ZSI_Reconnect_China/PATSTAT/patstat_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        define('plotly', function(require, exports, module) {\n            /**\n* plotly.js v2.20.0\n* Copyright 2012-2023, Plotly, Inc.\n* All rights reserved.\n* Licensed under the MIT license\n*/\n/*! For license information please see plotly.min.js.LICENSE.txt */\n!function(t,e){\"object\"==typeof exports&&\"object\"==typeof module?module.exports=e():\"function\"==typeof define&&define.amd?define([],e):\"object\"==typeof exports?exports.Plotly=e():t.Plotly=e()}(self,(function(){return function(){var t={98847:function(t,e,r){\"use strict\";var n=r(71828),i={\"X,X div\":'direction:ltr;font-family:\"Open Sans\",verdana,arial,sans-serif;margin:0;padding:0;',\"X input,X button\":'font-family:\"Open Sans\",verdana,arial,sans-serif;',\"X input:focus,X button:focus\":\"outline:none;\",\"X a\":\"text-decoration:none;\",\"X a:hover\":\"text-decoration:none;\",\"X .crisp\":\"shape-rendering:crispEdges;\",\"X .user-select-none\":\"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;\",\"X svg\":\"overflow:hidden;\",\"X svg a\":\"fill:#447adb;\",\"X svg a:hover\":\"fill:#3c6dc5;\",\"X .main-svg\":\"position:absolute;top:0;left:0;pointer-events:none;\",\"X .main-svg .draglayer\":\"pointer-events:all;\",\"X .cursor-default\":\"cursor:default;\",\"X .cursor-pointer\":\"cursor:pointer;\",\"X .cursor-crosshair\":\"cursor:crosshair;\",\"X .cursor-move\":\"cursor:move;\",\"X .cursor-col-resize\":\"cursor:col-resize;\",\"X .cursor-row-resize\":\"cursor:row-resize;\",\"X .cursor-ns-resize\":\"cursor:ns-resize;\",\"X .cursor-ew-resize\":\"cursor:ew-resize;\",\"X .cursor-sw-resize\":\"cursor:sw-resize;\",\"X .cursor-s-resize\":\"cursor:s-resize;\",\"X .cursor-se-resize\":\"cursor:se-resize;\",\"X .cursor-w-resize\":\"cursor:w-resize;\",\"X .cursor-e-resize\":\"cursor:e-resize;\",\"X .cursor-nw-resize\":\"cursor:nw-resize;\",\"X .cursor-n-resize\":\"cursor:n-resize;\",\"X .cursor-ne-resize\":\"cursor:ne-resize;\",\"X .cursor-grab\":\"cursor:-webkit-grab;cursor:grab;\",\"X .modebar\":\"position:absolute;top:2px;right:2px;\",\"X .ease-bg\":\"-webkit-transition:background-color .3s ease 0s;-moz-transition:background-color .3s ease 0s;-ms-transition:background-color .3s ease 0s;-o-transition:background-color .3s ease 0s;transition:background-color .3s ease 0s;\",\"X .modebar--hover>:not(.watermark)\":\"opacity:0;-webkit-transition:opacity .3s ease 0s;-moz-transition:opacity .3s ease 0s;-ms-transition:opacity .3s ease 0s;-o-transition:opacity .3s ease 0s;transition:opacity .3s ease 0s;\",\"X:hover .modebar--hover .modebar-group\":\"opacity:1;\",\"X .modebar-group\":\"float:left;display:inline-block;box-sizing:border-box;padding-left:8px;position:relative;vertical-align:middle;white-space:nowrap;\",\"X .modebar-btn\":\"position:relative;font-size:16px;padding:3px 4px;height:22px;cursor:pointer;line-height:normal;box-sizing:border-box;\",\"X .modebar-btn svg\":\"position:relative;top:2px;\",\"X .modebar.vertical\":\"display:flex;flex-direction:column;flex-wrap:wrap;align-content:flex-end;max-height:100%;\",\"X .modebar.vertical svg\":\"top:-1px;\",\"X .modebar.vertical .modebar-group\":\"display:block;float:none;padding-left:0px;padding-bottom:8px;\",\"X .modebar.vertical .modebar-group .modebar-btn\":\"display:block;text-align:center;\",\"X [data-title]:before,X [data-title]:after\":\"position:absolute;-webkit-transform:translate3d(0, 0, 0);-moz-transform:translate3d(0, 0, 0);-ms-transform:translate3d(0, 0, 0);-o-transform:translate3d(0, 0, 0);transform:translate3d(0, 0, 0);display:none;opacity:0;z-index:1001;pointer-events:none;top:110%;right:50%;\",\"X [data-title]:hover:before,X [data-title]:hover:after\":\"display:block;opacity:1;\",\"X [data-title]:before
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\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",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.offline as pyo\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "pyo.init_notebook_mode()\n",
    "\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"plotly_mimetype+notebook\"\n",
    "\n",
    "import country_converter as coco\n",
    "cc = coco.CountryConverter()\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
    "# tls_801.head()\n",
    "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'GB'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ctry_code iso_alpha3 st3_name organisation_flag continent eu_member   \n",
      "137        MA        MAR  Morocco                      Africa            \\\n",
      "\n",
      "    epo_member oecd_member discontinued  \n",
      "137                                      \n"
     ]
    }
   ],
   "source": [
    "print(tls_801[tls_801[\"ctry_code\"]==\"MA\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "outdir=\"raw_files_csv\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "           appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n8641708   531710630         CN  201811380088          A        2018-11-20  \\\n5246513   489770910         CN  201711004155          A        2017-10-24   \n6383260   503205376         CN  201810447617          A        2018-05-11   \n1437288   417129813         GR   20120100519          A        2012-10-12   \n4304665   479561975         CN  201510807721          A        2015-11-19   \n8099995   524172492         CN  201911055629          A        2019-10-31   \n2496404   443580404         CN  201510312381          A        2015-06-08   \n2862771   448112136         CN  201510736913          A        2015-11-03   \n8746800   533540106         CN  202010109483          A        2020-02-22   \n12407625  580940603         CN  202210840515          A        2022-07-18   \n\n          appln_filing_year appln_nr_original ipr_type receiving_office   \n8641708                2018      201811380088       PI              NaN  \\\n5246513                2017      201711004155       PI              NaN   \n6383260                2018      201810447617       PI              NaN   \n1437288                2012       20120100519       PI              NaN   \n4304665                2015      201510807721       PI              NaN   \n8099995                2019      201911055629       PI              NaN   \n2496404                2015     2015103123811       PI              NaN   \n2862771                2015     2015107369134       PI              NaN   \n8746800                2020      202010109483       PI              NaN   \n12407625               2022      202210840515       PI              NaN   \n\n          internat_appln_id  ... earliest_pat_publn_id granted   \n8641708                   0  ...             531710631       N  \\\n5246513                   0  ...             489770911       N   \n6383260                   0  ...             503205377       N   \n1437288                   0  ...             417129814       Y   \n4304665                   0  ...             479561976       N   \n8099995                   0  ...             524172493       N   \n2496404                   0  ...             443580405       N   \n2862771                   0  ...             448112137       N   \n8746800                   0  ...             533540107       Y   \n12407625                  0  ...             580940604       N   \n\n         docdb_family_id inpadoc_family_id  docdb_family_size   \n8641708         70744210         531710630                  1  \\\n5246513         61141626         489770910                  1   \n6383260         64213024         503205376                  1   \n1437288         50483672         417129813                  1   \n4304665         58885008         479561975                  1   \n8099995         68978631         524172492                  1   \n2496404         53812114         443580404                  1   \n2862771         54991770         448112136                  1   \n8746800         71149074         533540106                  1   \n12407625        83518531         580940603                  1   \n\n          nb_citing_docdb_fam nb_applicants  nb_inventors  is_prior is_pct  \n8641708                     0             1             1         1      0  \n5246513                     0             1             5         1      0  \n6383260                     0             1             3         1      0  \n1437288                     0             1             1         1      0  \n4304665                     0             1             1         1      0  \n8099995                     0             1             3         1      0  \n2496404                     3             2             3         1      0  \n2862771                     2             1             5         1      0  \n8746800                     0             1             5         1      0  \n12407625                    0             1             6         1      0  \n\n[10 rows x 28 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_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      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8641708</th>\n      <td>531710630</td>\n      <td>CN</td>\n      <td>201811380088</td>\n      <td>A</td>\n      <td>2018-11-20</td>\n      <td>2018</td>\n      <td>201811380088</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>531710631</td>\n      <td>N</td>\n      <td>70744210</td>\n      <td>531710630</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5246513</th>\n      <td>489770910</td>\n      <td>CN</td>\n      <td>201711004155</td>\n      <td>A</td>\n      <td>2017-10-24</td>\n      <td>2017</td>\n      <td>201711004155</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>489770911</td>\n      <td>N</td>\n      <td>61141626</td>\n      <td>489770910</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6383260</th>\n      <td>503205376</td>\n      <td>CN</td>\n      <td>201810447617</td>\n      <td>A</td>\n      <td>2018-05-11</td>\n      <td>2018</td>\n      <td>201810447617</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>503205377</td>\n      <td>N</td>\n      <td>64213024</td>\n      <td>503205376</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1437288</th>\n      <td>417129813</td>\n      <td>GR</td>\n      <td>20120100519</td>\n      <td>A</td>\n      <td>2012-10-12</td>\n      <td>2012</td>\n      <td>20120100519</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>417129814</td>\n      <td>Y</td>\n      <td>50483672</td>\n      <td>417129813</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4304665</th>\n      <td>479561975</td>\n      <td>CN</td>\n      <td>201510807721</td>\n      <td>A</td>\n      <td>2015-11-19</td>\n      <td>2015</td>\n      <td>201510807721</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>479561976</td>\n      <td>N</td>\n      <td>58885008</td>\n      <td>479561975</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8099995</th>\n      <td>524172492</td>\n      <td>CN</td>\n      <td>201911055629</td>\n      <td>A</td>\n      <td>2019-10-31</td>\n      <td>2019</td>\n      <td>201911055629</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>524172493</td>\n      <td>N</td>\n      <td>68978631</td>\n      <td>524172492</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2496404</th>\n      <td>443580404</td>\n      <td>CN</td>\n      <td>201510312
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln = pd.read_csv(f\"{outdir}/01_patent_subset.csv\", low_memory=False)\n",
    "appln.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "(54368949, 13184309)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers = pd.read_csv(f\"{outdir}/02_persons_2011_2022.csv\",low_memory=False)\n",
    "len(appln_pers[\"appln_id\"]), len(appln[\"appln_id\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(54368949, 12646904)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers[\"appln_id\"].isin(appln[\"appln_id\"]).sum(), appln[\"appln_id\"].isin(appln_pers[\"appln_id\"]).sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "12415"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ch_ids = appln_pers[appln_pers[\"person_ctry_code\"] == \"CN\"][\"appln_id\"].unique()\n",
    "appln_eu_ids = appln_pers[appln_pers[\"person_ctry_code\"].isin(scope_countries)][\"appln_id\"].unique()\n",
    "\n",
    "appln_scope_ids = np.intersect1d(appln_ch_ids, appln_eu_ids, assume_unique=False)\n",
    "appln_scope = appln_pers[((appln_pers[\"appln_id\"].isin(appln_scope_ids)) &\n",
    "                          (appln_pers[\"person_ctry_code\"].isin(list(scope_countries) + [\"CN\"])))]\n",
    "\n",
    "appln_scope[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(6584667, 18789)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_haninfo = pd.read_csv(f\"first_round/tls_206_scope_v2.csv\",\n",
    "                                 usecols=[\"person_id\", \"han_id\", \"han_name\", \"psn_sector\"])\n",
    "\n",
    "appln_pers_haninfo[\"han_id\"].nunique(), appln_scope[\"han_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pers_sector_primary = appln_pers_haninfo.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
    "    lambda x: \"UNKNOWN\" if len(pd.Series.mode(x))==0 else pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n       'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n       'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n       'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type',\n       'psn_sector_primary_x', 'psn_sector_primary_y'],\n      dtype='object')"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12415\n"
     ]
    }
   ],
   "source": [
    "appln_pers_f = appln_scope.merge(pers_sector_primary, on='han_id', how=\"left\").rename(columns={\"psn_sector_primary_y\":\"psn_sector_primary\"})\n",
    "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_primary\"].fillna(\"UNKNOWN\")\n",
    "print(appln_pers_f[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [],
   "source": [
    "appln_pers_f[\"han_name\"] = appln_pers_f[\"han_name\"].str.replace(\"Zhejiang Geely Holding Group Co., Ltd.\",\n",
    "                                                                            \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "g = appln_pers.groupby('appln_id')[\"person_ctry_code\"]\n",
    "missing_ctry_data = g.count().rsub(g.size(), axis=0).ge(1).astype(int).reset_index()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n",
      "21408  556911606         MA   42012433            2             0  \\\n",
      "\n",
      "            person_name person_ctry_code     han_id          han_name   \n",
      "21408  TURTINEN, SAMULI               FI  142012433  TURTINEN, SAMULI  \\\n",
      "\n",
      "      psn_sector_x psn_sector_y person_ctry_code_fullname appln_auth_fullname   \n",
      "21408   INDIVIDUAL   INDIVIDUAL                   Finland             Morocco  \\\n",
      "\n",
      "      person_ctry_ISO3 person_ctry_type auth_ISO3 auth_ctry_type   \n",
      "21408              FIN               EU       MAR             EU  \\\n",
      "\n",
      "      psn_sector_primary_x psn_sector_primary  \n",
      "21408           INDIVIDUAL         INDIVIDUAL  \n"
     ]
    }
   ],
   "source": [
    "print(appln_pers_f.sample())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "outputs": [
    {
     "data": {
      "text/plain": "(10810, 10084)"
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appl_eu_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n",
    "                              (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n",
    "\n",
    "appl_ch_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n",
    "                              (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n",
    "\n",
    "len(appl_eu_applt),len(appl_ch_applt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [
    {
     "data": {
      "text/plain": "(7638, 8788)"
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appl_eu_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n",
    "                              (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n",
    "\n",
    "appl_ch_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n",
    "                              (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n",
    "\n",
    "len(appl_eu_invt),len(appl_ch_invt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [
    {
     "data": {
      "text/plain": "12197"
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0))][\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# missing_ctry_data[missing_ctry_data[\"person_ctry_code\"]>0]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "appln_ana = appln.merge(missing_ctry_data,on=\"appln_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "(12646904, 13184309)"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ana[\"appln_id\"].nunique(),appln[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='appln_auth'>"
     },
     "execution_count": 45,
     "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": [
    "ctry_ana = appln_ana.groupby(\"appln_auth\")[\"person_ctry_code\"]\n",
    "(ctry_ana.sum()/ctry_ana.size()).sort_values().plot(kind=\"bar\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='appln_filing_year'>"
     },
     "execution_count": 51,
     "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": [
    "ch_ana = appln_ana.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"person_ctry_code\"]\n",
    "((1-(ch_ana.sum()/ch_ana.size()))*100).plot(kind=\"line\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# That one Chinese auth co-patent"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "      appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n124  419052362         CN    5361204            0             1  \\\n137  419052362         CN   51988464            1             0   \n\n                           person_name person_ctry_code     han_id   \n124  Schneider Electric Industries SAS               FR     808003  \\\n137                       JUZHONG SONG               CN  151988464   \n\n                    han_name psn_sector_x psn_sector_y  \n124  SCHNEIDER ELECT IND SAS      COMPANY      COMPANY  \n137             JUZHONG SONG      UNKNOWN      UNKNOWN  ",
      "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>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_x</th>\n      <th>psn_sector_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>124</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>5361204</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Schneider Electric Industries SAS</td>\n      <td>FR</td>\n      <td>808003</td>\n      <td>SCHNEIDER ELECT IND SAS</td>\n      <td>COMPANY</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>51988464</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JUZHONG SONG</td>\n      <td>CN</td>\n      <td>151988464</td>\n      <td>JUZHONG SONG</td>\n      <td>UNKNOWN</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.query(\"appln_auth=='CN'\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n1568550  419052362         CN  201210551425          A        2012-12-18  \\\n\n         appln_filing_year appln_nr_original ipr_type receiving_office   \n1568550               2012     2012105514252       PI              NaN  \\\n\n         internat_appln_id  ... earliest_pat_publn_id granted docdb_family_id   \n1568550                  0  ...             419052363       Y        49886800  \\\n\n        inpadoc_family_id  docdb_family_size  nb_citing_docdb_fam   \n1568550         413446088                  4                   15  \\\n\n        nb_applicants  nb_inventors  is_prior is_pct  \n1568550             1             1         1      0  \n\n[1 rows x 28 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_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      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1568550</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>201210551425</td>\n      <td>A</td>\n      <td>2012-12-18</td>\n      <td>2012</td>\n      <td>2012105514252</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>419052363</td>\n      <td>Y</td>\n      <td>49886800</td>\n      <td>413446088</td>\n      <td>4</td>\n      <td>15</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 28 columns</p>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.query('appln_id==419052362')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12415\n"
     ]
    },
    {
     "data": {
      "text/plain": "           appln_id appln_auth    appln_nr appln_kind appln_filing_date   \n863506    407017979         WO  2011083302          W        2011-12-01  \\\n7100916   510736006         WO  2019058105          W        2019-03-29   \n1603837   419656375         WO  2012087307          W        2012-12-24   \n7183441   511620156         WO  2017107161          W        2017-10-20   \n5588243   494153147         EP    18171845          A        2018-05-11   \n...             ...        ...         ...        ...               ...   \n2295082   440740099         WO  2013088240          W        2013-11-29   \n12569735  582929150         WO  2021092424          W        2021-05-08   \n8050187   523796882         WO  2018091667          W        2018-06-15   \n5582748   494089044         WO  2016104438          W        2016-11-03   \n716562    405080010         WO  2013056669          W        2013-03-28   \n\n          appln_filing_year appln_nr_original ipr_type receiving_office   \n863506                 2011     CN2011/083302       PI               CN  \\\n7100916                2019     EP2019/058105       PI               EP   \n1603837                2012     CN2012/087307       PI               CN   \n7183441                2017     CN2017/107161       PI               CN   \n5588243                2018          18171845       PI              NaN   \n...                     ...               ...      ...              ...   \n2295082                2013     CN2013/088240       PI               CN   \n12569735               2021     CN2021/092424       PI               CN   \n8050187                2018     CN2018/091667       PI               CN   \n5582748                2016     CN2016/104438       PI               CN   \n716562                 2013     EP2013/056669       PI               EP   \n\n          internat_appln_id  ... earliest_pat_publn_id granted   \n863506                    0  ...             407072959       N  \\\n7100916                   0  ...             538911303       N   \n1603837                   0  ...             419656376       N   \n7183441                   0  ...             511620157       N   \n5588243                   0  ...             520464429       N   \n...                     ...  ...                   ...     ...   \n2295082                   0  ...             440740100       N   \n12569735                  0  ...             582929151       N   \n8050187                   0  ...             523796883       N   \n5582748                   0  ...             494089045       N   \n716562                    0  ...             422430381       N   \n\n         docdb_family_id inpadoc_family_id  docdb_family_size   \n863506          48534637         407017979                  2  \\\n7100916         66001221         510736006                  4   \n1603837         51019632         419626782                  4   \n7183441         66173145         511620156                  4   \n5588243         62152456         494153147                  3   \n...                  ...               ...                ...   \n2295082         53198253         440740099                  4   \n12569735        84027865         582929150                  1   \n8050187         68841789         523796882                  4   \n5582748         62076012         494089044                  3   \n716562          48045501         405080010                  5   \n\n          nb_citing_docdb_fam nb_applicants  nb_inventors  is_prior is_pct  \n863506                      1             6             5         1      1  \n7100916                     0             2             2         1      1  \n1603837                    12             2             5         1      1  \n7183441                     1             2             3         1      1  \n5588243                     3             1             2         1      0  \n...                       ...           ...           ...       ...    ...  \n2295082                     2             5             4         1      1  \n12569735          
      "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_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      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>863506</th>\n      <td>407017979</td>\n      <td>WO</td>\n      <td>2011083302</td>\n      <td>W</td>\n      <td>2011-12-01</td>\n      <td>2011</td>\n      <td>CN2011/083302</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>407072959</td>\n      <td>N</td>\n      <td>48534637</td>\n      <td>407017979</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7100916</th>\n      <td>510736006</td>\n      <td>WO</td>\n      <td>2019058105</td>\n      <td>W</td>\n      <td>2019-03-29</td>\n      <td>2019</td>\n      <td>EP2019/058105</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>538911303</td>\n      <td>N</td>\n      <td>66001221</td>\n      <td>510736006</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1603837</th>\n      <td>419656375</td>\n      <td>WO</td>\n      <td>2012087307</td>\n      <td>W</td>\n      <td>2012-12-24</td>\n      <td>2012</td>\n      <td>CN2012/087307</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>419656376</td>\n      <td>N</td>\n      <td>51019632</td>\n      <td>419626782</td>\n      <td>4</td>\n      <td>12</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7183441</th>\n      <td>511620156</td>\n      <td>WO</td>\n      <td>2017107161</td>\n      <td>W</td>\n      <td>2017-10-20</td>\n      <td>2017</td>\n      <td>CN2017/107161</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>511620157</td>\n      <td>N</td>\n      <td>66173145</td>\n      <td>511620156</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5588243</th>\n      <td>494153147</td>\n      <td>EP</td>\n      <td>18171845</td>\n      <td>A</td>\n      <td>2018-05-11</td>\n      <td>2018</td>\n      <td>18171845</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>520464429</td>\n      <td>N</td>\n      <td>62152456</td>\n      <td>494153147</td>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2295082</th>\n      <td>440740099</td>\n      <td>WO</td>\n      <td>2013088240</td>\n      <td>W</td>\n      <td>2013-11-29<
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f = appln[appln[\"appln_id\"].isin(appln_scope['appln_id'])].copy()\n",
    "print(appln_f[\"appln_id\"].nunique())\n",
    "\n",
    "appln_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:4: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "appln_f[\"ch_applt\"] = appln_f[\"appln_id\"].isin(appl_ch_applt)\n",
    "appln_f[\"eu_applt\"] = appln_f[\"appln_id\"].isin(appl_eu_applt)\n",
    "appln_f[\"ch_invt\"] = appln_f[\"appln_id\"].isin(appl_ch_invt)\n",
    "appln_f[\"eu_invt\"] = appln_f[\"appln_id\"].isin(appl_eu_invt)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eu_applt  False  True \n",
      "ch_applt              \n",
      "False       218   2113\n",
      "True       1387   8697\n",
      "eu_invt  False  True \n",
      "ch_invt              \n",
      "False       74   3553\n",
      "True      4703   4085\n"
     ]
    }
   ],
   "source": [
    "applt_co_occur = pd.crosstab(appln_f[\"ch_applt\"], appln_f[\"eu_applt\"])\n",
    "print(applt_co_occur)\n",
    "invt_co_occur = pd.crosstab(appln_f[\"ch_invt\"], appln_f[\"eu_invt\"])\n",
    "print(invt_co_occur)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:11: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:12: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def bool_calc(eu,ch):\n",
    "    if eu==True and ch==False:\n",
    "        return \"European \"\n",
    "    if ch==True and eu==False:\n",
    "        return \"Chinese \"\n",
    "    if ch==False and eu==False:\n",
    "        return \"Third-party \"\n",
    "    if ch==True and eu==True:\n",
    "        return \"Co-\"\n",
    "\n",
    "appln_f[\"ownership\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_applt\"],x[\"ch_applt\"])+\"ownership\" ,axis=1)\n",
    "appln_f[\"invention\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_invt\"],x[\"ch_invt\"])+\"invention\" ,axis=1)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "invention              Chinese invention  Co-invention  European invention   \n",
      "ownership                                                                    \n",
      "Chinese ownership                      0           465                 922  \\\n",
      "Co-ownership                        3994          1998                2631   \n",
      "European ownership                   709          1404                   0   \n",
      "Third-party ownership                  0           218                   0   \n",
      "\n",
      "invention              Third-party invention  \n",
      "ownership                                     \n",
      "Chinese ownership                          0  \n",
      "Co-ownership                              74  \n",
      "European ownership                         0  \n",
      "Third-party ownership                      0  \n"
     ]
    }
   ],
   "source": [
    "ship_co_occur = pd.crosstab(appln_f[\"ownership\"], appln_f[\"invention\"])\n",
    "print(ship_co_occur)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [],
   "source": [
    "record_col=\"appln_id\"\n",
    "fromdir =outdir"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "outputs": [],
   "source": [
    "ship_co_occur.to_excel(f\"{fromdir}/plots/_ships.xlsx\",index=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
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         "orientation": "v",
         "showlegend": false,
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Co-patents per year\n",
    "data = (appln_f.groupby(['appln_filing_year'])[record_col].nunique(dropna=False)\n",
    "        .reset_index()\n",
    "        .rename(columns={0: record_col}))\n",
    "data[record_col + \"_relative_growth\"] = data[data[record_col] > 0].sort_values(by=[\"appln_filing_year\"], ascending=True)[record_col][0]\n",
    "data[record_col + \"_relative_growth\"] = (data[record_col] - data[record_col + \"_relative_growth\"]) / data[\n",
    "    record_col + \"_relative_growth\"]\n",
    "\n",
    "data = data.sort_values(by=[\"appln_filing_year\"], ascending=[True])\n",
    "data[record_col + \"_cumsum\"] = (data[record_col].cumsum())\n",
    "\n",
    "year_output = px.line(data, x=\"appln_filing_year\", y=record_col, markers=True, text=record_col)\n",
    "year_output.update_traces(textposition='top center', textfont_size=12)\n",
    "year_output.update_traces(hovertemplate='Year:%{x:d}<br>Number of co-publications:%{y:d}')\n",
    "\n",
    "year_rel_output = px.line(data, x=\"appln_filing_year\", y=record_col + \"_relative_growth\", markers=True,\n",
    "                          text=record_col + \"_relative_growth\")\n",
    "year_rel_output.update_traces(textposition='top center', textfont_size=12, texttemplate='%{y:.0%}')\n",
    "year_rel_output.update_traces(hovertemplate='Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}')\n",
    "\n",
    "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\", \"Relative growth of co-patents\"],\n",
    "                         specs=[\n",
    "                             [{\"type\": \"xy\"}],\n",
    "                             [{\"type\": \"xy\"}]\n",
    "                         ])\n",
    "\n",
    "for trace in list(year_output.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "                       row=1, col=1\n",
    "                       )\n",
    "\n",
    "for trace in list(year_rel_output.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "                       row=2, col=1\n",
    "                       )\n",
    "\n",
    "figsuper.update_layout(yaxis={'categoryorder': 'total ascending'}, barmode='relative')\n",
    "figsuper.update_yaxes(\n",
    "    showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "figsuper.update_xaxes(\n",
    "    showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "figsuper.update_layout({'template': \"plotly\", \"font_family\": \"Montserrat\"})\n",
    "figsuper['layout']['yaxis2'].update(zerolinecolor='grey', tickformat=\".0%\")\n",
    "# for i in[\"xaxis\",\"xaxis2\"]:\n",
    "#     figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n",
    "#\n",
    "figsuper['layout'][\"yaxis\"][\"range\"] = [0, 2500]\n",
    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1, 3.5]\n",
    "#\n",
    "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
    "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
    "\n",
    "figsuper.show(config=dict(displayModeBar=False, responsive=True), height=800, width=1600, scale=4)\n",
    "\n",
    "figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\", height=900, width=900, scale=4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
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     "metadata": {},
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    }
   ],
   "source": [
    "data = (appln_f.groupby(['appln_filing_year',\"appln_auth\"])[record_col]\n",
    "        .nunique(dropna=False).unstack()\n",
    "        .fillna(0)\n",
    "        .stack()\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "\n",
    "# data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n",
    "\n",
    "data = data.merge(data[data[record_col]>0].sort_values(by=[\"appln_filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n",
    "                  on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "data = data.sort_values(by =[\"appln_auth\",\"appln_filing_year\"], ascending=[True,True])\n",
    "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
    "\n",
    "data = data.merge(tls_801,left_on='appln_auth', right_on =\"ctry_code\")\n",
    "data[\"Country\"] = data[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")\n",
    "\n",
    "\n",
    "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n",
    "              y=record_col,\n",
    "              x='appln_filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
    "              markers=True,\n",
    "              labels={\n",
    "                     record_col: 'Number of co-patents',\n",
    "                 },\n",
    "                title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n",
    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 22\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)"
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   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
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       "config": {
        "plotlyServerURL": "https://plotly.com"
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      },
      "text/html": "<div>                            <div id=\"6b738783-0d4f-420e-a03c-92b6dc08ab4f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6b738783-0d4f-420e-a03c-92b6dc08ab4f\")) {                    Plotly.newPlot(                        \"6b738783-0d4f-420e-a03c-92b6dc08ab4f\",                        [{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x21\",\"y\":[-1.0,0.0,-1.0,0.0,0.0,0.0,1.0,-1.0,-1.0,0.0,-1.0,-1.0],\"yaxis\":\"y21\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x22\",\"y\":[-1.0,0.0,-1.0,-1.0,0.0,-1.0,-1.0,-1.0,1.0,0.0,0.0,-1.0],\"yaxis\":\"y22\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x23\",\"y\":[-1.0,0.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0],\"yaxis\":\"y23\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\",\"Czechia\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x24\",\"y\":[-1.0,-1.0,0.0,-1.0,0.0,-1.0,-1.0,0.0,1.0,0.0,-1.0,-1.0],\"yaxis\":\"y24\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x25\",\"y\":[-1.0,-1.0,0.0,-0.5,-0.5,-0.5,-1.0,-1.0,0.5,-0.5,-1.0,-1.0],\"yaxis\":\"y25\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\",\"EPO\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x16
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n",
    "              y=record_col+\"_relative_growth\",\n",
    "              x='appln_filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
    "              markers=True,\n",
    "              labels={\n",
    "                    record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n",
    "                 },\n",
    "                title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n",
    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 22\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [],
   "source": [
    "appln_pers_f = appln_scope\n",
    "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_x\"]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n       'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n       'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n       'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type'],\n      dtype='object')"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.columns"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ctry_code iso_alpha3                          st3_name organisation_flag   \n",
      "0          AD        AND                           Andorra                    \\\n",
      "1          AE        ARE              United Arab Emirates                     \n",
      "2          AF        AFG                       Afghanistan                     \n",
      "3          AG        ATG               Antigua and Barbuda                     \n",
      "4          AI        AIA                          Anguilla                     \n",
      "..        ...        ...                               ...               ...   \n",
      "235        YE        YEM                             Yemen                     \n",
      "236        YU        YUG  Yugoslavia/Serbia and Montenegro                     \n",
      "237        ZA        ZAF                      South Africa                     \n",
      "238        ZM        ZMB                            Zambia                     \n",
      "239        ZW        ZWE                          Zimbabwe                     \n",
      "\n",
      "         continent eu_member epo_member oecd_member discontinued  \n",
      "0           Europe                                                \n",
      "1             Asia                                                \n",
      "2             Asia                                                \n",
      "3    North America                                                \n",
      "4    North America                                                \n",
      "..             ...       ...        ...         ...          ...  \n",
      "235           Asia                                                \n",
      "236         Europe                                             Y  \n",
      "237         Africa                                                \n",
      "238         Africa                                                \n",
      "239         Africa                                                \n",
      "\n",
      "[240 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "print(tls_801)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [],
   "source": [
    "iso2_to_namedict = dict(zip(tls_801[\"ctry_code\"],tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")))\n",
    "name_to_iso3 = dict(zip(tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\"),tls_801[\"iso_alpha3\"]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [
    {
     "data": {
      "text/plain": "{'Andorra': 'AND',\n 'United Arab Emirates': 'ARE',\n 'Afghanistan': 'AFG',\n 'Antigua and Barbuda': 'ATG',\n 'Anguilla': 'AIA',\n 'Albania': 'ALB',\n 'Armenia': 'ARM',\n 'Netherlands Antilles ': 'ANT',\n 'Angola': 'AGO',\n 'ARIPO': '   ',\n 'Argentina': 'ARG',\n 'Austria': 'AUT',\n 'Australia': 'AUS',\n 'Aruba': 'ABW',\n 'Azerbaijan': 'AZE',\n 'Bosnia and Herzegovina': 'BIH',\n 'Barbados': 'BRB',\n 'Bangladesh': 'BGD',\n 'Belgium': 'BEL',\n 'Burkina Faso': 'BFA',\n 'Bulgaria': 'BGR',\n 'Bahrain': 'BHR',\n 'Burundi': 'BDI',\n 'Benin': 'BEN',\n 'Bermuda': 'BMU',\n 'Brunei Darussalam': 'BRN',\n 'Plurinational State of': 'BOL',\n 'Bonaire, Sint Eustatius and Saba': 'BES',\n 'Brazil': 'BRA',\n 'Bahamas': 'BHS',\n 'Bhutan': 'BTN',\n 'Burma': 'BUR',\n 'Bouvet Island': 'BVT',\n 'Botswana': 'BWA',\n 'BOIP': '   ',\n 'Belarus': 'BLR',\n 'Belize': 'BLZ',\n 'Canada': 'CAN',\n 'Democratic Republic of the Congo': 'COD',\n 'Central African Republic': 'CAF',\n 'Congo': 'COG',\n 'Switzerland': 'CHE',\n \"Côte d'Ivoire\": 'CIV',\n 'Cook Islands': 'COK',\n 'Chile': 'CHL',\n 'Cameroon': 'CMR',\n 'China': 'CHN',\n 'Colombia': 'COL',\n 'Costa Rica': 'CRI',\n 'Czechoslovakia': 'CSK',\n 'Cuba': 'CUB',\n 'Cabo Verde': 'CPV',\n 'Curaçao': 'CUW',\n 'Cyprus': 'CYP',\n 'Czechia': 'CZE',\n 'German Democratic Republic': 'DDR',\n 'Germany': 'DEU',\n 'Djibouti': 'DJI',\n 'Denmark': 'DNK',\n 'Dominica': 'DMA',\n 'Dominican Republic': 'DOM',\n 'Algeria': 'DZA',\n 'EAPO': '   ',\n 'Ecuador': 'ECU',\n 'Estonia': 'EST',\n 'Egypt': 'EGY',\n 'Western Sahara': 'ESH',\n 'EUIPO': '   ',\n 'EPO': '   ',\n 'Eritrea': 'ERI',\n 'Spain': 'ESP',\n 'Ethiopia': 'ETH',\n 'European Union': '   ',\n 'Finland': 'FIN',\n 'Fiji': 'FJI',\n 'Malvinas': 'FLK',\n 'Faroe Islands': 'FRO',\n 'France': 'FRA',\n 'Gabon': 'GAB',\n 'United Kingdom': 'GBR',\n 'GCC Patent Office': '   ',\n 'Grenada': 'GRD',\n 'Georgia': 'GEO',\n 'Guernsey': 'GGY',\n 'Ghana': 'GHA',\n 'Gibraltar': 'GIB',\n 'Greenland': 'GRL',\n 'Gambia': 'GMB',\n 'Guinea': 'GIN',\n 'Equatorial Guinea': 'GNQ',\n 'Greece': 'GRC',\n 'South Georgia and the South Sandwich Islands': 'SGS',\n 'Guatemala': 'GTM',\n 'Guinea-Bissau': 'GNB',\n 'Guyana': 'GUY',\n 'Hong Kong, China': 'HKG',\n 'Honduras': 'HND',\n 'Croatia': 'HRV',\n 'Haiti': 'HTI',\n 'Hungary': 'HUN',\n 'WIPO': '   ',\n 'Indonesia': 'IDN',\n 'Ireland': 'IRL',\n 'Israel': 'ISR',\n 'Isle of Man': 'IMN',\n 'India': 'IND',\n 'Iraq': 'IRQ',\n 'Islamic Republic of': 'IRN',\n 'Iceland': 'ISL',\n 'Italy': 'ITA',\n 'Jersey': 'JEY',\n 'Jamaica': 'JAM',\n 'Jordan': 'JOR',\n 'Japan': 'JPN',\n 'Kenya': 'KEN',\n 'Kyrgyzstan': 'KGZ',\n 'Cambodia': 'KHM',\n 'Kiribati': 'KIR',\n 'Comoros': 'COM',\n 'Saint Kitts and Nevis': 'KNA',\n \"Democratic People's Republic of Korea\": 'PRK',\n 'Republic of Korea': 'KOR',\n 'Kuwait': 'KWT',\n 'Cayman Islands': 'CYM',\n 'Kazakhstan': 'KAZ',\n \"Lao People's Democratic Republic\": 'LAO',\n 'Lebanon': 'LBN',\n 'Saint Lucia': 'LCA',\n 'Liechtenstein': 'LIE',\n 'Sri Lanka': 'LKA',\n 'Liberia': 'LBR',\n 'Lesotho': 'LSO',\n 'Lithuania': 'LTU',\n 'Luxembourg': 'LUX',\n 'Latvia': 'LVA',\n 'Libya': 'LBY',\n 'Morocco': 'MAR',\n 'Monaco': 'MCO',\n 'Republic of Moldova': 'MDA',\n 'Montenegro': 'MNE',\n 'Madagascar': 'MDG',\n 'North Macedonia': 'MKD',\n 'Mali': 'MLI',\n 'Myanmar': 'MMR',\n 'Mongolia': 'MNG',\n 'Macao, China': 'MAC',\n 'Northern Mariana Islands': 'MNP',\n 'Mauritania': 'MRT',\n 'Montserrat': 'MSR',\n 'Malta': 'MLT',\n 'Mauritius': 'MUS',\n 'Maldives': 'MDV',\n 'Malawi': 'MWI',\n 'Mexico': 'MEX',\n 'Malaysia': 'MYS',\n 'Mozambique': 'MOZ',\n 'Namibia': 'NAM',\n 'Niger': 'NER',\n 'Nigeria': 'NGA',\n 'Nicaragua': 'NIC',\n 'Netherlands': 'NLD',\n 'Norway': 'NOR',\n 'Nepal': 'NPL',\n 'Nauru': 'NRU',\n 'New Zealand': 'NZL',\n 'OAPI': '   ',\n 'Oman': 'OMN',\n 'Panama': 'PAN',\n 'Peru': 'PER',\n 'Papua New Guinea': 'PNG',\n 'Philippines': 'PHL',\n 'Pakistan': 'PAK',\n 'Poland': 'POL',\n 'Portugal': 'PRT',\n 'Palau': 'PLW',\n 'Paraguay': 'PRY',\n 'Qatar': 'QAT',\n 'CPVO': '   ',\n 'Romania': 'ROU',\n 'Serbia': 'SRB',\n 'Russian F
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_to_iso3"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Country type=China<br>Number of co-patents=%{x}<br>Name=%{y}<extra></extra>",
         "legendgroup": "China",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "China",
         "offsetgroup": "China",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "texttemplate": "%{x}",
         "x": [
          2706,
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          250,
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         ],
         "xaxis": "x",
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     "metadata": {},
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   ],
   "source": [
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    appln_pers_f[c+\"_fullname\"] = appln_pers_f[c].map(iso2_to_namedict)\n",
    "\n",
    "# appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
    "appln_pers_f[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
    "\n",
    "appln_pers_f[\"auth_ISO3\"] = appln_pers_f[\"appln_auth_fullname\"].map(name_to_iso3)\n",
    "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"Patent Office\" if x==\"   \" else \"EU\")\n",
    "\n",
    "# top 25\n",
    "# - inventor\n",
    "# - applicant\n",
    "# in china, EU, EUextended\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#9F2B68'}\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col: 'Number of co-patents',\n",
    "                     \"person_ctry_type\": \"Country type\",\n",
    "    \"psn_sector_primary\":\"Sector\",\n",
    "                                  \"han_label\": \"Name\",\n",
    "                                  \"Country_Type\":\"Country type\",\n",
    "                                  \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "for trait in  [\"person_ctry_type\",\"psn_sector_primary\"]:\n",
    "    for ctry in [\"China\",'EU']:\n",
    "        ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
    "        # for role in [\"invt\",\"applt\"]:\n",
    "        if True:\n",
    "            role = \"applt\"\n",
    "            pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
    "            data = (pers_sub\n",
    "                     .groupby([\"han_id\",'han_name'],as_index=False)\n",
    "                     .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n",
    "                     .sort_values(by=\"appln_id\", ascending=False)\n",
    "                    .head(15))\n",
    "            data[\"han_label\"] = (data[\"han_name\"]\n",
    "                                 .str.upper()\n",
    "                                 .str.strip(\".\")\n",
    "                                 .str.replace(\"&\",\"<br>\")\n",
    "                                 + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n",
    "\n",
    "            fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n",
    "                         color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
    "            fig.update_layout(font_family=\"Montserrat\",\n",
    "                          yaxis={'categoryorder':'total ascending'})\n",
    "\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=12\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "\n",
    "            figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "            figsuper.write_image(f\"{fromdir}/plots/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n"
     ]
    },
    {
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          "Germany",
          "Sweden",
          "Finland",
          "United Kingdom",
          "France",
          "Denmark",
          "Switzerland",
          "Italy",
          "Poland",
          "Netherlands",
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          "Spain",
          "Austria",
          "Ireland",
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          "Czechia",
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         "y": [
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          "ZHEJIANG GEELY HOLDINGGROUP CO LTD",
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   "source": [
    "applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n",
    "                          (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n",
    "invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n",
    "\n",
    "eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "#\n",
    "# patstat_applt_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "# patstat_applt_dipol.sample(100)\n",
    "\n",
    "patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "\n",
    "# co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
    "#                             values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
    "                            values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "\n",
    "\n",
    "co_occur = co_occur.T\n",
    "co_occur[\"sum\"] = co_occur.sum(axis=1)\n",
    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
    "\n",
    "co_occur = (co_occur\n",
    "            .sort_values(by=\"sum\", ascending=False)\n",
    "            .head(20)\n",
    "            .drop(columns=\"sum\")\n",
    "            .replace(0,np.nan)\n",
    "            .dropna(axis=1,how=\"all\")\n",
    "            .replace(np.nan,0)\n",
    "            .astype(int))\n",
    "\n",
    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
    "                labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
    "                x=list(co_occur.columns),\n",
    "                text_auto=True,\n",
    "                y=list(co_occur.index))\n",
    "\n",
    "\n",
    "fig.update_layout(title_x=0.5,\n",
    "                   width=1000, height=1000,\n",
    "                   xaxis_showgrid=False,\n",
    "                   yaxis_showgrid=False,\n",
    "                   yaxis_autorange='reversed',\n",
    "                  font_family=\"Montserrat\",\n",
    "                  template='plotly_white',\n",
    "                  coloraxis_colorbar=dict(\n",
    "                            thicknessmode=\"pixels\", thickness=25,\n",
    "                            ticks=\"outside\", ticksuffix=\" \",\n",
    "                            dtick=20,outlinewidth=1,\n",
    "                        ))\n",
    "\n",
    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.update_xaxes(tickangle= -45)\n",
    "fig.update_yaxes(\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    ticks=\"outside\")\n",
    "\n",
    "figsuper_ppt = fig\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 16\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "\n",
    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
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  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [],
   "source": [
    "patstat_applt_dipol_2 = (patstat_applt_dipol\n",
    "                         .groupby([\"han_name_ch\",\"han_name_eu\"], as_index=False)\n",
    "                         .agg({\"psn_sector_primary_ch\":\"first\",\n",
    "                               \"psn_sector_primary_eu\":\"first\",\n",
    "                               \"person_ctry_ISO3_eu\":\"first\",\n",
    "                               record_col:\"nunique\"})\n",
    "                         .sort_values(by=record_col, ascending=False)\n",
    "                         .head(-1))\n",
    "patstat_applt_dipol_2[\"applt_eu_label\"] = (patstat_applt_dipol_2[\"han_name_eu\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"person_ctry_ISO3_eu\"]\n",
    "                                           +\"; \"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_eu\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[\"applt_ch_label\"] = (patstat_applt_dipol_2[\"han_name_ch\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_ch\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[[\"applt_ch_label\",\"applt_eu_label\",\"appln_id\"]].to_excel(rf\"{fromdir}/plots/applt_table.xlsx\", index=False)"
   ],
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    "collapsed": false
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  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log10\n",
      "\n"
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     "metadata": {},
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   ],
   "source": [
    "co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
    "                            values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "# co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
    "#                             values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "\n",
    "\n",
    "co_occur = co_occur.T\n",
    "co_occur[\"SUM\"] = co_occur.sum(axis=1)\n",
    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
    "\n",
    "co_occur = co_occur.drop(columns = co_occur.columns[~co_occur.columns.str.isupper()])\n",
    "co_occur = co_occur.drop(index = co_occur.index[~co_occur.index.str.isupper()])\n",
    "\n",
    "co_occur.columns = co_occur.columns.str.split(\"&\").str[0:2].str.join(\"&\")\n",
    "\n",
    "top=20\n",
    "co_occur = (co_occur\n",
    "            .sort_values(by=\"SUM\", ascending=False)\n",
    "            .head(20)\n",
    "            .drop(columns=\"SUM\")\n",
    "            .replace(0,np.nan)\n",
    "            .dropna(axis=1,how=\"all\")\n",
    "            .replace(np.nan,0)\n",
    "            .astype(int)\n",
    "            .iloc[0:top,0:top]).copy()\n",
    "\n",
    "co_occur = co_occur.sort_index(axis=1).sort_index(axis=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
    "                labels=dict(x=\"Applicant (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
    "                x=list(co_occur.columns),\n",
    "                text_auto=True,\n",
    "                y=list(co_occur.index))\n",
    "\n",
    "\n",
    "fig.update_layout(title_x=0.5,\n",
    "                   width=1000, height=1000,\n",
    "                   xaxis_showgrid=False,\n",
    "                   yaxis_showgrid=False,\n",
    "                   yaxis_autorange='reversed',\n",
    "                  font_family=\"Montserrat\",\n",
    "                  template='plotly_white',\n",
    "                  coloraxis_colorbar=dict(\n",
    "                            thicknessmode=\"pixels\", thickness=25,\n",
    "                            ticks=\"outside\", ticksuffix=\" \",\n",
    "                            dtick=20,outlinewidth=1,\n",
    "                        ))\n",
    "\n",
    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.update_xaxes(tickangle= -45)\n",
    "fig.update_yaxes(\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    ticks=\"outside\")\n",
    "\n",
    "figsuper_ppt = fig\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 16\n",
    "\n",
    "s=12\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=30)\n",
    "\n",
    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_EU_coapplt.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([], dtype='object', name='han_name_eu')\n"
     ]
    }
   ],
   "source": [
    "print(co_occur.columns[~co_occur.columns.str.isupper()])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "346"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((co_occur.columns.str.isupper()))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
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   "source": [
    "# percent of publications related to\n",
    "# -authority country\n",
    "# -inventor country\n",
    "# -applicant country\n",
    "\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'Patent Office':'#7F00FF'}#CC8899\n",
    "\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col+\"_percent\": 'Percent of related co-patents',\n",
    "                    \"person_ctry_code_fullname\": \"Country\",\n",
    "                    \"person_ctry_type\": \"Country type\",\n",
    "                    \"appln_auth_fullname\": \"Application authority\",\n",
    "                    \"auth_ctry_type\": \"Authority type\",\n",
    "                    \"Country_Type\":\"Country type\",\n",
    "                    \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    pers_sub = appln_pers_f\n",
    "    if c == \"person_ctry_code\":\n",
    "        for role in [\"invt\",\"applt\"]:\n",
    "            pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
    "            role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n",
    "            labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n",
    "\n",
    "\n",
    "            data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
    "            data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "            fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "            fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=14\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "            figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "            figsuper.show()\n",
    "    else:\n",
    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
    "        data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "        fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "        fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "        figsuper = fig\n",
    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "        figsuper.update_yaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_xaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "        figsuper['layout'][\"font\"][\"size\"]=14\n",
    "        for a in figsuper['layout'][\"annotations\"]:\n",
    "            a[\"font\"][\"size\"] = 14\n",
    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "        figsuper.update_layout(uniformtext_minsize=10)\n",
    "        figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "        figsuper.show()"
   ],
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