n",
       "      <td>0.053371</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.082131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160</td>\n",
       "      <td>Novalis</td>\n",
       "      <td>0</td>\n",
       "      <td>2 mei 1772</td>\n",
       "      <td>25 maart 1801</td>\n",
       "      <td>Wiederstedt</td>\n",
       "      <td>Weissenfels</td>\n",
       "      <td>2723.552381</td>\n",
       "      <td>0.329892</td>\n",
       "      <td>0.042536</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>0.042402</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.082579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>Karel van de Woestijne</td>\n",
       "      <td>38</td>\n",
       "      <td>10 maart 1878</td>\n",
       "      <td>24 augustus 1929</td>\n",
       "      <td>Gent</td>\n",
       "      <td>Zwijnaarde</td>\n",
       "      <td>2711.552381</td>\n",
       "      <td>0.326469</td>\n",
       "      <td>0.047084</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.045211</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.097412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>238</td>\n",
       "      <td>Bergman</td>\n",
       "      <td>38</td>\n",
       "      <td>11 september 1921</td>\n",
       "      <td>12 januari 2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Den Haag</td>\n",
       "      <td>2710.735714</td>\n",
       "      <td>0.324944</td>\n",
       "      <td>0.048154</td>\n",
       "      <td>0.001964</td>\n",
       "      <td>0.048020</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.075239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Dirk Coster</td>\n",
       "      <td>0</td>\n",
       "      <td>7 juli 1887</td>\n",
       "      <td>8 oktober 1956</td>\n",
       "      <td>Delft</td>\n",
       "      <td>Delft</td>\n",
       "      <td>2708.919048</td>\n",
       "      <td>0.326663</td>\n",
       "      <td>0.058587</td>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.043874</td>\n",
       "      <td>0.014714</td>\n",
       "      <td>0.096214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>Menno ter Braak</td>\n",
       "      <td>0</td>\n",
       "      <td>26 januari 1902</td>\n",
       "      <td>15 mei 1940</td>\n",
       "      <td>Eibergen</td>\n",
       "      <td>Den Haag</td>\n",
       "      <td>2688.378571</td>\n",
       "      <td>0.321518</td>\n",
       "      <td>0.062734</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.046683</td>\n",
       "      <td>0.016051</td>\n",
       "      <td>0.085248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>163</td>\n",
       "      <td>Stijn Streuvels</td>\n",
       "      <td>38</td>\n",
       "      <td>3 oktober 1871</td>\n",
       "      <td>15 augustus 1969</td>\n",
       "      <td>Heule</td>\n",
       "      <td>Ingooigem (Anzegem)</td>\n",
       "      <td>2665.085714</td>\n",
       "      <td>0.318861</td>\n",
       "      <td>0.048020</td>\n",
       "      <td>0.002075</td>\n",
       "      <td>0.046683</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.097347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>2326</td>\n",
       "      <td>Lucebert</td>\n",
       "      <td>10</td>\n",
       "      <td>15 september 1924</td>\n",
       "      <td>10 mei 1994</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Alkmaar</td>\n",
       "      <td>2661.692857</td>\n",
       "      <td>0.315058</td>\n",
       "      <td>0.055778</td>\n",
       "      <td>0.002006</td>\n",
       "      <td>0.052836</td>\n",
       "      <td>0.002943</td>\n",
       "      <td>0.063955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>190</td>\n",
       "      <td>P.N. van Eyck</td>\n",
       "      <td>0</td>\n",
       "      <td>1 oktober 1887</td>\n",
       "      <td>10 april 1954</td>\n",
       "      <td>Breukelen</td>\n",
       "      <td>Wassenaar</td>\n",
       "      <td>2657.552381</td>\n",
       "      <td>0.316471</td>\n",
       "      <td>0.050829</td>\n",
       "      <td>0.000906</td>\n",
       "      <td>0.043339</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.083212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                  Author  Community                DOB  \\\n",
       "7              7                    Albe          2        8 juni 1902   \n",
       "33            33               Multatuli          0       2 maart 1820   \n",
       "330          330        Martinus Nijhoff          0      20 april 1894   \n",
       "127          127           Guido Gezelle         38         1 mei 1830   \n",
       "34            34           Albert Verwey          0        15 mei 1865   \n",
       "61            61         A. Roland Holst         10        23 mei 1888   \n",
       "427          427             Thomas Mann         10        6 juni 1875   \n",
       "69            69           Marnix Gijsen         38    20 oktober 1899   \n",
       "37            37           Herman Gorter          0   26 november 1864   \n",
       "51            51            Willem Kloos          0         6 mei 1859   \n",
       "119          119    Anton van Duinkerken          0     2 januari 1903   \n",
       "20            20      Frederik van Eeden          0       3 april 1860   \n",
       "160          160                 Novalis          0         2 mei 1772   \n",
       "47            47  Karel van de Woestijne         38      10 maart 1878   \n",
       "238          238                 Bergman         38  11 september 1921   \n",
       "14            14             Dirk Coster          0        7 juli 1887   \n",
       "91            91         Menno ter Braak          0    26 januari 1902   \n",
       "163          163         Stijn Streuvels         38     3 oktober 1871   \n",
       "2326        2326                Lucebert         10  15 september 1924   \n",
       "190          190           P.N. van Eyck          0     1 oktober 1887   \n",
       "\n",
       "                    DOD             POB                          POD  \\\n",
       "7       10 oktober 1973        Mechelen                      Brussel   \n",
       "33     19 februari 1887       Amsterdam             Nieder-Ingelheim   \n",
       "330     26 januari 1953        Den Haag                     Den Haag   \n",
       "127    27 november 1899          Brugge                       Brugge   \n",
       "34         8 maart 1937       Amsterdam                    Noordwijk   \n",
       "61      6 augustus 1976       Amsterdam                       Bergen   \n",
       "427    12 augustus 1955          Lübeck                       Zürich   \n",
       "69    29 september 1984       Antwerpen                       Leuven   \n",
       "37    15 september 1927      Wormerveer  St-Joost-Ten-Noode, Brussel   \n",
       "51        31 maart 1938       Amsterdam                     Den Haag   \n",
       "119        27 juli 1968  Bergen op Zoom                     Nijmegen   \n",
       "20         16 juni 1932         Haarlem                       Bussum   \n",
       "160       25 maart 1801     Wiederstedt                  Weissenfels   \n",
       "47     24 augustus 1929            Gent                   Zwijnaarde   \n",
       "238     12 januari 2009             NaN                     Den Haag   \n",
       "14       8 oktober 1956           Delft                        Delft   \n",
       "91          15 mei 1940        Eibergen                     Den Haag   \n",
       "163    15 augustus 1969           Heule          Ingooigem (Anzegem)   \n",
       "2326        10 mei 1994       Amsterdam                      Alkmaar   \n",
       "190       10 april 1954       Breukelen                    Wassenaar   \n",
       "\n",
       "         Harmonic  Closeness    Degree  Betweenness  In-degree  Out-degree  \\\n",
       "7     4004.716667   0.452943  0.300696     0.060445   0.297485    0.003210   \n",
       "33    3233.202381   0.375051  0.133494     0.013521   0.132156    0.001338   \n",
       "330   3143.335714   0.364776  0.144329     0.033983   0.118914    0.025415   \n",
       "127   2883.135714   0.339832  0.077849     0.001979   0.077047    0.000803   \n",
       "34    2832.685714   0.336064  0.070894     0.003619   0.065811    0.005083   \n",
       "61    2810.835714   0.334833  0.073970     0.007870   0.060861    0.013109   \n",
       "427   2749.669048   0.329342  0.053371     0.001044   0.053237    0.000134   \n",
       "69    2749.335714   0.327790  0.086811     0.009279   0.056447    0.030364   \n",
       "37    2742.185714   0.327964  0.051632     0.001945   0.051097    0.000535   \n",
       "51    2741.969048   0.329474  0.056314     0.005775   0.048422    0.007892   \n",
       "119   2740.861905   0.324602  0.135902     0.033313   0.059524    0.076378   \n",
       "20    2736.435714   0.326685  0.054708     0.002194   0.053371    0.001338   \n",
       "160   2723.552381   0.329892  0.042536     0.001992   0.042402    0.000134   \n",
       "47    2711.552381   0.326469  0.047084     0.001275   0.045211    0.001873   \n",
       "238   2710.735714   0.324944  0.048154     0.001964   0.048020    0.000134   \n",
       "14    2708.919048   0.326663  0.058587     0.003051   0.043874    0.014714   \n",
       "91    2688.378571   0.321518  0.062734     0.004400   0.046683    0.016051   \n",
       "163   2665.085714   0.318861  0.048020     0.002075   0.046683    0.001338   \n",
       "2326  2661.692857   0.315058  0.055778     0.002006   0.052836    0.002943   \n",
       "190   2657.552381   0.316471  0.050829     0.000906   0.043339    0.007491   \n",
       "\n",
       "      Eigenvector  \n",
       "7        0.284444  \n",
       "33       0.165608  \n",
       "330      0.136109  \n",
       "127      0.136622  \n",
       "34       0.109928  \n",
       "61       0.111440  \n",
       "427      0.070986  \n",
       "69       0.113986  \n",
       "37       0.092512  \n",
       "51       0.097182  \n",
       "119      0.104881  \n",
       "20       0.082131  \n",
       "160      0.082579  \n",
       "47       0.097412  \n",
       "238      0.075239  \n",
       "14       0.096214  \n",
       "91       0.085248  \n",
       "163      0.097347  \n",
       "2326     0.063955  \n",
       "190      0.083212  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 100 authors sorted by Harmonic centrality\n",
    "top_100_authors = merged_df.nlargest(100, 'Harmonic')\n",
    "\n",
    "top_100_authors.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c0d3ea89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bfvvty/0U+9xzz833reyIxVe1r7V2Wb169bIFCxbk27t3755ftnaKU1WPU6X3p/vvv3+95/7GN76RRXz5ifsFF1yQDRs2LBszZky2ZMmSSm230tvinXfeybc/88wz+fa99947y7Isu+qqqyp1LP/kk0/W227XXHNNpeqZOnVq/mdKSkrWm0a2Vsrxdr/99sv3veOOO/LtPXv2zLf/5Cc/ybf/+c9/Lnd7bezfz5QRi9L7zI9//ONya6zo+L+hvwuLFi3KmjVrVub3fdddd+V/pvQMBNIZsaAgtttuu7jkkksi4stP9CtzxZ+s1KcSlTk5rlOnThHx5SdhpXXs2DEiIv+JW8SXIxPlad++ff5+LpeLHXfcMf947ty5ZT7x3pB58+at19atW7dKX22o9POUrmmbbbbJf4K1atWqMldCqk4VPX9ElPnku7ztsW7/0o/nzp2bX0fpyxHncrmNuhxn69at8/fXvbRq6X1sbb+K+pfu26BBg2jRokWla9iY/W7BggX5fbp58+ZlRk/K277HHXdc/PjHP47GjRvH+PHj42c/+1l8//vfj69//evRvXv3+Pjjjytd78Za+zojyr7WjX1/rft47ty5Vd4upW233XZl3q+VsXjx4ujZs2fcfffd8f7778eKFSvW61PRCGvpK401adIkf7+i115Z//znP+OQQw6JJ598MmbNmlXuJZArqqmqx8wNadOmTWyzzTb5x+W9xpTjVPfu3ddr+8Mf/hBdu3aNzz77LIYOHRr/93//F4ccckhst912cfPNN1ep/tL1lHcMquyxvLzRy/JqL8/s2bPz9zt06BBNmzYtt1/K8far3pcRlXtvVsffz6ruf5vqPdSsWbM4++yzIyLi1VdfjQkTJuRH+Vq1ahXHH3980vopS7CgYC677LJo2bJlLFiwIJ588sly+zRq1Ch/v/RVXaZOnfqV669fv/yrKVfUXp4PPvggfz/Lsvjwww/zj1u1alXmH9Ndd901PwS77m3w4MHrrbtx48aVrqP085SuaeHChfHZZ59FxJevqyr/AK8rl8tV+fkjoszVOUr3q6h/6ccbmjKwMdZOcYiI9aaflX683377bbD/66+/nr/frVu3aNCgQaVr2Jj9rkWLFvnlixYtKhNsKtq+a694NXHixPjLX/4S559/fkR8+Yfzmmuuyffb0O81RXW/v9Z93KpVq43aLmtV9P7a0PZ47rnnYtasWRERsccee8T7778fa9asiSlTpnzlaym9j1T0HBvzuxg+fHh+2uWpp54a8+bNiyzL4rbbbiu3f1WPmZWtad33QHk/l3KcKu/31a1bt/j3v/8dH374YTzzzDPxm9/8JnbZZZdYtmxZXHbZZVUK0KXrKe8YVLr2IUOGlHscX7NmTeyyyy6Vqr08bdq0yd+fPn16LF26tNx+Kcfb6nhfpqwn5W92Zd5DG6t///759V911VUxfvz4iIg488wzq3R856sJFhRMs2bN4sorr4yIyP/hXFfpT2dGjRoVEREvvvjiBr/orTr9/ve/jwkTJsTixYtj8ODBMXPmzIj4ci7o7rvvHp06dYouXbpERMTbb78dl112Wf5Txffffz/uuOOO6Nq163p/HKrq2GOPzd+/5pprYurUqbFgwYLo379//lOh3r17lzmoV1XLli3z9996661YuXJl/nHPnj3zy1977bX47W9/G0uWLIlx48aVmRt/zDHHrLfel156Ke69995YsmRJPPXUU/HYY49FxJd/OA4//PCI+PKP5bp/xEvP8V64cGHMmzevzKjCmjVrYt68eTFv3rxYsmRJRETsvvvu+bAwffr0+NWvfhWffvpp/OpXv8r/Qd5///3zn4ydfvrpsdVWW0VExB133BGvvvpqfPzxxzFw4MD885S+PO2mUlxcnN8WWZbFJZdcEvPnz4/33nuvTEhYux+MGzcuBg8eHG+88UZ06NAh+vTpU+ZSwjNmzMjfL/17/fe//13mU8RCe+yxx+Kvf/1rLFmyJO677778+RrFxcVxwAEHVHm7VMaG9vPS/zTVr18/mjRpErNnz17ve0421oaeuyKla2rUqFEUFxfHv//977j11lvL7V/eMfORRx6Jf/7zn19Z05QpU2LNmjVfWVNFqvs49dOf/jRGjBgRq1atioMOOii+//3v5z9Jz7IsPvroo0rXdsUVV8ScOXNixowZZd7fvXv3jogo8/654YYbYtSoUbF06dJYsmRJvPzyy3HxxRcnf7K98847R9euXSPiy9GxU045Jd59991YtmxZTJkyJT8Kk3K8LbTS+99f//rXiPhyX7/77rs36fOu3V7z5s3L/50urV27dnHyySdHxJfnIWZZFvXq1cuPZFCNNtOUK+q4dc+xWOvzzz/P2rZtW2YOZ+lzLF544YUyy9bOk2zSpEm+raJzLEorvY61Sl8V5eCDDy631vKuChXrzPn817/+la+rotvaue0bc+7D2u1Uev75urdWrVqVmV+/Mc8zc+bMcueUr50n+8gjj2zwSkSlL9dX+vnX/f2uvZWeF/5VNnRFnXVf4+uvv561aNGi3H4tW7bMX2p2rdLnZax7O/rooyt1Hkx17HfvvvtuhVdJivjyXJW1c9P/+Mc/bnB73H777fn1/upXv1pveXnzkEurzHzo0udrlH79pdvLe77S667o/bX2HJiqbpeKnrO0De3nCxcuzNq0abPestLn4pReb0XnelX0/vuq91h5JkyYkNWrV2+DNZV+jgceeKBMv7VXrCt9zCz9fKUvuVne/lrR9ixvX6jqceqrzv/ZeeedK1zXDjvsUOb3Xp7S+2V5+9q6V4UaMGDABt9Xpd+vX3VuSkUqe1WojT3elt4XSr/XSrdX5hzF0srbLyo6jr3//vtZw4YNN7j/VeaYUlGNFe2Ppa/6V95rzrIsmzJlSpnlRx555Hq/H9IZsaCgiouL4+c//3mFy/fff/944IEHYvfdd4+ioqLYbrvt4rrrrsufn7GpXX311XHttdfGTjvtFA0bNowuXbrEww8/HCeddFK+z7777htTpkyJ8847Lzp16hRFRUXRtGnT6Ny5c3zve9+Le++9N9q2bZtUR3FxcYwZMyauu+666NatWzRu3DgaNmwYX/va1+L888+P1157LTp37pz0HG3bto0HHngg9txzz/Xm1UZEfPe7342XXnopvve970WbNm2ifv360bx58+jVq1cMHz48fv3rX5e73sMPPzweeeSR2GuvvaJhw4axww47xNVXXx1Dhw5NqrciXbp0iVdeeSX69esX22+/fTRo0CC23377OP300+OVV14pM483ImLAgAHx2GOPxUEHHRRNmzaNRo0axZ577hk33nhjPPbYY5vtW7c7d+4ckydPjgsuuCB23nnnaNiwYTRu3Di+8Y1vxJAhQ2LMmDH5T3q7d+8eZ511Vuy5557RokWL2GqrraKkpCS++c1vxl133RUXXHBBfr3nnXdenH/++dGuXbsa9w3iZ511VgwbNiy+/vWvR8OGDaNTp07xm9/8Jj+SGVG17VIZG9rPt95663jmmWfiW9/6VpSUlETLli3jzDPPLHNlnBRf9R4rT48ePeLhhx+Orl27RqNGjaJ9+/YxePDg+MlPflJu/5NPPjluvvnm/LFop512it/97ndxwgknlNv/+OOPj4EDB0aHDh2qPF1mXdV9nLrwwgvj29/+duywww7RqFGjaNCgQey4447Rr1+/GD9+fJV+7y+++GIcf/zxUVJSEk2bNo0+ffrE888/X2bEZvDgwTFq1Kj43//939huu+2ifv36sd1228Xee+8dl1xySQwZMqRK26M83bt3jylTpsSFF14YX//616OoqCgaN24cu+66axx33HH5fht7vC20jh07xqhRo2LvvfeO4uLiaN68eVx55ZVx4403btLnvf322+OYY44p8/tc15577hnf/va384/PPffcTVpTXZXLsho0Lg5sEcaOHRuHHHJIRHx5udvSw/fUbffee2/+UqkDBw7cYr7Zm5qnQ4cO+Wmo/tVh5cqVccABB8TEiROjY8eO8d///jc/FZbqk/bxBAAA1FAzZ86Mb33rWzFnzpz8eXrXXnutULGJCBYAAGyRvvjii3jnnXeiXr160bFjx+jfv3+ceuqphS5ri2UqFAAAkKxmnckHAADUSoIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFNcqQIUMil8tF//79y11+zjnnRC6Xi1tuuWWz1gUAwIYJFtQYEydOjLvuuiu6du1a7vLHH388/vnPf0bbtm03c2UAAHwVwYIaYcmSJXHqqafG7373u9hmm23WWz5z5sy44IIL4oEHHogGDRoUoEIAADZEsKBGOP/88+Ooo46Kww47bL1la9asib59+8bll18ee+yxRwGqAwDgq9QvdAEwfPjwePXVV2PixInlLr/++uujfv36cdFFF23mygAAqCzBgoL68MMP4+KLL45nn302GjVqtN7ySZMmxa233hqvvvpq5HK5AlQIAEBl5LIsywpdBHXX448/Ht/5zndiq622yretXr06crlc1KtXL66//vq4/PLLo169emWW16tXL3bccceYPn16AaoGAGBdggUFtXjx4vjggw/KtJ1xxhmx6667xpVXXhnbb799zJo1q8zyb3/729G3b98444wzYpdddtmc5QIAUAFToSiokpKS6NKlS5m2Jk2aRMuWLfPtLVu2LLO8QYMG0aZNG6ECAKAGcVUoAAAgmalQAABAMiMWAABAMudYbAGGjL2t0CWwkQb08t0cAMCWwYgFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZHUiWAwZMiRyuVz0798/35ZlWQwaNCjatm0bxcXF0atXr3jjjTcKVyQAANRiW3ywmDhxYtx1113RtWvXMu033HBD3HzzzTF06NCYOHFitGnTJg4//PBYvHhxgSoFAIDaa4sOFkuWLIlTTz01fve738U222yTb8+yLG655Za46qqr4vjjj48uXbrEfffdF59//nk8+OCDBawYAABqpy06WJx//vlx1FFHxWGHHVamfdq0aTF79uzo3bt3vq2oqCgOPvjgmDBhQoXrW7FiRXz22WdlbitWrNhk9QMAQG2xxQaL4cOHx6uvvhpDhgxZb9ns2bMjIqJ169Zl2lu3bp1fVp4hQ4ZE8+bNy9zKWz8AANQ19QtdwKbw4YcfxsUXXxzPPvtsNGrUqMJ+uVyuzOMsy9ZrK23AgAFx6aWXlmkrKipKKxYAALYAW2SwmDRpUsydOze6d++eb1u9enWMHz8+hg4dGu+8805EfDlysf322+f7zJ07d71RjNKKiooECQAAKMcWORXqW9/6Vrz++usxefLk/G2fffaJU089NSZPnhxf+9rXok2bNjF69Oj8z6xcuTLGjRsXPXv2LGDlAABQO22RIxYlJSXRpUuXMm1NmjSJli1b5tv79+8fgwcPjs6dO0fnzp1j8ODB0bhx4zjllFMKUTIAANRqW2SwqIwrrrgili1bFuedd14sXLgw9ttvv3j22WejpKSk0KUBAECtk8uyLCt0EaQZMva2QpfARhrQ66JClwAAUC22yHMsAACAzUuwAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJBAsAACCZYAEAACQTLAAAgGSCBQAAkEywAAAAkgkWAABAMsECAABIJlgAAADJtthgMWzYsOjatWs0a9YsmjVrFj169IinnnoqvzzLshg0aFC0bds2iouLo1evXvHGG28UsGIAAKi9tthgscMOO8R1110Xr7zySrzyyitx6KGHxnHHHZcPDzfccEPcfPPNMXTo0Jg4cWK0adMmDj/88Fi8eHGBKwcAgNonl2VZVugiNpcWLVrEjTfeGD/84Q+jbdu20b9//7jyyisjImLFihXRunXruP766+Occ84p9+dXrFgRK1asKNNWVFQURUVFm7z2DRky9raCPj8bb0CviwpdAgBAtdhiRyxKW716dQwfPjyWLl0aPXr0iGnTpsXs2bOjd+/e+T5FRUVx8MEHx4QJEypcz5AhQ6J58+ZlbkOGDNkcLwEAAGq0+oUuYFN6/fXXo0ePHrF8+fJo2rRpjBgxInbfffd8eGjdunWZ/q1bt44PPvigwvUNGDAgLr300jJthR6tAACAmmCLDha77LJLTJ48OT799NN49NFHo1+/fjFu3Lj88lwuV6Z/lmXrtZVWE6Y9AQBATbRFT4Vq2LBhdOrUKfbZZ58YMmRI7LXXXnHrrbdGmzZtIiJi9uzZZfrPnTt3vVEMAADgq23RwWJdWZbFihUromPHjtGmTZsYPXp0ftnKlStj3Lhx0bNnzwJWCAAAtdMWOxXqpz/9aRx55JGx4447xuLFi2P48OExduzYePrppyOXy0X//v1j8ODB0blz5+jcuXMMHjw4GjduHKecckqhSwcAgFpniw0Wc+bMib59+8asWbOiefPm0bVr13j66afj8MMPj4iIK664IpYtWxbnnXdeLFy4MPbbb7949tlno6SkpMCVAwBA7VOnvsdiS+V7LGov32MBAGwp6tQ5FgAAwKYhWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQrH7qCrIsi4cffjhmzJgRvXv3jq5du1ZHXQAAQC1S5RGLgQMHxk477RTXXnttRET84Ac/iJNPPjmuvPLK2HfffeOFF16o9iIBAICarcrB4rnnnouZM2fG//zP/8Qnn3wSDz30UGRZFlmWxRdffBHXX3/9pqgTAACowaocLKZOnRoREXvuuWdMnDgx1qxZE0cccURcffXVERHxr3/9q3orBAAAarwqB4sFCxZERETr1q3jnXfeiVwuF6ecckpcdtllERGxcOHC6q0QAACo8aocLBo0aBAREdOnT49JkyZFRETHjh1j9erVERHRuHHjaiwPAACoDap8VahOnTrF66+/HnvvvXcsXbo06tWrF3vuuWfMnDkzIiJatWpV7UUCAAA1W5VHLE499dTIsiwWL14ca9asiSOPPDKaNWsWzz//fEREdO/evdqLBAAAarYqj1hcfvnl0bBhw3juuedi5513joEDB+aXnXnmmfHd7363WgsEAABqvlyWZVllO69cuTJefvnliIjo0qVLtGjRYpMVRuUNGXtboUtgIw3odVGhSwAAqBZVGrFo0KBBHHrooRERMW3aNMECAACIiCqeY5HL5WL77bePLMuECgAAIG+jTt6OiBgxYkS1FwMAANROVT55+2tf+1q0bNkyzjrrrBg3blx07959ve+uOO2006qtQAAAoOar0snbERH16tWLXC4XWZZFLpdbf4W5XKxataraCuSrOXm79nLyNgCwpajyiEVExNosUsVMAgAAbKGqHCxKf28FAABAhGABAABUg42aChURsWLFipgwYULMmTMnWrduHT179oyioqLqrA0AAKglNipYjBw5Mn70ox/FvHnz8m3bbrtt3HnnndGnT5/qqg0AAKglqvw9Fq+99lp873vfi3nz5kWWZfnbJ598EieeeGJMmjRpU9QJAADUYFUOFtdff3188cUXkWVZ7LXXXnHcccfFXnvtFRERq1atihtvvLHaiwQAAGq2KgeL559/PnK5XAwdOjRee+21GDFiRLz22msxdOjQyLIsnn/++U1RJwAAUINVOVisPa+iX79+ZdrXPi593gUAAFA3VDlYNG3aNCJivXMp1j5euxwAAKg7qnxVqL333jv+8Y9/xHe+850444wzon379vHBBx/EvffeG7lcLrp167Yp6gQAAGqwKgeLc889N/7xj3/Ep59+Gr/+9a/z7VmWRS6Xi3PPPbdaCwQAAGq+Kk+F+u53vxuXXnppmUvNZlkWERH9+/ePE044odqLBAAAaraN+oK8m266KU466aR44okn8t+8feyxx8a+++5b3fUBAAC1wEYFi4iIffbZJ/bZZ5/qrAUAAKilNipYZFkWEydOjOnTp8fy5cvXW37aaaclFwYAANQeVQ4W77//fhx77LHx1ltvlbs8l8sJFgAAUMdUOVhccMEF8eabb26KWgAAgFqqysHi5ZdfjlwuF7vuumv87//+bzRp0iRyudymqA0AAKglqhws6tf/8kf+/ve/x/bbb1/tBQEAALVPlb/H4jvf+U5ERCxdurTaiwEAAGqnSo1YzJgxI3//nHPOiSeffDK++93vxuDBg2PXXXeNBg0alOm/0047VW+VAABAjVapYNGhQ4f1zqOYM2dOHHvssev1zeVysWrVquqpDgAAqBUqfY5FlmWbsg4AAKAWq1SwOOigg1z5CQAAqFClgsXYsWM3cRkAAEBtVuWrQl1zzTVx7bXXlrts/PjxMX78+OSiAACA2iWXVfHkiXr16kUul4vVq1eXu6xevXpO3t7Mhoy9rdAlsJEG9Lqo0CUAAFSLKo9YVOSLL76ICCd5AwBAXVSpcyzGjRsX48aNK9N2zTXXlHn81ltvRUREcXFxNZUGAADUFpU+ebt0kMiyLK6++ur1+uVyuejUqVP1VQcAANQKVf4ei7WXnS1vylNRUVEMHDiwmkoDAABqi0oFiz59+kSHDh0iIuKMM86IXC4X99xzT355LpeLli1bxt577x3bb7/9JikUAACouap8VagOHTpELpeLadOmbaqaqCJXhaq9XBUKANhSVHoq1FrTp0/fBGUAAAC1WZWDxbpXgyrPz3/+840qBgAAqJ02+gvyNqS8L89j0zEVqvYyFQoA2FJUecQiovwrQuVyuciy7CtDBwAAsOWpcrAofTWoiIhVq1bF1KlT4+67747ly5fHjTfeWG3FAQAAtUOVg0W/fv3KbT/ttNNijz32iBkzZiQXBQAA1C71qmtFu+22W5SUlMSf/vSn6lolAABQS1R5xGL8+PFlHmdZFp9//nn89a9/jcWLFztxGwAA6qAqB4tevXpVeIJ2LpeL3XbbLbkooO4ZMmRIPPbYY/H2229HcXFx9OzZM66//vrYZZddyu1/zjnnxF133RW//vWvo3///pu3WABgPRs1FSrLsnJvDRo0iOuuu666awTqgHHjxsX5558fL7/8cowePTpWrVoVvXv3jqVLl67X9/HHH49//vOf0bZt2wJUCgCUp1pO3m7UqFF06NAhTjzxxOjQoUN11AXUMU8//XSZx/fcc0+0atUqJk2aFAcddFC+febMmXHBBRfEM888E0cdddTmLhMAqEDy5WYBNoVFixZFRESLFi3ybWvWrIm+ffvG5ZdfHnvssUehSgMAylFtV4UCqC5ZlsWll14aBxxwQHTp0iXffv3110f9+vXjoot8YzkA1DQb9c3bL774Ytxyyy3x5ptvxrJly8osy+Vy8d5771VLcUDddMEFF8SUKVPihRdeyLdNmjQpbr311nj11VcrvIAEAFA4VQ4Wzz//fHzrW9+K1atXR5ZlEfFlmCh9H2BjXXjhhTFy5MgYP3587LDDDvn2559/PubOnRs77bRTvm316tXx4x//OG655ZaYPn16AaoFANaqcrC46aabYtWqVfnHpUMFwMbKsiwuvPDCGDFiRIwdOzY6duxYZnnfvn3jsMMOK9P27W9/O/r27RtnnHHG5iwVAChHlc+x+Ne//hW5XC6GDRuWb5syZUocddRRscsuu8Rrr71WrQUCdcP5558ff/rTn+LBBx+MkpKSmD17dsyePTs/3bJly5bRpUuXMrcGDRpEmzZtKvyuCwBg86lysJg/f35ERJxyyin5ti5dusRdd90V77zzTvzmN7+pvuqAOmPYsGGxaNGi6NWrV2y//fb520MPPVTo0gCASqjyVKhGjRrF0qVLo3HjxlFUVBQrV66MGTNmRHFxcUREjBgxIu68885qLxTYsm3MlErnVQBAzVHlYNGiRYtYunRpLFy4MNq1axfTpk2Lo48+OurX/3JVK1eurPYiAQCAmq3KwWK33XaLDz/8MKZOnRq9evWK999/P954442I+PJE7v/5n/+p9iKByhky9rZCl8BGGtDLd3MAULtV+RyLc845Jy666KJYs2ZN/OxnP4s2bdpElmWRZVm0bt06br755k1RJwAAUINVecSiT58+0adPn/zjd999N5577rmoX79+7L///tG8efPqrA8AAKgFNuqbt0tr2rRpHHvssdVRCwAAUEtVeSoUAADAugQLAAAgmWABAAAkEywAAIBklQoWI0eOjJEjR0ZExIwZM2LGjBmbtCgAAKB2qdRVofr06RP16tWLVatWRYcOHfL3AQAAIqowFSrLsli9enX+PgAAwFqVGrFo3rx5fPbZZ3HUUUfl2374wx+W2zeXy8Xdd99dPdUBAAC1QqWCxR577BEvvfRSjB49OiK+HLG47777KuwvWAAAQN1SqalQP//5z6NRo0aRZVnkcrnI5XKRZVm5NwAAoO6p1IhF7969Y+bMmfHmm2/GAQccELlcLsaMGbOpawMAAGqJSgWLiIitt946evbsGQcddFDkcrk4+OCDN2VdAABALVLpYLHW2LFj8/dXrlwZCxYsiBYtWkTDhg2rsy4AAKAW2ahv3p42bVocc8wxUVJSEu3atYuSkpI49thj47333qvu+gAAgFqgyiMWc+bMiZ49e8bcuXPzJ2t/8cUX8de//jVeeeWVePXVV6NNmzbVXigAAFBzVXnEYvDgwTFnzpz8FaKaN2+ev0rUnDlzYsiQIZuiTgAAoAarcrB46qmnIpfLRd++fWP+/PmxcOHCmD9/fvTt2zeyLIu//e1vm6JOAACgBqtysPjwww8jIuKWW26JrbfeOiK+vGLULbfcEhERH330UbUVBwAA1A5VDhb16395WsbChQvLtC9YsKDMcgAAoO6ocrDYZZddIiLi+9//fowaNSpef/31GDVqVJx88slllgMAAHVHlYcXTjrppHj11Vfjtddei+OOO67MslwuFyeeeGK1FQcAANQOVR6xuPjii2P//fePLMvWu/Xo0SP69++/CcoEAABqsiqPWDRo0CD+/ve/x2233RYjR46MOXPmROvWrePYY4+NCy+8MBo0aLAp6gQAAGqwjTrTuqioKC6//PK4/PLLq7seAACgFqryVCgAAIB1CRYAAEAywQIAAEgmWAAAAMkECwAAIFmVrgq1bNmyuPHGGyMiol+/ftG+fftNUhQAAFC7VClYFBcXxy9/+ctYtWpVXHTRRZuqJgAAoJap8lSozp07R0TEqlWrqr0YAACgdqpysLjssssiy7K47rrrNkU9AABALVTlb94eN25cbLvttvHrX/86nnzyyejWrVs0btw4vzyXy8Xdd99drUUCAAA1W5WDxX333Re5XC4iIqZOnRpTp05dr49gAQAAdUuVg0VERJZlFS5bGzoAAIC6o8rBYsyYMZuiDgAAoBarcrA4+OCDN0UdAABALbZRU6EiIt5888145plnYt68efHLX/4yZsyYERERbdu2jfr1N3q1AABALVTly81GRPz0pz+Nrl27xmWXXZa/7OwxxxwTHTt2jOHDh1drgQAAQM1X5WDxyCOPxHXXXRdr1qwpcxL3OeecE1mWxahRo6q1QAAAoOarcrC4/fbbI5fLxYEHHlim/cgjj4yIiMmTJ1dLYQAAQO1R5WCxNjg88MADZdp33HHHiIiYNWtWelUAAECtUuVgsWLFioiIaN26dZn2efPmRUTEF198UQ1lAQAAtUmVg0W7du0iIuKFF14o0/6rX/0qIv7/kQsAAKDuqHKwOOywwyLLsjjhhBPybXvssUfcfPPNkcvlonfv3tVaIAAAUPNVOVgMGDAgSkpK4tNPP41cLhcREW+//XZkWRbNmjWLK664otqLBAAAarYqB4sOHTrE3//+99h9990jy7L8rUuXLjF69GhToQAAoA7aqK/I3nfffeP111+P9957L+bMmROtW7eOnXfeubprAwAAaomN+ubtiIgsy2L58uWxYsWKWL58eZkvywMAAOqWjQoWDz/8cLRv3z66du0ahx12WHTt2jXat28fDz/8cHXXBwAA1AJVngr12GOPxUknnRQRUWaU4qOPPoqTTjop6tevH9/5zneqr0IAAKDGq/KIxdVXX50/Ybtdu3bxzW9+M//dFlmWxaBBg6q7RgAAoIarcrB45513IpfLxS9/+cv48MMPY8KECfHhhx/GtddeGxER7777brUXCQAA1GxVDhZt2rSJiIgLL7ywTPvFF19cZjkAAFB3VDlYnHXWWRER8frrr5dpX/v4Rz/6UTWUBQAA1CaVOnn7/vvvz99v165dtG3bNo477rg4++yzo3379vHBBx/E73//+9hhhx1i++2332TFAgAANVMuq8QXUNSrVy9yuVyZtizLym2rV69erFq1qnqrZIOGjL2t0CWwkQb0uqha12dfqL2qe18AgM2t0pebLS9/VLYNAADYslUqWAwcOHBT1wEAANRiggUAAJCsyleFAgAAWFelz7Eo7eWXX477778/pk+fHsuXLy+zLJfLxT/+8Y9qKQ4AAKgdqhwsHnzwwejbt2+5y8q7UhQAALDlq3KwGDx4sCs/AQAAZVQ5WEybNi1yuVxcd911cdxxx0VRUdGmqAsAAKhFqhwsdtlll/j3v/8dZ599djRv3nxT1AQAANQyVb4q1DXXXBMRERdeeGG89957sWbNmmovCgAAqF2qHCyOPvroOOGEE+JPf/pTfP3rX48GDRrEVlttlb/Vr79RF5oCAABqsSoHi5tuuikefvjhyOVykWVZubeaYMiQIbHvvvtGSUlJtGrVKvr06RPvvPNOmT5ZlsWgQYOibdu2UVxcHL169Yo33nijQBUDAEDtVeVgcdttt0VE1JgAUZFx48bF+eefHy+//HKMHj06Vq1aFb17946lS5fm+9xwww1x8803x9ChQ2PixInRpk2bOPzww2Px4sUFrBwAAGqfKs9bmj9/fuRyuRg+fHgcc8wx0ahRo01RV7Knn366zON77rknWrVqFZMmTYqDDjoosiyLW265Ja666qo4/vjjIyLivvvui9atW8eDDz4Y55xzznrrXLFiRaxYsaJMW1FRkStjAQBQ51V5xGL//fePiIjDDjusxoaK8ixatCgiIlq0aBERX142d/bs2dG7d+98n6Kiojj44INjwoQJ5a5jyJAh0bx58zK3IUOGbPriAQCghtuoqVDbbrtt/OhHP4p33323VlwVKsuyuPTSS+OAAw6ILl26RETE7NmzIyKidevWZfq2bt06v2xdAwYMiEWLFpW5DRgwYNMWDwAAtUCVp0LtscceERExYsSIGDFixHrLc7lcrFq1Kr2yanTBBRfElClT4oUXXlhvWS6XK/M4y7L12tYy7QkAAMpX5RGL0idt1+SrQq114YUXxsiRI2PMmDGxww475NvbtGkTEbHe6MTcuXPXG8UAAAA2rMojFjvttFOFn+jXJFmWxYUXXhgjRoyIsWPHRseOHcss79ixY7Rp0yZGjx4d3bp1i4iIlStXxrhx4+L6668vRMkAAFBrVTlYTJ8+fROUUf3OP//8ePDBB+OJJ56IkpKS/MhE8+bNo7i4OHK5XPTv3z8GDx4cnTt3js6dO8fgwYOjcePGccoppxS4egAAqF222K/JHjZsWERE9OrVq0z7PffcE6effnpERFxxxRWxbNmyOO+882LhwoWx3377xbPPPhslJSWbuVoAAKjdqhws7r///q/sc9ppp21UMdWpMud65HK5GDRoUAwaNGjTFwQAAFuwKgeL008/fYPnWORyuRoRLAAAgM1no6ZClTcakMvlatwVoQAAgM2jysFi4MCBZR6vWrUqpk6dGo8//ng0aNAgLrnkkmorDgAAqB2Sg8VaL774Yhx44IHRtGnT5KIAAIDapcpfkFeR/fffP5o1axa///3vq2uVAABALVHlEYsZM2aUeZxlWXz++ecxatSo+Oyzz2LlypXVVhwAAFA7VDlYdOjQocKrQuVyudh5552TiwIAAGqXarsq1FpXXXXVRhcDAADUTlUOFgcddNB6IxaNGjWKDh06RN++faNnz57VVhwAAFA7VDlYjB07dhOUAQAA1GbVdlUoAACg7qrUiMVdd91VpZWeffbZG1UMAABQO1UqWJx77rkVXgmqPIIFAADULZU+x2JDV4IqrSoBBAAA2DJUKlj069evwmWLFi2KJ598MtasWVPp8AEAAGxZKhUs7rnnnvXali9fHrfffnvccMMN+VDRtm3b+PnPf17tRQIAADVblS83u3r16vj9738fv/jFL+Ljjz+OLMuiZcuWceWVV8YFF1wQjRo12hR1AgAANViVgsWDDz4YAwcOjPfffz+yLIumTZvGJZdcEpdddlmUlJRsqhoBAIAarlLBYtSoUXHVVVfFf/7zn8iyLIqKiuK8886LAQMGxLbbbrupawQAAGq4SgWLY489NnK5XGRZFvXr148TTjghioqK4uabby63/+DBg6u1SAAAoGar0lSoXC4Xq1evjgcffHCD/QQLAACoW3yPBQAAkKxSwWLgwIGbug4AAKAWEywAAIBk9QpdAAAAUPsJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAqHHGjx8fxxxzTLRt2zZyuVw8/vjjZZbncrlybzfeeGNhCgZAsACg5lm6dGnstddeMXTo0HKXz5o1q8ztD3/4Q+Ryufjud7+7mSsFYK36hS4AANZ15JFHxpFHHlnh8jZt2pR5/MQTT8QhhxwSX/va1zZ1aQBUQLAAoFabM2dO/PWvf4377ruv0KUA1GmmQgFQq913331RUlISxx9/fKFLAajTBAsAarU//OEPceqpp0ajRo0KXQpAnWYqFAC11vPPPx/vvPNOPPTQQ4UuBaDOM2IBQK119913R/fu3WOvvfYqdCkAdZ4RCwBqnCVLlsTUqVPzj6dNmxaTJ0+OFi1axE477RQREZ999lk8/PDD8atf/apQZQJQimABQI3zyiuvxCGHHJJ/fOmll0ZERL9+/eLee++NiIjhw4dHlmVx8sknF6JEANYhWABQ4/Tq1SuyLNtgn7PPPjvOPvvszVQRAF/FORYAAEAyIxYAW6AhY28rdAlspAG9Lip0CQAbxYgFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQbIsNFuPHj49jjjkm2rZtG7lcLh5//PEyy7Msi0GDBkXbtm2juLg4evXqFW+88UZhigUAgFpuiw0WS5cujb322iuGDh1a7vIbbrghbr755hg6dGhMnDgx2rRpE4cffngsXrx4M1cKAAC1X/1CF7CpHHnkkXHkkUeWuyzLsrjlllviqquuiuOPPz4iIu67775o3bp1PPjgg3HOOeeU+3MrVqyIFStWlGkrKiqKoqKi6i0eAABqmS12xGJDpk2bFrNnz47evXvn24qKiuLggw+OCRMmVPhzQ4YMiebNm5e5DRkyZHOUDAAANdoWO2KxIbNnz46IiNatW5dpb926dXzwwQcV/tyAAQPi0ksvLdNmtAIAAOposFgrl8uVeZxl2XptpZn2BAAA5auTU6HatGkTEf//yMVac+fOXW8UAwAA+Gp1Mlh07Ngx2rRpE6NHj863rVy5MsaNGxc9e/YsYGUAAFA7bbFToZYsWRJTp07NP542bVpMnjw5WrRoETvttFP0798/Bg8eHJ07d47OnTvH4MGDo3HjxnHKKacUsGoAAKidtthg8corr8QhhxySf7z2pOt+/frFvffeG1dccUUsW7YszjvvvFi4cGHst99+8eyzz0ZJSUmhSgYAgFpri50K1atXr8iybL3bvffeGxFfnrg9aNCgmDVrVixfvjzGjRsXXbp0KWzRAMAGLV68OPr37x/t27eP4uLi6NmzZ0ycOLHQZQGxBQcLAGDLc9ZZZ8Xo0aPjj3/8Y7z++uvRu3fvOOyww2LmzJmFLg3qPMECAKgVli1bFo8++mjccMMNcdBBB0WnTp1i0KBB0bFjxxg2bFihy4M6T7AAAGqFVatWxerVq6NRo0Zl2ouLi+OFF14oUFXAWoIFAFArlJSURI8ePeLaa6+Njz/+OFavXh1/+tOf4p///GfMmjWr0OVBnSdYAAC1xh//+MfIsizatWsXRUVFcdttt8Upp5wSW221VaFLgzpPsAAAao2dd945xo0bF0uWLIkPP/ww/vWvf8UXX3wRHTt2LHRpUOcJFgBArdOkSZPYfvvtY+HChfHMM8/EcccdV+iSoM7bYr8gDwDY8jzzzDORZVnssssuMXXq1Lj88stjl112iTPOOKPQpUGdZ8QCAKg1Fi1aFOeff37suuuucdppp8UBBxwQzz77bDRo0KDQpUGdZ8QCAKg1vv/978f3v//9QpcBlMOIBQAAkMyIBQBsoYaMva3QJZBgQK+LCl0CVIkRCwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAANQ6M2fOjB/84AfRsmXLaNy4cXzjG9+ISZMmFbqsOq1+oQsAAICqWLhwYey///5xyCGHxFNPPRWtWrWK9957L7beeutCl1anCRYAANQq119/fey4445xzz335Ns6dOhQuIKICFOhAACoZUaOHBn77LNPfO9734tWrVpFt27d4ne/+12hy6rzBAsAAGqV999/P4YNGxadO3eOZ555Js4999y46KKL4v777y90aXWaqVAAANQqa9asiX322ScGDx4cERHdunWLN954I4YNGxannXZagauru4xYAABQq2y//fax++67l2nbbbfdYsaMGQWqiAjBAgCAWmb//fePd955p0zbu+++G+3bty9QRUQIFgAA1DKXXHJJvPzyyzF48OCYOnVqPPjgg3HXXXfF+eefX+jS6jTBAgCAWmXfffeNESNGxJ///Ofo0qVLXHvttXHLLbfEqaeeWujS6jQnbwMAUOscffTRcfTRRxe6DEoxYgEAACQzYgEAsIUbMva2QpfARhrQ66JCl1BpRiwAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASCZYAAAAyQSLiLjjjjuiY8eO0ahRo+jevXs8//zzhS4JAABqlTofLB566KHo379/XHXVVfHaa6/FgQceGEceeWTMmDGj0KUBAECtUeeDxc033xxnnnlmnHXWWbHbbrvFLbfcEjvuuGMMGzas0KUBAECtUaeDxcqVK2PSpEnRu3fvMu29e/eOCRMmrNd/xYoV8dlnn5W5rVixYnOVCwAANVYuy7Ks0EUUyscffxzt2rWLF198MXr27JlvHzx4cNx3333xzjvvlOk/aNCguPrqq8u0DRw4MAYNGrQ5yq2TVqxYEUOGDIkBAwZEUVFRocuhQOwHrGVfYC37AmvZF2oOwaJdu5gwYUL06NEj3/7LX/4y/vjHP8bbb79dpv+KFSvWG6EoKiqyE29Cn332WTRv3jwWLVoUzZo1K3Q5FIj9gLXsC6xlX2At+0LNUb/QBRTStttuG1tttVXMnj27TPvcuXOjdevW6/UXIgAAoHx1+hyLhg0bRvfu3WP06NFl2kePHl1mahQAALBhdXrEIiLi0ksvjb59+8Y+++wTPXr0iLvuuitmzJgR5557bqFLAwCAWqPOB4sTTzwx5s+fH9dcc03MmjUrunTpEn/729+iffv2hS6N+HL62cCBA01Bq+PsB6xlX2At+wJr2Rdqjjp98jYAAFA96vQ5FgAAQPUQLAAAgGSCBQAAkEywAAAAkgkW1Fh33HFHdOzYMRo1ahTdu3eP559/vtAlsZmNHz8+jjnmmGjbtm3kcrl4/PHHC10SBTJkyJDYd999o6SkJFq1ahV9+vSJd955p9BlUWBDhgyJXC4X/fv3L3QpbGbDhg2Lrl27RrNmzaJZs2bRo0ePeOqppwpdVp0nWFAjPfTQQ9G/f/+46qqr4rXXXosDDzwwjjzyyJgxY0ahS2MzWrp0aey1114xdOjQQpdCgY0bNy7OP//8ePnll2P06NGxatWq6N27dyxdurTQpVEgEydOjLvuuiu6du1a6FIogB122CGuu+66eOWVV+KVV16JQw89NI477rh44403Cl1aneZys9RI++23X+y9994xbNiwfNtuu+0Wffr0iSFDhhSwMgoll8vFiBEjok+fPoUuhRrgk08+iVatWsW4cePioIMOKnQ5bGZLliyJvffeO+644474xS9+Ed/4xjfilltuKXRZFFiLFi3ixhtvjDPPPLPQpdRZRiyocVauXBmTJk2K3r17l2nv3bt3TJgwoUBVATXJokWLIuLLfySoe84///w46qij4rDDDit0KdQAq1evjuHDh8fSpUujR48ehS6nTqvz37xNzTNv3rxYvXp1tG7dukx769atY/bs2QWqCqgpsiyLSy+9NA444IDo0qVLocthMxs+fHi8+uqrMXHixEKXQoG9/vrr0aNHj1i+fHk0bdo0RowYEbvvvnuhy6rTBAtqrFwuV+ZxlmXrtQF1zwUXXBBTpkyJF154odClsJl9+OGHcfHFF8ezzz4bjRo1KnQ5FNguu+wSkydPjk8//TQeffTR6NevX4wbN064KCDBghpn2223ja222mq90Ym5c+euN4oB1C0XXnhhjBw5MsaPHx877LBDocthM5s0aVLMnTs3unfvnm9bvXp1jB8/PoYOHRorVqyIrbbaqoAVsjk1bNgwOnXqFBER++yzT0ycODFuvfXWuPPOOwtcWd3lHAtqnIYNG0b37t1j9OjRZdpHjx4dPXv2LFBVQCFlWRYXXHBBPPbYY/Hcc89Fx44dC10SBfCtb30rXn/99Zg8eXL+ts8++8Spp54akydPFirquCzLYsWKFYUuo04zYkGNdOmll0bfvn1jn332iR49esRdd90VM2bMiHPPPbfQpbEZLVmyJKZOnZp/PG3atJg8eXK0aNEidtpppwJWxuZ2/vnnx4MPPhhPPPFElJSU5Ec0mzdvHsXFxQWujs2lpKRkvfNqmjRpEi1btnS+TR3z05/+NI488sjYcccdY/HixTF8+PAYO3ZsPP3004UurU4TLKiRTjzxxJg/f35cc801MWvWrOjSpUv87W9/i/bt2xe6NDajV155JQ455JD840svvTQiIvr16xf33ntvgaqiENZeerpXr15l2u+55544/fTTN39BQEHNmTMn+vbtG7NmzYrmzZtH165d4+mnn47DDz+80KXVab7HAgAASOYcCwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQIAAEgmWAAAAMkECwAAIJlgAQAAJBMsAACAZIIFAACQTLAAAACSCRYAAEAywQKgDpk/f3788pe/jJ49e0aLFi2iqKgodtxxxzj00EPj9ttvj6VLlxa6xBrr9NNPj1wuF7lcLsaOHZtvf/zxx2PQoEExaNCgmD59esHqAyi0+oUuAIDNY8yYMXHiiSfGJ598Uqb9o48+io8++ijGjBkTBx54YHzjG98oTIG11OOPPx733XdfRET06tUrOnToUNiCAArEiAVAHfDuu+/Gsccemw8VRx55ZLz66quxYsWKmD9/fjzxxBNx6KGHFrjKmu3ee++NLMsiy7Lo1atXocsBqHEEC4A64Oqrr44lS5ZERESXLl3iySefjG7dukXDhg2jRYsWceyxx8Y//vGP6NKlS0RELF68OH72s59Fly5donHjxlFcXBx77LFH/L//9//is88+K7PuDh065KcIvfXWW9G7d+9o3Lhx7LjjjnHjjTdGlmUxatSo2HvvvaNx48axxx57xAMPPFBmHYMGDcqv484774wrrrgiWrduHSUlJfHd7343Zs+eHR9//HGceOKJ0axZs2jXrl2ce+65+dcUETF27Nj8Ok4//fQy61/bXno04d577823Dxw4MG699dbYZZdd8q913RrXnQo1ffr0yOVy+dGKiIhDDjkk3+fJJ5+MrbfeOnK5XOywww6xatWqfL8vvvgitt1228jlctG6detYuXJl1X+pADWMqVAAW7g1a9bEqFGj8o+vuOKK2GqrrcrtW79+/Zg3b14ccMAB8c4775RZ9uabb8abb74ZjzzySLz44ovRsmXL9X7+4IMPzo+KfPTRR3HFFVfExIkT45FHHoksy/Lr+cEPfhAdO3aMnj17rreOn/3sZ2Wmaz322GPxwQcfxKJFi2Lq1KkR8WXwufPOOyPLsrjzzjuruEXWd/vtt8fChQvLvNYN1VgZJSUlcc4558QNN9wQM2fOjMceeyy+//3vR0TEU089FfPnz4+IiB/+8IfRsGHD5NcAUGhGLAC2cPPnzy8zyrDHHntssP/AgQPzoaJ3797x0UcfxcyZM+Owww6LiIh33nknfv7zn5f7s/vvv3/MmzcvHnrooXzbww8/HCeffHIsWLAgrr/++nz7/fffX+46siyLf/3rXzFr1qxo3759RERMmjQpli5dGlOmTIn//ve/0aRJk/w61gaWFJ999ln8+c9/jkWLFsUVV1zxlTVGfDlSk2VZ9OvXL982ZsyYMtOlLr744nxoGDp0aL7f2tGQevXqxdlnn51cP0BNIFgAbOHW/cc7l8ttsP8TTzyRv3/jjTdGu3btom3btnHjjTfm20eOHFnuzw4ZMiRatmwZxxxzTJn2QYMGxTbbbBPHHXdcvq2iKyideeaZse+++0abNm1iv/32y7efccYZseeee0anTp1izz33jIiI5cuXx5w5czb4eirj2GOPjZNOOimaNWsWffv2/coaK6tt27ZxyimnRETE888/H1OmTInFixfHk08+GRFfBreOHTsmPQdATSFYAGzhtt1222jWrFn+8RtvvLHB/qX/UV87YhARZc5PqOif+U6dOkVERHFxcZn2tf88FxUV5duWL1++wXWsu57S/4B/1XpKh6nS5zZUZLfddsvfXzsasqEaq+Kyyy7L3x86dGg8+uijsWzZsoiIOPfcc5PXD1BTCBYAW7h69eqVGUG48cYbY/Xq1eX2XbVqVbRu3Tr/+IMPPsjfL/3pfek+pdWvX/6pexW1V+c6GjVqlL//+eef5++vPS9jQxo0aJC//1UjOuv6qv577LFHHHnkkRHx5RSo3/72txERscMOO8TRRx9dpecCqMkEC4A6YODAgdG0adOIiJgyZUr06dMnJk+eHCtXrowFCxbEyJEj49BDD43//Oc/ceyxx+Z/7sorr4yPP/44Zs2aFVdeeWW+vXSfmqL0iMpLL70U8+fPjxUrVsRVV121SZ+39EnsU6ZMiTVr1qzX5/LLL4+ILwPPP//5z4iI+NGPflThSfQAtZFgAVAHdO7cOUaOHBnbbrttRESMGjUqunXrFkVFRdGyZcs47rjjYsyYMRHx5aVpO3fuHBERTz/9dP4ci2effTa/rquvvrowL2QD2rRpE4cffnhERMycOTPatm0bzZs3j6effnqTPm/pq0ZdfPHFsdVWW603inHIIYdE9+7d84/r168fZ5111iatC2BzEywA6ohDDjkk3nrrrbj22mtjv/32i6233joaNGgQ7dq1i0MOOSRuvfXW6Ny5c2y33XYxceLE+OlPfxq77757NGrUKIqKimK33XaLAQMGxMSJE/MBpab505/+FCeeeGJsu+220bBhwzj88MPzIwSbyvHHHx8DBw6MDh06bHC6VulzLY455pho27btJq0LYHPLZdVxnT4AYIN+/etfx6WXXhoREc8991wccsghBa4IoHr5gjwA2IROO+20GDduXMyYMSMiIg477DChAtgimQoFAJvQjBkzYsaMGbH11lvHiSeeWObLAwG2JKZCAQAAyYxYAAAAyQQLAAAgmWABAAAkEywAAIBkggUAAJBMsAAAAJIJFgAAQDLBAgAASPb/AZZbpCdYUP2uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To which communities do these 100 authors belong?\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming your DataFrame is named communities_df\n",
    "x= top_100_authors['Community'].value_counts()\n",
    "\n",
    "ax = x.plot(kind=\"bar\", figsize=(8, 10), color='#86bf91', zorder=2, width=0.85)\n",
    "\n",
    "# Despine\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Labels and title\n",
    "ax.set_xlabel(\"Community\", labelpad=20, weight='bold', size=12)\n",
    "ax.set_ylabel(\"Number of authors\", labelpad=20, weight='bold', size=12)\n",
    "ax.set_title(\"Number of top-100 most important authors per community\", size=12, weight='bold')\n",
    "\n",
    "plt.xticks(rotation=0)\n",
    "ax.bar_label(ax.containers[0])  # Add labels to the bars\n",
    "plt.tight_layout()  # Adjust layout\n",
    "ax.legend().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b82bd28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Author  Community    Harmonic\n",
      "                Guido Gezelle          0 2928.569048\n",
      "                Marnix Gijsen          0 2795.519048\n",
      "                Marnix Gijsen          0 2795.519048\n",
      "                 Willem Kloos          0 2789.769048\n",
      "         Anton van Duinkerken          0 2786.195238\n",
      "              A. Roland Holst          1 2857.435714\n",
      "                  Thomas Mann          1 2796.169048\n",
      "              Menno ter Braak          1 2733.811905\n",
      "                     Lucebert          1 2706.376190\n",
      "         Lodewijk van Deyssel          1 2701.885714\n",
      "                         Albe          2 4063.516667\n",
      "                    Multatuli          2 3282.969048\n",
      "             Martinus Nijhoff          2 3195.102381\n",
      "             Martinus Nijhoff          2 3195.102381\n",
      "             Martinus Nijhoff          2 3195.102381\n",
      "                Albert Verwey          3 2879.785714\n",
      "                      M. Stol          3 2680.895238\n",
      "                 A.A.M. Stols          3 2672.311905\n",
      "                      P. Geyl          3 2582.778571\n",
      "            E.J. Dijksterhuis          3 2576.311905\n",
      "                Herman Gorter          4 2788.535714\n",
      "           Frederik van Eeden          4 2780.869048\n",
      "                P.N. van Eyck          4 2704.152381\n",
      "                  W.A.P. Smit          4 2648.478571\n",
      "               Louis Couperus          4 2622.602381\n",
      "                  Jan Baetens          5 1704.495238\n",
      "                  Bertus Dijk          5 1640.823810\n",
      "              António Jacinto          5 1359.001190\n",
      "           José Cardoso Pires          5 1304.422619\n",
      "                Nicanor Parra          5 1284.681349\n",
      "                D.J. Opperman          6 2231.035714\n",
      "            N.P. van Wyk Louw          6 2196.211905\n",
      "                  T.T. Cloete          6 2105.902381\n",
      "                    Uys Krige          6 2099.838095\n",
      "              F.E.J. Malherbe          6 2094.190476\n",
      "            J. Menno Huizinga          7    1.000000\n",
      "                G.W. Bruinsma          7    0.000000\n",
      "          Jacobus Boelen Jhz.          8    1.000000\n",
      "                Hendrik Onnen          9    1.000000\n",
      "                 Zack Gilbert         10 1610.865476\n",
      "                Robert Hayden         10 1388.401587\n",
      "                 Waring Cuney         10 1063.111905\n",
      "            Leslie M. Collins         10 1063.111905\n",
      "                  Owen Dodson         10 1063.111905\n",
      "                Chairil Anwar         11 1945.630952\n",
      "                   Asrul Sani         11 1753.188095\n",
      "                   Rivai Apin         11 1721.113095\n",
      "                   Oscar Mohr         11 1606.047619\n",
      "                 H.M.J. Maier         11 1423.695635\n",
      "               Léon de Greiff         12    1.000000\n",
      "                  C.L.W. Fock         13    1.000000\n",
      "          Pieter S. Gerbrandy         13    0.000000\n",
      "                Jan Voorhoeve         14 1906.947619\n",
      "            Monika van Paemel         14 1887.080952\n",
      "          Geert van Istendael         14 1801.842857\n",
      "                   Bea Vianen         14 1799.202381\n",
      "                  Wim Rutgers         14 1770.307143\n",
      "                 H. Linnemann         15    1.000000\n",
      "                  A.J. Cuppen         15    1.000000\n",
      "             Monika Warnenska         16    1.000000\n",
      "            Strátis Myriwílis         17 1816.016667\n",
      "              Aryiris Chionis         17 1482.318651\n",
      "             Jórgos Theotokás         17 1396.444841\n",
      "    Konstantínos Chatzópoulos         17 1395.778175\n",
      "          Pandelis Prewelakis         17 1395.778175\n",
      "              A.L.R. Fernhout         18    1.000000\n",
      "              Adrian Pâunescu         19 1278.920238\n",
      "           Sorin Alexandrescu         19    0.000000\n",
      "                Eva Vondeling         20    1.000000\n",
      "            Józef Lewandowski         20    1.000000\n",
      "                Annie Gransch         21    1.000000\n",
      "                 I.F. Fedorov         21    1.000000\n",
      "                 C.W. Aakster         22    1.000000\n",
      "                   Chris Mast         22    0.000000\n",
      "            Francisco Marinho         23    1.000000\n",
      "            Jan Paul Hinrichs         24 1423.752778\n",
      "           Elisaveta Bagrjana         24 1147.326984\n",
      "    Peter Reinhart Gleichmann         25    1.000000\n",
      "                  Jacobus Bos         26    1.000000\n",
      "                 Tjalling Bos         26    0.000000\n",
      "      José Rentes de Carvalho         27    1.000000\n",
      "             Jan van der Meer         28 1853.945238\n",
      "           Wislawa Szymborska         28 1620.842857\n",
      "                   Gerard Ras         28 1513.603968\n",
      "            J.L.J. van Schoot         29    1.000000\n",
      "                  Lucy Grealy         30    1.000000\n",
      "                  Jan D'Haese         31 2031.797619\n",
      "             Ernst van Altena         31 2024.304762\n",
      "                 Guus Vleugel         31 1502.056746\n",
      "     Els Broeksma-van Capelle         32 1536.274603\n",
      "            Caroline Lamarche         32 1221.638095\n",
      "               Gustav Herling         32 1221.638095\n",
      "                 Marcel Beyer         33    1.000000\n",
      "         Mirna Velcic-Canivez         34    1.000000\n",
      "                Jelle Noorman         35    1.000000\n",
      "                Raj Kamal Jha         35    1.000000\n",
      "                  Brady Udall         36    1.000000\n",
      "                 Bert Meelker         36    1.000000\n",
      "              Clarissa Jacobi         37 1851.666667\n",
      "               Pszisko Jacobs         37 1788.046429\n",
      "                 Leo Landsman         37 1787.061905\n",
      "             Antoni Slonimski         37 1782.411905\n",
      "             Antoni Slonimski         37 1782.411905\n",
      "                 Cees Verraak         38    1.500000\n",
      "            Patrick Cornillie         38    1.500000\n",
      "               Leonard Beuger         39    1.000000\n",
      "             Hartmann von Aue         39    0.000000\n",
      "          Hubert de Vogelaere         40    1.000000\n",
      "                    U Win Tin         41    1.000000\n",
      "               H.P. Plantenga         42    1.000000\n",
      "                   Wanda Maya         42    1.000000\n",
      "                C.N. Dubelaar         43    1.000000\n",
      "           Jacob Dirk Veegens         44    1.000000\n",
      "Rudolf Cornelis Hendrik Römer         44    1.000000\n",
      "           Henriette van Duin         45    1.000000\n"
     ]
    }
   ],
   "source": [
    "# Top 5 authors for each community, again sorted by Harmonic centrality\n",
    "top_5_authors_per_community = (\n",
    "    merged_df.groupby('Community', group_keys=False)\n",
    "    .apply(lambda group: group.nlargest(5, 'Harmonic'))\n",
    ")\n",
    "\n",
    "# Displaying relevant columns and formatting the output\n",
    "columns_to_display = ['Author', 'Community', 'Harmonic']\n",
    "top_5_authors_per_community_display = top_5_authors_per_community[columns_to_display]\n",
    "\n",
    "# Use to_string() for a neat display\n",
    "print(top_5_authors_per_community_display.to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445f8450",
   "metadata": {},
   "source": [
    "# Visualize communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "57743190",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "CCLAMP_communities.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000px\"\n",
       "            height=\"700px\"\n",
       "            src=\"CCLAMP_communities.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1d0f40502b0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set communities as a node attribute\n",
    "nx.set_node_attributes(G, communities, 'group')\n",
    "\n",
    "# draw graph with communities\n",
    "com_net = Network(notebook = True, width=\"1000px\", height=\"700px\", bgcolor='#222222', font_color='white')\n",
    "com_net.from_nx(G)\n",
    "com_net.show(\"CCLAMP_communities.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dadf5b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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