ZSI_Reconnect_China/WOS/wos_analyses_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "id": "fb7baf32",
   "metadata": {},
   "outputs": [],
   "source": [
    "outdir=\"wos_processed_data\"\n",
    "\n",
    "wos = pd.read_excel(f\"{outdir}/wos_processed.xlsx\")\n",
    "\n",
    "wos_addresses = pd.read_excel(f\"{outdir}/wos_addresses.xlsx\")\n",
    "\n",
    "wos_affiliations = pd.read_excel(f\"{outdir}/wos_affiliations.xlsx\")\n",
    "wos_affiliations = wos_affiliations[wos_affiliations[\"Affiliations\"]!=\"UNKNOWN\"].copy()\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "id": "af12584f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "               Domain_English  UT (Unique WOS ID)\n0            Applied Sciences                5379\n5            Natural Sciences                1649\n3             Health Sciences                1106\n2  Economic & Social Sciences                 289\n4               Miscellaneous                 156\n1           Arts & Humanities                  13",
      "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>5379</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Natural Sciences</td>\n      <td>1649</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Health Sciences</td>\n      <td>1106</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Economic &amp; Social Sciences</td>\n      <td>289</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Miscellaneous</td>\n      <td>156</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Arts &amp; Humanities</td>\n      <td>13</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 6,
     "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": 7,
   "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(0,6000)\n",
    "g.set_ylabel(\"Domain\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88742c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    Publication Year              Domain_English  UT (Unique WOS ID)\n65              2022            Natural Sciences                 524\n64              2022               Miscellaneous                  41\n63              2022             Health Sciences                 368\n62              2022  Economic & Social Sciences                 106\n61              2022           Arts & Humanities                   4\n..               ...                         ...                 ...\n4               2012               Miscellaneous                   3\n3               2012             Health Sciences                   2\n2               2012  Economic & Social Sciences                   0\n1               2012           Arts & Humanities                   0\n0               2012            Applied Sciences                  21\n\n[66 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>65</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>524</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>2022</td>\n      <td>Miscellaneous</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>2022</td>\n      <td>Health Sciences</td>\n      <td>368</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>2022</td>\n      <td>Economic &amp; Social Sciences</td>\n      <td>106</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>2022</td>\n      <td>Arts &amp; Humanities</td>\n      <td>4</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>2012</td>\n      <td>Miscellaneous</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>Health Sciences</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012</td>\n      <td>Economic &amp; Social Sciences</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>Arts &amp; Humanities</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n<p>66 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 8,
     "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": 9,
   "id": "151a7a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1ba2d8a4e20>"
     },
     "execution_count": 9,
     "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcae04bd",
   "metadata": {},
   "source": [
    "## Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3807072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     Publication Year    Domain_English   \n176              2022  Natural Sciences  \\\n175              2022  Natural Sciences   \n174              2022  Natural Sciences   \n173              2022  Natural Sciences   \n172              2022  Natural Sciences   \n..                ...               ...   \n4                2012     Miscellaneous   \n3                2012   Health Sciences   \n2                2012  Applied Sciences   \n1                2012  Applied Sciences   \n0                2012  Applied Sciences   \n\n                                Field_English  UT (Unique WOS ID)  \n176                       Physics & Astronomy                 205  \n175                  Mathematics & Statistics                  61  \n174            Earth & Environmental Sciences                 134  \n173                                 Chemistry                  81  \n172                                   Biology                  43  \n..                                        ...                 ...  \n4                               Miscellaneous                   3  \n3                           Clinical Medicine                   2  \n2    Information & Communication Technologies                  14  \n1                                 Engineering                   5  \n0           Agriculture, Fisheries & Forestry                   2  \n\n[177 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>176</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>205</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Mathematics &amp; Statistics</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Earth &amp; Environmental Sciences</td>\n      <td>134</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Chemistry</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Biology</td>\n      <td>43</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>2012</td>\n      <td>Miscellaneous</td>\n      <td>Miscellaneous</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>Health Sciences</td>\n      <td>Clinical Medicine</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>177 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 10,
     "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": 11,
   "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": [
    "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",
    "    #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": "markdown",
   "id": "09a6de71",
   "metadata": {},
   "source": [
    "## SubField"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0397eb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     Publication Year    Domain_English   \n774              2022  Natural Sciences  \\\n773              2022  Natural Sciences   \n772              2022  Natural Sciences   \n771              2022  Natural Sciences   \n770              2022  Natural Sciences   \n..                ...               ...   \n4                2012  Applied Sciences   \n3                2012  Applied Sciences   \n2                2012  Applied Sciences   \n1                2012  Applied Sciences   \n0                2012  Applied Sciences   \n\n                                Field_English   \n774                       Physics & Astronomy  \\\n773                       Physics & Astronomy   \n772                       Physics & Astronomy   \n771                       Physics & Astronomy   \n770                       Physics & Astronomy   \n..                                        ...   \n4    Information & Communication Technologies   \n3                                 Engineering   \n2                                 Engineering   \n1                                 Engineering   \n0           Agriculture, Fisheries & Forestry   \n\n                               SubField_English  UT (Unique WOS ID)  \n774                                      Optics                  56  \n773                  Nuclear & Particle Physics                  28  \n772                        Mathematical Physics                   2  \n771                             General Physics                  14  \n770                            Fluids & Plasmas                  21  \n..                                          ...                 ...  \n4    Artificial Intelligence & Image Processing                  10  \n3           Mechanical Engineering & Transports                   1  \n2           Industrial Engineering & Automation                   3  \n1            Geological & Geomatics Engineering                   1  \n0                                  Food Science                   2  \n\n[775 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>774</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Optics</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>773</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>28</td>\n    </tr>\n    <tr>\n      <th>772</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Mathematical Physics</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>771</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>General Physics</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>770</th>\n      <td>2022</td>\n      <td>Natural Sciences</td>\n      <td>Physics &amp; Astronomy</td>\n      <td>Fluids &amp; Plasmas</td>\n      <td>21</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>2012</td>\n      <td>Applied Sciences</td>\n      <td>Information &amp; Communication Technologies</td>\n      <td>Artificial Intelligence &amp; Image Processing</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Mechanical Engineering &amp; Transports</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Industrial Engineering &amp; Automation</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Engineering</td>\n      <td>Geological &amp; Geomatics Engineering</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2012</td>\n      <td>Applied Sciences</td>\n      <td>Agriculture, Fisheries &amp; Forestry</td>\n      <td>Food Science</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>775 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 12,
     "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": 13,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAzQAAAHHCAYAAABp6x3pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1xV9f/A8dflsjcCMhQBFRBxgDM1N+YuFbepuAdqafotv1mi5cqcOSpTUHNlqam4NbTIgQM1NXIgaOJCBBHZ5/eHP+7XK6Cg4HW8n4/HeTy653zO5/M+B4nzvp9xVIqiKAghhBBCCCHEK0hP1wEIIYQQQgghxLOShEYIIYQQQgjxypKERgghhBBCCPHKkoRGCCGEEEII8cqShEYIIYQQQgjxypKERgghhBBCCPHKkoRGCCGEEEII8cqShEYIIYQQQgjxypKERgghhBBCCPHKkoRGiJdIaGgoKpWKo0ePvrA2L1++jEqlIjQ09IW1KYQQQghRXCShEeIFyE1UcjdjY2M8PT0ZMWIEN27c0HV4OhEWFkbt2rUxMzPDycmJgIAAzp49+0x1bdu2DZVKhbOzMzk5Oc9Ux7Vr1wgODiYqKuqZzhdCCCGEbujrOgAh3iSTJ0/G3d2dtLQ0/vjjDxYvXsy2bdv466+/MDU11UlMrq6uPHjwAAMDgxfWZmRkJO+99x4+Pj589dVXJCcns3XrViIjI6lcuXKR61u1ahVubm5cvnyZffv24e/vX+Q6rl27xqRJk3Bzc8PX17fI5wshhBBCNyShEeIFat26NbVq1QJg4MCB2NraMnv2bH799Vd69Oihk5hye4xepJ9//pmcnBx27dqFg4MDAOPHjyc9Pb3Idd2/f59ff/2VadOmERISwqpVq54poSmq1NRUnSWhQgghhPgfGXImhA41a9YMgJiYGK396enpjBkzBnt7e8zMzOjYsSO3bt3SHO/bty92dnZkZmbmqfOdd97By8tL83n37t28/fbbWFtbY25ujpeXF//97381xwuaQ/P333/TtWtX7O3tMTExwcvLi08//VRz/N69e3z44Ye4ublhZGRE6dKladGiBcePH3/qdevp5f+/HiMjo6ee+7iNGzfy4MEDunTpQvfu3dmwYQNpaWl5yj3pPoSHh1O7dm0A+vXrpxkamHtPmjRpQpUqVTh27BiNGjXC1NRUc+7NmzcZMGAADg4OGBsbU716dZYvX67Vdu49/vrrr/n++++pUKECRkZG1K5dm8jIyDyx7tu3j4YNG2JmZoa1tTXvvfce586d0yoTHByMSqXin3/+4f3338fKygp7e3s+++wzFEXhypUrvPfee1haWuLo6MisWbM056akpGBmZsYHH3yQp+2rV6+iVquZNm1aEX4KQgghhO5IQiOEDl28eBEAW1tbrf0jR47k5MmTTJw4kWHDhrFlyxZGjBihOd67d28SEhLYuXOn1nnXr19n3759vP/++wCcOXOGdu3akZ6ezuTJk5k1axbvvvsuERERT4zr1KlT1K1bl3379jFo0CDmzZtHhw4d2LJli6bM0KFDWbx4MQEBASxatIixY8diYmKS58E7P71790atVjN69GgURXlq+SdZtWoVTZs2xdHRke7du3Pv3j2tOOHp98Hb25vJkycDMHjwYFauXMnKlStp1KiRpo6EhARat26Nr68vc+fOpWnTpjx48IAmTZqwcuVKevXqxcyZM7GysiIwMJB58+bliXX16tXMnDmTIUOG8OWXX3L58mU6deqklZju2bOHli1bcvPmTYKDgxkzZgx//vknDRo04PLly3nq7NatGzk5OUyfPp26devy5ZdfMnfuXFq0aEGZMmWYMWMGFStWZOzYsRw4cAAAc3NzOnbsyLp168jOztaqb82aNSiKQq9evZ7tByKEEEK8aIoQosSFhIQogLJnzx7l1q1bypUrV5S1a9cqtra2iomJiXL16lWtcv7+/kpOTo7m/NGjRytqtVq5e/euoiiKkp2drZQtW1bp1q2bVjuzZ89WVCqVcunSJUVRFGXOnDkKoNy6davA2GJiYhRACQkJ0exr1KiRYmFhocTGxmqVfTQmKysrJSgo6Jnux6ZNmxRTU1NFrVYrY8aMeaY6FEVRbty4oejr6ytLlizR7Ktfv77y3nvvaZUrzH2IjIzMcx9yNW7cWAGUb7/9Vmv/3LlzFUD58ccfNfsyMjKUevXqKebm5kpycrKiKP+7x7a2tsqdO3c0ZX/99VcFULZs2aLZ5+vrq5QuXVpJSEjQ7Dt58qSip6en9OnTR7Nv4sSJCqAMHjxYsy8rK0spW7asolKplOnTp2v2JyYmKiYmJkrfvn01+3bu3KkAyvbt27WuqVq1akrjxo0LvE9CCCHEy0Z6aIR4gfz9/bG3t8fFxYXu3btjbm7Oxo0bKVOmjFa5wYMHo1KpNJ8bNmxIdnY2sbGxwMMhW7169WLz5s3cu3dPU27VqlXUr18fd3d3AKytrQH49ddfC736161btzhw4AD9+/enXLlyWscejcna2prDhw9z7dq1wt8A4OjRo3Tt2pWvvvqKxYsXM3v2bIKDg7XKtGzZkoYNGz61rrVr16Knp0dAQIBmX48ePdi+fTuJiYlasULR7sPjjIyM6Nevn9a+bdu24ejoqDX/ycDAgFGjRpGSksL+/fu1ynfr1g0bGxvN59xrvHTpEgDx8fFERUURGBhIqVKlNOWqVatGixYt2LZtW564Bg4cqPlvtVpNrVq1UBSFAQMGaPZbW1vj5eWlaQce/lt0dnZm1apVmn1//fUXp06d0vTwCSGEEK8CSWiEeIEWLlzI7t27+e233zh79iyXLl2iZcuWeco9nkjkPgQ/+pDep08fHjx4wMaNGwGIjo7m2LFj9O7dW1OmW7duNGjQgIEDB+Lg4ED37t356aefnvhQn/vQW6VKlSdey1dffcVff/2Fi4sLderUITg4WOuBuSATJkzAw8ODoKAgBg0axBdffMGkSZOYM2eOpsyZM2eoW7fuU+v68ccfqVOnDgkJCVy4cIELFy7g5+dHRkYG69ev15R7lvvwuDJlymBoaKi1LzY2Fg8Pjzxzgry9vTXHH/W0n2tu+UfnQD1a5+3bt7l///4T67SyssLY2Bg7O7s8+x/995ObFG/atInU1FTgYUJsbGxMly5d8rQvhBBCvKwkoRHiBapTpw7+/v40adIEb2/vAifHq9XqfPcrj8w3qVy5MjVr1uTHH38EHj7cGxoa0rVrV00ZExMTDhw4wJ49e+jduzenTp2iW7dutGjRIs/ciaLq2rUrly5d4ptvvsHZ2ZmZM2fi4+PD9u3bn3jen3/+qZWsTJgwgREjRjBmzBh++OEHwsLC+Pfff586h+P8+fNERkbyxx9/4OHhodnefvttAK2eh+K4DyYmJoUq9ySF+bkWR52FbadPnz6kpKSwadMmFEVh9erVtGvXDisrq2eORwghhHjRJKER4hXWp08f9u3bR3x8PKtXr6Zt27ZaQ5rg4TfxzZs3Z/bs2Zw9e5YpU6awb98+fvvtt3zrLF++PPBw+NHTODk5MXz4cDZt2kRMTAy2trZMmTLlieeoVCquXLmitW/evHl07dqVIUOGMHz4cDp06ICfn98T61m1ahUGBgasXbuW9evXa20ffPABv//+O3FxcYW+D48OpyssV1dXzp8/n6en5++//9YcL2p98LC37XF///03dnZ2mJmZFTnOglSpUgU/Pz9WrVqluV+P9vAJIYQQrwJJaIR4hfXo0QOVSsUHH3zApUuX8sx9uHPnTp5zcl8aWdA7X+zt7WnUqBHLli3TSgjgf9/wZ2dnk5SUpHWsdOnSODs7P/VdMv7+/uzdu1drfomenh4//PADtra2xMXF0aFDhyfWAQ8TmoYNG9KtWzc6d+6stY0bNw54uGIXFO4+5CYKd+/efWrbudq0acP169dZt26dZl9WVhbffPMN5ubmNG7cuNB1wcME0dfXl+XLl2vF8ddff7Fr1y7atGlTpPoKo3fv3uzatYu5c+dia2tL69ati70NIYQQoiTJizWFeIXZ29vTqlUr1q9fj7W1NW3bttU6Pnn
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8EAAAHHCAYAAACfjlo0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADuEklEQVR4nOzdd3yN5//48dfJ3tNIEElI7FlbWjE/QalRs0pCxKaKFi21arRqK2olalOlqdqzhNpbkBBbjJBEJDLv3x/55XwdSUg4yUni/Xw8zqM9933d1/U+d85JvM+1VIqiKAghhBBCCCGEEB8APV0HIIQQQgghhBBC5BZJgoUQQgghhBBCfDAkCRZCCCGEEEII8cGQJFgIIYQQQgghxAdDkmAhhBBCCCGEEB8MSYKFEEIIIYQQQnwwJAkWQgghhBBCCPHBkCRYCCGEEEIIIcQHQ5JgIYQQQgghhBAfDEmChXhHLi4utGrVSmv13bx5E5VKRUBAgNbqFO9PpVIxfvz4LJV1cXHBx8cnR+PJaQcOHEClUnHgwAFdhyKEEEIIkSMkCRZ5XkBAACqVSv0wMTGhTJkyDBo0iIcPH+o6vA/Oy5cvGT16NC4uLpiZmVGuXDlGjBjxTnUdOHCA9u3b4+DggJGREUWKFKF169b8+eefWo5ae44cOcL48eOJjIzUdShqCQkJzJkzh+rVq2NlZYWNjQ0VK1akT58+XLlyRdfhCSGEEELkKQa6DkCIrJo4cSKurq68fPmSw4cPs3DhQrZt28bFixcxMzPTdXgfjJEjRzJ37lx69epFnTp1uHr1KqtWreKXX37JVj3jxo1j4sSJuLu707dvX5ydnYmIiGDbtm18/vnnrF69mi+++CKHXkXWxcXFYWDwf78qjxw5woQJE/Dx8cHGxkaj7NWrV9HTy/3vFj///HO2b99O165d8fPzIzExkStXrrB161bq169PuXLlslxXgwYNiIuLw8jIKAcjFkIIIYTQHUmCRb7RokULatasCUDv3r2xt7dn5syZ/PXXX3Tt2lXH0X041q1bR8uWLVm2bJn62JQpU7JVxx9//MHEiRPp0KEDa9aswdDQUH3um2++YefOnSQmJmot5vdhYmKS5bLGxsY5GEnGTpw4wdatW5k8eTLfffedxrn58+dnu8daT08vW69ZCCGEECK/keHQIt9q3LgxAGFhYQCEh4fTs2dPSpQogbGxMY6OjrRp04abN28C4O3tTaFChTJMrv73v/9RtmxZjWOrVq2idu3amJmZYWtrS4MGDdi1a1e6aw8fPkzt2rUxMTGhVKlS/P777+nK3Lhxg44dO2JnZ4eZmRl169bln3/+ydLr3LdvH5988gnm5ubY2NjQpk0bgoOD05U7cOAANWvWxMTEhNKlS/Pbb78xfvx4VCqVuoynpydVq1bNsJ2yZcvi5eX11nj09PRQFEXjWHaTv7Fjx2JnZ8fy5cs1EuA0Xl5eGvOtHz16hK+vL0WLFsXExISqVauyYsWKdNdFRETQvXt39ZBgb29vzp07l26utY+PDxYWFty7d4+2bdtiYWFB4cKFGTFiBMnJyRp1vjonePz48XzzzTcAuLq6qofop73HXp0TfPLkSVQqVYZx7ty5E5VKxdatW9XH7t27R69evShatCjGxsZUrFiR5cuXv/VeXr9+HQAPD4905/T19bG3t9c4du/ePXx9fSlWrBjGxsa4urrSv39/EhISgMznBB87dozmzZtjbW2NmZkZnp6eBAUFaZRJe7+Fhoaqe8qtra3p2bMnsbGx6eLLymds+/bt6ve/paUln376KZcuXdIo87bPvhBCCCHEqyQJFvlW2j/+0/6R//nnn7N582Z69uzJggULGDJkCM+fP+f27dsAdO/enYiICHbu3KlRT3h4OPv27ePLL79UH5swYQLdu3fH0NCQiRMnMmHCBJycnNi3b5/GtaGhoXTo0IFmzZoxY8YMbG1t8fHx0fhH+sOHD6lfvz47d+5kwIABTJ48mZcvX/LZZ5+xefPmN77GPXv24OXlxaNHjxg/fjzDhg3jyJEjeHh4aPwD/8yZMzRv3pyIiAgmTJiAr68vEydOZMuWLRr1de/enfPnz3Px4kWN4ydOnODatWsa9yAzPXv2ZMeOHWzfvv2tZTMSEhLClStXaNu2LZaWlm8tHxcXR8OGDVm5ciXdunVj+vTpWFtb4+Pjw5w5c9TlUlJSaN26NWvXrsXb25vJkyfz4MEDvL29M6w3OTkZLy8v7O3t+eWXX/D09GTGjBksXrw401jat2+vHnUwa9YsVq5cycqVKylcuHC6sjVr1qRUqVJs2LAh3bn169dja2ur/tLh4cOH1K1blz179jBo0CDmzJmDm5sbvr6+zJ49+433x9nZGYDVq1eTlJT0xrL379+ndu3arFu3js6dOzN37ly6d+/OwYMHM0xS0+zbt48GDRoQHR3NuHHjmDJlCpGRkTRu3Jjjx4+nK9+pUyeeP3/O1KlT6dSpEwEBAUyYMEGjTFY+YytXruTTTz/FwsKCn376ibFjx3L58mU+/vhjjff/2z77QgghhBAaFCHyOH9/fwVQ9uzZozx+/Fi5c+eOsm7dOsXe3l4xNTVV7t69qzx79kwBlOnTp2daT3JyslKiRAmlc+fOGsdnzpypqFQq5caNG4qiKEpISIiip6entGvXTklOTtYom5KSov5/Z2dnBVD+/fdf9bFHjx4pxsbGyvDhw9XHhg4dqgDKoUOH1MeeP3+uuLq6Ki4uLuo2wsLCFEDx9/dXl6tWrZpSpEgRJSIiQn3s3Llzip6entKjRw/1sdatWytmZmbKvXv31MdCQkIUAwMD5dWPeWRkpGJiYqKMHDlS43UNGTJEMTc3V2JiYjK9f4qiKImJicqXX36pGBkZKebm5sqRI0feWD4jf/31lwIos2bNylL52bNnK4CyatUq9bGEhASlXr16ioWFhRIdHa0oiqJs2rRJAZTZs2eryyUnJyuNGzdOd1+9vb0VQJk4caJGW9WrV1dq1KihcQxQxo0bp34+ffp0BVDCwsLSxers7Kx4e3urn48ePVoxNDRUnj59qj4WHx+v2NjYKL169VIf8/X1VRwdHZUnT55o1NelSxfF2tpaiY2NzfT+pKSkKJ6engqgFC1aVOnatavy66+/Krdu3UpXtkePHoqenp5y4sSJDOtRFEXZv3+/Aij79+9XH3d3d1e8vLw03v+xsbGKq6ur0qxZM/WxcePGKYDGa1MURWnXrp1ib2+vfp6Vz9jz588VGxsbxc/PT+N8eHi4Ym1trT6elc++EEIIIcSrpCdY5BtNmzalcOHCODk50aVLFywsLNi8eTPFixfH1NQUIyMjDhw4wLNnzzK8Xk9Pj27duhEYGMjz58/Vx1evXk39+vVxdXUFYMuWLaSkpPDDDz+kW+To1aHFABUqVOCTTz5RPy9cuDBly5blxo0b6mPbtm2jdu3afPzxx+pjFhYW9OnTh5s3b3L58uUM433w4AFnz57Fx8cHOzs79fEqVarQrFkztm3bBqT2aO7Zs4e2bdtSrFgxdTk3NzdatGihUae1tTVt2rRh7dq16iHNycnJrF+/nrZt22Jubp5hLGm+/fZbtm/fzoULF6hTpw4tW7bk7NmzGjGrVCqN+cKvi46OBshSLzCk3j8HBweNed+GhoYMGTKEmJgYDh48CMCOHTswNDTEz89PXU5PT4+BAwdmWne/fv00nn/yyScaP7v31blzZxITEzVWu961axeRkZF07twZAEVR2LRpE61bt0ZRFJ48eaJ+eHl5ERUVxenTpzNtQ6VSsXPnTn788UdsbW1Zu3YtAwcOxNnZmc6dO6vnBKekpLBlyxZat26tnlv/ej0ZOXv2LCEhIXzxxRdERESoY3vx4gVNmjTh33//JSUlReOajO5rRESE+meflc/Y7t27iYyMpGvXrhr3RF9fnzp16rB//36ALH32hRBCCCFeJUmwyDd+/fVXdu/ezf79+7l8+TI3btxQDyc1Njbmp59+Yvv27RQtWpQGDRrw888/Ex4erlFHjx49iIuLUw9Dvnr1KqdOnaJ79+7qMtevX0dPT48
     },
     "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\")\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(cat)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d2cc8a",
   "metadata": {},
   "source": [
    "## Collabs"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Country"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "wos_collabs = wos_addresses[wos_addresses[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3adb06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "           Country  count   percent    weight\n0          Germany   2309  0.233492  0.101095\n1           France   1632  0.165032  0.071454\n2            Italy   1260  0.127414  0.055166\n3      Netherlands   1063  0.107493  0.046541\n4            Spain    987  0.099808  0.043214\n5           Sweden    832  0.084134  0.036427\n6          Finland    700  0.070786  0.030648\n7          Denmark    566  0.057235  0.024781\n8          Ireland    552  0.055820  0.024168\n9          Belgium    499  0.050460  0.021848\n10          Poland    486  0.049146  0.021278\n11         Austria    373  0.037719  0.016331\n12        Portugal    365  0.036910  0.015981\n13          Greece    320  0.032359  0.014011\n14         Hungary    181  0.018303  0.007925\n15  Czech Republic    144  0.014562  0.006305\n16         Romania    133  0.013449  0.005823\n17        Slovenia    111  0.011225  0.004860\n18        Slovakia     75  0.007584  0.003284\n19       Lithuania     68  0.006876  0.002977\n20         Estonia     65  0.006573  0.002846\n21      Luxembourg     59  0.005966  0.002583\n22         Croatia     56  0.005663  0.002452\n23        Bulgaria     48  0.004854  0.002102\n24          Cyprus     35  0.003539  0.001532\n25          Latvia     20  0.002022  0.000876\n26           Malta     13  0.001315  0.000569",
      "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>count</th>\n      <th>percent</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Germany</td>\n      <td>2309</td>\n      <td>0.233492</td>\n      <td>0.101095</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>France</td>\n      <td>1632</td>\n      <td>0.165032</td>\n      <td>0.071454</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Italy</td>\n      <td>1260</td>\n      <td>0.127414</td>\n      <td>0.055166</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Netherlands</td>\n      <td>1063</td>\n      <td>0.107493</td>\n      <td>0.046541</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Spain</td>\n      <td>987</td>\n      <td>0.099808</td>\n      <td>0.043214</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Sweden</td>\n      <td>832</td>\n      <td>0.084134</td>\n      <td>0.036427</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Finland</td>\n      <td>700</td>\n      <td>0.070786</td>\n      <td>0.030648</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Denmark</td>\n      <td>566</td>\n      <td>0.057235</td>\n      <td>0.024781</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Ireland</td>\n      <td>552</td>\n      <td>0.055820</td>\n      <td>0.024168</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Belgium</td>\n      <td>499</td>\n      <td>0.050460</td>\n      <td>0.021848</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Poland</td>\n      <td>486</td>\n      <td>0.049146</td>\n      <td>0.021278</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Austria</td>\n      <td>373</td>\n      <td>0.037719</td>\n      <td>0.016331</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Portugal</td>\n      <td>365</td>\n      <td>0.036910</td>\n      <td>0.015981</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Greece</td>\n      <td>320</td>\n      <td>0.032359</td>\n      <td>0.014011</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Hungary</td>\n      <td>181</td>\n      <td>0.018303</td>\n      <td>0.007925</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Czech Republic</td>\n      <td>144</td>\n      <td>0.014562</td>\n      <td>0.006305</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Romania</td>\n      <td>133</td>\n      <td>0.013449</td>\n      <td>0.005823</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Slovenia</td>\n      <td>111</td>\n      <td>0.011225</td>\n      <td>0.004860</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Slovakia</td>\n      <td>75</td>\n      <td>0.007584</td>\n      <td>0.003284</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Lithuania</td>\n      <td>68</td>\n      <td>0.006876</td>\n      <td>0.002977</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Estonia</td>\n      <td>65</td>\n      <td>0.006573</td>\n      <td>0.002846</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Luxembourg</td>\n      <td>59</td>\n      <td>0.005966</td>\n      <td>0.002583</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Croatia</td>\n      <td>56</td>\n      <td>0.005663</td>\n      <td>0.002452</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Bulgaria</td>\n      <td>48</td>\n      <td>0.004854</td>\n      <td>0.002102</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Cyprus</td>\n      <td>35</td>\n      <td>0.003539</td>\n      <td>0.001532</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Latvia</td>\n      <td>20</td>\n      <td>0.002022</td>\n      <td>0.000876</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Malta</td>\n      <td>13</td>\n      <td>0.001315</td>\n      <td>0.000
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_collab = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n",
    "country_collab[\"percent\"] = country_collab[\"count\"]/wos_collabs[record_col].nunique()\n",
    "country_collab[\"weight\"] = country_collab[\"count\"]/wos_collabs[record_col].size\n",
    "country_collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAAGxCAYAAADPkWbQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADPVUlEQVR4nOzdd1QVx9vA8e+ld0REgUhTLGAAEY2iiV0BFWvUGKNir1E0GMWKFbtJrLEEsP1MjCXE3ntXsMQKig0UYwGxIMJ9//CwrzeAogK253POnsPuzsw+c0Nk7uwUlVqtViOEEEIIIUQ+0XrXAQghhBBCiI+bNDiFEEIIIUS+kganEEIIIYTIV9LgFEIIIYQQ+UoanEIIIYQQIl9Jg1MIIYQQQuQraXAKIYQQQoh8JQ1OIYQQQgiRr3TedQDiw5aRkUF8fDympqaoVKp3HY4QQgghckGtVvPgwQNsbW3R0sr//kdpcIq3Eh8fj52d3bsOQwghhBBv4Nq1axQvXjzfnyMNTvFWTE1NAShefD9aWibvOBohhBDi/XPqlANTp07l77//5uLFixgYGFC5cmVGjRpFqVKllHT9+vVj586d3Lx5E2NjYyVN6dKllTTXrl1jwIAB7NmzB2NjY9q0aUNISAg6Ov/fpJs/fz7z5s3j6tWrFC9enKCgINq0aaMRU3JyMnZ2dsrf8fwmDU7xVjJfo2tpmaClVTC/tEIIIcSHxMzMjEOHDtG3b18qVarEs2fPGDJkCM2bN+fMmTMYGxsD4O3tTceOHbG3t+fu3buEhITQvHlzLl++jLa2Nunp6XzzzTdYW1uzf/9+EhISaN++PSYmJowfPx6AOXPmMGrUKObPn0+lSpU4fPgwXbt2xdbWFn9//yyxFdRwOJVarVYXyJM+cDdv3iQ0NJR169Zx/fp1zM3NcXZ25rvvvqNDhw4YGRm96xDfieTkZMzNzbG3PykNTiGEECIbly87Zrl2+/ZtihYtyq5du6hevXq2+U6ePImHhwcxMTGULFmSDRs20KhRI+Lj4ylWrBgAc+fOZdCgQdy+fRs9PT2qVq1KtWrVmDx5slLODz/8wKFDh9i7d69yLfPvd1JSEmZmZnlb4WzILPVcuHTpEp6enmzevJnx48cTFRXFgQMH+PHHH1m7di1bt259o3KfPn2ax5EKIYQQ4kOQlJQEQOHChbO9//DhQ8LCwnByclLmShw4cAA3NzelsQng4+NDcnIy//zzDwCpqakYGBholGVoaMjhw4dJS0vLj6rkijQ4c6FXr17o6Ohw9OhRWrVqhYuLCyVKlKBJkyasW7dO6aK+f/8+Xbp0wcrKCjMzM2rXrs2JEyeUckJCQihfvjwLFizAyclJ+YVQqVT8+uuvNGrUCCMjI1xcXDhw4AAxMTHUrFkTY2NjqlatSmxsrFJWbGwsTZo0oVixYpiYmFCpUqUsDV9HR0fGjx9Pp06dMDU1xd7ennnz5in3a9euTZ8+fTTyZH5D2rZtW55/jkIIIYR4vsJLYGAg1apV4/PPP9e4N3v2bExMTDAxMWHDhg1s2bIFPT094Pnb1hcbm4ByfvPmTeB5A3TBggUcO3YMtVrN0aNHWbBgAWlpafz7778FULvsSYPzFe7cucPmzZvp3bu3MsbivzLHP7Rs2ZLExEQ2bNjAsWPHqFChAnXq1OHu3btK2piYGFauXMmqVauIjo5Wro8ZM4b27dsTHR1N2bJl+fbbb+nevTvBwcEcPXoUtVqt0ThMSUmhQYMGbNu2jaioKHx9ffH39+fq1asasU2dOpWKFSsSFRVFr1696NmzJ+fPnwegS5cuLFu2jNTUVCX9kiVL+Oyzz6hdu3a2dU1NTSU5OVnjEEIIIUTu9e7dm9OnT7N8+fIs99q2bUtUVBS7du2idOnStGrViidPnuS67OHDh+Pn50eVKlXQ1dWlSZMmdOjQAaBAlj/KiTQ4XyEmJga1Wk2ZMmU0rhcpUkT5BjJo0CD27t3L4cOHWbFiBRUrVqRUqVJMmTKFQoUK8eeffyr5nj59yqJFi/D09MTd3V253rFjR1q1akXp0qUZNGgQcXFxtG3bFh8fH1xcXJSZa5k8PDzo3r07n3/+OaVKlWLMmDGULFmSyMhIjTgbNGhAr169cHZ2ZtCgQRQpUoQdO3YA0Lx5cwD++usvJX14eDgBAQE5DiIODQ3F3NxcOWRJJCGEECL3+vTpw9q1a9mxY0e2yxGZm5tTqlQpqlevzp9//sm5c+dYvXo1ANbW1ty6dUsjfea5tbU18Pz1+W+//cajR4+Ii4vj6tWrODo6YmpqipWVVT7XLmfS4HxDhw8fJjo6mnLlypGamsqJEydISUnB0tJSaYiamJhw+fJljVfhDg4O2f4Hf7Hxmdk97ubmpnHtyZMnSo9iSkoKQUFBuLi4UKhQIUxMTDh79myWHs4Xy1WpVFhbW5OYmAiAgYEB7dq147fffgPg+PHjnD59moCAgBzrHRwcTFJSknJcu3Yttx+ZEEII8cnKfFO5evVqtm/fjpOTU67yqNVq5U2kt7c3p06dUv6OA2zZsgUzMzNcXV018urq6lK8eHG0tbVZvnw5jRo1eqc9nLIs0is4OzujUqmU19CZSpQoATz/JgHPG4A2NjYavZCZChUqpPyc02t5XV1d5efM3sXsrmVkZAAQFBTEli1bmDJlCs7OzhgaGvL1119nmYj0YhmZ5WSWAc9fq5cvX57r168TFhZG7dq1cXBwyDZGAH19ffT19XO8L4QQQoisevfuzbJly/jrr78wNTVVxlyam5tjaGjIpUuX+P3336lfvz5WVlZcv36dCRMmYGhoSIMGDQCoX78+rq6utGvXjkmTJnHz5k2GDRtG7969lb/NFy5c4PDhw1SuXJl79+4xbdo0Tp8+TURExDurO0iD85UsLS2pV68eM2fO5Pvvv8+xwVihQgVu3ryJjo4Ojo6O+R7Xvn37CAgIoFmzZsDzBm9cXNxrl+Pm5kbFihWZP38+y5YtY+bMmXkcqRBCCCHmzJkDQM2aNTWuh4WFERAQgIGBAXv27OGnn37i3r17FCtWjOrVq7N//36KFi0KgLa2NmvXrqVnz554e3tjbGxMhw4dGD16tFJeeno6U6dO5fz58+jq6lKrVi32799fIG2Tl5EGZy7Mnj2batWqUbFiRUJCQnB3d0dLS4sjR45w7tw5vLy8qFu3Lt7e3jRt2pRJkyZRunRp4uPjWbduHc2aNaNixYp5GlOpUqVYtWoV/v7+qFQqhg8frtFz+Tq6dOlCnz59MDY2VhqwQgghhMg7r1r23NbWlvXr17+yHAcHh5emc3FxISoq6rXjy2/S4MyFkiVLEhUVxfjx4wkODub69evo6+vj6upKUFAQvXr1QqVSsX79eoYOHUrHjh25ffs21tbWVK9ePcsSBnlh2rRpdOrUiapVq1KkSBEGDRr0xjPG27RpQ2BgIG3atMmydldunTrlUCALxwohhBDiwyM7DQni4uIoWbIkR44coUKFCq+Vt6B3KhBCCCHE2yvov9/Sw/kJS0tL486dOwwbNowqVaq8dmNTCCGEECI3pMH5Cdu3bx+1atWidOnSGmuFvgk3tyuyl7oQ4pN3+bIju3fvZvLkyRw7doyEhARWr15N06ZNNdKdPXuWQYMGsWvXLp49e4arqysrV67E3t4egO7du7N161bi4+MxMTGhatWqTJw4kbJlywJw4sQJJkyYwN69e/n3339xdHSkR48e9OvXr6CrLESuyDqcBSRzMfX/HjExMe8sppo1a6JWqzl//rzGmp9CCCHe3MOHD/Hw8GDWrFnZ3o+NjeXLL7+kbNmy7Ny5k5MnTzJ8+HCNMfReXl6EhYVx9uxZNm3ahFqtpn79+qSnpwNw7NgxihYtypIlS/jnn38YOnQowcHBstKIeG/JGM4CEhAQwK1btwgLC9O4bmVlhba2tnL+9OlTZc/UD0HmGBB7+5PSwymE+ORdvuyoca5SqbL0cH7zzTfo6uqyePHiXJd78uRJPDw8iImJoWTJktmm6d27N2fPnmX79u1vErr4xBT0GE7p4SxA+vr6WFtbaxx16tShT58+BAY
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = country_collab\n",
    "g = sns.barplot(data, x=\"count\", y=\"Country\", color=\"blue\")\n",
    "g.set_xlim(0,2500)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = country_collab\n",
    "data[\"percent_round\"] = (data[\"percent\"]*100).round(2)\n",
    "g = sns.barplot(data, x=\"percent_round\", y=\"Country\", color=\"blue\")\n",
    "g.set_xlim(0,25)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Percentage of co-publications\")\n",
    "for i in g.containers:\n",
    "    # g.bar_label(i,fmt='%.2f%%')\n",
    "    g.bar_label(i,fmt='%.2f')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Per year"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "wos_collabs = wos_addresses[wos_addresses[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()\n",
    "wos_collabs_y = wos_collabs.merge(wos, on=record_col)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "     Publication Year   Country  count  UT (Unique WOS ID)   percent\n1                2012   Germany     15                  40  0.375000\n2                2012     Italy      9                  40  0.225000\n3                2012    France      9                  40  0.225000\n4                2012     Spain      6                  40  0.150000\n5                2012   Ireland      5                  40  0.125000\n..                ...       ...    ...                 ...       ...\n263              2022  Slovenia     13                2646  0.004913\n264              2022    Cyprus     10                2646  0.003779\n265              2022    Latvia      7                2646  0.002646\n266              2022     Malta      6                2646  0.002268\n267              2022  Bulgaria      5                2646  0.001890\n\n[257 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>Country</th>\n      <th>count</th>\n      <th>UT (Unique WOS ID)</th>\n      <th>percent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>Germany</td>\n      <td>15</td>\n      <td>40</td>\n      <td>0.375000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2012</td>\n      <td>Italy</td>\n      <td>9</td>\n      <td>40</td>\n      <td>0.225000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2012</td>\n      <td>France</td>\n      <td>9</td>\n      <td>40</td>\n      <td>0.225000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2012</td>\n      <td>Spain</td>\n      <td>6</td>\n      <td>40</td>\n      <td>0.150000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2012</td>\n      <td>Ireland</td>\n      <td>5</td>\n      <td>40</td>\n      <td>0.125000</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>263</th>\n      <td>2022</td>\n      <td>Slovenia</td>\n      <td>13</td>\n      <td>2646</td>\n      <td>0.004913</td>\n    </tr>\n    <tr>\n      <th>264</th>\n      <td>2022</td>\n      <td>Cyprus</td>\n      <td>10</td>\n      <td>2646</td>\n      <td>0.003779</td>\n    </tr>\n    <tr>\n      <th>265</th>\n      <td>2022</td>\n      <td>Latvia</td>\n      <td>7</td>\n      <td>2646</td>\n      <td>0.002646</td>\n    </tr>\n    <tr>\n      <th>266</th>\n      <td>2022</td>\n      <td>Malta</td>\n      <td>6</td>\n      <td>2646</td>\n      <td>0.002268</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>2022</td>\n      <td>Bulgaria</td>\n      <td>5</td>\n      <td>2646</td>\n      <td>0.001890</td>\n    </tr>\n  </tbody>\n</table>\n<p>257 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countall = wos_collabs_y.groupby(\"Publication Year\", as_index=False)[record_col].nunique()\n",
    "data = wos_collabs_y.groupby(\"Publication Year\", as_index=False)[\"Country\"].value_counts().merge(countall, on=\"Publication Year\")\n",
    "data[\"percent\"] = data[\"count\"]/data[record_col]\n",
    "data = data[data[\"Country\"]!=\"China\"]\n",
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Number of co-publications per year')"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "g = sns.heatmap(pd.pivot_table(data,columns=\"Publication Year\", index=\"Country\", values=\"count\").fillna(0).astype(int),\n",
    "            annot=True, fmt=\".0f\",linewidth=.5)\n",
    "g.set_title(\"Number of co-publications per year\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Percentage of co-publications related to country per year')"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "g = sns.heatmap(pd.pivot_table(data,columns=\"Publication Year\", index=\"Country\", values=\"percent\").fillna(0)*100,\n",
    "            annot=True, fmt=\".2f\",linewidth=.5)\n",
    "g.set_title(\"Percentage of co-publications related to country per year\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1ba3237ed90>"
     },
     "execution_count": 22,
     "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,x=\"Publication Year\",y=\"count\",hue=\"Country\", marker=\"o\")\n",
    "g.set(xticks=list(range(2012,2022+1,2)))\n",
    "g.set_ylabel(\"Number of co-publications\")\n",
    "g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncols=math.ceil(len(g.legend_.texts)/5))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\Anaconda3\\envs\\MOME_BIGDATA\\lib\\site-packages\\seaborn\\utils.py:456: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
      "  handles = old_legend.legendHandles\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#(data,x=\"Publication Year\",y=\"count\",hue=\"Country\", marker=\"o\")\n",
    "data = wos_collabs_y\n",
    "data = data[data[\"Country\"]!=\"China\"]\n",
    "g=sns.histplot(\n",
    "    data,\n",
    "    x=\"Publication Year\", hue=\"Country\",\n",
    "    multiple=\"stack\")\n",
    "sns.move_legend(g,title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncols=math.ceil(len(g.legend_.texts)/5))\n",
    "# g._legend(bbox_to_anchor=(1.05, 1))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Institution"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations           4873\nAffiliations_merged    4240\nInstitution            6357\ndtype: int64"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_affiliations[[\"Affiliations\",\"Affiliations_merged\",\"Institution\"]].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "aff = \"Affiliations\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_8232\\4001275826.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  wos_affiliations.loc[wos_affiliations[\"Affiliations\"].str.contains(\"(CNRS)\", regex=True),[\"Affiliations\",\"Country\",\"Country_Type\",\"City\"]] = \"CNRS\",\"France\",\"EU\",None\n"
     ]
    }
   ],
   "source": [
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].str.contains(\"(CNRS)\", regex=True),[\"Affiliations\",\"Country\",\"Country_Type\",\"City\"]] = \"CNRS\",\"France\",\"EU\",None\n",
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].str.contains(\"UDICE\", regex=True),[\"Country\",\"Country_Type\",\"City\"]] = \"France\",\"EU\",None\n",
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].str.contains(\"HELMHOLTZ\", regex=True),[\"Country\",\"Country_Type\",\"City\"]] = \"Germany\",\"EU\",None"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "wos_inst_collabs = wos_affiliations[wos_affiliations[\"Country_Type\"]!=\"Other\"][[record_col,aff,\"Country\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "2761"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_inst_collabs[aff].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "df1f03ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                           CHINESE ACADEMY OF SCIENCES   1128  0.114611  \\\n1        UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS    400  0.040642   \n2                                   TSINGHUA UNIVERSITY    393  0.039931   \n3                         SHANGHAI JIAO TONG UNIVERSITY    354  0.035968   \n4                                   ZHEJIANG UNIVERSITY    337  0.034241   \n...                                                 ...    ...       ...   \n1371            BEIJING ACADEMY OF SCIENCE & TECHNOLOGY      1  0.000102   \n1372   INSTITUT D'ESTUDIS ESPACIALS DE CATALUNYA (IEEC)      1  0.000102   \n1373                HOSPITAL UNIVERSITARI VALL D'HEBRON      1  0.000102   \n1374                                            SERVIER      1  0.000102   \n1375  INSTITUTE OF QUALITY STANDARDS & TESTING TECHN...      1  0.000102   \n\n        weight  \n0     0.027703  \n1     0.009824  \n2     0.009652  \n3     0.008694  \n4     0.008276  \n...        ...  \n1371  0.000025  \n1372  0.000025  \n1373  0.000025  \n1374  0.000025  \n1375  0.000025  \n\n[1376 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>Affiliations</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CHINESE ACADEMY OF SCIENCES</td>\n      <td>1128</td>\n      <td>0.114611</td>\n      <td>0.027703</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS</td>\n      <td>400</td>\n      <td>0.040642</td>\n      <td>0.009824</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TSINGHUA UNIVERSITY</td>\n      <td>393</td>\n      <td>0.039931</td>\n      <td>0.009652</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>354</td>\n      <td>0.035968</td>\n      <td>0.008694</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ZHEJIANG UNIVERSITY</td>\n      <td>337</td>\n      <td>0.034241</td>\n      <td>0.008276</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>1371</th>\n      <td>BEIJING ACADEMY OF SCIENCE &amp; TECHNOLOGY</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1372</th>\n      <td>INSTITUT D'ESTUDIS ESPACIALS DE CATALUNYA (IEEC)</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1373</th>\n      <td>HOSPITAL UNIVERSITARI VALL D'HEBRON</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1374</th>\n      <td>SERVIER</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1375</th>\n      <td>INSTITUTE OF QUALITY STANDARDS &amp; TESTING TECHN...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n  </tbody>\n</table>\n<p>1376 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Institutions in China\n",
    "inst_collab = wos_inst_collabs[wos_inst_collabs[\"Country\"]==\"China\"][aff].value_counts().reset_index()\n",
    "inst_collab[\"percent\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].nunique()\n",
    "inst_collab[\"weight\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].size\n",
    "\n",
    "top50_ch = inst_collab[0:50][aff].to_list()\n",
    "inst_collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = inst_collab[0:50]\n",
    "g = sns.barplot(data, x=\"count\", y=aff, color=\"blue\")\n",
    "g.set_ylabel(\"Institution\")\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "* observe: CNRS --> Institution - country merge needs some more work"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4c50e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                    UDICE-FRENCH RESEARCH UNIVERSITIES    647  0.065739  \\\n1                                                  CNRS    640  0.065027   \n2                                 HELMHOLTZ ASSOCIATION    427  0.043385   \n3                        TECHNICAL UNIVERSITY OF MUNICH    308  0.031294   \n4                        DELFT UNIVERSITY OF TECHNOLOGY    242  0.024588   \n...                                                 ...    ...       ...   \n1892            GUY'S & ST THOMAS' NHS FOUNDATION TRUST      1  0.000102   \n1893                     GUGLIELMO DA SALICETO HOSPITAL      1  0.000102   \n1894  GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS ...      1  0.000102   \n1895                                    GELRE HOSPITALS      1  0.000102   \n1896                      HOSPITAL UNIVERSITARIO LA PAZ      1  0.000102   \n\n        weight  \n0     0.015890  \n1     0.015718  \n2     0.010487  \n3     0.007564  \n4     0.005943  \n...        ...  \n1892  0.000025  \n1893  0.000025  \n1894  0.000025  \n1895  0.000025  \n1896  0.000025  \n\n[1897 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>Affiliations</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>UDICE-FRENCH RESEARCH UNIVERSITIES</td>\n      <td>647</td>\n      <td>0.065739</td>\n      <td>0.015890</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CNRS</td>\n      <td>640</td>\n      <td>0.065027</td>\n      <td>0.015718</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HELMHOLTZ ASSOCIATION</td>\n      <td>427</td>\n      <td>0.043385</td>\n      <td>0.010487</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TECHNICAL UNIVERSITY OF MUNICH</td>\n      <td>308</td>\n      <td>0.031294</td>\n      <td>0.007564</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DELFT UNIVERSITY OF TECHNOLOGY</td>\n      <td>242</td>\n      <td>0.024588</td>\n      <td>0.005943</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>1892</th>\n      <td>GUY'S &amp; ST THOMAS' NHS FOUNDATION TRUST</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1893</th>\n      <td>GUGLIELMO DA SALICETO HOSPITAL</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1894</th>\n      <td>GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS ...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1895</th>\n      <td>GELRE HOSPITALS</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1896</th>\n      <td>HOSPITAL UNIVERSITARIO LA PAZ</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n  </tbody>\n</table>\n<p>1897 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Institutions in EU\n",
    "inst_collab = wos_inst_collabs[wos_inst_collabs[\"Country\"]!=\"China\"][aff].value_counts().reset_index()\n",
    "inst_collab[\"percent\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].nunique()\n",
    "inst_collab[\"weight\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].size\n",
    "\n",
    "top50_eu = inst_collab[0:50][aff].to_list()\n",
    "inst_collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = inst_collab[0:50]\n",
    "g = sns.barplot(data, x=\"count\", y=aff, color=\"blue\")\n",
    "# g.set_xlim(0,6000)\n",
    "g.set_ylabel(\"Institution\")\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Again but cleaning up a bit for top50 and plotting top25"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].isin(top50_eu),\"Country_Type\"] = \"EU\"\n",
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].isin(top50_ch),\"Country_Type\"] = \"China\"\n",
    "wos_inst_collabs = wos_affiliations[wos_affiliations[\"Country_Type\"]!=\"Other\"][[record_col,aff,\"Country_Type\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                           CHINESE ACADEMY OF SCIENCES   1188  0.120707  \\\n1        UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS    411  0.041760   \n2                                   TSINGHUA UNIVERSITY    393  0.039931   \n3                         SHANGHAI JIAO TONG UNIVERSITY    355  0.036070   \n4                                   ZHEJIANG UNIVERSITY    337  0.034241   \n...                                                 ...    ...       ...   \n1344  FOUNDATION FOR RESEARCH & TECHNOLOGY - HELLAS ...      1  0.000102   \n1345                             NANYANG NORMAL COLLEGE      1  0.000102   \n1346                            UNIVERSITY OF GOTTINGEN      1  0.000102   \n1347  CSIC - INSTITUT DE ROBOTICA I INFORMATICA INDU...      1  0.000102   \n1348  INSTITUTE OF QUALITY STANDARDS & TESTING TECHN...      1  0.000102   \n\n        weight  \n0     0.029176  \n1     0.010094  \n2     0.009652  \n3     0.008719  \n4     0.008276  \n...        ...  \n1344  0.000025  \n1345  0.000025  \n1346  0.000025  \n1347  0.000025  \n1348  0.000025  \n\n[1349 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>Affiliations</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CHINESE ACADEMY OF SCIENCES</td>\n      <td>1188</td>\n      <td>0.120707</td>\n      <td>0.029176</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS</td>\n      <td>411</td>\n      <td>0.041760</td>\n      <td>0.010094</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TSINGHUA UNIVERSITY</td>\n      <td>393</td>\n      <td>0.039931</td>\n      <td>0.009652</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>355</td>\n      <td>0.036070</td>\n      <td>0.008719</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ZHEJIANG UNIVERSITY</td>\n      <td>337</td>\n      <td>0.034241</td>\n      <td>0.008276</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>1344</th>\n      <td>FOUNDATION FOR RESEARCH &amp; TECHNOLOGY - HELLAS ...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1345</th>\n      <td>NANYANG NORMAL COLLEGE</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1346</th>\n      <td>UNIVERSITY OF GOTTINGEN</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1347</th>\n      <td>CSIC - INSTITUT DE ROBOTICA I INFORMATICA INDU...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1348</th>\n      <td>INSTITUTE OF QUALITY STANDARDS &amp; TESTING TECHN...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n  </tbody>\n</table>\n<p>1349 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Institutions in China\n",
    "inst_collab = wos_inst_collabs[wos_inst_collabs[\"Country_Type\"]==\"China\"][aff].value_counts().reset_index()\n",
    "inst_collab[\"percent\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].nunique()\n",
    "inst_collab[\"weight\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].size\n",
    "inst_collab"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = inst_collab[0:25]\n",
    "g = sns.barplot(data, x=\"count\", y=aff, color=\"blue\")\n",
    "g.set_ylabel(\"Institution\")\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                    UDICE-FRENCH RESEARCH UNIVERSITIES    647  0.065739  \\\n1                                                  CNRS    640  0.065027   \n2                                 HELMHOLTZ ASSOCIATION    427  0.043385   \n3                        TECHNICAL UNIVERSITY OF MUNICH    312  0.031701   \n4                               UNIVERSITE PARIS SACLAY    254  0.025808   \n...                                                 ...    ...       ...   \n1870               UNIVERSITY OF AGRICULTURE FAISALABAD      1  0.000102   \n1871                          CLINICAL CENTRE OF SERBIA      1  0.000102   \n1872          AZIENDA OSPEDALIERA SAN CAMILLO-FORLANINI      1  0.000102   \n1873  IRCCS ISTITUTO DI RICERCA DIAGNOSTICA E NUCLEA...      1  0.000102   \n1874                      HOSPITAL UNIVERSITARIO LA PAZ      1  0.000102   \n\n        weight  \n0     0.015890  \n1     0.015718  \n2     0.010487  \n3     0.007662  \n4     0.006238  \n...        ...  \n1870  0.000025  \n1871  0.000025  \n1872  0.000025  \n1873  0.000025  \n1874  0.000025  \n\n[1875 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>Affiliations</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>UDICE-FRENCH RESEARCH UNIVERSITIES</td>\n      <td>647</td>\n      <td>0.065739</td>\n      <td>0.015890</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CNRS</td>\n      <td>640</td>\n      <td>0.065027</td>\n      <td>0.015718</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HELMHOLTZ ASSOCIATION</td>\n      <td>427</td>\n      <td>0.043385</td>\n      <td>0.010487</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TECHNICAL UNIVERSITY OF MUNICH</td>\n      <td>312</td>\n      <td>0.031701</td>\n      <td>0.007662</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSITE PARIS SACLAY</td>\n      <td>254</td>\n      <td>0.025808</td>\n      <td>0.006238</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>1870</th>\n      <td>UNIVERSITY OF AGRICULTURE FAISALABAD</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1871</th>\n      <td>CLINICAL CENTRE OF SERBIA</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1872</th>\n      <td>AZIENDA OSPEDALIERA SAN CAMILLO-FORLANINI</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1873</th>\n      <td>IRCCS ISTITUTO DI RICERCA DIAGNOSTICA E NUCLEA...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1874</th>\n      <td>HOSPITAL UNIVERSITARIO LA PAZ</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n  </tbody>\n</table>\n<p>1875 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Institutions in EU\n",
    "inst_collab = wos_inst_collabs[wos_inst_collabs[\"Country_Type\"]!=\"China\"][aff].value_counts().reset_index()\n",
    "inst_collab[\"percent\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].nunique()\n",
    "inst_collab[\"weight\"] = inst_collab[\"count\"]/wos_inst_collabs[record_col].size\n",
    "inst_collab"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = inst_collab[0:25]\n",
    "g = sns.barplot(data, x=\"count\", y=aff, color=\"blue\")\n",
    "g.set_ylabel(\"Institution\")\n",
    "g.set_xlabel(\"Number of co-publications\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "        UT (Unique WOS ID)                         Affiliations Country_Type\n0      WOS:000947693400001  UNIVERSITAT POLITECNICA DE VALENCIA           EU\n1      WOS:000947693400001              SHANGHAITECH UNIVERSITY        China\n2      WOS:000947693400001            SHANGHAI OCEAN UNIVERSITY        China\n3      WOS:000947693400001        SHANGHAI JIAO TONG UNIVERSITY        China\n4      WOS:000947693400001                    HUZHOU UNIVERSITY        China\n...                    ...                                  ...          ...\n63580  WOS:000301090100061                 UNIVERSITY OF LUBECK           EU\n63584  WOS:000301090100061           CAPITAL MEDICAL UNIVERSITY        China\n63586  WOS:000297893800037    UNIVERSIDAD POLITECNICA DE MADRID           EU\n63587  WOS:000297893800037      BEIJING INSTITUTE OF TECHNOLOGY        China\n63589  WOS:000209536100003        BULGARIAN ACADEMY OF SCIENCES           EU\n\n[40718 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>UT (Unique WOS ID)</th>\n      <th>Affiliations</th>\n      <th>Country_Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>WOS:000947693400001</td>\n      <td>UNIVERSITAT POLITECNICA DE VALENCIA</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAITECH UNIVERSITY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAI OCEAN UNIVERSITY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>WOS:000947693400001</td>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>WOS:000947693400001</td>\n      <td>HUZHOU UNIVERSITY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>63580</th>\n      <td>WOS:000301090100061</td>\n      <td>UNIVERSITY OF LUBECK</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>63584</th>\n      <td>WOS:000301090100061</td>\n      <td>CAPITAL MEDICAL UNIVERSITY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>63586</th>\n      <td>WOS:000297893800037</td>\n      <td>UNIVERSIDAD POLITECNICA DE MADRID</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>63587</th>\n      <td>WOS:000297893800037</td>\n      <td>BEIJING INSTITUTE OF TECHNOLOGY</td>\n      <td>China</td>\n    </tr>\n    <tr>\n      <th>63589</th>\n      <td>WOS:000209536100003</td>\n      <td>BULGARIAN ACADEMY OF SCIENCES</td>\n      <td>EU</td>\n    </tr>\n  </tbody>\n</table>\n<p>40718 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "wos_inst_collabs"
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