ZSI_Reconnect_China/WOS/wos_analyses_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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": 21,
     "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": 22,
   "id": "f8e72c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAGwCAYAAADouNBjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABtp0lEQVR4nO3dd1xW5f/H8dcNKg4UFM1cIS5yi+LGhabFN/fIhYKaVpa7cC/c5kRNy5WmpiZqhiNnZY5y5EhxgjM3KKCIwPn94c+77sCFcKP0fj4ePPK+zjnX9TkXaO/7cJ1zmwzDMBAREREREauwSe0CRERERET+SxTARURERESsSAFcRERERMSKFMBFRERERKxIAVxERERExIoUwEVERERErEgBXERERETEihTARURERESsSAFcRERERMSK0qV2ASJp2a1bEcTHp3YVrzaTCZycsnLzZgT63N6k0zwmH81l8tFcJg/NY/KxsYEcObKm+DgK4CIpyDDQP4bJRHOZPDSPyUdzmXw0l8lD8/jirDV/WoIiIiIiImJFCuAiIiIiIlakJSgiKcjGxgYbvc1NFra2msjkoHlMPprL5KO5TB6pOY/x8Qbx8Vr/8qxMhqHVQiIiIiKSdHFx8YSH333lQ7iNzcMbWlOaroCLpCD/FbsIvnwrtcsQERFJMS6vOTCqbQ1sbEyvfAC3FgVwkRR07sYdgi8pgIuIiMjftOhKRERERMSKFMBFRERERKxIAVxERERExIoUwEVERERErEgBXERERETEihTARURERESsSAFcRERERMSKFMBFRERERKxIAVxERERExIoUwEVERERErEgBXERERETEihTARURERESsSAFcRERERMSKFMBFRERERKxIAVxERERExIoUwEVERERErChdahcgIiIiIq++HTu20b9/P4u22rU9GTVqAj/+uIH587/i2rWrFCvmSo8efShRohQAHh7uifY3aNBw3nnnXcLCbjFp0jj27fsNBwdHOnbsjJdXwxQ/n5SkK+AvscDAQFxdXVm5cmWy9dm/f3/69+8PQEBAAN7e3knuy9XVlb179z52+6JFi/Dy8qJUqVJUr16dgQMHcv369Wfq29PTk8DAwCTXJiIiItYVEnKW6tVrsHbtRvOXn98QDh06yLhx/vj6dmHx4uWUKlWGfv16cvfuXQCL/deu3Ujbth14/fU81KhRG8MwGDiwH9euXWP69Nn06NGXgIAp/PTTtlQ+2xejAP4SCwoK4o033mDt2rUp0n+nTp0ICAhIkb4XLVrEvHnz6NevHxs3biQgIIDQ0FC6dOlCfHz8U4//7rvv8PLySpHaREREJPmFhoZQqFARnJxymr+yZs3KzZs36dixMw0aeJEvX358fbtw585tQkPPAljsf//+fb77bjl+foOxt7fnxInjHDlymGHDRlGs2JtUr16Ddu06sHTp4lQ+2xejAP6SunnzJrt376Z79+7s27ePCxcuJPsYWbJkwdHRMdn7BVi9ejW+vr54enqSP39+ypcvz+TJkwkODubw4cNPPT5HjhxkzJgxRWoTERGR5BcSEkKBAm8kaPf0rEfHjp0BuH8/muXLl5I9ew4KFiyUYN9582bj7l6RihUrA3Dp0iUcHbOTL19+8z6FCxclOPgYsbGxKXQmKU8B/CW1ceNGsmbNSqNGjXjttdcsroJ7enqycOFCGjZsSLly5ejatat5acfevXupWbMmixYtonLlylSrVo0vvvgi0TH+vQRl3759NGvWjDJlytCwYUM2bdpksf+MGTOoWrUqlStXfuqyGJPJxL59+4iJiTG3vf7666xfv54333wTgNjYWCZPnoyHhwcVKlSgR48ehIWFmc/x0RIUwzCYOXMmHh4euLu788EHH3D58mVzv66urqxdu5Z3332XUqVK0bZtW4s3LIcPH6ZNmzaULVuWBg0aEBQU9EznfPnyZTp16oSbmxtVq1bF39+fBw8ePPG8RURE/osMw+D8+VD27t1N69bNaNWqMV98EWDx/819+37jrbdqsmDBV/To0YfMmTNb9HHlyhU2b95Ex45dzG05cuQgMjKC6Ohoc9u1a1eJi4sjMjIy5U8shSiAv6SCgoKoXbs2NjY2eHp6smbNGgzDMG8PCAigS5cuLF++nHv37vHJJ5+Yt928eZM1a9Ywf/58Ro4cydy5c1mxYsUTx7t+/TrdunWjWbNmrFu3ji5dutC/f3/27dsHwPLly1m0aBFjxoxh4cKFrFq16on9dejQgc2bN1OrVi0GDBjA2rVrCQ8Pp3DhwuYr29OmTWP16tWMGTOG5cuXc/PmTYYNG5agr2+++YZ169YxadIkli9fjpOTE506dbL4Sx0QEMCgQYMIDAwkLCyMqVOnmueiU6dOFC9enNWrV9OtWzf8/PwIDg5+6jn7+/uTOXNm1qxZw8yZM9m0adNT51FEROS/6PLly0RHR5MhQwb8/cfSvXsvNm/eyMyZ08z7FCpUmHnzFtO5czfGjBnB0aNHLPoIClqLq2txSpYsZW4rUaIUOXPmYsqUCdy7d4+LFy+wfPkSAGJjX92LYnoKykvor7/+4sCBA/j6+gJQv359li1bxv79+3F3f3incPPmzWncuDEAY8aMoV69epw8eRJ4eGV5zJgxvPnmm5QsWZKOHTvy7bff0qpVq8eOuWTJEqpVq0b79u0BcHZ25vjx43z99de4u7uzYsUKOnbsSJ06dQAYNWoU//vf/x7bX5MmTciePTtff/0169atIzAwkAwZMvDRRx/x4YcfYhgGK1aswM/Pj5o1awIwYsQINmzYkKCvuXPnMmzYMCpXfvjrqJEjR+Lh4cEvv/yCp6cnAL6+vlStWhWANm3asGTJw7+cQUFBODg4MHjwYGxsbChUqBC3b98mOjr6qed86dIlSpYsSd68eXF2dubLL78kW7ZsT/3+iYiI/Nfky5ePTZu2kzmzPSaTiaJFXTGMeEaOHMonn/TG1taWHDmcyJHDiaJFXTl27Chr166iVKnS5j62b99KkybNLPq1s7PD338cQ4cOoEGDWmTPnp22bTsQEDCFzJmzWPs0k40C+EsoKCgIOzs7PDw8AKhUqRIODg6sXr3aHMDLly9v3r9AgQI4Ojpy5swZcuTIQebMmc3LPABKlSrF/Pnznzjm2bNn2b59O25ubua2Bw8e4OLiAsCZM2fo3r27eVuRIkUS/Oro32rVqkWtWrWIjIxkz549fPvtt0ydOpUiRYpQoUIFwsPDKVmypEWf/7ySDxAVFcWVK1fo3bs3NjZ//8ImOjqa0NBQ82tnZ2fzn+3t7c1Xx0NCQihRooTFsY/e2MyfP/+J59ylSxcGDhzI5s2bqVmzJl5eXpQoUeKJ5ywiIvJf5eDgQGzs3w9acHZ2ISbmPidPBmNjY4ur69/ZpGBBF0JDQ8yvr169QmjoWTw8aifot3jxkqxc+T03b97AwcGR33/fg6Oj41NzyMtMAfwlFBQURHR0NBUqVDC3xcXFsXHjRoYMGQJAunSW37q4uDhzyPz3tvj4eEwm0xPHjI2NpWHDhnzwwQcW7f/s659LYBIb55G//vqL2bNnM2jQIDJkyIC9vT316tWjbt26tG7dml27dpmvZj9NXFwc8HC5yqNg/IiDg4P5z+nTp0/0+MfVCE8/50aNGlG1alW2bNnCjh076NGjB++//z69e/d+ptpFRET+K3755Rf69OlLYGCQeanpqVMncXBw4Icf1vLXX5eZPHmGef8TJ4IpVuzvQH7s2FFeey03r7/+ukW/d+7cxs+vD+PGTcLJKScAu3b9SrlyFXiVaQ34SyYkJIRjx44xePBg1qxZY/6aMmUKkZGRbN68GYDg4GDzMefOnSMiIgJXV1cA7ty5w8WLF83bjxw5Yt72OC4uLpw7dw5nZ2fz19atW1m3bh0ARYsW5ciRv9dqXbx4kTt37iTaV4YMGVi5ciU///yzRbvJZMLe3p4cOXKQLVs2smfPbnEex48fp2bNmhY3WmTLlg0nJyeuX79uritPnjxMnDiRkJAQnqZgwYKcOHHC4s1Dr169mDt37lPPecqUKdy8eZM2bdowZ84cevXqxY8
     },
     "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": 23,
   "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": 23,
     "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": 24,
   "id": "151a7a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1aaa6075880>"
     },
     "execution_count": 24,
     "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": 25,
   "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": 25,
     "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": 26,
   "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": 27,
   "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": 27,
     "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": 28,
   "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\")\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": 29,
   "outputs": [],
   "source": [
    "wos_collabs = wos_addresses[wos_addresses[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "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": 31,
   "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",
    "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": 32,
   "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": 56,
   "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": 111,
   "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": 111,
     "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": 112,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Number of co-publications per year')"
     },
     "execution_count": 112,
     "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": 116,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Percentage of co-publications related to country per year')"
     },
     "execution_count": 116,
     "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": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1aab6206100>"
     },
     "execution_count": 67,
     "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": 89,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Roaming\\Python\\Python39\\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": "iVBORw0KGgoAAAANSUhEUgAABXcAAAGwCAYAAADi9FZwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADSbUlEQVR4nOzdd1RURxvA4d8uXXpVULEb7C0qxlhQCfYglthjw15ij71XxI4FsNfYY9QvxhhLmkYx9q4oKjYsVAHZ3e+PjasIKigsIu9zjkd2b5s7M3vv7LtzZxQajUaDEEIIIYQQQgghhBBCiGxFmdUJEEIIIYQQQgghhBBCCJF+EtwVQgghhBBCCCGEEEKIbEiCu0IIIYQQQgghhBBCCJENSXBXCCGEEEIIIYQQQgghsiEJ7gohhBBCCCGEEEIIIUQ2JMFdIYQQQgghhBBCCCGEyIYkuCuEEEIIIYQQQgghhBDZkGFWJ0AIIYQQIjtSqVQ8f/48q5MhhBBCCCGE+MQYGRlhYGCQpnUluCuEEEIIkQ4ajYa7d+/y9OlTNJqsTo0QQgghhBDiU6NQgI2NDc7OzigUireum6XB3Zs3bzJx4kROnDiBtbU17du3p1u3bgBMnjyZNWvWJFt/zJgxtG/fHoBdu3Yxd+5cHj58yJdffsmkSZOws7MDtF+6/P392bJlC2q1mhYtWjBkyBCUyvSNQvHoUXSGf2lTKMDe3jJT9i1eknzWD8ln/ZB81h/Ja/3IrHx+sd/MdvfuXZ48eYqlpQ0mJibA2xtbQgghhBBCCJF2GhISEnjy5CkALi4ub107y4K7arWa7t27U6ZMGbZv387NmzcZNGgQuXPnpkmTJly7do3BgwfTrFkz3TYWFhYAnD59mlGjRjFhwgTc3NyYMmUKI0aMYOnSpQCsWLGCXbt2sXDhQpKSkhg6dCj29vZ07do1XWnUaMi0L/eZuW/xkuSzfkg+64fks/5IXutHdsxnlUrF06fawK6lpXVWJ0cIIYQQQgjxCTI2NgXg6dOn5M6d+61DNGTZhGoRERGUKFGC8ePHU7BgQWrVqkW1atUICQkB4Nq1a5QsWRJHR0fdPzMzMwDWrl1LgwYN8Pb2xs3NjZkzZ3Lo0CFu3boFwOrVq+nfvz+ff/457u7uDBkyhHXr1mXVqQohhBDiE/H8+XM0Gv7rsSuEEEIIIYQQmcPExASNhnfO85FlwV0nJyfmzp2LhYUFGo2GkJAQjh07RpUqVYiJieH+/fsULFgw1W1PnTrF559/rnvt7OyMi4sLp06d4v79+9y9e5fKlSvrlleqVIk7d+7w4MGDzD4tIYQQQuQIMhSDEEIIIYQQIjOl7TvHRzGhWp06dQgPD8fDwwMvLy/Onj2LQqFgyZIlHD58GBsbGzp37qwbouHBgwc4OTkl24e9vT337t3j4cOHAMmWOzg4AHDv3r0U273NO8Yrfi8v9pkZ+xYvST7rh+Szfkg+64/ktX5kVj5LuQkhhBBCCCFymo8iuDt//nwiIiIYP34806ZNo1SpUigUCgoXLkz79u05duwYY8aMwcLCAk9PT+Lj4zE2Nk62D2NjYxITE4mPj9e9fnUZQGJiYrrSlZmTsuhjwhch+awvks/6IfmsP5LX+vGp5bNCoUCp1E+EWa3WoMluAxaLFPRZZ0DqjXg7qY85h5T1p03aIzmHfJbFCx9FcLdMmTIAJCQkMGTIEE6cOIGHhwc2NjYAuLm5cePGDTZs2ICnpycmJiYpArWJiYmYmZklC+S+GA/vxbovxuxNq8yYLV1mYtcPyWf9kHzWD8ln/ZG81o/MyucX+80KCoUCaytTFEr9jHilUauJjIpPVwPb3b1istc2NjbUrOnBd98NJleuXO/cPjw8HB+fxmzbtuudM/a+D2/vRnTr1oPGjZtm+L4/RgqFAisrE5TKN0+OkdHUahVRUQnpqjdRUVGsWBHMwYO/8fjxY/LkyUOzZs1p1aoNykys70FBSzhxIoTFi4PStP7EiePYs+enZO+ZmZlRqFAR+vUbQIUKlTIjmSnSADB27IRUl79ax3v18qVixUr4+vbM9HSlhUKhwNLKFAM9XcMAVGo10em4jnl7N+LevbuANr2mpqYULVqcrl19cXf/IjOTmiHeVT/0RVvWZhjoMSCkUmuIjnqWrmvPh3xG3N0rEhAQSKVKn7975XT62D67r9PeW8z0GtyNSmfZgv7vLY8fP+bff0OoW9cTyNw6oi/atqcZCj1+ljVqDZHpKO/skM/pbW98rLIsuBsREcHJkyepV6+e7r2iRYvy/PlzYmJisLOzS7Z+4cKFOXLkCAC5c+cmIiIixf4cHR3JnTs3AA8fPiRfvny6vwEcHR3TlcbMnMU7O84Qnh1JPuuH5LN+SD7rj+S1fnxK+axUKlAolcT//Ceax5GZeiyFnTWm9aujVCpQqdKXgdOm+VG2bDlUKjUPHtxj+vQpLFgwl+HDR2ZSatNuxYq16f4hPjtTKhUolQZcDAkiLvpuph8vl6UzbpV801VvIiOf0rXrtzg6OjJq1FhcXPJy7txZZs+eye3btxkyZHgmpzp96tb1ZNCgobrXDx8+ZPHiBQwbNogdO3Zjbm6RhalLbvr0WRgZGWV1MnSUSgUGSiWBF3cSHhfx7g0+kEsuB7q7NU33dWzgwCHUq/fVfwGlSPbs2cXgwQOYM2chVapUzcQUfzq0Za0g+Oxl7sXGZfrx8pjnolvp4u91zxLpp723KNh7PJ4n0Zmb37aWCrw+N0132WbFvSUgYB4aDbrg7u7dv2BlZZ3hx9EnbdtTwbNtcagfqjP/eI5KzHxyyWf5I5Vlwd3bt2/Tt29fDh06pAvInj17Fjs7O9asWcO///7LypUrdetfvHiRwoULA1CuXDlCQkLw8fEB4O7du9y9e5dy5cqRO3duXFxcCAkJ0QV3Q0JCcHFxSdd4u0IIIYQQ6aV5HIn64ZNMPcaH9GexsrLG3l47F4GTkxPfftsFP79pH0Vw19bWNquTkCXiou8SGxmW1clIVUDAAoyNjZk7N0D3RJyLS15MTU0ZNmwQrVq1xtW1QBan8iUTE1Nd/Qawt3dg1KjxNG1an5CQ49SsWTvrEvcaa+uPM6gQHhdBWMz9rE7GG5mbW+jK2NHRkX79vuPRowjmzfNn3bpNWZy67OVebBxh0bFZnQyRSZ5Ea3gYmdkBv/drkWTFveX1zgSv3iuyO/VDNep7mR/cFR83/T1385oyZcpQqlQpRo4cydWrVzl06BB+fn707NkTDw8Pjh07xrJlywgLC2P9+vXs2LGDLl26ANCmTRt+/PFHNm/ezMWLFxk2bBi1a9cmf/78uuWzZs3i6NGjHD16FH9/fzp27JhVpyqEEEII8VEyNTVN9trbuxG7du3UvQ4JOZ5iOIcXIiOfMnz4YDw8quPj04Rt27YkW/fw4UN07NiGmjXdqVevJmPGjCAuTttLLChoCcOGDaJnz6589VVtTpwISXbs2NgYJk8eT4MGdfnyyyp8840Phw4dyOjTF2+RmJjIr7/upUWLb3Rfvl/48suaLFy4hDx5nNm1ayfu7hVT/AsOXgrA/fv3GDLkO2rV+gJv70YEBy9FpVLp9vX333/SsWNbatX6gvbtv+HYsaO6ZUlJSfj5TaNOnRo0aFCP9evXpvs8jI21vWMNDLTDX2g0GpYvD6Jx46+oV68mgwcP0D3qD9pHSHfu3EHz5k2pU+dLxo4dpau3u3btxNu7UbL99+rlS1DQEt3r2NgYhg0bRM2a7rRr14qQkGOppuv17davX4u3dyM8PKozYEBvwsPvpPtccypvbx+uXbvKrVthREdHM378aOrUqUHjxl8xa9YM3ZwsISHH8fZuxNatm2nSxIvatb9g/PjRuiH8goKWMGHCGGbNmoGHR3W8vRtx9OjfbN68kQYN6lG/fh1++GGD7rihodcZMKA3dep8Sc2a7vTo0YXQ0OvJjjVjxlTq1q3J6tUrk6X56dMntGrVjEmTxsn4le+wa9dOfH07M3z4YOrWrcnPP+955+f4VQ8ePGDEiKF4etaiRo2qdOzYllOnTgLa4Ybc3Sty4MB+mjdvSs2
     },
     "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": 157,
   "outputs": [
    {
     "data": {
      "text/plain": "Affiliations           4873\nAffiliations_merged    4240\nInstitution            6357\ndtype: int64"
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_affiliations[[\"Affiliations\",\"Affiliations_merged\",\"Institution\"]].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "outputs": [],
   "source": [
    "aff = \"Affiliations\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_17172\\580976551.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"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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": 161,
   "outputs": [
    {
     "data": {
      "text/plain": "2761"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wos_inst_collabs[aff].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "df1f03ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                           CHINESE ACADEMY OF SCIENCES   1128  0.114657  \\\n1        UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS    400  0.040659   \n2                                   TSINGHUA UNIVERSITY    393  0.039947   \n3                         SHANGHAI JIAO TONG UNIVERSITY    354  0.035983   \n4                                   ZHEJIANG UNIVERSITY    337  0.034255   \n...                                                 ...    ...       ...   \n1376                      UNIVERSITY SYSTEM OF MARYLAND      1  0.000102   \n1377             LUNENFELD TANENBAUM RESEARCH INSTITUTE      1  0.000102   \n1378  WUHAN RESEARCH INSTITUTE OF POST & TELECOMMUNI...      1  0.000102   \n1379                                ZHAOTONG UNIVERSITY      1  0.000102   \n1380  INSTITUTE OF QUALITY STANDARDS & TESTING TECHN...      1  0.000102   \n\n        weight  \n0     0.027827  \n1     0.009868  \n2     0.009695  \n3     0.008733  \n4     0.008314  \n...        ...  \n1376  0.000025  \n1377  0.000025  \n1378  0.000025  \n1379  0.000025  \n1380  0.000025  \n\n[1381 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>0.114657</td>\n      <td>0.027827</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>0.040659</td>\n      <td>0.009868</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TSINGHUA UNIVERSITY</td>\n      <td>393</td>\n      <td>0.039947</td>\n      <td>0.009695</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>354</td>\n      <td>0.035983</td>\n      <td>0.008733</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ZHEJIANG UNIVERSITY</td>\n      <td>337</td>\n      <td>0.034255</td>\n      <td>0.008314</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>1376</th>\n      <td>UNIVERSITY SYSTEM OF MARYLAND</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1377</th>\n      <td>LUNENFELD TANENBAUM RESEARCH INSTITUTE</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1378</th>\n      <td>WUHAN RESEARCH INSTITUTE OF POST &amp; TELECOMMUNI...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1379</th>\n      <td>ZHAOTONG UNIVERSITY</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1380</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>1381 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 162,
     "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",
    "top25_ch= inst_collab[0:25][aff].to_list()\n",
    "inst_collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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": "markdown",
   "source": [
    "* observe: CNRS --> Institution - country merge needs some more work"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "e4c50e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                               Affiliations  count   percent    weight\n0        UDICE-FRENCH RESEARCH UNIVERSITIES    647  0.065765  0.015961\n1                                      CNRS    640  0.065054  0.015788\n2                     HELMHOLTZ ASSOCIATION    311  0.031612  0.007672\n3            TECHNICAL UNIVERSITY OF MUNICH    308  0.031307  0.007598\n4            DELFT UNIVERSITY OF TECHNOLOGY    242  0.024598  0.005970\n...                                     ...    ...       ...       ...\n1890                           ADVENTHEALTH      1  0.000102  0.000025\n1891               ACIBADEM HOSPITALS GROUP      1  0.000102  0.000025\n1892                      INSTITUT BERGONIE      1  0.000102  0.000025\n1893  TEHRAN UNIVERSITY OF MEDICAL SCIENCES      1  0.000102  0.000025\n1894          HOSPITAL UNIVERSITARIO LA PAZ      1  0.000102  0.000025\n\n[1895 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>0.065765</td>\n      <td>0.015961</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CNRS</td>\n      <td>640</td>\n      <td>0.065054</td>\n      <td>0.015788</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HELMHOLTZ ASSOCIATION</td>\n      <td>311</td>\n      <td>0.031612</td>\n      <td>0.007672</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TECHNICAL UNIVERSITY OF MUNICH</td>\n      <td>308</td>\n      <td>0.031307</td>\n      <td>0.007598</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DELFT UNIVERSITY OF TECHNOLOGY</td>\n      <td>242</td>\n      <td>0.024598</td>\n      <td>0.005970</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>1890</th>\n      <td>ADVENTHEALTH</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1891</th>\n      <td>ACIBADEM HOSPITALS GROUP</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1892</th>\n      <td>INSTITUT BERGONIE</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>TEHRAN UNIVERSITY OF MEDICAL SCIENCES</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>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>1895 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 164,
     "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",
    "top25_eu = inst_collab[0:25][aff].to_list()\n",
    "inst_collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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_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 top25"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [],
   "source": [
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].isin(top25_eu),\"Country_Type\"] = \"EU\"\n",
    "wos_affiliations.loc[wos_affiliations[\"Affiliations\"].isin(top25_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": 167,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                           CHINESE ACADEMY OF SCIENCES   1188  0.120756  \\\n1        UNIVERSITY OF CHINESE ACADEMY OF SCIENCES, CAS    411  0.041777   \n2                                   TSINGHUA UNIVERSITY    393  0.039947   \n3                         SHANGHAI JIAO TONG UNIVERSITY    355  0.036085   \n4                                   ZHEJIANG UNIVERSITY    337  0.034255   \n...                                                 ...    ...       ...   \n1361                      UNIVERSITY SYSTEM OF MARYLAND      1  0.000102   \n1362             LUNENFELD TANENBAUM RESEARCH INSTITUTE      1  0.000102   \n1363  WUHAN RESEARCH INSTITUTE OF POST & TELECOMMUNI...      1  0.000102   \n1364                                ZHAOTONG UNIVERSITY      1  0.000102   \n1365  INSTITUTE OF QUALITY STANDARDS & TESTING TECHN...      1  0.000102   \n\n        weight  \n0     0.029307  \n1     0.010139  \n2     0.009695  \n3     0.008757  \n4     0.008313  \n...        ...  \n1361  0.000025  \n1362  0.000025  \n1363  0.000025  \n1364  0.000025  \n1365  0.000025  \n\n[1366 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>0.120756</td>\n      <td>0.029307</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>0.041777</td>\n      <td>0.010139</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TSINGHUA UNIVERSITY</td>\n      <td>393</td>\n      <td>0.039947</td>\n      <td>0.009695</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>SHANGHAI JIAO TONG UNIVERSITY</td>\n      <td>355</td>\n      <td>0.036085</td>\n      <td>0.008757</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ZHEJIANG UNIVERSITY</td>\n      <td>337</td>\n      <td>0.034255</td>\n      <td>0.008313</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>1361</th>\n      <td>UNIVERSITY SYSTEM OF MARYLAND</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1362</th>\n      <td>LUNENFELD TANENBAUM RESEARCH INSTITUTE</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1363</th>\n      <td>WUHAN RESEARCH INSTITUTE OF POST &amp; TELECOMMUNI...</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1364</th>\n      <td>ZHAOTONG UNIVERSITY</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1365</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>1366 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 167,
     "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": 168,
   "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": 169,
   "outputs": [
    {
     "data": {
      "text/plain": "                                           Affiliations  count   percent   \n0                    UDICE-FRENCH RESEARCH UNIVERSITIES    647  0.065765  \\\n1                                                  CNRS    640  0.065054   \n2                                 HELMHOLTZ ASSOCIATION    427  0.043403   \n3                        TECHNICAL UNIVERSITY OF MUNICH    312  0.031714   \n4                               UNIVERSITE PARIS SACLAY    254  0.025818   \n...                                                 ...    ...       ...   \n1878  GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS ...      1  0.000102   \n1879                                    GELRE HOSPITALS      1  0.000102   \n1880                                   EMORY UNIVERSITY      1  0.000102   \n1881              ELISABETH-TWEESTEDEN ZIEKENHUIS (ETZ)      1  0.000102   \n1882                      HOSPITAL UNIVERSITARIO LA PAZ      1  0.000102   \n\n        weight  \n0     0.015961  \n1     0.015788  \n2     0.010534  \n3     0.007697  \n4     0.006266  \n...        ...  \n1878  0.000025  \n1879  0.000025  \n1880  0.000025  \n1881  0.000025  \n1882  0.000025  \n\n[1883 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>0.065765</td>\n      <td>0.015961</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CNRS</td>\n      <td>640</td>\n      <td>0.065054</td>\n      <td>0.015788</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HELMHOLTZ ASSOCIATION</td>\n      <td>427</td>\n      <td>0.043403</td>\n      <td>0.010534</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TECHNICAL UNIVERSITY OF MUNICH</td>\n      <td>312</td>\n      <td>0.031714</td>\n      <td>0.007697</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>UNIVERSITE PARIS SACLAY</td>\n      <td>254</td>\n      <td>0.025818</td>\n      <td>0.006266</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>1878</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>1879</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>1880</th>\n      <td>EMORY UNIVERSITY</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1881</th>\n      <td>ELISABETH-TWEESTEDEN ZIEKENHUIS (ETZ)</td>\n      <td>1</td>\n      <td>0.000102</td>\n      <td>0.000025</td>\n    </tr>\n    <tr>\n      <th>1882</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>1883 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 169,
     "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": 170,
   "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
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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