ZSI_Reconnect_China/PATSTAT/patstat_analysis_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "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": 162,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\n"
     ]
    }
   ],
   "source": [
    "print(os.getcwd())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [],
   "source": [
    "outdir=\"EU_CH_scope/v2_\"\n",
    "\n",
    "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")\n",
    "\n",
    "appln_title = pd.read_csv(f\"{outdir}/tls_202_scope.csv\")\n",
    "\n",
    "pers = pd.read_csv(f\"{outdir}/tls_206_scope.csv\")\n",
    "\n",
    "appln_pers = pd.read_csv(f\"{outdir}/tls_207_scope.csv\")\n",
    "\n",
    "appln_cpc = pd.read_csv(f\"{outdir}/tls_224_scope.csv\")\n",
    "\n",
    "cpc_desc = pd.read_csv(r\"CPC_data/cpc_defs.csv\")\n",
    "\n",
    "country_defs = pd.read_csv(f\"{outdir}/table_tls801.csv\").rename(columns={\"st3_name\":\"Country\"})"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [],
   "source": [
    "ch_codes = [\"CN\",\"HK\",\"MO\",\"TW\"]\n",
    "ch_names = country_defs[country_defs[\"ctry_code\"].isin(ch_codes)][\"Country\"].unique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [
    {
     "data": {
      "text/plain": "    ctry_code iso_alpha3                           Country organisation_flag   \n0                                                  unknown                 Y  \\\n1          AD        AND                           Andorra                     \n2          AE        ARE              United Arab Emirates                     \n3          AF        AFG                       Afghanistan                     \n4          AG        ATG               Antigua and Barbuda                     \n..        ...        ...                               ...               ...   \n237        YE        YEM                             Yemen                     \n238        YU        YUG  Yugoslavia/Serbia and Montenegro                     \n239        ZA        ZAF                      South Africa                     \n240        ZM        ZMB                            Zambia                     \n241        ZW        ZWE                          Zimbabwe                     \n\n         continent eu_member epo_member oecd_member discontinued  \n0              NaN                                                \n1           Europe                                                \n2             Asia                                                \n3             Asia                                                \n4    North America                                                \n..             ...       ...        ...         ...          ...  \n237           Asia                                                \n238         Europe                                             Y  \n239         Africa                                                \n240         Africa                                                \n241         Africa                                                \n\n[242 rows x 9 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>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>Country</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td></td>\n      <td></td>\n      <td>unknown</td>\n      <td>Y</td>\n      <td>NaN</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AD</td>\n      <td>AND</td>\n      <td>Andorra</td>\n      <td></td>\n      <td>Europe</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>AE</td>\n      <td>ARE</td>\n      <td>United Arab Emirates</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AF</td>\n      <td>AFG</td>\n      <td>Afghanistan</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>AG</td>\n      <td>ATG</td>\n      <td>Antigua and Barbuda</td>\n      <td></td>\n      <td>North America</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>YE</td>\n      <td>YEM</td>\n      <td>Yemen</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>YU</td>\n      <td>YUG</td>\n      <td>Yugoslavia/Serbia and Montenegro</td>\n      <td></td>\n      <td>Europe</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>Y</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>ZA</td>\n      <td>ZAF</td>\n      <td>South Africa</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>ZM</td>\n      <td>ZMB</td>\n      <td>Zambia</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>ZW</td>\n      <td>ZWE</td>\n      <td>Zimbabwe</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>242 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_defs"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [
    {
     "data": {
      "text/plain": "    appln_id appln_auth    appln_nr appln_kind appln_filing_date   \n0  330225325         EP    11150195         A         2011-01-05  \\\n1  330322632         EP    11150485         A         2011-01-10   \n2  330350961         EP    11150683         A         2011-01-12   \n3  330374780         WO  2011050339         W         2011-01-12   \n4  330424360         WO  2011050199         W         2011-01-10   \n\n   appln_filing_year appln_nr_original ipr_type receiving_office   \n0               2011          11150195       PI                   \\\n1               2011          11150485       PI                    \n2               2011          11150683       PI                    \n3               2011     EP2011/050339       PI               EP   \n4               2011     EP2011/050199       PI               EP   \n\n   internat_appln_id  ... earliest_publn_date earliest_publn_year   \n0                  0  ...          2011-07-13                2011  \\\n1                  0  ...          2012-07-11                2012   \n2                  0  ...          2012-07-18                2012   \n3                  0  ...          2011-07-21                2011   \n4                  0  ...          2012-07-19                2012   \n\n  earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n0             335277427       Y         43754737          330225325  \\\n1             364719889       Y         43991052          330322632   \n2             364923578       N         43881056          330350961   \n3             335927718       N         43923624          330374780   \n4             365345607       N         43533009          330424360   \n\n  docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n0                 4                   16              1            1  \n1                 2                    5              1            2  \n2                 7                   12              2            5  \n3                 2                    8              5            4  \n4                 4                   13              3            2  \n\n[5 rows x 26 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>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>330225325</td>\n      <td>EP</td>\n      <td>11150195</td>\n      <td>A</td>\n      <td>2011-01-05</td>\n      <td>2011</td>\n      <td>11150195</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2011-07-13</td>\n      <td>2011</td>\n      <td>335277427</td>\n      <td>Y</td>\n      <td>43754737</td>\n      <td>330225325</td>\n      <td>4</td>\n      <td>16</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>330322632</td>\n      <td>EP</td>\n      <td>11150485</td>\n      <td>A</td>\n      <td>2011-01-10</td>\n      <td>2011</td>\n      <td>11150485</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-07-11</td>\n      <td>2012</td>\n      <td>364719889</td>\n      <td>Y</td>\n      <td>43991052</td>\n      <td>330322632</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>330350961</td>\n      <td>EP</td>\n      <td>11150683</td>\n      <td>A</td>\n      <td>2011-01-12</td>\n      <td>2011</td>\n      <td>11150683</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-07-18</td>\n      <td>2012</td>\n      <td>364923578</td>\n      <td>N</td>\n      <td>43881056</td>\n      <td>330350961</td>\n      <td>7</td>\n      <td>12</td>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>330374780</td>\n      <td>WO</td>\n      <td>2011050339</td>\n      <td>W</td>\n      <td>2011-01-12</td>\n      <td>2011</td>\n      <td>EP2011/050339</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2011-07-21</td>\n      <td>2011</td>\n      <td>335927718</td>\n      <td>N</td>\n      <td>43923624</td>\n      <td>330374780</td>\n      <td>2</td>\n      <td>8</td>\n      <td>5</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>330424360</td>\n      <td>WO</td>\n      <td>2011050199</td>\n      <td>W</td>\n      <td>2011-01-10</td>\n      <td>2011</td>\n      <td>EP2011/050199</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-07-19</td>\n      <td>2012</td>\n      <td>365345607</td>\n      <td>N</td>\n      <td>43533009</td>\n      <td>330424360</td>\n      <td>4</td>\n      <td>13</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_id             64266\nappln_nr             63242\nappln_nr_original    62651\ndtype: int64"
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[[\"appln_id\",\"appln_nr\",\"appln_nr_original\"]].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "outputs": [],
   "source": [
    " # only first submissions\n",
    "appln = appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]]\n",
    "appln_pers = appln_pers[appln_pers[\"appln_id\"].isin(appln[\"appln_id\"])]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "outputs": [
    {
     "data": {
      "text/plain": "        person_id   appln_id  applt_seq_nr  invt_seq_nr  Country Country_Type\n0               1  340314532             1            0  Finland           EU\n1               1  413601768             1            0  Finland           EU\n2             128  332888018             1            0  Finland           EU\n3             128  333546132             1            0  Finland           EU\n4             128  334765473             1            0  Finland           EU\n...           ...        ...           ...          ...      ...          ...\n106442   65263479  504779814             2            0   Latvia           EU\n106443   65263479  544264361             2            0   Latvia           EU\n106444   69866789  481190056             2            0   Latvia           EU\n106445   80730412  554759601             2            0   Latvia           EU\n106446   84881241  569497458             0            1   Latvia           EU\n\n[106447 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>person_id</th>\n      <th>appln_id</th>\n      <th>applt_seq_nr</th>\n      <th>invt_seq_nr</th>\n      <th>Country</th>\n      <th>Country_Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>340314532</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>413601768</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>128</td>\n      <td>332888018</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>128</td>\n      <td>333546132</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>128</td>\n      <td>334765473</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Finland</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>106442</th>\n      <td>65263479</td>\n      <td>504779814</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>106443</th>\n      <td>65263479</td>\n      <td>544264361</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>106444</th>\n      <td>69866789</td>\n      <td>481190056</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>106445</th>\n      <td>80730412</td>\n      <td>554759601</td>\n      <td>2</td>\n      <td>0</td>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>106446</th>\n      <td>84881241</td>\n      <td>569497458</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Latvia</td>\n      <td>EU</td>\n    </tr>\n  </tbody>\n</table>\n<p>106447 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ctry = appln_pers.merge(pers[[\"person_id\",\"person_ctry_code\"]],\n",
    "                 on=\"person_id\").merge(country_defs[[\"Country\",\"ctry_code\"]],\n",
    "                                       left_on=\"person_ctry_code\", right_on=\"ctry_code\").drop(columns=[\"ctry_code\",\"person_ctry_code\"])\n",
    "appln_ctry[\"Country_Type\"] = \"EU\"\n",
    "appln_ctry.loc[appln_ctry[\"Country\"].isin(ch_names),\"Country_Type\"] = \"CH\"\n",
    "appln_ctry"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Year')"
     },
     "execution_count": 170,
     "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": [
    "data = appln.groupby([\"appln_filing_year\"], as_index=False)[\"appln_id\"].count()\n",
    "data\n",
    "g = sns.lineplot(data, x=\"appln_filing_year\", y=\"appln_id\", marker=\"o\")\n",
    "g.set_title(\"Number of co-patents\")\n",
    "g.set_ylabel(\"Count\")\n",
    "g.set_xlabel(\"Year\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [],
   "source": [
    "\n",
    "eu_invt = appln_ctry[((appln_ctry[\"Country_Type\"]==\"EU\") & (appln_ctry['invt_seq_nr']>0))][\"appln_id\"].unique()\n",
    "eu_applt = appln_ctry[((appln_ctry[\"Country_Type\"]==\"EU\") & (appln_ctry['applt_seq_nr']>0))][\"appln_id\"].unique()\n",
    "ch_invt = appln_ctry[((appln_ctry[\"Country_Type\"]==\"CH\" )& (appln_ctry['invt_seq_nr']>0))][\"appln_id\"].unique()\n",
    "ch_applt = appln_ctry[((appln_ctry[\"Country_Type\"]==\"CH\") & (appln_ctry['applt_seq_nr']>0))][\"appln_id\"].unique()\n",
    "\n",
    "appln[\"co_inventors\"] = appln[\"appln_id\"].isin(eu_invt) & appln[\"appln_id\"].isin(ch_invt)\n",
    "appln[\"co_applicants\"] = appln[\"appln_id\"].isin(eu_applt) & appln[\"appln_id\"].isin(ch_applt)\n",
    "\n",
    "appln[\"foreign ownership (CH inventors; EU owned)\"] = (appln[\"appln_id\"].isin(eu_applt) & appln[\"appln_id\"].isin(ch_invt) &\n",
    "                                             ~appln[\"appln_id\"].isin(eu_invt) & ~appln[\"appln_id\"].isin(ch_applt))\n",
    "appln[\"foreign ownership (EU inventors; CH owned)\"] = (appln[\"appln_id\"].isin(ch_applt) & appln[\"appln_id\"].isin(eu_invt) &\n",
    "                                             ~appln[\"appln_id\"].isin(ch_invt)& ~appln[\"appln_id\"].isin(eu_applt))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x169596040a0>"
     },
     "execution_count": 172,
     "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": [
    "cols = [\"co_inventors\",\"co_applicants\",\n",
    "        \"foreign ownership (CH inventors; EU owned)\",\n",
    "        \"foreign ownership (EU inventors; CH owned)\"]\n",
    "\n",
    "for c in cols:\n",
    "    data = appln[appln[c]==True].groupby([\"appln_filing_year\"], as_index=False)[\"appln_id\"].count()\n",
    "    g = sns.lineplot(data, x=\"appln_filing_year\", y=\"appln_id\", marker=\"o\")\n",
    "g.set_title(f\"Number of co-patents\")\n",
    "g.set_ylabel(\"Count\")\n",
    "g.set_xlabel(\"Year\")\n",
    "g.legend(handles=g.lines,labels=[\"patents with \" + c.replace(\"_\",\"-\") for c in cols], bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16953bb12b0>"
     },
     "execution_count": 173,
     "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": [
    "data = appln.groupby([\"appln_filing_year\",\"appln_kind\"], as_index=False)[\"appln_id\"].count()\n",
    "data\n",
    "g = sns.lineplot(data, x=\"appln_filing_year\", y=\"appln_id\", marker=\"o\", hue=\"appln_kind\")\n",
    "g.set_title(\"Number of co-patents\")\n",
    "g.set_ylabel(\"Count\")\n",
    "g.set_xlabel(\"Year\")\n",
    "g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, 'Year')"
     },
     "execution_count": 174,
     "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": [
    "data = appln.groupby([\"appln_filing_year\",\"granted\"], as_index=False)[\"appln_id\"].count()\n",
    "data\n",
    "g = sns.lineplot(data, x=\"appln_filing_year\", y=\"appln_id\", hue=\"granted\", marker=\"o\")\n",
    "g.set_title(\"Number of co-patents\")\n",
    "g.set_ylabel(\"Count\")\n",
    "g.set_xlabel(\"Year\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1691af2f3d0>"
     },
     "execution_count": 175,
     "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": [
    "data = appln.groupby([\"appln_filing_year\",\"granted\",\"appln_kind\"], as_index=False)[\"appln_id\"].count()\n",
    "g = sns.lineplot(data, x=\"appln_filing_year\", y=\"appln_id\", hue=\"appln_kind\",style=\"granted\", marker=\"o\")\n",
    "g.set_title(\"Number of co-patents\")\n",
    "g.set_ylabel(\"Count\")\n",
    "g.set_xlabel(\"Year\")\n",
    "g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "outputs": [
    {
     "data": {
      "text/plain": "    appln_filing_year granted appln_kind  appln_id\n0                2011       N         A        892\n1                2011       N         F          6\n2                2011       N         T         10\n3                2011       N         U          1\n4                2011       Y         A        966\n..                ...     ...        ...       ...\n70               2021       Y         F          1\n71               2021       Y         U         30\n72               2022       N         A          6\n73               2022       N         U          1\n74               2022       Y         U          4\n\n[75 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>appln_filing_year</th>\n      <th>granted</th>\n      <th>appln_kind</th>\n      <th>appln_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>N</td>\n      <td>A</td>\n      <td>892</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>N</td>\n      <td>F</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>N</td>\n      <td>T</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>N</td>\n      <td>U</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>Y</td>\n      <td>A</td>\n      <td>966</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>70</th>\n      <td>2021</td>\n      <td>Y</td>\n      <td>F</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>2021</td>\n      <td>Y</td>\n      <td>U</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2022</td>\n      <td>N</td>\n      <td>A</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2022</td>\n      <td>N</td>\n      <td>U</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2022</td>\n      <td>Y</td>\n      <td>U</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n<p>75 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1696b530850>"
     },
     "execution_count": 177,
     "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": [
    "perc = data.groupby(['appln_filing_year',\"appln_kind\"], as_index=False)[\"appln_id\"].sum().rename(columns={\"appln_id\":\"sum\"})\n",
    "p_data = data.merge(perc, on = ['appln_filing_year',\"appln_kind\"])\n",
    "p_data[\"percent\"] = p_data[\"appln_id\"]/p_data[\"sum\"]\n",
    "# p_data\n",
    "g = sns.lineplot(p_data[p_data[\"granted\"]==\"Y\"], x=\"appln_filing_year\", y=\"percent\", hue=\"appln_kind\", marker=\"o\")\n",
    "g.set_title(\"Number of co-patents\")\n",
    "g.set_ylabel(\"Percent of accepted co-patents\")\n",
    "g.set_xlabel(\"Year\")\n",
    "g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "outputs": [],
   "source": [
    "for kind in sorted(appln[\"appln_kind\"].unique()):\n",
    "    sub_data = data[data[\"appln_kind\"]==kind]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "outputs": [],
   "source": [
    "# Granted patents\n",
    "# granted = appln[appln[\"granted\"]==\"Y\"][\"appln_id\"].unique()\n",
    "# appln_pers=[appln_pers[\"appln_id\"].isin(granted)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "outputs": [],
   "source": [
    "patstat_collabs = appln_pers.merge(pers, on=\"person_id\")\n",
    "patstat_collabs = patstat_collabs.merge(country_defs, left_on=\"person_ctry_code\", right_on=\"ctry_code\")\n",
    "patstat_collabs = patstat_collabs.groupby(\"appln_id\",as_index=False)[\"Country\"].unique().explode('Country')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "outputs": [
    {
     "data": {
      "text/plain": "                    Country  count   percent    weight\n0                     China  22137  0.888073  0.415765\n1  Taiwan Province Of China   2593  0.104024  0.048700\n2          Hong Kong, China    581  0.023308  0.010912\n3         Macao SAR (China)     17  0.000682  0.000319",
      "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>China</td>\n      <td>22137</td>\n      <td>0.888073</td>\n      <td>0.415765</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Taiwan Province Of China</td>\n      <td>2593</td>\n      <td>0.104024</td>\n      <td>0.048700</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Hong Kong, China</td>\n      <td>581</td>\n      <td>0.023308</td>\n      <td>0.010912</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Macao SAR (China)</td>\n      <td>17</td>\n      <td>0.000682</td>\n      <td>0.000319</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_collab = patstat_collabs[patstat_collabs[\"Country\"].isin(ch_names)][\"Country\"].value_counts().reset_index()\n",
    "country_collab[\"percent\"] = country_collab[\"count\"]/patstat_collabs[\"appln_id\"].nunique()\n",
    "country_collab[\"weight\"] = country_collab[\"count\"]/patstat_collabs[\"appln_id\"].size\n",
    "country_collab"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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(0,30000)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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(0,100)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Percentage of co-patents\")\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": "code",
   "execution_count": 184,
   "outputs": [
    {
     "data": {
      "text/plain": "        Country  count   percent    weight\n0       Germany   9725  0.390139  0.182650\n1        France   4381  0.175753  0.082282\n2        Sweden   3667  0.147110  0.068872\n3   Netherlands   2900  0.116340  0.054466\n4       Finland   1644  0.065953  0.030877\n5       Belgium   1028  0.041240  0.019307\n6         Italy   1000  0.040117  0.018781\n7       Denmark    705  0.028283  0.013241\n8         Spain    678  0.027199  0.012734\n9       Austria    484  0.019417  0.009090\n10      Ireland    469  0.018815  0.008809\n11       Poland    289  0.011594  0.005428\n12       Greece    234  0.009387  0.004395\n13   Luxembourg    146  0.005857  0.002742\n14     Portugal    111  0.004453  0.002085\n15      Hungary     75  0.003009  0.001409\n16      Czechia     72  0.002888  0.001352\n17     Bulgaria     69  0.002768  0.001296\n18      Romania     68  0.002728  0.001277\n19     Slovakia     43  0.001725  0.000808\n20       Cyprus     35  0.001404  0.000657\n21     Slovenia     28  0.001123  0.000526\n22      Croatia     25  0.001003  0.000470\n23        Malta     15  0.000602  0.000282\n24      Estonia     11  0.000441  0.000207\n25    Lithuania      8  0.000321  0.000150\n26       Latvia      6  0.000241  0.000113",
      "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>9725</td>\n      <td>0.390139</td>\n      <td>0.182650</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>France</td>\n      <td>4381</td>\n      <td>0.175753</td>\n      <td>0.082282</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Sweden</td>\n      <td>3667</td>\n      <td>0.147110</td>\n      <td>0.068872</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Netherlands</td>\n      <td>2900</td>\n      <td>0.116340</td>\n      <td>0.054466</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Finland</td>\n      <td>1644</td>\n      <td>0.065953</td>\n      <td>0.030877</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Belgium</td>\n      <td>1028</td>\n      <td>0.041240</td>\n      <td>0.019307</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Italy</td>\n      <td>1000</td>\n      <td>0.040117</td>\n      <td>0.018781</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Denmark</td>\n      <td>705</td>\n      <td>0.028283</td>\n      <td>0.013241</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Spain</td>\n      <td>678</td>\n      <td>0.027199</td>\n      <td>0.012734</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Austria</td>\n      <td>484</td>\n      <td>0.019417</td>\n      <td>0.009090</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Ireland</td>\n      <td>469</td>\n      <td>0.018815</td>\n      <td>0.008809</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Poland</td>\n      <td>289</td>\n      <td>0.011594</td>\n      <td>0.005428</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Greece</td>\n      <td>234</td>\n      <td>0.009387</td>\n      <td>0.004395</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Luxembourg</td>\n      <td>146</td>\n      <td>0.005857</td>\n      <td>0.002742</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Portugal</td>\n      <td>111</td>\n      <td>0.004453</td>\n      <td>0.002085</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Hungary</td>\n      <td>75</td>\n      <td>0.003009</td>\n      <td>0.001409</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Czechia</td>\n      <td>72</td>\n      <td>0.002888</td>\n      <td>0.001352</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Bulgaria</td>\n      <td>69</td>\n      <td>0.002768</td>\n      <td>0.001296</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Romania</td>\n      <td>68</td>\n      <td>0.002728</td>\n      <td>0.001277</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Slovakia</td>\n      <td>43</td>\n      <td>0.001725</td>\n      <td>0.000808</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Cyprus</td>\n      <td>35</td>\n      <td>0.001404</td>\n      <td>0.000657</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Slovenia</td>\n      <td>28</td>\n      <td>0.001123</td>\n      <td>0.000526</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Croatia</td>\n      <td>25</td>\n      <td>0.001003</td>\n      <td>0.000470</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Malta</td>\n      <td>15</td>\n      <td>0.000602</td>\n      <td>0.000282</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Estonia</td>\n      <td>11</td>\n      <td>0.000441</td>\n      <td>0.000207</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Lithuania</td>\n      <td>8</td>\n      <td>0.000321</td>\n      <td>0.000150</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Latvia</td>\n      <td>6</td>\n      <td>0.000241</td>\n      <td>0.000113</td>\
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_collab = patstat_collabs[~patstat_collabs[\"Country\"].isin(ch_names)][\"Country\"].value_counts().reset_index()\n",
    "country_collab[\"percent\"] = country_collab[\"count\"]/patstat_collabs[\"appln_id\"].nunique()\n",
    "country_collab[\"weight\"] = country_collab[\"count\"]/patstat_collabs[\"appln_id\"].size\n",
    "country_collab"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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"
    }
   ],
   "source": [
    "data = country_collab\n",
    "g = sns.barplot(data, x=\"count\", y=\"Country\", color=\"blue\")\n",
    "g.set_xlim(0,10000)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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(0,50)\n",
    "g.set_ylabel(\"Country\")\n",
    "g.set_xlabel(\"Percentage of co-patents\")\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": [
    "# Pivot country - year"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id  Country  appln_filing_year\n0      330225325  Germany               2011\n1      330225325    China               2011\n2      330322632  Germany               2011\n3      330322632    China               2011\n4      330350961   Sweden               2011\n...          ...      ...                ...\n53239  575355937    China               2017\n53240  575399552   Sweden               2020\n53241  575399552    China               2020\n53242  575406608  Germany               2014\n53243  575406608    China               2014\n\n[53244 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>appln_id</th>\n      <th>Country</th>\n      <th>appln_filing_year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>330225325</td>\n      <td>Germany</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>330225325</td>\n      <td>China</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>330322632</td>\n      <td>Germany</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>330322632</td>\n      <td>China</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>330350961</td>\n      <td>Sweden</td>\n      <td>2011</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>53239</th>\n      <td>575355937</td>\n      <td>China</td>\n      <td>2017</td>\n    </tr>\n    <tr>\n      <th>53240</th>\n      <td>575399552</td>\n      <td>Sweden</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>53241</th>\n      <td>575399552</td>\n      <td>China</td>\n      <td>2020</td>\n    </tr>\n    <tr>\n      <th>53242</th>\n      <td>575406608</td>\n      <td>Germany</td>\n      <td>2014</td>\n    </tr>\n    <tr>\n      <th>53243</th>\n      <td>575406608</td>\n      <td>China</td>\n      <td>2014</td>\n    </tr>\n  </tbody>\n</table>\n<p>53244 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "record_col = \"appln_id\"\n",
    "patstat_collabs_y=patstat_collabs.merge(appln[[record_col,\"appln_filing_year\"]], on=\"appln_id\")\n",
    "patstat_collabs_y"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "outputs": [
    {
     "data": {
      "text/plain": "     appln_filing_year                   Country  count  appln_id   percent\n0                 2011                     China   1719      1969  0.873032\n5                 2011  Taiwan Province Of China    195      1969  0.099035\n9                 2011          Hong Kong, China     75      1969  0.038090\n24                2011         Macao SAR (China)      3      1969  0.001524\n27                2012                     China   2101      2425  0.866392\n31                2012  Taiwan Province Of China    297      2425  0.122474\n37                2012          Hong Kong, China     60      2425  0.024742\n53                2013                     China   1970      2226  0.884996\n58                2013  Taiwan Province Of China    221      2226  0.099281\n63                2013          Hong Kong, China     47      2226  0.021114\n73                2013         Macao SAR (China)      4      2226  0.001797\n79                2014                     China   2172      2427  0.894932\n84                2014  Taiwan Province Of China    236      2427  0.097239\n91                2014          Hong Kong, China     39      2427  0.016069\n105               2014         Macao SAR (China)      1      2427  0.000412\n106               2015                     China   2079      2378  0.874264\n111               2015  Taiwan Province Of China    275      2378  0.115643\n117               2015          Hong Kong, China     48      2378  0.020185\n130               2015         Macao SAR (China)      1      2378  0.000421\n132               2016                     China   2201      2477  0.888575\n137               2016  Taiwan Province Of China    255      2477  0.102947\n144               2016          Hong Kong, China     54      2477  0.021801\n160               2016         Macao SAR (China)      2      2477  0.000807\n161               2017                     China   2417      2654  0.910701\n166               2017  Taiwan Province Of China    211      2654  0.079503\n171               2017          Hong Kong, China     55      2654  0.020723\n188               2018                     China   2596      2859  0.908010\n193               2018  Taiwan Province Of China    252      2859  0.088143\n199               2018          Hong Kong, China     68      2859  0.023785\n215               2019                     China   2460      2725  0.902752\n220               2019  Taiwan Province Of China    269      2725  0.098716\n228               2019          Hong Kong, China     61      2725  0.022385\n238               2019         Macao SAR (China)      4      2725  0.001468\n243               2020                     China   1718      1963  0.875191\n248               2020  Taiwan Province Of China    249      1963  0.126847\n254               2020          Hong Kong, China     59      1963  0.030056\n269               2020         Macao SAR (China)      2      1963  0.001019\n271               2021                     China    694       813  0.853629\n274               2021  Taiwan Province Of China    133       813  0.163592\n286               2021          Hong Kong, China     13       813  0.015990\n297               2022                     China     10        11  0.909091\n300               2022          Hong Kong, China      2        11  0.181818",
      "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>appln_filing_year</th>\n      <th>Country</th>\n      <th>count</th>\n      <th>appln_id</th>\n      <th>percent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>China</td>\n      <td>1719</td>\n      <td>1969</td>\n      <td>0.873032</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2011</td>\n      <td>Taiwan Province Of China</td>\n      <td>195</td>\n      <td>1969</td>\n      <td>0.099035</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2011</td>\n      <td>Hong Kong, China</td>\n      <td>75</td>\n      <td>1969</td>\n      <td>0.038090</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2011</td>\n      <td>Macao SAR (China)</td>\n      <td>3</td>\n      <td>1969</td>\n      <td>0.001524</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>2012</td>\n      <td>China</td>\n      <td>2101</td>\n      <td>2425</td>\n      <td>0.866392</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>2012</td>\n      <td>Taiwan Province Of China</td>\n      <td>297</td>\n      <td>2425</td>\n      <td>0.122474</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>2012</td>\n      <td>Hong Kong, China</td>\n      <td>60</td>\n      <td>2425</td>\n      <td>0.024742</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>2013</td>\n      <td>China</td>\n      <td>1970</td>\n      <td>2226</td>\n      <td>0.884996</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>2013</td>\n      <td>Taiwan Province Of China</td>\n      <td>221</td>\n      <td>2226</td>\n      <td>0.099281</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>2013</td>\n      <td>Hong Kong, China</td>\n      <td>47</td>\n      <td>2226</td>\n      <td>0.021114</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2013</td>\n      <td>Macao SAR (China)</td>\n      <td>4</td>\n      <td>2226</td>\n      <td>0.001797</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>2014</td>\n      <td>China</td>\n      <td>2172</td>\n      <td>2427</td>\n      <td>0.894932</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>2014</td>\n      <td>Taiwan Province Of China</td>\n      <td>236</td>\n      <td>2427</td>\n      <td>0.097239</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>2014</td>\n      <td>Hong Kong, China</td>\n      <td>39</td>\n      <td>2427</td>\n      <td>0.016069</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>2014</td>\n      <td>Macao SAR (China)</td>\n      <td>1</td>\n      <td>2427</td>\n      <td>0.000412</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>2015</td>\n      <td>China</td>\n      <td>2079</td>\n      <td>2378</td>\n      <td>0.874264</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>2015</td>\n      <td>Taiwan Province Of China</td>\n      <td>275</td>\n      <td>2378</td>\n      <td>0.115643</td>\n    </tr>\n    <tr>\n      <th>117</th>\n      <td>2015</td>\n      <td>Hong Kong, China</td>\n      <td>48</td>\n      <td>2378</td>\n      <td>0.020185</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>2015</td>\n      <td>Macao SAR (China)</td>\n      <td>1</td>\n      <td>2378</td>\n      <td>0.000421</td>\n    </tr>\n    <tr>\n      <th>132</th>\n      <td>2016</td>\n      <td>China</td>\n      <td>2201</td>\n      <td>2477</td>\n      <td>0.888575</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>2016</td>\n      <td>Taiwan Province Of China</td>\n      <td>255</td>\n      <td>2477</td>\n      <td>0.102947</td>\n    </tr>\n    <tr>\n      <th>144</th>\n      <td>2016</td>\n      <td>Hong Kong, China</td>\n      <td>54</td>\n      <td>2477</td>\n      <td>0.021801</td>\n    </tr>\n    <tr>\n      <th>160</th>\n      <td>2016</td>\n
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countall = patstat_collabs_y.groupby(\"appln_filing_year\", as_index=False)[record_col].nunique()\n",
    "data = patstat_collabs_y.groupby(\"appln_filing_year\", as_index=False)[\"Country\"].value_counts().merge(countall, on=\"appln_filing_year\")\n",
    "data[\"percent\"] = data[\"count\"]/data[record_col]\n",
    "data_ch = data[data[\"Country\"].isin(ch_names)]\n",
    "data_ch"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(95.7222222222222, 0.5, '')"
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x300 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "g = sns.heatmap(pd.pivot_table(data_ch,columns=\"appln_filing_year\", index=\"Country\", values=\"count\").fillna(0).astype(int),\n",
    "            annot=True, fmt=\".0f\",linewidth=.5)\n",
    "g.set_title(\"Number of co-patents per year\")\n",
    "g.set_xlabel(\"\")\n",
    "g.set_ylabel(\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(95.7222222222222, 0.5, '')"
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x300 with 2 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "g = sns.heatmap(pd.pivot_table(data_ch,columns=\"appln_filing_year\", index=\"Country\", values=\"percent\").fillna(0)*100,\n",
    "            annot=True, fmt=\".2f\",linewidth=.5)\n",
    "g.set_title(\"Percentage of co-patents related to country per year\")\n",
    "g.set_xlabel(\"\")\n",
    "g.set_ylabel(\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "outputs": [
    {
     "data": {
      "text/plain": "     appln_filing_year      Country  count  appln_id   percent\n1                 2011      Germany    745      1969  0.378365\n2                 2011       France    407      1969  0.206704\n3                 2011  Netherlands    220      1969  0.111732\n4                 2011       Sweden    209      1969  0.106145\n6                 2011      Finland    166      1969  0.084307\n..                 ...          ...    ...       ...       ...\n298               2022        Spain      4        11  0.363636\n299               2022      Germany      3        11  0.272727\n301               2022  Netherlands      2        11  0.181818\n302               2022        Italy      1        11  0.090909\n303               2022      Denmark      1        11  0.090909\n\n[262 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>appln_filing_year</th>\n      <th>Country</th>\n      <th>count</th>\n      <th>appln_id</th>\n      <th>percent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>Germany</td>\n      <td>745</td>\n      <td>1969</td>\n      <td>0.378365</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>France</td>\n      <td>407</td>\n      <td>1969</td>\n      <td>0.206704</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>Netherlands</td>\n      <td>220</td>\n      <td>1969</td>\n      <td>0.111732</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>Sweden</td>\n      <td>209</td>\n      <td>1969</td>\n      <td>0.106145</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2011</td>\n      <td>Finland</td>\n      <td>166</td>\n      <td>1969</td>\n      <td>0.084307</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>298</th>\n      <td>2022</td>\n      <td>Spain</td>\n      <td>4</td>\n      <td>11</td>\n      <td>0.363636</td>\n    </tr>\n    <tr>\n      <th>299</th>\n      <td>2022</td>\n      <td>Germany</td>\n      <td>3</td>\n      <td>11</td>\n      <td>0.272727</td>\n    </tr>\n    <tr>\n      <th>301</th>\n      <td>2022</td>\n      <td>Netherlands</td>\n      <td>2</td>\n      <td>11</td>\n      <td>0.181818</td>\n    </tr>\n    <tr>\n      <th>302</th>\n      <td>2022</td>\n      <td>Italy</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0.090909</td>\n    </tr>\n    <tr>\n      <th>303</th>\n      <td>2022</td>\n      <td>Denmark</td>\n      <td>1</td>\n      <td>11</td>\n      <td>0.090909</td>\n    </tr>\n  </tbody>\n</table>\n<p>262 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_eu = data[~data[\"Country\"].isin(ch_names)]\n",
    "data_eu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(95.72222222222221, 0.5, '')"
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1oAAANECAYAAACpfl51AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddVwU+R/H8dciiICECggmYJ7d3Xp2t57drWe36Nkdp94ZZ59n95lYZyMG2IoiiKQIikHu7w9+7rmCAS4M7H2ej8c8Huzk+7szO8t35zvfUanVajVCCCGEEEIIIXTGQOkAQgghhBBCCKFvpKIlhBBCCCGEEDomFS0hhBBCCCGE0DGpaAkhhBBCCCGEjklFSwghhBBCCCF0TCpaQgghhBBCCKFjUtESQgghhBBCCB2TipYQQgghhBBC6JhUtIQQQgghhBBCx6SiJYQQn3H69GlUKhU7d+5UOso3CQgIoHXr1mTJkgWVSsXixYuVjiSEEEL8Z0lFSwihqPXr16NSqciQIQO+vr7xpteoUYMiRYookCzt+fnnnzl69Cjjxo1j06ZN1K9fX+lIOvX27VucnZ05ffp0imxvxYoVrF+/PkW2JYQQQv8YKh1ACCEAIiIimD17NsuWLVM6Spp18uRJmjVrxsiRI5WOkizevn3L1KlTgbgKeHJbsWIF1tbWdOvWLdm3JYQQQv/IFS0hRKpQokQJVq9ezfPnz5WOkuLevHmjk/UEBgZiZWWlk3WJtEtXx1Nye//+PbGxsUrHEEKIZCMVLSFEqjB+/HhiYmKYPXv2F+fz8vJCpVIl2KRLpVLh7Oysee3s7IxKpeLBgwd06tQJS0tLbGxsmDRpEmq1Gh8fH5o1a4aFhQV2dnYsWLAgwW3GxMQwfvx47OzsMDMzo2nTpvj4+MSb7/Lly9SvXx9LS0tMTU2pXr0658+f15rnQ6Y7d+7QsWNHMmXKRJUqVb5Y5sePH9OmTRsyZ86MqakpFSpU4NChQ5rpH5pfqtVqli9fjkqlQqVSfXGdsbGxLFmyhKJFi5IhQwZsbGyoX78+V69e1cwTHR3NL7/8Qp48eTA2NsbBwYHx48cTERHxxXV/8KHZp5ubG5UqVcLExARHR0d+++03rfkiIyOZPHkypUuXxtLSEjMzM6pWrcqpU6c083h5eWFjYwPA1KlTNWX8eH/fu3eP1q1bkzlzZjJkyECZMmXYv3+/1rY+vFfnz59n+PDh2NjYYGZmRosWLQgKCtLM5+DgwO3btzlz5oxmWx+uokVFRTF16lTy5ctHhgwZyJIlC1WqVOH48eNffD8+bPvs2bP07duXLFmyYGFhQZcuXXj58mW8+Q8fPkzVqlUxMzPD3NycRo0acfv2ba15unXrRsaMGfH09KRhw4aYm5vz008/Jbj9U6dOoVKp2LNnT7xpf/75JyqViosXLybq/QwJCWHkyJEULVqUjBkzYmFhQYMGDbh586bWfB/ud/zrr7+YOHEi2bNnx9TUlFevXn3xPRNCiLRMKlpCiFTB0dGRLl26JMtVrXbt2hEbG8vs2bMpX74806dPZ/Hixfz4449kz56dOXPmkDdvXkaOHMnZs2fjLT9jxgwOHTrEmDFjGDJkCMePH6dOnTq8e/dOM8/JkyepVq0ar169YsqUKcycOZPQ0FBq1arFlStX4q2zTZs2vH37lpkzZ9K7d+/PZg8ICKBSpUocPXqUAQMGMGPGDN6/f0/Tpk01/zBXq1aNTZs2AfDjjz+yadMmzevP6dmzJ8OGDSNnzpzMmTOHsWPHkiFDBi5duqSZp1evXkyePJlSpUqxaNEiqlevzqxZs2jfvv2X3/CPvHz5koYNG1K6dGnmzp1Ljhw56N+/P3/88YdmnlevXrFmzRpq1KjBnDlzcHZ2JigoiHr16nHjxg0AbGxsWLlyJQAtWrTQlLFly5YA3L59mwoVKnD37l3Gjh3LggULMDMzo3nz5glWLAYPHszNmzeZMmUK/fv358CBAwwaNEgzffHixeTIkYOCBQtqtjVhwgQgrrI8depUatasya+//sqECRPIlSsX165d+6b3ZNCgQdy9exdnZ2e6dOnCli1baN68OWq1WjPPpk2baNSoERkzZmTOnDlMmjSJO3fuUKVKFby8vLTWFx0dTb169bC1tWX+/Pm0atUqwe3WqFGDnDlzsmXLlnjTtmzZQp48eahYsWKi3s/Hjx+zd+9eGjduzMKFCxk1ahQeHh5Ur149wc/xL7/8wqFDhxg5ciQzZ84kffr03/SeCSFEmqQWQggFrVu3Tg2oXV1d1Z6enmpDQ0P1kCFDNNOrV6+uLly4sOb1kydP1IB63bp18dYFqKdMmaJ5PWXKFDWg7tOnj2ZcdHS0OkeOHGqVSqWePXu2ZvzLly/VJiYm6q5du2rGnTp1Sg2os2fPrn716pVm/Pbt29WAesmSJWq1Wq2OjY1V58uXT12vXj11bGysZr63b9+qHR0d1T/++GO8TB06dPim92fYsGFqQP3PP/9oxr1+/Vrt6OiodnBwUMfExGiVf+DAgV9d58mTJ9WA1vv8wYf8N27cUAPqXr16aU0fOXKkGlCfPHnyq9upXr26GlAvWLBAMy4iIkJdokQJta2trToyMlKtVsftk4iICK1lX758qc6aNau6R48emnFBQUHx9vEHtWvXVhctWlT9/v17rbJUqlRJnS9fPs24D8dbnTp1tPbVzz//rE6XLp06NDRUM65w4cLq6tWrx9tW8eLF1Y0aNfpq+T/1YdulS5fWlF2tVqvnzp2rBtT79u1Tq9Vx+9fKykrdu3dvreX9/f3VlpaWWuO7du2qBtRjx479pgzjxo1TGxsba5UzMDBQbWhoqPW+fuv7+f79e61jUK2O+4waGxurp02bphn34bPk5OSkfvv27TdlFUKItE6uaAkhUg0nJyc6d+7MqlWr8PPz09l6e/Xqpfk7Xbp0lClTBrVaTc+ePTXjraysKFCgAI8fP463fJcuXTA3N9e8bt26Nfb29vz9998A3Lhxg4cPH9KxY0devHhBcHAwwcHBvHnzhtq1a3P27Nl496L069fvm7L//ffflCtXTqt5YcaMGenTpw9eXl7cuXPn296Ej+zatQuVSsWUKVPiTfvQ5PBD2YYPH641fcSIEQBaTRe/xNDQkL59+2pep0+fnr59+xIYGIibmxsQt08+XNmIjY0lJCSE6OhoypQp801XiUJCQjh58iRt27bl9evXmvf/xYsX1KtXj4cPH8br0bJPnz5azSurVq1KTEwMT58+/er2rKysuH37Ng8fPvym9+BTffr0wcjISPO6f//+GBoaat7z48ePExoaSocOHTRlCQ4OJl26dJQvX16rSeXH6/gWXbp0ISIiQuuRBdu2bSM6OppOnToBiXs/jY2NMTCI+1ciJiaGFy9ekDFjRgoUKJDgvuvatSsmJibf+E4JIUTaJhUtIUSqMnHiRKKjo796r1Zi5MqVS+u1paUlGTJkwNraOt74hO6VyZcvn9ZrlUpF3rx5NU24PvzD3bVrV2xsbLSGNWvWEBERQVhYmNY6HB0dvyn706dPKVCgQLzxP/zwg2Z6Ynl6epItWzYyZ878xe0aGBiQN29erfF2dnZYWVlptvvu3Tv8/f21ho9ly5YNMzMzrXH58+cH0GoCt2HDBooVK6a558nGxoZDhw7Fe98S8ujRI9RqNZMmTYr3/n+oTAYGBmot8+kxkSlTJoAE9/+npk2bRmhoKPnz56do0aKMGjUKd3f3ry73wafHU8aMGbG3t493PNWqVSteeY4dOxavLIaGhuTIkeObtl2wYEHKli2r1Xxwy5YtVKhQQbOvE/N+xsbGsmjRIvLly4exsTHW1tbY2Njg7u6e4L771uNeCCH0gXTvLoRIVZycnOjUqROrVq1i7Nix8aZ/rpOHmJiYz64zXbp03zQO0LpP5lt9uFo1b948SpQokeA8GTNm1HqdVn7V/1qnGtu2baN79+5a4xL7Hm7evJlu3brRvHlzRo0aha2tLenSpWPWrFl4enp+dfkP7//IkSOpV69egvN8WmH8nv1frVo1PD092bdvH8eOHWPNmjUsWrS
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "g = sns.heatmap(pd.pivot_table(data_eu,columns=\"appln_filing_year\", index=\"Country\", values=\"count\").fillna(0).astype(int),\n",
    "            annot=True, fmt=\".0f\",linewidth=.5)\n",
    "g.set_title(\"Number of co-patents per year\")\n",
    "g.set_xlabel(\"\")\n",
    "g.set_ylabel(\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(95.72222222222221, 0.5, '')"
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANECAYAAACKJQ+gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddVgUXRvA4d8iISGiUooFdgd2d2ILdnd3F3Zg96vyYhfWaycmFoICoiJIGJQFEkrIfn8Aqysh6K4g37mvay9l9szM8+ycmd0zc+aMRCqVShEEQRAEQRAEQRBQyewABEEQBEEQBEEQsgrRQBIEQRAEQRAEQUgkGkiCIAiCIAiCIAiJRANJEARBEARBEAQhkWggCYIgCIIgCIIgJBINJEEQBEEQBEEQhESigSQIgiAIgiAIgpBINJAEQRAEQRAEQRASiQaSIAiCIAiCIAhCItFAEgQh2zh//jyVK1cmZ86cSCQSQkNDMzskQcGKFi1K//79MzsMOdbW1kgkkswOQxAEQVAQ0UAShBTs3LkTiUQie+XMmZOSJUsyevRogoODMzu83/bkyROsra3x8/PL7FAU5v3791hZWaGpqcmmTZvYs2cP2tramR2WQt2+fRtra+s/0vALCAjA2tqaR48eKX1df0JWyGf//v2sXbs209afWc6ePYu1tXVmhyEIgpBuooEkCGlYsGABe/bsYePGjdSpU4ctW7ZQu3ZtoqKiMju03/LkyRPmz5+frRpITk5OhIeHs3DhQgYNGkTv3r1RU1PL7LAU6vbt28yfP/+PNZDmz5+frRpImZ3P/3MDaf78+ZkdhiAIQrqpZnYAgpCVtW7dmmrVqgEwePBg8uXLx+rVq/nvv//o0aPHby07KioKLS0tRYQpACEhIQDo6ellbiBChoj9QPheXFwc8fHxqKurZ3YoPxUZGflXXKWOj48nJiaGnDlzZnYogvDXEFeQBCEDmjRpAoCvr69s2t69ezE3N0dTU5O8efPSvXt3Xr16JTdfo0aNKF++PM7OzjRo0AAtLS1mzpwJwJcvX7C2tqZkyZLkzJmT/Pnz07lzZ168eCGbPz4+nrVr11KuXDly5syJkZERw4YN4+PHj3LrKVq0KBYWFty6dYsaNWqQM2dOzMzM2L17t6zMzp07sbS0BKBx48ayboTXrl0D4L///qNt27YUKFAADQ0NihUrxsKFC/n69Wuyz2PTpk2YmZmhqalJjRo1uHnzJo0aNaJRo0Zy5aKjo5k3bx7FixdHQ0ODQoUKMXXqVKKjo9P1udvb28s+Y319fXr37s2bN2/kPt9+/foBUL16dSQSyU/vU3nz5g2DBg2S5WlqasqIESOIiYmRlfHx8cHS0pK8efOipaVFrVq1OHPmTLpi9vPzQyKRsHLlStasWUORIkXQ1NSkYcOGPH78WK6sm5sb/fv3x8zMjJw5c2JsbMzAgQN5//69rIy1tTVTpkwBwNTUVLbdvr8KmJG6+OTJExo3boyWlhYmJiasWLFCVubatWtUr14dgAEDBsjWtXPnTgC8vLzo0qULxsbG5MyZk4IFC9K9e3fCwsLS/EzS2g9+tY58+PCByZMnU6FCBXR0dNDV1aV169a4urqmOx+Ae/fu0apVK3Lnzo2WlhYNGzbE0dEx2fpu3bpF9erVyZkzJ8WKFeOff/5JM77vcz9z5gz+/v6y9RctWlT2fkhICIMGDcLIyIicOXNSqVIldu3ala5lA5w7d46GDRuSK1cudHV1qV69Ovv375cr87P9KCnOH/dfgP79+8vF+3393rZtG8WKFUNDQ4Pq1avj5OQkN9+mTZsA5Lot/7iMtWvXypZx//59tLW1GTduXLI4Xr9+TY4cOVi6dGmqn0VG9j2AZ8+e0bVrV/LmzUvOnDmpVq0aJ0+elCuT1O36+vXrjBw5EkNDQwoWLJji+iMiIjIUf2hoKOPHj6dQoUJoaGhQvHhxli9fTnx8vNy8K1eupE6dOuTLlw9NTU3Mzc05cuRIsnVIJBJGjx7Nvn37KFeuHBoaGpw/fz7Vz0sQhOTEFSRByICkRku+fPkAWLx4MXPmzMHKyorBgwfz9u1bNmzYQIMGDXj48KHc1Yz379/TunVrunfvTu/evTEyMuLr169YWFhw5coVunfvzrhx4wgPD+fSpUs8fvyYYsWKATBs2DB27tzJgAEDGDt2LL6+vmzcuJGHDx/i6Ogo15XM29ubrl27MmjQIPr168e///5L//79MTc3p1y5cjRo0ICxY8eyfv16Zs6cSZkyZQBk/+7cuRMdHR0mTpyIjo4ODg4OzJ07l0+fPmFjYyNbz5YtWxg9ejT169dnwoQJ+Pn50bFjR/LkySP3wyE+Pp727dtz69Ythg4dSpkyZXB3d2fNmjU8f/6cEydOpPmZJ+VdvXp1li5dSnBwMOvWrcPR0VH2Gc+aNYtSpUqxbds2FixYgKmpqeyzS0lAQAA1atQgNDSUoUOHUrp0ad68ecORI0eIiopCXV2d4OBg6tSpQ1RUFGPHjiVfvnzs2rWL9u3bc+TIETp16pSOGgO7d+8mPDycUaNG8eXLF9atW0eTJk1wd3fHyMgIgEuXLuHj48OAAQMwNjbGw8ODbdu24eHhwd27d5FIJHTu3Jnnz59z4MAB1qxZg76+PgAGBgZAxurix48fadWqFZ07d8bKyoojR44wbdo0KlSoQOvWrSlTpgwLFixg7ty5DB06lPr16wNQp04dYmJiaNmyJdHR0YwZMwZjY2PevHnD6dOnCQ0NJXfu3Gl+HintB79TR3x8fDhx4gSWlpaYmpoSHBzMP//8Q8OGDXny5AkFChRIMx8ABwcHWrdujbm5OfPmzUNFRQU7OzuaNGnCzZs3qVGjBgDu7u60aNECAwMDrK2tiYuLY968ebLtmJZZs2YRFhbG69evWbNmDQA6OjoAfP78mUaNGuHt7c3o0aMxNTXF3t6e/v37ExoamuIP7e/t3LmTgQMHUq5cOWbMmIGenh4PHz7k/Pnz9OzZU1bmZ/vRr9i/fz/h4eEMGzYMiUTCihUr6Ny5Mz4+PqipqTFs2DACAgK4dOkSe/bsSXEZdnZ2fPnyhaFDh6KhoUHhwoXp1KkThw4dYvXq1eTIkUNW9sCBA0ilUnr16vXT2NKz73l4eFC3bl1MTEyYPn062traHD58mI4dO3L06NFk+/nIkSMxMDBg7ty5REZGprheHR2ddMcfFRVFw4YNefPmDcOGDaNw4cLcvn2bGTNmEBgYKNclc926dbRv355evXoRExPDwYMHsbS05PTp07Rt21YuBgcHBw4fPszo0aPR19eXa9wKgpAOUkEQkrGzs5MC0suXL0vfvn0rffXqlfTgwYPSfPnySTU1NaWvX7+W+vn5SXPkyCFdvHix3Lzu7u5SVVVVuekNGzaUAtKtW7fKlf3333+lgHT16tXJYoiPj5dKpVLpzZs3pYB03759cu+fP38+2fQiRYpIAemNGzdk00JCQqQaGhrSSZMmyabZ29tLAenVq1eTrTcqKirZtGHDhkm1tLSkX758kUqlUml0dLQ0X7580urVq0tjY2Nl5Xbu3CkFpA0bNpRN27Nnj1RFRUV68+ZNuWVu3bpVCkgdHR2TrS9JTEyM1NDQUFq+fHnp58+fZdNPnz4tBaRz586VTUvaZk5OTqkuL0nfvn2lKioqKZZN+tzHjx8vBeTiDg8Pl5qamkqLFi0q/fr1a5rr8PX1lQKy+pLk3r17UkA6YcIE2bSUPvMDBw4k25Y2NjZSQOrr6ytX9lfq4u7du2XToqOjpcbGxtIuXbrIpjk5OUkBqZ2dndwyHz58KAWk9vb2aeafktT2g4zUkSJFikj79esn+/vLly/JtoWvr69UQ0NDumDBgp/mEx8fLy1RooS0ZcuWsm0vlSZsE1NTU2nz5s1l0zp27CjNmTOn1N/fXzbtyZMn0hw5ckjT83Xatm1baZEiRZJNX7t2rRSQ7t27VzYtJiZGWrt2bamOjo7006dPqS4zNDRUmitXLmnNmjXl9pGk3JKWld79qGHDhnL7b5J+/frJxZ5Uv/Plyyf98OGDbPp///0nBaSnTp2STRs
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "g = sns.heatmap(pd.pivot_table(data_eu,columns=\"appln_filing_year\", index=\"Country\", values=\"percent\").fillna(0)*100,\n",
    "            annot=True, fmt=\".2f\",linewidth=.5)\n",
    "g.set_title(\"Percentage of co-patents related to country per year\")\n",
    "g.set_xlabel(\"\")\n",
    "g.set_ylabel(\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Let's see about 'organizations'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [],
   "source": [
    "# harmonized entities (sector and country too)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "outputs": [],
   "source": [
    "pers_han = pers[[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\",\"person_ctry_code\"]].drop_duplicates()\n",
    "pers_han[\"psn_sector\"].fillna(\"UNKNOWN\", inplace=True)\n",
    "pers_han = pers_han.sort_values(by=\"psn_sector\", ascending=True)\n",
    "pers_han.drop(columns=\"person_id\", inplace=True)\n",
    "pers_han = pers_han.groupby(\"han_id\", as_index=False)[[\"han_name\",\"psn_sector\",\"person_ctry_code\"]].agg(\n",
    "    lambda x: pd.Series.mode(x)[0])\n",
    "pers_han = pers_han.merge(country_defs,left_on=\"person_ctry_code\",right_on=\"ctry_code\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id     han_id                 han_name  psn_sector  Country\n0       340314532    2125445               NOKIA CORP     COMPANY  Finland\n1       413601768    2125445               NOKIA CORP     COMPANY  Finland\n2       332888018    2125445               NOKIA CORP     COMPANY  Finland\n3       333546132    2125445               NOKIA CORP     COMPANY  Finland\n4       334765473    2125445               NOKIA CORP     COMPANY  Finland\n...           ...        ...                      ...         ...      ...\n106442  504779814    3652317        VAVILOVS VALERIJS  INDIVIDUAL   Latvia\n106443  544264361    3652317        VAVILOVS VALERIJS  INDIVIDUAL   Latvia\n106444  481190056    3652317        VAVILOVS VALERIJS  INDIVIDUAL   Latvia\n106445  554759601    3652317        VAVILOVS VALERIJS  INDIVIDUAL   Latvia\n106446  569497458  184881241  ZAVORONKOVS, ALEKSANDRS     UNKNOWN   Latvia\n\n[106421 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>appln_id</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>340314532</td>\n      <td>2125445</td>\n      <td>NOKIA CORP</td>\n      <td>COMPANY</td>\n      <td>Finland</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>413601768</td>\n      <td>2125445</td>\n      <td>NOKIA CORP</td>\n      <td>COMPANY</td>\n      <td>Finland</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>332888018</td>\n      <td>2125445</td>\n      <td>NOKIA CORP</td>\n      <td>COMPANY</td>\n      <td>Finland</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>333546132</td>\n      <td>2125445</td>\n      <td>NOKIA CORP</td>\n      <td>COMPANY</td>\n      <td>Finland</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>334765473</td>\n      <td>2125445</td>\n      <td>NOKIA CORP</td>\n      <td>COMPANY</td>\n      <td>Finland</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>106442</th>\n      <td>504779814</td>\n      <td>3652317</td>\n      <td>VAVILOVS VALERIJS</td>\n      <td>INDIVIDUAL</td>\n      <td>Latvia</td>\n    </tr>\n    <tr>\n      <th>106443</th>\n      <td>544264361</td>\n      <td>3652317</td>\n      <td>VAVILOVS VALERIJS</td>\n      <td>INDIVIDUAL</td>\n      <td>Latvia</td>\n    </tr>\n    <tr>\n      <th>106444</th>\n      <td>481190056</td>\n      <td>3652317</td>\n      <td>VAVILOVS VALERIJS</td>\n      <td>INDIVIDUAL</td>\n      <td>Latvia</td>\n    </tr>\n    <tr>\n      <th>106445</th>\n      <td>554759601</td>\n      <td>3652317</td>\n      <td>VAVILOVS VALERIJS</td>\n      <td>INDIVIDUAL</td>\n      <td>Latvia</td>\n    </tr>\n    <tr>\n      <th>106446</th>\n      <td>569497458</td>\n      <td>184881241</td>\n      <td>ZAVORONKOVS, ALEKSANDRS</td>\n      <td>UNKNOWN</td>\n      <td>Latvia</td>\n    </tr>\n  </tbody>\n</table>\n<p>106421 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patstat_collabs = appln_pers.merge(pers, on=\"person_id\")\n",
    "patstat_collabs = patstat_collabs.merge(country_defs, left_on=\"person_ctry_code\", right_on=\"ctry_code\")\n",
    "org_collabs =  patstat_collabs[[record_col,\"han_id\",\"han_name\",\"psn_sector\",\"Country\"]].drop_duplicates()\n",
    "org_collabs[\"psn_sector\"].fillna(\"UNKNOWN\", inplace=True)\n",
    "org_collabs"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "data": {
      "text/plain": "          han_id  count   percent    weight   \n0        1337324   1723  0.069122  0.016190  \\\n1        3697072    270  0.010832  0.002537   \n2          62077    204  0.008184  0.001917   \n3        1912470    130  0.005215  0.001222   \n4        2607939    101  0.004052  0.000949   \n...          ...    ...       ...       ...   \n29061  151900860      1  0.000040  0.000009   \n29062  151899043      1  0.000040  0.000009   \n29063  151898719      1  0.000040  0.000009   \n29064  151894061      1  0.000040  0.000009   \n29065  183287157      1  0.000040  0.000009   \n\n                                        han_name  psn_sector person_ctry_code   \n0                             HUAWEI TECH CO LTD     COMPANY               CN  \\\n1             NINGBO GEELY AUTOMOBILE R&D CO LTD     COMPANY               CN   \n2      TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD     COMPANY               TW   \n3                                   MEDIATEK INC     COMPANY               TW   \n4                              SELF ELECT CO LTD     COMPANY               CN   \n...                                          ...         ...              ...   \n29061                                 LI, FAHONG     UNKNOWN               CN   \n29062                                  ZHANG, YI     UNKNOWN               CN   \n29063                                  XU, EASON     UNKNOWN               CN   \n29064                                  WENG, RAN  INDIVIDUAL               CN   \n29065            LEFEBVRE, Marc René André Louis     UNKNOWN               MO   \n\n      ctry_code iso_alpha3                   Country organisation_flag   \n0            CN        CHN                     China                    \\\n1            CN        CHN                     China                     \n2            TW        TWN  Taiwan Province Of China                     \n3            TW        TWN  Taiwan Province Of China                     \n4            CN        CHN                     China                     \n...         ...        ...                       ...               ...   \n29061        CN        CHN                     China                     \n29062        CN        CHN                     China                     \n29063        CN        CHN                     China                     \n29064        CN        CHN                     China                     \n29065        MO        MAC         Macao SAR (China)                     \n\n      continent eu_member epo_member oecd_member discontinued   \n0          Asia                                                \\\n1          Asia                                                 \n2          Asia                                                 \n3          Asia                                                 \n4          Asia                                                 \n...         ...       ...        ...         ...          ...   \n29061      Asia                                                 \n29062      Asia                                                 \n29063      Asia                                                 \n29064      Asia                                                 \n29065      Asia                                                 \n\n                                                  org  \n0                             HUAWEI TECH CO LTD (CN)  \n1             NINGBO GEELY AUTOMOBILE R&D CO LTD (CN)  \n2      TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD (TW)  \n3                                   MEDIATEK INC (TW)  \n4                              SELF ELECT CO LTD (CN)  \n...                                               ...  \n29061                                 LI, FAHONG (CN)  \n29062                                  ZHANG, YI (CN)  \n29063                                  XU, EASON (CN)  \n29064                                  WENG, RAN (CN)  \n29065            LEFEBVRE, Marc René André Louis (MO)  \n\n[29066 rows x 17 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>han_id</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n      <th>person_ctry_code</th>\n      <th>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>Country</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n      <th>org</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1337324</td>\n      <td>1723</td>\n      <td>0.069122</td>\n      <td>0.016190</td>\n      <td>HUAWEI TECH CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>HUAWEI TECH CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3697072</td>\n      <td>270</td>\n      <td>0.010832</td>\n      <td>0.002537</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>62077</td>\n      <td>204</td>\n      <td>0.008184</td>\n      <td>0.001917</td>\n      <td>TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD</td>\n      <td>COMPANY</td>\n      <td>TW</td>\n      <td>TW</td>\n      <td>TWN</td>\n      <td>Taiwan Province Of China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD (TW)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1912470</td>\n      <td>130</td>\n      <td>0.005215</td>\n      <td>0.001222</td>\n      <td>MEDIATEK INC</td>\n      <td>COMPANY</td>\n      <td>TW</td>\n      <td>TW</td>\n      <td>TWN</td>\n      <td>Taiwan Province Of China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>MEDIATEK INC (TW)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2607939</td>\n      <td>101</td>\n      <td>0.004052</td>\n      <td>0.000949</td>\n      <td>SELF ELECT CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>SELF ELECT CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>29061</th>\n      <td>151900860</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>LI, FAHONG</td>\n      <td>UNKNOWN</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>LI, FAHONG (CN)</td>\n    </tr>\n    <tr>\n      <th>29062</th>\n      <td>151899043</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>ZHANG, YI</td>\n      <td>UNKNOWN</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "          han_id  count   percent    weight   \n0        1337324   1723  0.069122  0.016190  \\\n1        3697072    270  0.010832  0.002537   \n2          62077    204  0.008184  0.001917   \n3        1912470    130  0.005215  0.001222   \n4        2607939    101  0.004052  0.000949   \n...          ...    ...       ...       ...   \n29061  151900860      1  0.000040  0.000009   \n29062  151899043      1  0.000040  0.000009   \n29063  151898719      1  0.000040  0.000009   \n29064  151894061      1  0.000040  0.000009   \n29065  183287157      1  0.000040  0.000009   \n\n                                        han_name  psn_sector person_ctry_code   \n0                             HUAWEI TECH CO LTD     COMPANY               CN  \\\n1             NINGBO GEELY AUTOMOBILE R&D CO LTD     COMPANY               CN   \n2      TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD     COMPANY               TW   \n3                                   MEDIATEK INC     COMPANY               TW   \n4                              SELF ELECT CO LTD     COMPANY               CN   \n...                                          ...         ...              ...   \n29061                                 LI, FAHONG     UNKNOWN               CN   \n29062                                  ZHANG, YI     UNKNOWN               CN   \n29063                                  XU, EASON     UNKNOWN               CN   \n29064                                  WENG, RAN  INDIVIDUAL               CN   \n29065            LEFEBVRE, Marc René André Louis     UNKNOWN               MO   \n\n      ctry_code iso_alpha3                   Country organisation_flag   \n0            CN        CHN                     China                    \\\n1            CN        CHN                     China                     \n2            TW        TWN  Taiwan Province Of China                     \n3            TW        TWN  Taiwan Province Of China                     \n4            CN        CHN                     China                     \n...         ...        ...                       ...               ...   \n29061        CN        CHN                     China                     \n29062        CN        CHN                     China                     \n29063        CN        CHN                     China                     \n29064        CN        CHN                     China                     \n29065        MO        MAC         Macao SAR (China)                     \n\n      continent eu_member epo_member oecd_member discontinued   \n0          Asia                                                \\\n1          Asia                                                 \n2          Asia                                                 \n3          Asia                                                 \n4          Asia                                                 \n...         ...       ...        ...         ...          ...   \n29061      Asia                                                 \n29062      Asia                                                 \n29063      Asia                                                 \n29064      Asia                                                 \n29065      Asia                                                 \n\n                                                  org  \n0                             HUAWEI TECH CO LTD (CN)  \n1             NINGBO GEELY AUTOMOBILE R&D CO LTD (CN)  \n2      TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD (TW)  \n3                                   MEDIATEK INC (TW)  \n4                              SELF ELECT CO LTD (CN)  \n...                                               ...  \n29061                                 LI, FAHONG (CN)  \n29062                                  ZHANG, YI (CN)  \n29063                                  XU, EASON (CN)  \n29064                                  WENG, RAN (CN)  \n29065            LEFEBVRE, Marc René André Louis (MO)  \n\n[29066 rows x 17 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>han_id</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n      <th>person_ctry_code</th>\n      <th>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>Country</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n      <th>org</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1337324</td>\n      <td>1723</td>\n      <td>0.069122</td>\n      <td>0.016190</td>\n      <td>HUAWEI TECH CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>HUAWEI TECH CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3697072</td>\n      <td>270</td>\n      <td>0.010832</td>\n      <td>0.002537</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>62077</td>\n      <td>204</td>\n      <td>0.008184</td>\n      <td>0.001917</td>\n      <td>TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD</td>\n      <td>COMPANY</td>\n      <td>TW</td>\n      <td>TW</td>\n      <td>TWN</td>\n      <td>Taiwan Province Of China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>TAIWAN SEMICONDUCTOR MANUFACTURING CO LTD (TW)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1912470</td>\n      <td>130</td>\n      <td>0.005215</td>\n      <td>0.001222</td>\n      <td>MEDIATEK INC</td>\n      <td>COMPANY</td>\n      <td>TW</td>\n      <td>TW</td>\n      <td>TWN</td>\n      <td>Taiwan Province Of China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>MEDIATEK INC (TW)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2607939</td>\n      <td>101</td>\n      <td>0.004052</td>\n      <td>0.000949</td>\n      <td>SELF ELECT CO LTD</td>\n      <td>COMPANY</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>SELF ELECT CO LTD (CN)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>29061</th>\n      <td>151900860</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>LI, FAHONG</td>\n      <td>UNKNOWN</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>LI, FAHONG (CN)</td>\n    </tr>\n    <tr>\n      <th>29062</th>\n      <td>151899043</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>ZHANG, YI</td>\n      <td>UNKNOWN</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>CHN</td>\n      <td>China</td
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# China\n",
    "org_collabs_ch = org_collabs[org_collabs[\"Country\"].isin(ch_names)][\"han_id\"].value_counts().reset_index()\n",
    "org_collabs_ch[\"percent\"] = org_collabs_ch[\"count\"]/org_collabs[record_col].nunique()\n",
    "org_collabs_ch[\"weight\"] = org_collabs_ch[\"count\"]/org_collabs[record_col].size\n",
    "org_collabs_ch = org_collabs_ch.merge(pers_han, on='han_id')\n",
    "org_collabs_ch[\"org\"] = org_collabs_ch[\"han_name\"].str.strip() + \" (\"+org_collabs_ch[\"person_ctry_code\"]+\")\"\n",
    "org_collabs_ch"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1690cb0b8b0>"
     },
     "execution_count": 155,
     "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"
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16962050640>"
     },
     "execution_count": 198,
     "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": [
    "data = org_collabs_ch[0:25]\n",
    "g = sns.barplot(data, x=\"count\", y=\"han_name\", hue=\"psn_sector\", dodge=False)\n",
    "g.set_ylabel(\"Entity\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "g.legend(title=None)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x169069f7fd0>"
     },
     "execution_count": 156,
     "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"
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x169075f4970>"
     },
     "execution_count": 199,
     "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.barplot(data, x=\"count\", y=\"han_name\", hue=\"Country\", dodge=False)\n",
    "g.set_ylabel(\"Entity\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "g.legend(title=None, loc=4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "outputs": [
    {
     "data": {
      "text/plain": "          han_id  count   percent    weight                     han_name   \n0          13378   1851  0.074257  0.017393  TELEFON AB LM ERICSSON PUBL  \\\n1         228842    716  0.028724  0.006728                      BASF SE   \n2        2562294    693  0.027801  0.006512               NOKIA TECH LTD   \n3          68848    586  0.023509  0.005506               ALCATEL LUCENT   \n4        2456791    524  0.021021  0.004924            ROBERT BOSCH GMBH   \n...          ...    ...       ...       ...                          ...   \n25665  151189488      1  0.000040  0.000009              Hundt, Wolfgang   \n25666  151187794      1  0.000040  0.000009                Fischer, Peer   \n25667  151187133      1  0.000040  0.000009                Daryani, Neha   \n25668  151186834      1  0.000040  0.000009                 HEIL, Nadine   \n25669  184881241      1  0.000040  0.000009      ZAVORONKOVS, ALEKSANDRS   \n\n      psn_sector person_ctry_code ctry_code iso_alpha3  Country   \n0        COMPANY               SE        SE        SWE   Sweden  \\\n1        COMPANY               DE        DE        DEU  Germany   \n2        COMPANY               FI        FI        FIN  Finland   \n3        COMPANY               FR        FR        FRA   France   \n4        COMPANY               DE        DE        DEU  Germany   \n...          ...              ...       ...        ...      ...   \n25665    UNKNOWN               DE        DE        DEU  Germany   \n25666    UNKNOWN               DE        DE        DEU  Germany   \n25667    UNKNOWN               DE        DE        DEU  Germany   \n25668    UNKNOWN               DE        DE        DEU  Germany   \n25669    UNKNOWN               LV        LV        LVA   Latvia   \n\n      organisation_flag continent eu_member epo_member oecd_member   \n0                          Europe         Y          Y           Y  \\\n1                          Europe         Y          Y           Y   \n2                          Europe         Y          Y           Y   \n3                          Europe         Y          Y           Y   \n4                          Europe         Y          Y           Y   \n...                 ...       ...       ...        ...         ...   \n25665                      Europe         Y          Y           Y   \n25666                      Europe         Y          Y           Y   \n25667                      Europe         Y          Y           Y   \n25668                      Europe         Y          Y           Y   \n25669                      Europe         Y          Y           Y   \n\n      discontinued  \n0                   \n1                   \n2                   \n3                   \n4                   \n...            ...  \n25665               \n25666               \n25667               \n25668               \n25669               \n\n[25670 rows x 16 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>han_id</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n      <th>person_ctry_code</th>\n      <th>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>Country</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13378</td>\n      <td>1851</td>\n      <td>0.074257</td>\n      <td>0.017393</td>\n      <td>TELEFON AB LM ERICSSON PUBL</td>\n      <td>COMPANY</td>\n      <td>SE</td>\n      <td>SE</td>\n      <td>SWE</td>\n      <td>Sweden</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>228842</td>\n      <td>716</td>\n      <td>0.028724</td>\n      <td>0.006728</td>\n      <td>BASF SE</td>\n      <td>COMPANY</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2562294</td>\n      <td>693</td>\n      <td>0.027801</td>\n      <td>0.006512</td>\n      <td>NOKIA TECH LTD</td>\n      <td>COMPANY</td>\n      <td>FI</td>\n      <td>FI</td>\n      <td>FIN</td>\n      <td>Finland</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68848</td>\n      <td>586</td>\n      <td>0.023509</td>\n      <td>0.005506</td>\n      <td>ALCATEL LUCENT</td>\n      <td>COMPANY</td>\n      <td>FR</td>\n      <td>FR</td>\n      <td>FRA</td>\n      <td>France</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2456791</td>\n      <td>524</td>\n      <td>0.021021</td>\n      <td>0.004924</td>\n      <td>ROBERT BOSCH GMBH</td>\n      <td>COMPANY</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>25665</th>\n      <td>151189488</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Hundt, Wolfgang</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>25666</th>\n      <td>151187794</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Fischer, Peer</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>25667</th>\n      <td>151187133</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Daryani, Neha</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n  
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "          han_id  count   percent    weight                     han_name   \n0          13378   1851  0.074257  0.017393  TELEFON AB LM ERICSSON PUBL  \\\n1         228842    716  0.028724  0.006728                      BASF SE   \n2        2562294    693  0.027801  0.006512               NOKIA TECH LTD   \n3          68848    586  0.023509  0.005506               ALCATEL LUCENT   \n4        2456791    524  0.021021  0.004924            ROBERT BOSCH GMBH   \n...          ...    ...       ...       ...                          ...   \n25665  151189488      1  0.000040  0.000009              Hundt, Wolfgang   \n25666  151187794      1  0.000040  0.000009                Fischer, Peer   \n25667  151187133      1  0.000040  0.000009                Daryani, Neha   \n25668  151186834      1  0.000040  0.000009                 HEIL, Nadine   \n25669  184881241      1  0.000040  0.000009      ZAVORONKOVS, ALEKSANDRS   \n\n      psn_sector person_ctry_code ctry_code iso_alpha3  Country   \n0        COMPANY               SE        SE        SWE   Sweden  \\\n1        COMPANY               DE        DE        DEU  Germany   \n2        COMPANY               FI        FI        FIN  Finland   \n3        COMPANY               FR        FR        FRA   France   \n4        COMPANY               DE        DE        DEU  Germany   \n...          ...              ...       ...        ...      ...   \n25665    UNKNOWN               DE        DE        DEU  Germany   \n25666    UNKNOWN               DE        DE        DEU  Germany   \n25667    UNKNOWN               DE        DE        DEU  Germany   \n25668    UNKNOWN               DE        DE        DEU  Germany   \n25669    UNKNOWN               LV        LV        LVA   Latvia   \n\n      organisation_flag continent eu_member epo_member oecd_member   \n0                          Europe         Y          Y           Y  \\\n1                          Europe         Y          Y           Y   \n2                          Europe         Y          Y           Y   \n3                          Europe         Y          Y           Y   \n4                          Europe         Y          Y           Y   \n...                 ...       ...       ...        ...         ...   \n25665                      Europe         Y          Y           Y   \n25666                      Europe         Y          Y           Y   \n25667                      Europe         Y          Y           Y   \n25668                      Europe         Y          Y           Y   \n25669                      Europe         Y          Y           Y   \n\n      discontinued  \n0                   \n1                   \n2                   \n3                   \n4                   \n...            ...  \n25665               \n25666               \n25667               \n25668               \n25669               \n\n[25670 rows x 16 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>han_id</th>\n      <th>count</th>\n      <th>percent</th>\n      <th>weight</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n      <th>person_ctry_code</th>\n      <th>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>Country</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>13378</td>\n      <td>1851</td>\n      <td>0.074257</td>\n      <td>0.017393</td>\n      <td>TELEFON AB LM ERICSSON PUBL</td>\n      <td>COMPANY</td>\n      <td>SE</td>\n      <td>SE</td>\n      <td>SWE</td>\n      <td>Sweden</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>228842</td>\n      <td>716</td>\n      <td>0.028724</td>\n      <td>0.006728</td>\n      <td>BASF SE</td>\n      <td>COMPANY</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2562294</td>\n      <td>693</td>\n      <td>0.027801</td>\n      <td>0.006512</td>\n      <td>NOKIA TECH LTD</td>\n      <td>COMPANY</td>\n      <td>FI</td>\n      <td>FI</td>\n      <td>FIN</td>\n      <td>Finland</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68848</td>\n      <td>586</td>\n      <td>0.023509</td>\n      <td>0.005506</td>\n      <td>ALCATEL LUCENT</td>\n      <td>COMPANY</td>\n      <td>FR</td>\n      <td>FR</td>\n      <td>FRA</td>\n      <td>France</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2456791</td>\n      <td>524</td>\n      <td>0.021021</td>\n      <td>0.004924</td>\n      <td>ROBERT BOSCH GMBH</td>\n      <td>COMPANY</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>25665</th>\n      <td>151189488</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Hundt, Wolfgang</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>25666</th>\n      <td>151187794</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Fischer, Peer</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>DEU</td>\n      <td>Germany</td>\n      <td></td>\n      <td>Europe</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td>Y</td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>25667</th>\n      <td>151187133</td>\n      <td>1</td>\n      <td>0.000040</td>\n      <td>0.000009</td>\n      <td>Daryani, Neha</td>\n      <td>UNKNOWN</td>\n      <td>DE</td>\n      <td>DE</td>\n  
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eu\n",
    "org_collabs_eu = org_collabs[~org_collabs[\"Country\"].isin(ch_names)][\"han_id\"].value_counts().reset_index()\n",
    "org_collabs_eu[\"percent\"] = org_collabs_eu[\"count\"]/org_collabs[record_col].nunique()\n",
    "org_collabs_eu[\"weight\"] = org_collabs_eu[\"count\"]/org_collabs[record_col].size\n",
    "org_collabs_eu = org_collabs_eu.merge(pers_han, on='han_id')\n",
    "org_collabs_eu"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16905fef370>"
     },
     "execution_count": 158,
     "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"
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16912396370>"
     },
     "execution_count": 201,
     "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": [
    "data = org_collabs_eu[0:25]\n",
    "g = sns.barplot(data, x=\"count\", y=\"han_name\", hue=\"psn_sector\", dodge=False)\n",
    "g.set_ylabel(\"Entity\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "g.legend(title=None)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x16907562700>"
     },
     "execution_count": 159,
     "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"
    },
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x169155ecc40>"
     },
     "execution_count": 202,
     "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.barplot(data, x=\"count\", y=\"han_name\", hue=\"Country\", dodge=False)\n",
    "g.set_ylabel(\"Entity\")\n",
    "g.set_xlabel(\"Number of co-patents\")\n",
    "for i in g.containers:\n",
    "    g.bar_label(i,)\n",
    "g.legend(title=None, loc=4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Patent classes?"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}