ZSI_Reconnect_China/WOS/wos_analysis/wos_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40038234",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import janitor\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'okabe' is not a valid palette name",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\palettes.py:235\u001B[0m, in \u001B[0;36mcolor_palette\u001B[1;34m(palette, n_colors, desat, as_cmap)\u001B[0m\n\u001B[0;32m    233\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    234\u001B[0m     \u001B[38;5;66;03m# Perhaps a named matplotlib colormap?\u001B[39;00m\n\u001B[1;32m--> 235\u001B[0m     palette \u001B[38;5;241m=\u001B[39m \u001B[43mmpl_palette\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpalette\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_colors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mas_cmap\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mas_cmap\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    236\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mValueError\u001B[39;00m, \u001B[38;5;167;01mKeyError\u001B[39;00m):  \u001B[38;5;66;03m# Error class changed in mpl36\u001B[39;00m\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\palettes.py:406\u001B[0m, in \u001B[0;36mmpl_palette\u001B[1;34m(name, n_colors, as_cmap)\u001B[0m\n\u001B[0;32m    405\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 406\u001B[0m     cmap \u001B[38;5;241m=\u001B[39m \u001B[43mget_colormap\u001B[49m\u001B[43m(\u001B[49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    408\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01min\u001B[39;00m MPL_QUAL_PALS:\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\_compat.py:133\u001B[0m, in \u001B[0;36mget_colormap\u001B[1;34m(name)\u001B[0m\n\u001B[0;32m    132\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 133\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmpl\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolormaps\u001B[49m\u001B[43m[\u001B[49m\u001B[43mname\u001B[49m\u001B[43m]\u001B[49m\n\u001B[0;32m    134\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mAttributeError\u001B[39;00m:\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\matplotlib\\cm.py:82\u001B[0m, in \u001B[0;36mColormapRegistry.__getitem__\u001B[1;34m(self, item)\u001B[0m\n\u001B[0;32m     81\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m:\n\u001B[1;32m---> 82\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mitem\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m is not a known colormap name\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28mNone\u001B[39m\n",
      "\u001B[1;31mKeyError\u001B[0m: \"'okabe' is not a known colormap name\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[62], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43msns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mset_theme\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcontext\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mnotebook\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstyle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mticks\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpalette\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mokabe\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfont\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msans-serif\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfont_scale\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolor_codes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrc\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m      2\u001B[0m sns\u001B[38;5;241m.\u001B[39mpalplot(sns\u001B[38;5;241m.\u001B[39mcolor_palette())\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\rcmod.py:121\u001B[0m, in \u001B[0;36mset_theme\u001B[1;34m(context, style, palette, font, font_scale, color_codes, rc)\u001B[0m\n\u001B[0;32m    119\u001B[0m set_context(context, font_scale)\n\u001B[0;32m    120\u001B[0m set_style(style, rc\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfont.family\u001B[39m\u001B[38;5;124m\"\u001B[39m: font})\n\u001B[1;32m--> 121\u001B[0m \u001B[43mset_palette\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpalette\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolor_codes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcolor_codes\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    122\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m rc \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    123\u001B[0m     mpl\u001B[38;5;241m.\u001B[39mrcParams\u001B[38;5;241m.\u001B[39mupdate(rc)\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\rcmod.py:527\u001B[0m, in \u001B[0;36mset_palette\u001B[1;34m(palette, n_colors, desat, color_codes)\u001B[0m\n\u001B[0;32m    502\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mset_palette\u001B[39m(palette, n_colors\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, desat\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, color_codes\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[0;32m    503\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Set the matplotlib color cycle using a seaborn palette.\u001B[39;00m\n\u001B[0;32m    504\u001B[0m \n\u001B[0;32m    505\u001B[0m \u001B[38;5;124;03m    Parameters\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    525\u001B[0m \n\u001B[0;32m    526\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 527\u001B[0m     colors \u001B[38;5;241m=\u001B[39m \u001B[43mpalettes\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolor_palette\u001B[49m\u001B[43m(\u001B[49m\u001B[43mpalette\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_colors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdesat\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    528\u001B[0m     cyl \u001B[38;5;241m=\u001B[39m cycler(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcolor\u001B[39m\u001B[38;5;124m'\u001B[39m, colors)\n\u001B[0;32m    529\u001B[0m     mpl\u001B[38;5;241m.\u001B[39mrcParams[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maxes.prop_cycle\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m cyl\n",
      "File \u001B[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\seaborn\\palettes.py:237\u001B[0m, in \u001B[0;36mcolor_palette\u001B[1;34m(palette, n_colors, desat, as_cmap)\u001B[0m\n\u001B[0;32m    235\u001B[0m             palette \u001B[38;5;241m=\u001B[39m mpl_palette(palette, n_colors, as_cmap\u001B[38;5;241m=\u001B[39mas_cmap)\n\u001B[0;32m    236\u001B[0m         \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mValueError\u001B[39;00m, \u001B[38;5;167;01mKeyError\u001B[39;00m):  \u001B[38;5;66;03m# Error class changed in mpl36\u001B[39;00m\n\u001B[1;32m--> 237\u001B[0m             \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpalette\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m is not a valid palette name\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    239\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m desat \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    240\u001B[0m     palette \u001B[38;5;241m=\u001B[39m [desaturate(c, desat) \u001B[38;5;28;01mfor\u001B[39;00m c \u001B[38;5;129;01min\u001B[39;00m palette]\n",
      "\u001B[1;31mValueError\u001B[0m: 'okabe' is not a valid palette name"
     ]
    }
   ],
   "source": [
    "sns.set_theme(context='notebook', style='ticks', palette='colorblind', font='sans-serif', font_scale=1, color_codes=True, rc=None)\n",
    "sns.palplot(sns.color_palette())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "fb7baf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\"\n",
    "\n",
    "wos = pd.read_excel(f\"../{outdir}/wos_processed.xlsx\")\n",
    "wos_univ = pd.read_excel(f\"../{outdir}/wos_institution_locations_harmonized.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [],
   "source": [
    "wos_country = pd.read_excel(f\"../{outdir}/wos_countries.xlsx\")\n",
    "wos_country_types = pd.read_excel(f\"../{outdir}/wos_country_types.xlsx\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [
    {
     "data": {
      "text/plain": "           Country      Country_Type\n0            China             China\n1      Netherlands                EU\n2           Norway  Non-EU associate\n3   United Kingdom  Non-EU associate\n4           France                EU\n5            Italy                EU\n6          Denmark                EU\n7          Germany                EU\n8          Belgium                EU\n9         Slovenia                EU\n10         Estonia                EU\n11         Finland                EU\n12        Bulgaria                EU\n13           Spain                EU\n14          Poland                EU\n15  Czech Republic                EU\n16          Sweden                EU\n17          Greece                EU\n18         Austria                EU\n19     Switzerland  Non-EU associate\n20         Ireland                EU\n21        Portugal                EU\n22      Luxembourg                EU\n23         Romania                EU\n24        Slovakia                EU\n25         Hungary                EU\n26          Cyprus                EU\n27         Croatia                EU\n28       Lithuania                EU\n29          Latvia                EU\n30           Malta                EU",
      "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>Country</th>\n      <th>Country_Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>China</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Netherlands</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Norway</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>United Kingdom</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>France</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Italy</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Denmark</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Germany</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Belgium</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Slovenia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Estonia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Bulgaria</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Spain</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Poland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Czech Republic</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Sweden</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Greece</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Austria</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Switzerland</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Ireland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Portugal</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Luxembourg</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Romania</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Slovakia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Hungary</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Cyprus</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Croatia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Lithuania</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Malta</td>\n      <td>EU</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_country_types"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "(41377, 155067)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wos),len(wos_univ_locations)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "\n",
    "# wos_addresses = pd.read_excel(f\"/{outdir}/wos_addresses.xlsx\")\n",
    "\n",
    "# wos_affiliations = pd.read_excel(f\"/{outdir}/wos_affiliations.xlsx\")\n",
    "\n",
    "# wos_author_locations = pd.read_excel(f\"/{outdir}/wos_author_locations.xlsx\")\n",
    "\n",
    "# wos_univ_locations = pd.read_excel(f\"/{outdir}/wos_univ_locations.xlsx\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e737dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "record_col = \"UT (Unique WOS ID)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a97f1cbb",
   "metadata": {},
   "source": [
    "# Output - per yer, by Metrix taxonomy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e34c6b",
   "metadata": {},
   "source": [
    "## Domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af12584f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "               Domain_English  UT (Unique WOS ID)\n0            Applied Sciences               27805\n5            Natural Sciences                7869\n3             Health Sciences                3748\n2  Economic & Social Sciences                1194\n4           Multidisciplinary                 703\n1           Arts & Humanities                  58",
      "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>Domain_English</th>\n      <th>UT (Unique WOS ID)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Applied Sciences</td>\n      <td>27805</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Natural Sciences</td>\n      <td>7869</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Health Sciences</td>\n      <td>3748</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Economic &amp; Social Sciences</td>\n      <td>1194</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Multidisciplinary</td>\n      <td>703</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Arts &amp; Humanities</td>\n      <td>58</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = 'Domain_English'\n",
    "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f8e72c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.barplot(data, x=record_col, y=group)\n",
    "g.set_xlim(0,35000)\n",
    "g.set_ylabel(None)\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "g.set_title(\"Distribution of Domains\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "88742c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    Publication Year              Domain_English  UT (Unique WOS ID)\n71              2022            Natural Sciences                1595\n70              2022           Multidisciplinary                 125\n69              2022             Health Sciences                 904\n68              2022  Economic & Social Sciences                 336\n67              2022           Arts & Humanities                  10\n..               ...                         ...                 ...\n4               2011           Multidisciplinary                  11\n3               2011             Health Sciences                  69\n2               2011  Economic & Social Sciences                  18\n1               2011           Arts & Humanities                   0\n0               2011            Applied Sciences                 446\n\n[72 rows x 3 columns]",
      "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>Publication Year</th>\n      <th>Domain_English</th>\n      <th>UT (Unique WOS ID)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>71</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>1595</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>2022</td>\n      <td>Multidisciplinary</td>\n      <td>125</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>2022</td>\n      <td>Health Sciences</td>\n      <td>904</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>2022</td>\n      <td>Economic &amp; Social Sciences</td>\n      <td>336</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>2022</td>\n      <td>Arts &amp; Humanities</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>Multidisciplinary</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>Health Sciences</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>Economic &amp; Social Sciences</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>Arts &amp; Humanities</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>446</td>\n    </tr>\n  </tbody>\n</table>\n<p>72 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = ['Publication Year','Domain_English']\n",
    "data = wos.groupby(group)[record_col].nunique().unstack(fill_value=0).stack().reset_index().rename(columns={0:record_col}).sort_values(ascending=False, by=group+[record_col])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "151a7a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Yearly output of co-publications')"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.lineplot(data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0], hue=group[-1], marker=\"o\")\n",
    "g.set(xticks=list(range(2012,2022+1,2)))\n",
    "g.legend(title=None)\n",
    "g.set_xlabel(None)\n",
    "g.set_ylabel(None)\n",
    "g.set_title(\"Yearly output of co-publications\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "outputs": [
    {
     "data": {
      "text/plain": "Publication Year            2011  2012  2013  2014  2015  2016  2017  2018   \nDomain_English                                                               \nApplied Sciences             446   537   678   960  1116  1450  1749  2593  \\\nArts & Humanities              0     0     0     4     0     3     5     1   \nEconomic & Social Sciences    18    18    23    25    33    38    72    85   \nHealth Sciences               69    64    91   113   130   168   206   270   \nMultidisciplinary             11    16    34    38    43    58    56    62   \nNatural Sciences             168   208   278   278   347   391   512   692   \n\nPublication Year            2019  2020  2021  2022  \nDomain_English                                      \nApplied Sciences            3427  4072  4939  5838  \nArts & Humanities              9    10    16    10  \nEconomic & Social Sciences   141   181   224   336  \nHealth Sciences              421   530   782   904  \nMultidisciplinary             71    90    99   125  \nNatural Sciences             930  1157  1313  1595  ",
      "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>Publication Year</th>\n      <th>2011</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n      <th>2020</th>\n      <th>2021</th>\n      <th>2022</th>\n    </tr>\n    <tr>\n      <th>Domain_English</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Applied Sciences</th>\n      <td>446</td>\n      <td>537</td>\n      <td>678</td>\n      <td>960</td>\n      <td>1116</td>\n      <td>1450</td>\n      <td>1749</td>\n      <td>2593</td>\n      <td>3427</td>\n      <td>4072</td>\n      <td>4939</td>\n      <td>5838</td>\n    </tr>\n    <tr>\n      <th>Arts &amp; Humanities</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>1</td>\n      <td>9</td>\n      <td>10</td>\n      <td>16</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>Economic &amp; Social Sciences</th>\n      <td>18</td>\n      <td>18</td>\n      <td>23</td>\n      <td>25</td>\n      <td>33</td>\n      <td>38</td>\n      <td>72</td>\n      <td>85</td>\n      <td>141</td>\n      <td>181</td>\n      <td>224</td>\n      <td>336</td>\n    </tr>\n    <tr>\n      <th>Health Sciences</th>\n      <td>69</td>\n      <td>64</td>\n      <td>91</td>\n      <td>113</td>\n      <td>130</td>\n      <td>168</td>\n      <td>206</td>\n      <td>270</td>\n      <td>421</td>\n      <td>530</td>\n      <td>782</td>\n      <td>904</td>\n    </tr>\n    <tr>\n      <th>Multidisciplinary</th>\n      <td>11</td>\n      <td>16</td>\n      <td>34</td>\n      <td>38</td>\n      <td>43</td>\n      <td>58</td>\n      <td>56</td>\n      <td>62</td>\n      <td>71</td>\n      <td>90</td>\n      <td>99</td>\n      <td>125</td>\n    </tr>\n    <tr>\n      <th>Natural Sciences</th>\n      <td>168</td>\n      <td>208</td>\n      <td>278</td>\n      <td>278</td>\n      <td>347</td>\n      <td>391</td>\n      <td>512</td>\n      <td>692</td>\n      <td>930</td>\n      <td>1157</td>\n      <td>1313</td>\n      <td>1595</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_data = pd.pivot_table(data, values=record_col, index=['Domain_English'],\n",
    "\n",
    "                       columns=['Publication Year'], fill_value=0)\n",
    "pivot_data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "outputs": [
    {
     "data": {
      "text/plain": "[Text(0.5, 33.249999999999986, ''), Text(79.74999999999999, 0.5, '')]"
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 900x600 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(9, 6))\n",
    "g = sns.heatmap(pivot_data, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
    "g.set(xlabel=\"\", ylabel=\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "outputs": [
    {
     "data": {
      "text/plain": "Publication Year                 2011       2012       2013       2014   \nDomain_English                                                           \nApplied Sciences            62.640449  63.701068  61.413043  67.700987  \\\nArts & Humanities            0.000000   0.000000   0.000000   0.282087   \nEconomic & Social Sciences   2.528090   2.135231   2.083333   1.763047   \nHealth Sciences              9.691011   7.591934   8.242754   7.968970   \nMultidisciplinary            1.544944   1.897983   3.079710   2.679831   \nNatural Sciences            23.595506  24.673784  25.181159  19.605078   \n\nPublication Year                 2015       2016       2017       2018   \nDomain_English                                                           \nApplied Sciences            66.866387  68.785579  67.269231  70.024305  \\\nArts & Humanities            0.000000   0.142315   0.192308   0.027005   \nEconomic & Social Sciences   1.977232   1.802657   2.769231   2.295436   \nHealth Sciences              7.789095   7.969639   7.923077   7.291385   \nMultidisciplinary            2.576393   2.751423   2.153846   1.674318   \nNatural Sciences            20.790893  18.548387  19.692308  18.687551   \n\nPublication Year                 2019       2020       2021       2022  \nDomain_English                                                          \nApplied Sciences            68.553711  67.417219  66.987658  66.280654  \nArts & Humanities            0.180036   0.165563   0.217008   0.113533  \nEconomic & Social Sciences   2.820564   2.996689   3.038112   3.814714  \nHealth Sciences              8.421684   8.774834  10.606266  10.263397  \nMultidisciplinary            1.420284   1.490066   1.342737   1.419164  \nNatural Sciences            18.603721  19.155629  17.808219  18.108538  ",
      "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>Publication Year</th>\n      <th>2011</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n      <th>2020</th>\n      <th>2021</th>\n      <th>2022</th>\n    </tr>\n    <tr>\n      <th>Domain_English</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Applied Sciences</th>\n      <td>62.640449</td>\n      <td>63.701068</td>\n      <td>61.413043</td>\n      <td>67.700987</td>\n      <td>66.866387</td>\n      <td>68.785579</td>\n      <td>67.269231</td>\n      <td>70.024305</td>\n      <td>68.553711</td>\n      <td>67.417219</td>\n      <td>66.987658</td>\n      <td>66.280654</td>\n    </tr>\n    <tr>\n      <th>Arts &amp; Humanities</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.282087</td>\n      <td>0.000000</td>\n      <td>0.142315</td>\n      <td>0.192308</td>\n      <td>0.027005</td>\n      <td>0.180036</td>\n      <td>0.165563</td>\n      <td>0.217008</td>\n      <td>0.113533</td>\n    </tr>\n    <tr>\n      <th>Economic &amp; Social Sciences</th>\n      <td>2.528090</td>\n      <td>2.135231</td>\n      <td>2.083333</td>\n      <td>1.763047</td>\n      <td>1.977232</td>\n      <td>1.802657</td>\n      <td>2.769231</td>\n      <td>2.295436</td>\n      <td>2.820564</td>\n      <td>2.996689</td>\n      <td>3.038112</td>\n      <td>3.814714</td>\n    </tr>\n    <tr>\n      <th>Health Sciences</th>\n      <td>9.691011</td>\n      <td>7.591934</td>\n      <td>8.242754</td>\n      <td>7.968970</td>\n      <td>7.789095</td>\n      <td>7.969639</td>\n      <td>7.923077</td>\n      <td>7.291385</td>\n      <td>8.421684</td>\n      <td>8.774834</td>\n      <td>10.606266</td>\n      <td>10.263397</td>\n    </tr>\n    <tr>\n      <th>Multidisciplinary</th>\n      <td>1.544944</td>\n      <td>1.897983</td>\n      <td>3.079710</td>\n      <td>2.679831</td>\n      <td>2.576393</td>\n      <td>2.751423</td>\n      <td>2.153846</td>\n      <td>1.674318</td>\n      <td>1.420284</td>\n      <td>1.490066</td>\n      <td>1.342737</td>\n      <td>1.419164</td>\n    </tr>\n    <tr>\n      <th>Natural Sciences</th>\n      <td>23.595506</td>\n      <td>24.673784</td>\n      <td>25.181159</td>\n      <td>19.605078</td>\n      <td>20.790893</td>\n      <td>18.548387</td>\n      <td>19.692308</td>\n      <td>18.687551</td>\n      <td>18.603721</td>\n      <td>19.155629</td>\n      <td>17.808219</td>\n      <td>18.108538</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "percent_pivot = pd.crosstab(data['Domain_English'], data['Publication Year'], values=data[record_col], aggfunc=np.sum, normalize='columns')*100\n",
    "percent_pivot"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "outputs": [
    {
     "data": {
      "text/plain": "[Text(0.5, 33.249999999999986, ''), Text(154.75, 0.5, '')]"
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1500x600 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 6))\n",
    "g = sns.heatmap(percent_pivot, annot=True, fmt='.2f', linewidths=.5, ax=ax, cbar=False)\n",
    "for t in ax.texts: t.set_text(t.get_text() + \" %\")\n",
    "g.set(xlabel=\"\", ylabel=\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='Publication Year'>"
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_pivot.T.plot(kind='bar',\n",
    "                    stacked=True,\n",
    "                    figsize=(10, 6))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x800 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_pivot.T.plot(kind='bar',\n",
    "                        stacked=True,\n",
    "                        figsize=(15, 8))\n",
    "\n",
    "plt.legend(loc=\"lower left\", ncol=2)\n",
    "# plt.ylabel(\"Release Year\")\n",
    "# plt.xlabel(\"Proportion\")\n",
    "\n",
    "\n",
    "for n, x in enumerate([*pivot_data.T.index.values]):\n",
    "    for (proportion, count, y_loc) in zip(percent_pivot.T.loc[x],\n",
    "                                          pivot_data.T.loc[x],\n",
    "                                          percent_pivot.T.loc[x].cumsum()):\n",
    "\n",
    "        plt.text(y=(y_loc - proportion) + (proportion / 2),\n",
    "                 x=n - 0.11,\n",
    "                 s=f'{count}',# ({np.round(proportion, 1)}%)',\n",
    "                 color=\"black\",\n",
    "                 fontsize=8,\n",
    "                 fontweight=\"bold\")\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "dcae04bd",
   "metadata": {},
   "source": [
    "## Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d3807072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     Publication Year    Domain_English   \n232              2022  Natural Sciences  \\\n231              2022  Natural Sciences   \n230              2022  Natural Sciences   \n229              2022  Natural Sciences   \n228              2022  Natural Sciences   \n..                ...               ...   \n4                2011  Applied Sciences   \n3                2011  Applied Sciences   \n2                2011  Applied Sciences   \n1                2011  Applied Sciences   \n0                2011  Applied Sciences   \n\n                                Field_English  UT (Unique WOS ID)  \n232                       Physics & Astronomy                 575  \n231                  Mathematics & Statistics                 219  \n230            Earth & Environmental Sciences                 382  \n229                                 Chemistry                 237  \n228                                   Biology                 182  \n..                                        ...                 ...  \n4    Information & Communication Technologies                 238  \n3                                 Engineering                 148  \n2           Enabling & Strategic Technologies                  46  \n1                  Built Environment & Design                   6  \n0           Agriculture, Fisheries & Forestry                   8  \n\n[233 rows x 4 columns]",
      "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>Publication Year</th>\n      <th>Domain_English</th>\n      <th>Field_English</th>\n      <th>UT (Unique WOS ID)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>232</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>575</td>\n    </tr>\n    <tr>\n      <th>231</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Mathematics &amp; Statistics</td>\n      <td>219</td>\n    </tr>\n    <tr>\n      <th>230</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Earth &amp; Environmental Sciences</td>\n      <td>382</td>\n    </tr>\n    <tr>\n      <th>229</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Chemistry</td>\n      <td>237</td>\n    </tr>\n    <tr>\n      <th>228</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Biology</td>\n      <td>182</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>148</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Enabling &amp; Strategic Technologies</td>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Built Environment &amp; Design</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>8</td>\n    </tr>\n  </tbody>\n</table>\n<p>233 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = ['Publication Year',\"Domain_English\",'Field_English']\n",
    "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "6"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data[group[-2]].unique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "'Publication Year'"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "756513b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_complete = pd.DataFrame()\n",
    "\n",
    "for cat in sorted(data[group[-2]].unique()):\n",
    "    #data segment\n",
    "    sub_data = data[data[group[-2]]==cat]\n",
    "    sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n",
    "                                 ,group[-1],fill_value=0)\n",
    "    data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n",
    "    #plot\n",
    "    g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),\n",
    "                   y=record_col,x=group[0], hue=group[-1], marker=\"o\")\n",
    "    g.set(xticks=list(range(2012,2022+1,2)))\n",
    "    g.legend(title=None)\n",
    "    g.set_title(cat)\n",
    "    g.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1500 with 6 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABM0AAATuCAYAAADeEtRgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddVgV2f/A8TeNiGLH2gUGgmCjImK3YIPiqmt3rCsmFgaI3Z1rItbaazfqqmuuiAomBiogPb8/+N35egkBY9X183oeHr0zZ86ciXvvuZ85oaMoioIQQgghhBBCCCGEEEKl+7ULIIQQQgghhBBCCCHEt0aCZkIIIYQQQgghhBBCJCJBMyGEEEIIIYQQQgghEpGgmRBCCCGEEEIIIYQQiUjQTAghhBBCCCGEEEKIRCRoJoQQQgghhBBCCCFEIhI0E0IIIYQQQgghhBAiEQmaCSGEEEIIIYQQQgiRiATNhBBCCCGEEEIIIYRIRIJmQgjxjZozZw4WFhZ07NgxxTRv3rxJNc2XpinnwYMHv1oZPkZsbCxTp06lWrVqlC1blqZNm37tIn1xjo6OVKhQIU1pO3bsiIWFBW/evAEgODgYCwsLevfu/SWLCEBgYCB79uzRWmZhYUHz5s2/+L4/VeLz9iXduHEDCwsLhg8f/sX39Tl8r58VH6t3795YWFgQHBz8tYvyzfnW3uPJfR+cPXsWCwsLJk2a9FF5puezQO4VIYT4dul/7QIIIYT4sHPnzrF582Zat279tYvyn7JlyxaWL19OkSJFcHJyInv27F+7SN+0zJkz07dvX4oWLfpF93Pz5k1atWpF+/btadiwobq8b9++5MiR44vu+3NwcnKiUqVKGBkZfe2ifHMqVapE3759KVKkyNcuiviKvsX3eHLfB/ny5aNv375YW1t/lTIJIYT4NkjQTAghvgNeXl7UqlXruwgafC+uX78OwJgxY7Czs/vKpfn2Zc6cmX79+n3x/bx+/ZqYmJgky/+NfX8Ozs7OX7sI36zKlStTuXLlr10M8ZV9i+/xlL4PvpfPHSGEEF+OdM8UQohvXOnSpXn9+jUTJ0782kX5T4mOjgYga9asX7kkQgghvib5PhBCCJESCZoJIcQ3rlu3bhQpUoQ9e/Zw+PDhVNP7+vpiYWHBypUrk6xLaZyq+fPns3//fpycnLCyssLR0ZEVK1YAcOHCBVxcXChXrhyOjo7MmTOH2NjYJHlHRkbi6elJ1apVKVeuHB07duTs2bPJlnHPnj20a9cOGxsbbG1t6dSpE2fOnNFKoxlPZv369QwePBgrKyuqV6/OhQsXPnj8J0+epHPnztja2mJlZYWTkxPr1q0jPj5e65i3bdsGQIsWLbCwsEixrBrBwcGMHDkSe3t7rK2tady4McuXL0/SYiIwMJChQ4diZ2eHpaUlderUYdq0abx9+/aD+b/PwsKCoUOHcubMGVq1aqVekxkzZhAVFZUkbXLjAH3oPrh9+zadOnXC2tqaatWqMWbMGF68eJHq8Sc3ptnLly/x9PTE0dERKysr6tevz4wZMwgPD0+yz19//ZWaNWtiaWmJra0t7dq1Y9++fWqaOXPm4ObmBsDq1au1rktyx/n27VumTZtGnTp1sLS0xM7OjiFDhhAYGJjsuTh9+jTLli2jXr166rVZsGABcXFxWulPnDhBp06dqFq1KlZWVjRt2pRFixapP6w/JPF7THMf+/r6smXLFpo2bUrZsmWxt7dn6tSpvHv3LtU8IaFLW69evahUqRIVK1bE3d2d0NDQZNOm97ycO3eOxYsXq9ewRYsWHD9+HEjottawYUOsra1p2rQpe/fuTbK/tFxbSH5MM82YbBcvXqRjx47Y2NhQsWJFBg4cmGR8p+fPnzNixAjq1q1L2bJlqV69Or/++iv3799P0zl8+fIlU6dOVY9H8z5euHCh1mdaeq9ZXFwcS5cupX79+ur9sn///jSVCWD48OFYWFhw5coVGjVqRNmyZWnXrh2KogBw//59rc+Uhg0bsmjRoiSfPeHh4Xh6etKgQQPKli1L1apV6du3L9euXdNK17FjR+zt7Xn48CE9e/bExsYGOzs7fv31Vx49epSkfGFhYXh7e6v3U40aNRg7dmyynxnv3r1j9uzZNGjQAGtraxwdHRk3bhwvX74E0v4eX7FiBRYWFmzYsCHJPp4+fUqpUqXo1q2buiw6OppFixap569q1aoMGTKEoKCgD577D30fpDSmWUhICB4eHtjb22NpaYmjoyNeXl6EhYV9cF+Qvnvl/v37DBgwgFq1aqn78fDwICQkJNX9CCGE+HwkaCaEEN84Q0NDJkyYgI6ODuPGjUsSjPgc9u/fz+DBgylWrBht27YlPDycKVOmMHHiRH7++WeyZs1K+/btURSFuXPnsm7duiR5TJkyhe3bt9OoUSMaNGjA1atX6dy5M0eOHNFKN2vWLAYOHMizZ89wcnLCycmJO3fu0LlzZ7Zv354k33nz5nH16lU6dOhA6dKlKVOmTIrHsWbNGrp06cLVq1epW7cuLVu25O3bt4wfP54hQ4agKIo6NlfJkiUBaNu2LX379iVfvnwp5nv79m1atmzJ1q1bKV26NC4uLhgbGzN16lRGjRqlprt8+TLOzs7s3r2bcuXK4erqSvbs2Vm2bBlt2rRJMciRnFu3bvHLL7+QIUMGXF1dMTMzY+HChXTv3l0NAH6MyMhIOnTowNu3b3F1daVo0aJs3LgRFxeXNP3oe19ISAitWrVi1apV5M+fH1dXV/LkycPChQvp06ePGoi4cuUKrVu35siRI1SvXp3OnTtTvXp1rl69Sv/+/dVgcKVKlXBycgLA2tr6g9fl1atXtG7dmmXLlpE9e3ZcXV0pV64cf/zxB61ateLy5ctJtvHy8mLu3LmUL18eV1dXIiMjmTlzJrNnz1bT+Pv707NnT+7evUujRo3o0KEDenp6+Pj44OHhka7z8761a9fi4eFBiRIl6NixI0ZGRixfvlzr/knJjRs3cHFx4fjx49SoUYMmTZpw8uRJfv31189yXjw9PVm+fDm1atWicePG3L59m169ejFx4kQmTZqEra0tzs7OBAcHM2jQILUrG6T92n7ItWvXcHNzQ1dXl/bt22NhYcGePXv4+eef1UBlVFQU3bp1Y/v27ZQpU4aff/6Z8uXLs3v3btq1a5fqe+vt27e0adOG1atXU7x4cdzc3GjSpAkhISHMmDGD6dOnJ9kmrdds+PDheHl5oa+vT9u2bcmTJw/9+/dP9lx/SK9evShYsCDt2rWjcuXK6OjocO3aNVq2bMnevXupUqUKP//8M2ZmZvj4+NCrVy+tgO/AgQNZtWoVhQsXplOnTtSsWZNjx47h6urK3bt3tfYVGRmJm5sbgYGBtGvXDktLS3bs2EG7du14+vSp1nlr3749S5YsIX/+/Li5uWFjY8OmTZto3bo1z549U9O+e/eO9u3bM2/ePExNTWnXrp364KNTp06EhYWl+T3euHFj9PT0kkwWAAkPXeLj42nWrBkAMTExdOvWDR8fHzJmzEiHDh2oUaMG+/fvp1WrVty+fTvFc57e74NHjx7RqlUrNmzYoN6HRYoUYenSpXTs2JGIiIgU9wVpv1devnzJzz//zNGjR6lUqRKdO3emePHi/P7777i5uSXbvVUIIcQXogghhPgmzZ49WzE3N1cOHDigKIqijB49WjE3N1cmTJigpnn9+rVibm6udOjQQV22detWxdzcXFmxYkWSPDt06KCYm5srr1+/VhRFUYKCghRzc3Ot/SiKohw/flxdvnbtWnW5Jn2rVq2SlLNixYpKUFCQuvzatWuKtbW14uDgoMTGxiqKoiiXL19WLCwslA4dOigRERFq2pcvXyp169ZVrK2tlRcvXiiKoihnzpxRzM3NFWtra+XZs2epnq8HDx4opUuXVhwcHJQHDx6oy8PDwxU3NzfF3Nxc2bZtm7r8t99+U8zNzZXr16+nmreLi4tiYWGh7Nu3T10WHx+vdOnSRTE3N1f+/vtvJTY2VqlXr55SunRp5ejRo1rbe3l5Kebm5oq7u3uq+1IURT3348aNU5fFxMQoffr
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_complete = pd.DataFrame()\n",
    "\n",
    "# Creating subplot axes\n",
    "fig, axes = plt.subplots(nrows=3,ncols=2,figsize=(15, 15))\n",
    "\n",
    "for cat,ax in zip(sorted(data[group[-2]].unique()),axes.flatten()):\n",
    "    #data segment\n",
    "    sub_data = data[data[group[-2]]==cat]\n",
    "    sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n",
    "                                 ,group[-1],fill_value=0)\n",
    "    data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n",
    "    #plot\n",
    "    g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),\n",
    "                   y=record_col,x=group[0], hue=group[-1], marker=\"o\", ax=ax)\n",
    "    g.set(xticks=list(range(2012,2022+1,2)))\n",
    "    g.legend(title=None)\n",
    "    g.set_title(cat)\n",
    "    g.set_xlabel(None)\n",
    "    g.set_ylabel(None)\n",
    "    g.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "fig.suptitle(\"Number of co-publications in domains and respective fields\", y=0.92)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "09a6de71",
   "metadata": {},
   "source": [
    "## SubField"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0397eb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      Publication Year    Domain_English                      Field_English   \n1464              2022  Natural Sciences                Physics & Astronomy  \\\n1463              2022  Natural Sciences                Physics & Astronomy   \n1462              2022  Natural Sciences                Physics & Astronomy   \n1461              2022  Natural Sciences                Physics & Astronomy   \n1460              2022  Natural Sciences                Physics & Astronomy   \n...                ...               ...                                ...   \n4                 2011  Applied Sciences         Built Environment & Design   \n3                 2011  Applied Sciences  Agriculture, Fisheries & Forestry   \n2                 2011  Applied Sciences  Agriculture, Fisheries & Forestry   \n1                 2011  Applied Sciences  Agriculture, Fisheries & Forestry   \n0                 2011  Applied Sciences  Agriculture, Fisheries & Forestry   \n\n                SubField_English  UT (Unique WOS ID)  \n1464                      Optics                 130  \n1463  Nuclear & Particle Physics                  62  \n1462        Mathematical Physics                  10  \n1461             General Physics                  31  \n1460            Fluids & Plasmas                  72  \n...                          ...                 ...  \n4        Building & Construction                   3  \n3                   Food Science                   1  \n2                      Fisheries                   2  \n1         Dairy & Animal Science                   2  \n0         Agronomy & Agriculture                   3  \n\n[1465 rows x 5 columns]",
      "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>Publication Year</th>\n      <th>Domain_English</th>\n      <th>Field_English</th>\n      <th>SubField_English</th>\n      <th>UT (Unique WOS ID)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1464</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Optics</td>\n      <td>130</td>\n    </tr>\n    <tr>\n      <th>1463</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Nuclear &amp; Particle Physics</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>1462</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Mathematical Physics</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1461</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>General Physics</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>1460</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Fluids &amp; Plasmas</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Built Environment &amp; Design</td>\n      <td>Building &amp; Construction</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>Food Science</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>Fisheries</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>Dairy &amp; Animal Science</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>Agronomy &amp; Agriculture</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>1465 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group = ['Publication Year',\"Domain_English\",'Field_English',\"SubField_English\"]\n",
    "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "846596cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cat in sorted(data[group[-2]].unique()):\n",
    "    sub_data = data[data[group[-2]]==cat]\n",
    "    sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n",
    "                                 ,group[-1],fill_value=0)\n",
    "    g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0],\n",
    "                   hue=group[-1], marker=\"o\", errorbar=None)\n",
    "    g.set(xticks=list(range(2012,2022+1,2)))\n",
    "    g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncols=math.ceil(len(g.legend_.texts)/12))\n",
    "    g.set_title(f'Number or co-publications in {cat}')\n",
    "    g.set_ylabel(None)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "20"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sorted(data[group[-2]].unique()))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "outputs": [],
   "source": [
    "from  matplotlib.ticker import FuncFormatter\n",
    "import math\n",
    "def orderOfMagnitude(number):\n",
    "    return math.floor(math.log(number, 10))\n",
    "\n",
    "def roundToNearest(number):\n",
    "    order = orderOfMagnitude(number)\n",
    "    # if order!=0:\n",
    "    #     order+=1\n",
    "    near = math.ceil(number/10**order)*10**order\n",
    "    return near"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "91d2cc8a",
   "metadata": {},
   "source": [
    "## Collabs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "outputs": [
    {
     "data": {
      "text/plain": "         UT (Unique WOS ID)                        Institution   \n90877   WOS:000579154000008       Ctr Therapeut Target Validat  \\\n20703   WOS:000453784400017                         Fudan Univ   \n101741  WOS:000929537500051                  Publ Hlth England   \n137547  WOS:000596812400006                               CSIC   \n221     WOS:000288412600011  PKU HKUST Shenzhen Hong Kong Inst   \n...                     ...                                ...   \n119321  WOS:000310863300008                 Tech Univ Dortmund   \n27889   WOS:000496830500113                Harbin Inst Technol   \n150774  WOS:000744133100006                   Dublin City Univ   \n136356  WOS:000450361100005             Univ Politecn Valencia   \n59076   WOS:000781418000001                        Tongji Univ   \n\n               Country              Institution_harm  merge_iter   \n90877   United Kingdom  Ctr Therapeut Target Validat           0  \\\n20703            China                    Fudan Univ           0   \n101741  United Kingdom             Publ Hlth England           0   \n137547           Spain                          CSIC           0   \n221              China       Shenzhen Hong Kong Inst           0   \n...                ...                           ...         ...   \n119321         Germany            Tech Univ Dortmund           0   \n27889            China           Harbin Inst Technol           0   \n150774         Ireland              Dublin City Univ           0   \n136356           Spain        Univ Politecn Valencia           0   \n59076            China                   Tongji Univ           0   \n\n            Country_Type  \n90877   Non-EU associate  \n20703              China  \n101741  Non-EU associate  \n137547                EU  \n221                China  \n...                  ...  \n119321                EU  \n27889              China  \n150774                EU  \n136356                EU  \n59076              China  \n\n[100 rows x 6 columns]",
      "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>UT (Unique WOS ID)</th>\n      <th>Institution</th>\n      <th>Country</th>\n      <th>Institution_harm</th>\n      <th>merge_iter</th>\n      <th>Country_Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>90877</th>\n      <td>WOS:000579154000008</td>\n      <td>Ctr Therapeut Target Validat</td>\n      <td>United Kingdom</td>\n      <td>Ctr Therapeut Target Validat</td>\n      <td>0</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>20703</th>\n      <td>WOS:000453784400017</td>\n      <td>Fudan Univ</td>\n      <td>China</td>\n      <td>Fudan Univ</td>\n      <td>0</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>101741</th>\n      <td>WOS:000929537500051</td>\n      <td>Publ Hlth England</td>\n      <td>United Kingdom</td>\n      <td>Publ Hlth England</td>\n      <td>0</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>137547</th>\n      <td>WOS:000596812400006</td>\n      <td>CSIC</td>\n      <td>Spain</td>\n      <td>CSIC</td>\n      <td>0</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>221</th>\n      <td>WOS:000288412600011</td>\n      <td>PKU HKUST Shenzhen Hong Kong Inst</td>\n      <td>China</td>\n      <td>Shenzhen Hong Kong Inst</td>\n      <td>0</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>119321</th>\n      <td>WOS:000310863300008</td>\n      <td>Tech Univ Dortmund</td>\n      <td>Germany</td>\n      <td>Tech Univ Dortmund</td>\n      <td>0</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>27889</th>\n      <td>WOS:000496830500113</td>\n      <td>Harbin Inst Technol</td>\n      <td>China</td>\n      <td>Harbin Inst Technol</td>\n      <td>0</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>150774</th>\n      <td>WOS:000744133100006</td>\n      <td>Dublin City Univ</td>\n      <td>Ireland</td>\n      <td>Dublin City Univ</td>\n      <td>0</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>136356</th>\n      <td>WOS:000450361100005</td>\n      <td>Univ Politecn Valencia</td>\n      <td>Spain</td>\n      <td>Univ Politecn Valencia</td>\n      <td>0</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>59076</th>\n      <td>WOS:000781418000001</td>\n      <td>Tongji Univ</td>\n      <td>China</td>\n      <td>Tongji Univ</td>\n      <td>0</td>\n      <td>China</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n",
    "wos_univ_locations.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [],
   "source": [
    "wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "b3adb06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 900x1200 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_1020\\556627507.py:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  g.set_xticklabels([str(int(val))+'%' for val in vals])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 900x1200 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_1020\\556627507.py:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  g.set_xticklabels([str(int(val))+'%' for val in vals])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 900x1200 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAP0CAYAAACpiK2OAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1zV5f//8QeghAqoqBGpucWFggs1XAwHuQdO/JmpqOWkDHPlChFLS4U0LWc5MUeaMy0tUbFc5EBIxRWakosh8PvDj+frCVAKEJHn/XY7t877fV3v6/263ofP93teXuOYpKSkpCAiIiIiIiL/mWlOByAiIiIiIpLbKbESERERERHJJCVWIiIiIiIimaTESkREREREJJOUWImIiIiIiGSSEisREREREZFMUmIlIiIiIiKSSUqsREREREREMkmJlYiI/Gc5/Rvzef3+eZWeu4g8j5RYiYi8AI4fP857771Hs2bNqFmzJu7u7owfP56LFy9my/3+/vtvRo8ezeHDh59YLzo6Gnt7e0JCQgAICQnB3t6e6OjoTMewa9cu3n//fcNxaGgo9vb2hIaGZrrtp8lo/18UGfnc/vn858yZg729fZbGkZCQwEcffcSmTZsM5/z8/HB1dc3S+4iI/BdKrEREcrkVK1bQvXt3bty4ga+vL1988QUDBw7k4MGDdOnShVOnTmX5PX///Xc2bNhAcnLyE+u9/PLLrFq1imbNmmV5DIsXL+bKlSuG4+rVq7Nq1SqqV6+e5ff6p4z2Py/r2rUrq1atytI2//zzT5YsWcKDBw8M54YMGcLcuXOz9D4iIv9FvpwOQERE/ruwsDCmTZtGr169GDt2rOG8s7Mz7u7udOjQgQ8++MAwYvSsmZub4+jo+EzuZWlp+czuJU/3yiuv8Morr2T7fV577bVsv4eISEZoxEpEJBdbtGgRVlZWjBo1KlWZjY0Nfn5+uLm5ce/ePQCSkpJYsWIFbdu2pWbNmjRr1oyZM2cSHx9vuM7Pz4++ffuybt06WrZsSY0aNWjfvj0//vgj8HDKV58+fQDo06cP3t7eAHh7e/Puu+8ybNgwHB0defPNN1NNBXzkyJEjdOjQgRo1atCmTRu2bNliKEtvSp+3t7fRvQ4ePMjBgwcNddO67vjx47z11ls4OztTu3ZtBg0axNmzZ1Pd65dffqFfv37UqlWL119/ncDAQJKSktJ85un1H2DLli106tQJJycnXn/9dSZMmEBsbGya7TySkpLC4sWLad26NTVr1sTDw4NFixYZrSPav38/PXv2pE6dOjg7O+Pr62s0WpceV1dXZs2axUcffUS9evVwdnZm9OjR3Lp1K83n+s/n8s/P4Emf2z+lNRXw22+/pWPHjtSqVYtmzZrx8ccfk5CQYCjfuXMnPXv2xMnJiRo1atCqVStWrFgBPJxW6ubmBsCYMWMM0//+ORUwK/7GAZKTk5k1axaurq7UqFEDV1dXPv74YxITE5/4zEUk71JiJSKSS6WkpLBv3z4aNmxIgQIF0qzj6enJ22+/TcGCBQGYMGEC/v7+uLu7ExwcTK9evVi+fDlDhgwx+iJ/4sQJFi1axLBhw5g3bx5mZmYMHTqU2NhYqlevzoQJEwztTZw40XDd1q1bKVSoEMHBwfTv3z/d2CdMmEDr1q0JCgqiUqVKjBw5kp07d2a47xMnTqRatWpUq1Yt3el/Bw4coEePHgB89NFHTJ06lStXrtC9e3fOnTtnVPfdd9+lTp06fP7557Rp04aFCxeyZs2aNO+dXv+DgoIYNWoUjo6OfPbZZ7z99tts27YNb29v4uLi0u3LjBkzmDFjBq6urnz++ed06dKFmTNnsmDBAuBhMtKvXz/s7Oz45JNPGDNmDL/++ivdunXjxo0bT31WX3/9NUeOHMHf3x9fX1/27t2Lj4/Pf9oAIjOf24oVK3j//fepXr06c+fOZeDAgSxbtoypU6cCsGfPHt5++22qV69OUFAQc+bMoXTp0kyePJmjR4/y8ssvG6b8DR48ON3pf1nxNw7wxRdf8M033/D222/z5Zdf0qNHDxYtWkRwcPC/fm4ikjdoKqCISC518+ZN4uPjKVWqVIbqR0REsHbtWnx9fRk4cCAAr7/+Oi+//DKjR4/mxx9/pGnTpgDcvn2bkJAQwzSrggUL0rt3bw4cOEDLli2pWLEiABUrVjS8B8ifPz+TJk3C3NwcIN3NDoYOHcpbb70FQJMmTfjjjz8ICgrC3d09Q32pWLEilpaWAOlO//v4448pU6YMCxYswMzMDAAXFxc8PDz47LPP+PTTTw11u3btyttvvw1Aw4YN2blzJ3v27KF79+6p2rW0tEzV/9jYWIKDg/Hy8jIkXQCVK1emV69erFu3jl69eqVq6++//2bp0qX07t2b9957D4BGjRoRExPDoUOHGDBgADNnzsTFxYWPP/7YcF3t2rXx9PRk0aJFjB49+onPytTUlK+++gorKyvg4Ujm22+/zU8//USTJk2eeO0//dfPLTk5mXnz5uHu7m5IpADu37/Pd999R2JiIhEREXTs2NFoSquTkxPOzs6EhoZSq1YtqlatCjyc/letWrVU98nKv/GDBw9So0YNOnfuDED9+vUpUKCA4TmKiPyTRqxERHKpR8lCelPW/ungwYMAvPHGG0bn33jjDczMzIymfdnY2BitXXm0Vub+/ftPvEf58uUNSdWTeHp6Gh27u7sTHh7O3bt3n3ptRty7d4/jx4/TunVrw3MCsLa2pnnz5oZn8YiTk5PR8SuvvGKYPpkRv/32GwkJCbRp08bofN26dSlZsmSq+z1+3YMHD2jRooXR+XHjxrFw4UKioqKIiYlJ1e5rr72Gk5OTod0HDx4YvR7fVMPV1dUoGXB1dSVfvnwcOnQow/175L9+blFRUdy4cQMPDw+j82+99RYhISHkz5+f/v37M336dO7evcuJEyfYsmUL8+fPBzCaLvgkWfk37uzsbJiCuXDhQiIiIujduzft27fPUCwikvcosRIRyaUKFy5MoUKFuHz5crp17t27Z5ja9Oi/JUqUMKqTL18+ihYtyu3btw3n/jm10MTEBOCpu+AVKlQoQ7EXL17c6LhYsWKkpKRw586dDF3/NLdv3yYlJSXVfR7d+/G+AlhYWBgdm5qa/qupco+ebUbv98ijtU42NjZPLH9au9WrVzd6zZs3z1DP1tbW6DpTU1OKFi361LVfafmvn9ujfhQrVizdOn/99RdDhw6lbt26eHl5MWfOHEO7Gf0ssvJvvH///kyYMIG4uDhmzpzJG2+8QZs2bThw4ECGYhGRvEdTAUVEcjEXFxdCQ0OJj4/npZdeSlW+evVqAgICWLt2LYULFwYgJiaGkiVLGuokJiZy8+ZNihYt+szijo2NNfqSfv36dczMzChcuHC6Sdzdu3cznLhZWVlhYmLC9evXU5XFxMRQpEiR/x58Gh492+vXr1O+fPlU9ytdunSa11lbWwMPk4rHr7t8+TIXLlwwfCbp9eNR+dq1a43KXn75ZcP7mzdvGpUlJSVx8+ZNo2Tun6Oe6Y3WPelze5LH+/m4mzdvEh4ejpOTE++++y6RkZEsXrwYJycnzM3NuX//PqtXr35i24/Lyr9xU1NTevXqRa9evbhx4wZ79+7l888/Z+jQoezfvz9DI7MikrdoxEpEJBfr168ft27dYvbs2anKYmJi+PLLL6lYsSLVq1enfv36AHz33XdG9b777juSkpKoU6dOhu/7+PS6/2LPnj2G98nJyXz//ffUqlULCwsLw9qpq1evGurExsam2nDC1DT9/xdWsGBBatSowdatW42Shtu3b7Nnz55/1de0/LP/tWrVwtzcnM2bNxudP3z4MJcvX6Z27dpptlOzZk3y58/PDz/8YHT+yy+/ZNSoUVSqVIkSJUqkavfixYv89ttvhnYdHByMXo+PUv34449GU+l27drFgwcPaNiwIfBwzdjjzxoebuOflid9bk9Svnx5ihYtmqqfGzZsYODAgSQmJhIWFkaLFi1wdnY2JC2Pdul7lGQ/7e8uK//Gu3fvblgPVqxYMTp16kSvXr34+++/s2xkVUReLBq
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "collab_desc = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n",
    "collab_desc[\"percent_of_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].nunique()*100\n",
    "collab_desc[\"percent_contrib_in_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].size*100\n",
    "collab_desc = collab_desc.merge(wos_country_types, on=\"Country\")\n",
    "collab_desc\n",
    "\n",
    "c_dict = {\"count\":\"Number of co-publications\",\n",
    "          \"percent_of_copubs\":\"Percent of co-publications\",\n",
    "          \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n",
    "\n",
    "\n",
    "# Creating subplot axes\n",
    "# fig, axes = plt.subplots(ncols=3,figsize=(15, 15))\n",
    "# for c,ax in zip(c_dict.keys(),axes.flatten()):\n",
    "for c in c_dict.keys():\n",
    "    data = collab_desc[[\"Country\",c,\"Country_Type\"]]\n",
    "    plt.figure(figsize=(9,12))\n",
    "    g = sns.barplot(data, x=c, y=\"Country\", hue=\"Country_Type\", dodge=False)\n",
    "    g.set_xlim(0,roundToNearest(data[c].max()))\n",
    "    g.set_ylabel(None)\n",
    "    g.set_xlabel(c_dict.get(c))\n",
    "    g.set_title(c_dict.get(c))\n",
    "    g.legend(title=None, loc=\"right\")\n",
    "    for i in g.containers:\n",
    "        g.bar_label(i,fontsize=10, fmt='%.1f%%' if 'percent' in c else '%.0f')\n",
    "    if 'percent' in c:\n",
    "        g.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "        vals = g.get_xticks()\n",
    "        g.set_xticklabels([str(int(val))+'%' for val in vals])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "140395ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 71.74999999999994, '')"
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1100x900 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n",
    "wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n",
    "EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique', normalize='all').fillna(0)\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(EU_co_occur, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "g = sns.heatmap(EU_co_occur, mask=mask,\n",
    "            square=True, linewidths=.5)\n",
    "\n",
    "g.set_ylabel(None)\n",
    "g.set_xlabel(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "df1f03ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Yearly output of co-publications with China')"
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "collab_year = wos_collabs[wos_collabs[\"Country\"]!=\"China\"].copy()\n",
    "collab_year = collab_year.merge(wos_country_types, on=\"Country\").merge(wos[[record_col,\"Publication Year\"]],on=record_col).drop_duplicates()\n",
    "data = collab_year.groupby([\"Publication Year\",'Country_Type'],as_index=False)[record_col].nunique()\n",
    "\n",
    "\n",
    "g=sns.lineplot(data,y=record_col,x=\"Publication Year\", hue=\"Country_Type\", marker=\"o\")\n",
    "g.set(xticks=list(range(2012,2022+1,2)))\n",
    "g.legend(title=None)\n",
    "g.set_xlabel(None)\n",
    "g.set_ylabel(None)\n",
    "g.set_title(\"Yearly output of co-publications with China\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "e4c50e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Publication Year  2011  2012  2013  2014  2015  2016  2017  2018  2019  2020   \nCountry                                                                        \nAustria             19    23    22    32    37    47    63    71   120   120  \\\nBelgium             28    34    32    54    63    76    73   113   158   188   \nBulgaria             2     5     8     8     6    14    17    16    10    22   \nCroatia              1     2     6     6     6     7     9    18    22    25   \nCyprus               2     1     5     4     5     5     8     7    15    25   \nCzech Republic      10    13    12    17    12    31    34    53    55    72   \nDenmark             29    26    34    48    55    60    83   164   197   216   \nEstonia              3     3     7     9    10    10    14    13    11    31   \nFinland             25    30    37    68    86   113   110   171   212   227   \nFrance              97   114   157   201   241   287   311   441   568   619   \nGermany             98   149   171   221   269   322   382   525   707   819   \nGreece              10    18    17    25    24    43    37    70    95   108   \nHungary              8    10    17    12    16    32    29    37    50    56   \nIreland             11    12    22    24    21    43    53    69    73   106   \nItaly               46    59    71    97   151   162   206   276   376   516   \nLatvia               0     0     1     0     0     7    10    13     8     7   \nLithuania            1     1     8     3     3    12    12    22    32    32   \nLuxembourg           2     2     3     1     6     9    12    14    18    17   \nMalta                0     0     0     0     1     1     0     0     2     1   \nNetherlands         59    49    61    82   117   141   173   255   353   394   \nNorway              23    36    54    62    51    76    75   106   192   230   \nPoland              14    27    31    51    62    73    89   101   120   165   \nPortugal            12    17    27    37    37    49    64   105   107   132   \nRomania              4    12     9    12    20    21    28    45    45    48   \nSlovakia             5     5     2     8     7    17    15    23    22    32   \nSlovenia             4     6     9    10    11    23    19    41    48    28   \nSpain               43    36    52    94   116   163   210   241   312   360   \nSweden              26    41    48    69    92   141   198   195   315   307   \nSwitzerland         28    38    43    58    55    83   129   174   202   224   \nUnited Kingdom     318   366   467   584   677   903  1188  1637  2176  2776   \n\nPublication Year  2021  2022  \nCountry                       \nAustria            159   189  \nBelgium            221   271  \nBulgaria            29    18  \nCroatia             32    33  \nCyprus              30    38  \nCzech Republic      80   109  \nDenmark            265   306  \nEstonia             36    34  \nFinland            255   353  \nFrance             741   794  \nGermany           1103  1288  \nGreece             120   167  \nHungary             76    88  \nIreland            148   162  \nItaly              569   734  \nLatvia              10    17  \nLithuania           34    33  \nLuxembourg          30    46  \nMalta                6    10  \nNetherlands        460   564  \nNorway             270   279  \nPoland             260   337  \nPortugal           192   191  \nRomania             42    53  \nSlovakia            32    42  \nSlovenia            43    37  \nSpain              436   593  \nSweden             352   442  \nSwitzerland        320   402  \nUnited Kingdom    3343  3889  ",
      "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>Publication Year</th>\n      <th>2011</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n      <th>2020</th>\n      <th>2021</th>\n      <th>2022</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Austria</th>\n      <td>19</td>\n      <td>23</td>\n      <td>22</td>\n      <td>32</td>\n      <td>37</td>\n      <td>47</td>\n      <td>63</td>\n      <td>71</td>\n      <td>120</td>\n      <td>120</td>\n      <td>159</td>\n      <td>189</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td>28</td>\n      <td>34</td>\n      <td>32</td>\n      <td>54</td>\n      <td>63</td>\n      <td>76</td>\n      <td>73</td>\n      <td>113</td>\n      <td>158</td>\n      <td>188</td>\n      <td>221</td>\n      <td>271</td>\n    </tr>\n    <tr>\n      <th>Bulgaria</th>\n      <td>2</td>\n      <td>5</td>\n      <td>8</td>\n      <td>8</td>\n      <td>6</td>\n      <td>14</td>\n      <td>17</td>\n      <td>16</td>\n      <td>10</td>\n      <td>22</td>\n      <td>29</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>Croatia</th>\n      <td>1</td>\n      <td>2</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>7</td>\n      <td>9</td>\n      <td>18</td>\n      <td>22</td>\n      <td>25</td>\n      <td>32</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>Cyprus</th>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>4</td>\n      <td>5</td>\n      <td>5</td>\n      <td>8</td>\n      <td>7</td>\n      <td>15</td>\n      <td>25</td>\n      <td>30</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>Czech Republic</th>\n      <td>10</td>\n      <td>13</td>\n      <td>12</td>\n      <td>17</td>\n      <td>12</td>\n      <td>31</td>\n      <td>34</td>\n      <td>53</td>\n      <td>55</td>\n      <td>72</td>\n      <td>80</td>\n      <td>109</td>\n    </tr>\n    <tr>\n      <th>Denmark</th>\n      <td>29</td>\n      <td>26</td>\n      <td>34</td>\n      <td>48</td>\n      <td>55</td>\n      <td>60</td>\n      <td>83</td>\n      <td>164</td>\n      <td>197</td>\n      <td>216</td>\n      <td>265</td>\n      <td>306</td>\n    </tr>\n    <tr>\n      <th>Estonia</th>\n      <td>3</td>\n      <td>3</td>\n      <td>7</td>\n      <td>9</td>\n      <td>10</td>\n      <td>10</td>\n      <td>14</td>\n      <td>13</td>\n      <td>11</td>\n      <td>31</td>\n      <td>36</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>Finland</th>\n      <td>25</td>\n      <td>30</td>\n      <td>37</td>\n      <td>68</td>\n      <td>86</td>\n      <td>113</td>\n      <td>110</td>\n      <td>171</td>\n      <td>212</td>\n      <td>227</td>\n      <td>255</td>\n      <td>353</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>97</td>\n      <td>114</td>\n      <td>157</td>\n      <td>201</td>\n      <td>241</td>\n      <td>287</td>\n      <td>311</td>\n      <td>441</td>\n      <td>568</td>\n      <td>619</td>\n      <td>741</td>\n      <td>794</td>\n    </tr>\n    <tr>\n      <th>Germany</th>\n      <td>98</td>\n      <td>149</td>\n      <td>171</td>\n      <td>221</td>\n      <td>269</td>\n      <td>322</td>\n      <td>382</td>\n      <td>525</td>\n      <td>707</td>\n      <td>819</td>\n      <td>1103</td>\n      <td>1288</td>\n    </tr>\n    <tr>\n      <th>Greece</th>\n      <td>10</td>\n      <td>18</td>\n      <td>17</td>\n      <td>25</td>\n      <td>24</td>\n      <
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_pivot = pd.crosstab(collab_year['Country'], collab_year['Publication Year'], values=collab_year[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "year_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1500 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 15))\n",
    "g = sns.heatmap(year_pivot, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
    "g.set(xlabel=\"\", ylabel=\"\")\n",
    "for i in range(year_pivot.shape[0]+1):\n",
    "    ax.axhline(i, color='white', lw=10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "outputs": [
    {
     "data": {
      "text/plain": "Publication Year       2011       2012       2013       2014       2015   \nCountry                                                                   \nAustria            2.047414   2.026432   1.535241   1.686874   1.639344  \\\nBelgium            3.017241   2.995595   2.233077   2.846600   2.791316   \nBulgaria           0.215517   0.440529   0.558269   0.421719   0.265840   \nCroatia            0.107759   0.176211   0.418702   0.316289   0.265840   \nCyprus             0.215517   0.088106   0.348918   0.210859   0.221533   \nCzech Republic     1.077586   1.145374   0.837404   0.896152   0.531679   \nDenmark            3.125000   2.290749   2.372645   2.530311   2.436863   \nEstonia            0.323276   0.264317   0.488486   0.474433   0.443066   \nFinland            2.693966   2.643172   2.581996   3.584607   3.810368   \nFrance            10.452586  10.044053  10.956036  10.595677  10.677891   \nGermany           10.560345  13.127753  11.933008  11.649974  11.918476   \nGreece             1.077586   1.585903   1.186322   1.317870   1.063358   \nHungary            0.862069   0.881057   1.186322   0.632578   0.708906   \nIreland            1.185345   1.057269   1.535241   1.265156   0.930439   \nItaly              4.956897   5.198238   4.954641   5.113337   6.690297   \nLatvia             0.000000   0.000000   0.069784   0.000000   0.000000   \nLithuania          0.107759   0.088106   0.558269   0.158144   0.132920   \nLuxembourg         0.215517   0.176211   0.209351   0.052715   0.265840   \nMalta              0.000000   0.000000   0.000000   0.000000   0.044307   \nNetherlands        6.357759   4.317181   4.256804   4.322615   5.183872   \nNorway             2.478448   3.171806   3.768318   3.268318   2.259637   \nPoland             1.508621   2.378855   2.163294   2.688455   2.747009   \nPortugal           1.293103   1.497797   1.884159   1.950448   1.639344   \nRomania            0.431034   1.057269   0.628053   0.632578   0.886132   \nSlovakia           0.538793   0.440529   0.139567   0.421719   0.310146   \nSlovenia           0.431034   0.528634   0.628053   0.527148   0.487373   \nSpain              4.633621   3.171806   3.628751   4.955192   5.139566   \nSweden             2.801724   3.612335   3.349616   3.637322   4.076207   \nSwitzerland        3.017241   3.348018   3.000698   3.057459   2.436863   \nUnited Kingdom    34.267241  32.246696  32.588974  30.785451  29.995569   \n\nPublication Year       2016       2017       2018       2019       2020   \nCountry                                                                   \nAustria            1.581959   1.725555   1.415470   1.812415   1.518411  \\\nBelgium            2.558061   1.999452   2.252791   2.386346   2.378843   \nBulgaria           0.471222   0.465626   0.318979   0.151035   0.278375   \nCroatia            0.235611   0.246508   0.358852   0.332276   0.316336   \nCyprus             0.168294   0.219118   0.139553   0.226552   0.316336   \nCzech Republic     1.043420   0.931252   1.056619   0.830690   0.911046   \nDenmark            2.019522   2.273350   3.269537   2.975381   2.733139   \nEstonia            0.336587   0.383457   0.259171   0.166138   0.392256   \nFinland            3.803433   3.012873   3.409091   3.201933   2.872327   \nFrance             9.660047   8.518214   8.791866   8.578765   7.832469   \nGermany           10.838102  10.462887  10.466507  10.678145  10.363153   \nGreece             1.447324   1.013421   1.395534   1.434829   1.366570   \nHungary            1.077078   0.794303   0.737640   0.755173   0.708592   \nIreland            1.447324   1.451657   1.375598   1.102552   1.341263   \nItaly              5.452710   5.642290   5.502392   5.678900   6.529166   \nLatvia             0.235611   0.273898   0.259171   0.120828   0.088574   \nLithuania          0.403904   0.328677   0.438596   0.483311   0.404910   \nLuxembourg         0.302928   0.328677   0.279107   0.271862   0.215108   \nMalta              0.033659   0.000000   0.000000   0.030207   0.012653   \nNetherlands        4.745877   4.738428   5
      "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>Publication Year</th>\n      <th>2011</th>\n      <th>2012</th>\n      <th>2013</th>\n      <th>2014</th>\n      <th>2015</th>\n      <th>2016</th>\n      <th>2017</th>\n      <th>2018</th>\n      <th>2019</th>\n      <th>2020</th>\n      <th>2021</th>\n      <th>2022</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Austria</th>\n      <td>2.047414</td>\n      <td>2.026432</td>\n      <td>1.535241</td>\n      <td>1.686874</td>\n      <td>1.639344</td>\n      <td>1.581959</td>\n      <td>1.725555</td>\n      <td>1.415470</td>\n      <td>1.812415</td>\n      <td>1.518411</td>\n      <td>1.640190</td>\n      <td>1.640767</td>\n    </tr>\n    <tr>\n      <th>Belgium</th>\n      <td>3.017241</td>\n      <td>2.995595</td>\n      <td>2.233077</td>\n      <td>2.846600</td>\n      <td>2.791316</td>\n      <td>2.558061</td>\n      <td>1.999452</td>\n      <td>2.252791</td>\n      <td>2.386346</td>\n      <td>2.378843</td>\n      <td>2.279761</td>\n      <td>2.352635</td>\n    </tr>\n    <tr>\n      <th>Bulgaria</th>\n      <td>0.215517</td>\n      <td>0.440529</td>\n      <td>0.558269</td>\n      <td>0.421719</td>\n      <td>0.265840</td>\n      <td>0.471222</td>\n      <td>0.465626</td>\n      <td>0.318979</td>\n      <td>0.151035</td>\n      <td>0.278375</td>\n      <td>0.299154</td>\n      <td>0.156264</td>\n    </tr>\n    <tr>\n      <th>Croatia</th>\n      <td>0.107759</td>\n      <td>0.176211</td>\n      <td>0.418702</td>\n      <td>0.316289</td>\n      <td>0.265840</td>\n      <td>0.235611</td>\n      <td>0.246508</td>\n      <td>0.358852</td>\n      <td>0.332276</td>\n      <td>0.316336</td>\n      <td>0.330101</td>\n      <td>0.286483</td>\n    </tr>\n    <tr>\n      <th>Cyprus</th>\n      <td>0.215517</td>\n      <td>0.088106</td>\n      <td>0.348918</td>\n      <td>0.210859</td>\n      <td>0.221533</td>\n      <td>0.168294</td>\n      <td>0.219118</td>\n      <td>0.139553</td>\n      <td>0.226552</td>\n      <td>0.316336</td>\n      <td>0.309470</td>\n      <td>0.329890</td>\n    </tr>\n    <tr>\n      <th>Czech Republic</th>\n      <td>1.077586</td>\n      <td>1.145374</td>\n      <td>0.837404</td>\n      <td>0.896152</td>\n      <td>0.531679</td>\n      <td>1.043420</td>\n      <td>0.931252</td>\n      <td>1.056619</td>\n      <td>0.830690</td>\n      <td>0.911046</td>\n      <td>0.825253</td>\n      <td>0.946263</td>\n    </tr>\n    <tr>\n      <th>Denmark</th>\n      <td>3.125000</td>\n      <td>2.290749</td>\n      <td>2.372645</td>\n      <td>2.530311</td>\n      <td>2.436863</td>\n      <td>2.019522</td>\n      <td>2.273350</td>\n      <td>3.269537</td>\n      <td>2.975381</td>\n      <td>2.733139</td>\n      <td>2.733650</td>\n      <td>2.656481</td>\n    </tr>\n    <tr>\n      <th>Estonia</th>\n      <td>0.323276</td>\n      <td>0.264317</td>\n      <td>0.488486</td>\n      <td>0.474433</td>\n      <td>0.443066</td>\n      <td>0.336587</td>\n      <td>0.383457</td>\n      <td>0.259171</td>\n      <td>0.166138</td>\n      <td>0.392256</td>\n      <td>0.371364</td>\n      <td>0.295165</td>\n    </tr>\n    <tr>\n      <th>Finland</th>\n      <td>2.693966</td>\n      <td>2.643172</td>\n      <td>2.581996</td>\n      <td>3.584607</td>\n      <td>3.810368</td>\n      <td>3.803433</td>\n      <td>3.012873</td>\n      <td>3.409091</td>\n      <td>3.201933</td>\n      <td>2.872327</td>\n      <td>2.630493</td>\n      <td>3.064502</td>\n    </tr>\n    <tr>\n      <th>France</th>\n      <td>10.452586</
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_percent_pivot = pd.crosstab(collab_year['Country'], collab_year['Publication Year'], values=collab_year[record_col], aggfunc='nunique', normalize='columns').fillna(0)*100\n",
    "year_percent_pivot"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1500 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(15, 15))\n",
    "g = sns.heatmap(year_percent_pivot, annot=True, fmt='.1f', linewidths=(.5), ax=ax, cbar=False)\n",
    "for t in ax.texts: t.set_text(t.get_text() + \" %\")\n",
    "g.set(xlabel=\"\", ylabel=\"\")\n",
    "for i in range(year_percent_pivot.shape[1]+1):\n",
    "    ax.axvline(i, color='white', lw=10)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}