ZSI_Reconnect_China/PATSTAT/patstat_analysis_pipeline.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "outputs": [],
   "source": [
    "outdir=\"EU_CH_scope\"\n",
    "\n",
    "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "\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",
    "pers['psn_sector'] = pers['psn_sector'].fillna(\"UNKNOWN\")\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_def = pd.read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [
    {
     "data": {
      "text/plain": "       person_id                         person_name   \n27780   38752029                        BOTH HENDRIK  \\\n42972   47186505                   Zhang, Richard S.   \n39368   45225635  Roche Diagnostics Operations, Inc.   \n17349   13164592                         LI, QINGHUA   \n38005   44432440                     YANG, CHENGGUAN   \n17354   13164808                   JAVAHERIAN, KASHI   \n48330   48886066                         KRAUSE FALK   \n25002   24615058                        BUTLER, HANS   \n70951   54647336                 Schmieder, Wolfgang   \n8906    11399816    De Boer, Dirk Kornelis Gerhardus   \n\n                      person_name_orig_lg    person_address person_ctry_code   \n27780                        BOTH HENDRIK               NaN               NL  \\\n42972                   Zhang, Richard S.          Shanghai               CN   \n39368  Roche Diagnostics Operations, Inc.        Indiana,IN               US   \n17349                         LI, QINGHUA               NaN               US   \n38005                     YANG, CHENGGUAN               NaN               CN   \n17354                   JAVAHERIAN, KASHI               NaN               US   \n48330                         KRAUSE FALK               NaN               DE   \n25002                        BUTLER, HANS               NaN               NL   \n70951                 Schmieder, Wolfgang  Baden-Wurtemberg               DE   \n8906     De Boer, Dirk Kornelis Gerhardus         Den Bosch               NL   \n\n      nuts  nuts_level  doc_std_name_id                     doc_std_name   \n27780   NL           0           695378                     BOTH HENDRIK  \\\n42972  NaN           9           414822             HARBOURT CYRUS DAVID   \n39368  NaN           9           167898                      WANG XIAOBO   \n17349  NaN           9            77857                       LI QINGHUA   \n38005  NaN           9         19222547                   YANG CHENGGUAN   \n17354  NaN           9           245041                 JAVAHERIAN KASHI   \n48330   DE           0           597644                      KRAUSE FALK   \n25002   NL           0           103820                      BUTLER HANS   \n70951   DE           0         19636429               MERUVA JAYAPRAKASH   \n8906    NL           0          1980622  DE BOER DIRK KORNELIS GERHARDUS   \n\n         psn_id                          psn_name  psn_level  psn_sector   \n27780   3219299                      BOTH HENDRIK          0     UNKNOWN  \\\n42972  37071034                  ZHANG RICHARD S.          1  INDIVIDUAL   \n39368  27413631      ROCHE DIAGNOSTICS OPERATIONS          2     COMPANY   \n17349  19316195                        LI QINGHUA          1  INDIVIDUAL   \n38005  36198377                   YANG, CHENGGUAN          1  INDIVIDUAL   \n17354  14769626                 JAVAHERIAN, KASHI          1  INDIVIDUAL   \n48330  17940076                       KRAUSE FALK          0     UNKNOWN   \n25002   3827604                      BUTLER, HANS          1  INDIVIDUAL   \n70951  28724833               SCHMIEDER, WOLFGANG          0     UNKNOWN   \n8906    6348065  DE BOER, DIRK KORNELIS GERHARDUS          1  INDIVIDUAL   \n\n          han_id                          han_name  han_harmonized  \n27780  138752029                      BOTH HENDRIK               0  \n42972  147186505                 Zhang, Richard S.               0  \n39368    2461595  ROCHE DIAGNOSTICS OPERATIONS INC               2  \n17349    4304276                        LI QINGHUA               1  \n38005  144432440                   YANG, CHENGGUAN               0  \n17354  113164808                 JAVAHERIAN, KASHI               0  \n48330  148886066                       KRAUSE FALK               0  \n25002  124615058                      BUTLER, HANS               0  \n70951  154647336               Schmieder, Wolfgang               0  \n8906   111399816  De Boer, Dirk Kornelis Gerhardus               0  ",
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     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pers.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "              cpc_id                                           cpc_name   \n0                  A                                  HUMAN NECESSITIES  \\\n1                A01  AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n2               A01B  SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n3           A01B1/00  Hand tools (edge trimmers for lawns A01G3/06  ...   \n4           A01B1/02  Spades; Shovels {(hand-operated dredgers E02F3...   \n...              ...                                                ...   \n260486  Y10T483/1873                                    Indexing matrix   \n260487  Y10T483/1882                                        Rotary disc   \n260488  Y10T483/1891                                      Chain or belt   \n260489    Y10T483/19                                      Miscellaneous   \n260490           NaN                                                NaN   \n\n       section  class subclass  group  main_group  cpc_version   \n0            A    NaN      NaN    NaN         NaN         2023  \\\n1            A    1.0      NaN    NaN         NaN         2023   \n2            A    1.0        B    NaN         NaN         2023   \n3            A    1.0        B    1.0         0.0         2023   \n4            A    1.0        B    1.0         2.0         2023   \n...        ...    ...      ...    ...         ...          ...   \n260486       Y   10.0        T  483.0      1873.0         2023   \n260487       Y   10.0        T  483.0      1882.0         2023   \n260488       Y   10.0        T  483.0      1891.0         2023   \n260489       Y   10.0        T  483.0        19.0         2023   \n260490     NaN    NaN      NaN    NaN         NaN         2022   \n\n               version https://git-lfs.github.com/spec/v1   \n0                                                     NaN  \\\n1                                                     NaN   \n2                                                     NaN   \n3                                                     NaN   \n4                                                     NaN   \n...                                                   ...   \n260486                                                NaN   \n260487                                                NaN   \n260488                                                NaN   \n260489                                                NaN   \n260490  oid sha256:f138d6bdf2939ba576b96b633d81366123b...   \n\n                                             cpc_taxonomy   \n0                            [('A', 'HUMAN NECESSITIES')]  \\\n1       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n2       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n3       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n4       [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...   \n...                                                   ...   \n260486  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260487  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260488  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260489  [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...   \n260490                                                 []   \n\n                                             cpc_fullname   \n0                                       HUMAN NECESSITIES  \\\n1       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n2       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n3       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n4       HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...   \n...                                                   ...   \n260486  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260487  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260488  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260489  GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...   \n260490                                                NaN   \n\n                                              tax_level_0   \n0                                       HUMAN NEC
      "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>cpc_id</th>\n      <th>cpc_name</th>\n      <th>section</th>\n      <th>class</th>\n      <th>subclass</th>\n      <th>group</th>\n      <th>main_group</th>\n      <th>cpc_version</th>\n      <th>version https://git-lfs.github.com/spec/v1</th>\n      <th>cpc_taxonomy</th>\n      <th>cpc_fullname</th>\n      <th>tax_level_0</th>\n      <th>tax_level_1</th>\n      <th>tax_level_2</th>\n      <th>tax_level_3</th>\n      <th>tax_level_4</th>\n      <th>tax_level_5</th>\n      <th>tax_level_6</th>\n      <th>tax_level_7</th>\n      <th>data_scope</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>A</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES')]</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A01</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>A01B</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>A01B1/00</td>\n      <td>Hand tools (edge trimmers for lawns A01G3/06  ...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...</td>\n      <td>Hand tools (edge trimmers for lawns A01G3/06  ...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>A01B1/02</td>\n      <td>Spades; Shovels {(hand-operated dredgers E02F3...</td>\n      <td>A</td>\n      <td>1.0</td>\n      <td>B</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...</td>\n      <td>HUMAN NECESSITIES&lt;&gt;AGRICULTURE; FORESTRY; ANIM...</td>\n      <td>HUMAN NECESSITIES</td>\n      <td>AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...</td>\n      <td>SOIL WORKING IN AGRICULTURE OR FORESTR
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc_def"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id cpc_class_symbol\n461523  550229597    C08G  18/6674\n511948  569882441      C08K   5/06\n67828   414454432     G06F  21/105\n83905   418994277    H04B   7/0452\n393107  530331540      H04N  19/70\n406153  534165470      C07K2317/52\n253473  490601228      D06M2200/50\n256305  491586869     G01G  11/003\n279783  497310121    A61K  39/3955\n432151  541221711    G01N  15/1012",
      "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>cpc_class_symbol</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>461523</th>\n      <td>550229597</td>\n      <td>C08G  18/6674</td>\n    </tr>\n    <tr>\n      <th>511948</th>\n      <td>569882441</td>\n      <td>C08K   5/06</td>\n    </tr>\n    <tr>\n      <th>67828</th>\n      <td>414454432</td>\n      <td>G06F  21/105</td>\n    </tr>\n    <tr>\n      <th>83905</th>\n      <td>418994277</td>\n      <td>H04B   7/0452</td>\n    </tr>\n    <tr>\n      <th>393107</th>\n      <td>530331540</td>\n      <td>H04N  19/70</td>\n    </tr>\n    <tr>\n      <th>406153</th>\n      <td>534165470</td>\n      <td>C07K2317/52</td>\n    </tr>\n    <tr>\n      <th>253473</th>\n      <td>490601228</td>\n      <td>D06M2200/50</td>\n    </tr>\n    <tr>\n      <th>256305</th>\n      <td>491586869</td>\n      <td>G01G  11/003</td>\n    </tr>\n    <tr>\n      <th>279783</th>\n      <td>497310121</td>\n      <td>A61K  39/3955</td>\n    </tr>\n    <tr>\n      <th>432151</th>\n      <td>541221711</td>\n      <td>G01N  15/1012</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_cpc.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "325243"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln_pers)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "# Number of collaborative applications\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "record_col = \"appln_id\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "count    65136.000000\nmean      2016.312546\nstd          3.189059\nmin       2011.000000\n25%       2013.000000\n50%       2017.000000\n75%       2019.000000\nmax       2023.000000\nName: appln_filing_year, dtype: float64"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[\"appln_filing_year\"].describe()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n445321  902305835         EP          A         2017              0  \\\n480488  515101583         CN          A         2018              2   \n307745  458113107         CN          A         2016              1   \n352956  449856958         EP          A         2016              1   \n452231  510599290         CN          A         2018              1   \n...           ...        ...        ...          ...            ...   \n424840  496723291         CN          A         2017              1   \n84346   378450888         GB          A         2012              1   \n736784  576111604         CN          A         2021              1   \n617397  528722596         CN          A         2019              1   \n65667   411664922         DE          A         2012              1   \n\n        nb_inventors  \n445321             0  \n480488             5  \n307745             3  \n352956             4  \n452231             3  \n...              ...  \n424840             1  \n84346              0  \n736784             3  \n617397             1  \n65667              1  \n\n[100 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>445321</th>\n      <td>902305835</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>480488</th>\n      <td>515101583</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>307745</th>\n      <td>458113107</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>352956</th>\n      <td>449856958</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>452231</th>\n      <td>510599290</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>3</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>424840</th>\n      <td>496723291</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>84346</th>\n      <td>378450888</td>\n      <td>GB</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>736784</th>\n      <td>576111604</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>617397</th>\n      <td>528722596</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>65667</th>\n      <td>411664922</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outdir=\"first_round\"\n",
    "\n",
    "appln_f = pd.read_csv(f\"{outdir}/first-filings-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors'])\n",
    "\n",
    "appln_f[\"filing_year\"] = appln_f[\"filing_year\"].astype(int)\n",
    "\n",
    "appln_f = appln_f[appln_f[\"filing_year\"]>2010]\n",
    "\n",
    "appln_f[\"appln_auth\"] = appln_f[\"appln_auth\"].str.replace(\"MA\",\"CN\")\n",
    "\n",
    "appln_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_kind\nA    735889\nU     37157\nF         9\nD         3\nName: count, dtype: int64"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f[\"appln_kind\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Co-patents per year"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "hovertemplate": "Year:%{x:d}<br>Number of co-publications:%{y:d}",
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         "yaxis": "y",
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      "text/html": "<div>                            <div id=\"8a77105c-5c9a-4803-ba01-aecc4d00c9e5\" 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(\"8a77105c-5c9a-4803-ba01-aecc4d00c9e5\")) {                    Plotly.newPlot(                        \"8a77105c-5c9a-4803-ba01-aecc4d00c9e5\",                        [{\"hovertemplate\":\"Year:%{x:d}<br>Number of co-publications:%{y:d}\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"text+markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[52293.0,52911.0,57065.0,59627.0,66787.0,72521.0,79736.0,83326.0,88838.0,94178.0,54385.0,101.0],\"textfont\":{\"size\":12},\"textposition\":\"top center\",\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x\",\"y\":[52293,52911,57065,59627,66787,72521,79736,83326,88838,94178,54385,101],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"hovertemplate\":\"Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"text+markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[0.0,0.011818025357122367,0.091255043696097,0.14024821677853633,0.27716902835943624,0.3868204157344195,0.5247929933260665,0.5934446293002887,0.6988507065955291,0.8009676247298874,0.04000535444514562,-0.9980685751439007],\"textfont\":{\"size\":12},\"textposition\":\"top center\",\"texttemplate\":\"%{y:.0%}\",\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],\"xaxis\":\"x2\",\"y\":[0.0,0.011818025357122367,0.091255043696097,0.14024821677853633,0.27716902835943624,0.3868204157344195,0.5247929933260665,0.5934446293002887,0.6988507065955291,0.8009676247298874,0.04000535444514562,-0.9980685751439007],\"yaxis\":\"y2\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.33333333333
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = (appln_f.groupby(['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=[\"filing_year\"], ascending=True)[record_col][0]\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "\n",
    "data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n",
    "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n",
    "\n",
    "year_output = px.line(data,x=\"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=\"filing_year\", y=record_col+\"_relative_growth\", markers=True, 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\"] = [0,150000]\n",
    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,1.5]\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))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n"
     ]
    },
    {
     "data": {
      "text/plain": "     filing_year appln_auth  appln_id  filing_year_relative_growth   \n0           2011         AT     705.0                         2011  \\\n1           2012         AT     607.0                         2011   \n2           2013         AT     673.0                         2011   \n3           2014         AT     658.0                         2011   \n4           2015         AT     710.0                         2011   \n..           ...        ...       ...                          ...   \n355         2018         SK      16.0                         2011   \n356         2019         SK      15.0                         2011   \n357         2020         SK      22.0                         2011   \n358         2021         SK      14.0                         2011   \n359         2022         SK       0.0                         2011   \n\n     appln_id_relative_growth  appln_id_cumsum   Country  \n0                    0.000000            705.0   Austria  \n1                   -0.139007           1312.0   Austria  \n2                   -0.045390           1985.0   Austria  \n3                   -0.066667           2643.0   Austria  \n4                    0.007092           3353.0   Austria  \n..                        ...              ...       ...  \n355                 -0.407407            191.0  Slovakia  \n356                 -0.444444            206.0  Slovakia  \n357                 -0.185185            228.0  Slovakia  \n358                 -0.481481            242.0  Slovakia  \n359                 -1.000000            242.0  Slovakia  \n\n[360 rows x 7 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>filing_year</th>\n      <th>appln_auth</th>\n      <th>appln_id</th>\n      <th>filing_year_relative_growth</th>\n      <th>appln_id_relative_growth</th>\n      <th>appln_id_cumsum</th>\n      <th>Country</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>AT</td>\n      <td>705.0</td>\n      <td>2011</td>\n      <td>0.000000</td>\n      <td>705.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>AT</td>\n      <td>607.0</td>\n      <td>2011</td>\n      <td>-0.139007</td>\n      <td>1312.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>AT</td>\n      <td>673.0</td>\n      <td>2011</td>\n      <td>-0.045390</td>\n      <td>1985.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>AT</td>\n      <td>658.0</td>\n      <td>2011</td>\n      <td>-0.066667</td>\n      <td>2643.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>AT</td>\n      <td>710.0</td>\n      <td>2011</td>\n      <td>0.007092</td>\n      <td>3353.0</td>\n      <td>Austria</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    </tr>\n    <tr>\n      <th>355</th>\n      <td>2018</td>\n      <td>SK</td>\n      <td>16.0</td>\n      <td>2011</td>\n      <td>-0.407407</td>\n      <td>191.0</td>\n      <td>Slovakia</td>\n    </tr>\n    <tr>\n      <th>356</th>\n      <td>2019</td>\n      <td>SK</td>\n      <td>15.0</td>\n      <td>2011</td>\n      <td>-0.444444</td>\n      <td>206.0</td>\n      <td>Slovakia</td>\n    </tr>\n    <tr>\n      <th>357</th>\n      <td>2020</td>\n      <td>SK</td>\n      <td>22.0</td>\n      <td>2011</td>\n      <td>-0.185185</td>\n      <td>228.0</td>\n      <td>Slovakia</td>\n    </tr>\n    <tr>\n      <th>358</th>\n      <td>2021</td>\n      <td>SK</td>\n      <td>14.0</td>\n      <td>2011</td>\n      <td>-0.481481</td>\n      <td>242.0</td>\n      <td>Slovakia</td>\n    </tr>\n    <tr>\n      <th>359</th>\n      <td>2022</td>\n      <td>SK</td>\n      <td>0.0</td>\n      <td>2011</td>\n      <td>-1.000000</td>\n      <td>242.0</td>\n      <td>Slovakia</td>\n    </tr>\n  </tbody>\n</table>\n<p>360 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (appln_f.groupby(['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=[\"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\",\"filing_year\"], ascending=[True,True])\n",
    "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
    "data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n",
    "data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n",
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
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         "hovertext": [
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     "metadata": {},
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   ],
   "source": [
    "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
    "              y=record_col,\n",
    "              x='filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=6,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\"})\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",
    "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()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
    "              y=record_col+\"_relative_growth\",\n",
    "              x='filing_year'\n",
    "              ,facet_col=\"Country\",facet_col_wrap=6,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 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\"})\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()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n"
     ]
    },
    {
     "data": {
      "text/plain": "          appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n987983   449313178         FR   53981953            1             0  \\\n896220   438489934         FR   52986688            4             0   \n1703159  484622346         EP   57708518            1             0   \n20051    471352386         DE    5235181            0             1   \n151664   337008716         FR   42812566            1             0   \n...            ...        ...        ...          ...           ...   \n818875   436949973         FR   51872306            2             0   \n195230   523590443         DE   16958864            2             0   \n129138   578626678         DE    6193358            0             1   \n276045   449666385         DE   41815193            2             0   \n24195    530683500         DE    5235181            0             1   \n\n                         person_name person_ctry_code  psn_sector     han_id   \n987983               BREARD MATTHIEU               FR         NaN  153981953  \\\n896220              ENGBLOM CHRISTER               FR         NaN  152986688   \n1703159         VAN INGELGEM, Werner               BE         NaN  157708518   \n20051              Robert Bosch GmbH               DE     COMPANY    2456791   \n151664                   BRESOUS LUC               FR         NaN  142812566   \n...                              ...              ...         ...        ...   \n818875              BORDERIEUX, LOIC               FR         NaN  151872306   \n195230             WEYDANZ, WOLFGANG               DE  INDIVIDUAL  116958864   \n129138   Clariant International Ltd.               CH     COMPANY     524840   \n276045                WELTER, MARTIN               DE  INDIVIDUAL  141815193   \n24195              Robert Bosch GmbH               DE     COMPANY    2456791   \n\n                     han_name person_ctry_code_fullname   \n987983        BREARD MATTHIEU                    France  \\\n896220       ENGBLOM CHRISTER                    France   \n1703159  VAN INGELGEM, Werner                   Belgium   \n20051       ROBERT BOSCH GMBH                   Germany   \n151664            BRESOUS LUC                    France   \n...                       ...                       ...   \n818875       BORDERIEUX, LOIC                    France   \n195230      WEYDANZ, WOLFGANG                   Germany   \n129138       CLARIANT INT LTD               Switzerland   \n276045         WELTER, MARTIN                   Germany   \n24195       ROBERT BOSCH GMBH                   Germany   \n\n            appln_auth_fullname  \n987983                   France  \n896220                   France  \n1703159  European Patent Office  \n20051                   Germany  \n151664                   France  \n...                         ...  \n818875                   France  \n195230                  Germany  \n129138                  Germany  \n276045                  Germany  \n24195                   Germany  \n\n[100 rows x 12 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>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>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>person_ctry_code_fullname</th>\n      <th>appln_auth_fullname</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>987983</th>\n      <td>449313178</td>\n      <td>FR</td>\n      <td>53981953</td>\n      <td>1</td>\n      <td>0</td>\n      <td>BREARD MATTHIEU</td>\n      <td>FR</td>\n      <td>NaN</td>\n      <td>153981953</td>\n      <td>BREARD MATTHIEU</td>\n      <td>France</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>896220</th>\n      <td>438489934</td>\n      <td>FR</td>\n      <td>52986688</td>\n      <td>4</td>\n      <td>0</td>\n      <td>ENGBLOM CHRISTER</td>\n      <td>FR</td>\n      <td>NaN</td>\n      <td>152986688</td>\n      <td>ENGBLOM CHRISTER</td>\n      <td>France</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>1703159</th>\n      <td>484622346</td>\n      <td>EP</td>\n      <td>57708518</td>\n      <td>1</td>\n      <td>0</td>\n      <td>VAN INGELGEM, Werner</td>\n      <td>BE</td>\n      <td>NaN</td>\n      <td>157708518</td>\n      <td>VAN INGELGEM, Werner</td>\n      <td>Belgium</td>\n      <td>European Patent Office</td>\n    </tr>\n    <tr>\n      <th>20051</th>\n      <td>471352386</td>\n      <td>DE</td>\n      <td>5235181</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Robert Bosch GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>2456791</td>\n      <td>ROBERT BOSCH GMBH</td>\n      <td>Germany</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>151664</th>\n      <td>337008716</td>\n      <td>FR</td>\n      <td>42812566</td>\n      <td>1</td>\n      <td>0</td>\n      <td>BRESOUS LUC</td>\n      <td>FR</td>\n      <td>NaN</td>\n      <td>142812566</td>\n      <td>BRESOUS LUC</td>\n      <td>France</td>\n      <td>France</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    </tr>\n    <tr>\n      <th>818875</th>\n      <td>436949973</td>\n      <td>FR</td>\n      <td>51872306</td>\n      <td>2</td>\n      <td>0</td>\n      <td>BORDERIEUX, LOIC</td>\n      <td>FR</td>\n      <td>NaN</td>\n      <td>151872306</td>\n      <td>BORDERIEUX, LOIC</td>\n      <td>France</td>\n      <td>France</td>\n    </tr>\n    <tr>\n      <th>195230</th>\n      <td>523590443</td>\n      <td>DE</td>\n      <td>16958864</td>\n      <td>2</td>\n      <td>0</td>\n      <td>WEYDANZ, WOLFGANG</td>\n      <td>DE</td>\n      <td>INDIVIDUAL</td>\n      <td>116958864</td>\n      <td>WEYDANZ, WOLFGANG</td>\n      <td>Germany</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>129138</th>\n      <td>578626678</td>\n      <td>DE</td>\n      <td>6193358</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Clariant International Ltd.</td>\n      <td>CH</td>\n      <td>COMPANY</td>\n      <td>524840</td>\n      <td>CLARIANT INT LTD</td>\n      <td>Switzerland</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>276045</th>\n      <td>449666385</td>\n      <td>DE</td>\n      <td>41815193</td>\n      <td>2</td>\n      <td>0</td>\n      <td>WELTER, MARTIN</td>\n      <td>DE</td>\n      <td>INDIVIDUAL</td>\n      <td>141815193</td>\n      <td>WELTER, MARTIN</td>\n      <td>Germany</td>\n      <td>Germany</td>\n    </tr>\n    <tr>\n      <th>24195</th>\n      <td>530683500</td>\n 
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
    "\n",
    "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
    "\n",
    "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n",
    "\n",
    "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 == 'CN')|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'UK'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()\n",
    "\n",
    "appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n",
    "\n",
    "appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
    "\n",
    "appln_pers_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [
    {
     "data": {
      "text/plain": "283091"
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     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "outputs": [
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            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3",
            "gridwidth": 2
           },
           "zaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3",
            "gridwidth": 2
           }
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          "shapedefaults": {
           "line": {
            "color": "#f2f5fa"
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          "annotationdefaults": {
           "arrowcolor": "#f2f5fa",
           "arrowhead": 0,
           "arrowwidth": 1
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          "geo": {
           "bgcolor": "rgb(17,17,17)",
           "landcolor": "rgb(17,17,17)",
           "subunitcolor": "#506784",
           "showland": true,
           "showlakes": true,
           "lakecolor": "rgb(17,17,17)"
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          "title": {
           "x": 0.05
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          "updatemenudefaults": {
           "bgcolor": "#506784",
           "borderwidth": 0
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          "sliderdefaults": {
           "bgcolor": "#C8D4E3",
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           "bordercolor": "rgb(17,17,17)",
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          "mapbox": {
           "style": "dark"
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         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "appln_id_percent"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "appln_auth_fullname"
         }
        },
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "barmode": "relative"
       },
       "config": {
        "plotlyServerURL": "https://plotly.com"
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      },
      "text/html": "<div>                            <div id=\"64df034e-7982-4fde-bd47-ea794757f921\" 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(\"64df034e-7982-4fde-bd47-ea794757f921\")) {                    Plotly.newPlot(                        \"64df034e-7982-4fde-bd47-ea794757f921\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"appln_id_percent=%{x}<br>appln_auth_fullname=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"h\",\"showlegend\":false,\"textposition\":\"auto\",\"x\":[0.021208727935540163,0.006238276737868742,0.0008053947317293732,6.0051361576312915e-05,0.0004768784595766026,0.004846498122511842,0.005178546827698514,0.00016249191955943496,0.1840115016019584,0.016662486620909883,0.20245786690498815,0.4374494420522023,0.0018121381463910898,0.0028436085922901116,0.00022607571416964864,0.006626844371597825,0.00021194598203404559,0.0002013486829323433,0.003694924953460195,1.059729910170228e-05,0.019612068204217018,0.006535001112716406,0.0013599867180517926,0.0008265893299327778,0.0007524082362208619,0.0015189462045773266,0.02970776181510539,0.02148072527915052,0.00673988222868265,0.016280983853248603],\"xaxis\":\"x\",\"y\":[\"Austria\",\"Belgium\",\"Bulgaria\",\"China\",\"Croatia\",\"Czech Republic\",\"Denmark\",\"Estonia\",\"European Patent Office\",\"Finland\",\"France\",\"Germany\",\"Greece\",\"Hungary\",\"Ireland\",\"Italy\",\"Latvia\",\"Lithuania\",\"Luxembourg\",\"Morocco\",\"Netherlands\",\"Poland\",\"Portugal\",\"Romania\",\"Slovakia\",\"Slovenia\",\"Spain\",\"Sweden\",\"Switzerland\",\"United Kingdom\"],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "\n",
    "            data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\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\")\n",
    "            fig.show()\n",
    "    else:\n",
    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\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\")\n",
    "        fig.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [
    {
     "data": {
      "text/plain": "   appln_auth  appln_id  appln_id_percent\n0          AT      6016          0.009362\n1          BE      1772          0.002758\n2          BG       229          0.000356\n3          CH      1926          0.002997\n4          CN    304654          0.474115\n5          CZ      1373          0.002137\n6          DE    124035          0.193028\n7          DK      1480          0.002303\n8          EE        46          0.000072\n9          EP     54082          0.084165\n10         ES      8484          0.013203\n11         FI      4719          0.007344\n12         FR     57391          0.089314\n13         GB     51069          0.079476\n14         GR       515          0.000801\n15         HR       135          0.000210\n16         HU       807          0.001256\n17         IE        70          0.000109\n18         IT      5535          0.008614\n19         LT        58          0.000090\n20         LU      1054          0.001640\n21         LV        60          0.000093\n22         MA       363          0.000565\n23         NL      7377          0.011480\n24         PL      1854          0.002885\n25         PT       385          0.000599\n26         RO       235          0.000366\n27         SE      6207          0.009660\n28         SI       430          0.000669\n29         SK       213          0.000331",
      "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_auth</th>\n      <th>appln_id</th>\n      <th>appln_id_percent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AT</td>\n      <td>6016</td>\n      <td>0.009362</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BE</td>\n      <td>1772</td>\n      <td>0.002758</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BG</td>\n      <td>229</td>\n      <td>0.000356</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CH</td>\n      <td>1926</td>\n      <td>0.002997</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CN</td>\n      <td>304654</td>\n      <td>0.474115</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CZ</td>\n      <td>1373</td>\n      <td>0.002137</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>DE</td>\n      <td>124035</td>\n      <td>0.193028</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>DK</td>\n      <td>1480</td>\n      <td>0.002303</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>EE</td>\n      <td>46</td>\n      <td>0.000072</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>EP</td>\n      <td>54082</td>\n      <td>0.084165</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>ES</td>\n      <td>8484</td>\n      <td>0.013203</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>FI</td>\n      <td>4719</td>\n      <td>0.007344</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>FR</td>\n      <td>57391</td>\n      <td>0.089314</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>GB</td>\n      <td>51069</td>\n      <td>0.079476</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>GR</td>\n      <td>515</td>\n      <td>0.000801</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>HR</td>\n      <td>135</td>\n      <td>0.000210</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>HU</td>\n      <td>807</td>\n      <td>0.001256</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>IE</td>\n      <td>70</td>\n      <td>0.000109</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>IT</td>\n      <td>5535</td>\n      <td>0.008614</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>LT</td>\n      <td>58</td>\n      <td>0.000090</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>LU</td>\n      <td>1054</td>\n      <td>0.001640</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>LV</td>\n      <td>60</td>\n      <td>0.000093</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>MA</td>\n      <td>363</td>\n      <td>0.000565</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>NL</td>\n      <td>7377</td>\n      <td>0.011480</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>PL</td>\n      <td>1854</td>\n      <td>0.002885</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>PT</td>\n      <td>385</td>\n      <td>0.000599</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>RO</td>\n      <td>235</td>\n      <td>0.000366</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>SE</td>\n      <td>6207</td>\n      <td>0.009660</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>SI</td>\n      <td>430</td>\n      <td>0.000669</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>SK</td>\n      <td>213</td>\n      <td>0.000331</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# percent of publications related to\n",
    "# -authority country\n",
    "# -inventor country\n",
    "# -applicant country"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# top 25\n",
    "# - inventor\n",
    "# - applicant\n",
    "# in china, EU, EUextended"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "        person_id      psn_sector     han_id   \n269495   48715290         COMPANY  148715290  \\\n225401   45096499  GOV NON-PROFIT     986555   \n426484   58455492             NaN  158455492   \n50805    15325724      INDIVIDUAL  115325724   \n112503   28064269             NaN  128064269   \n...           ...             ...        ...   \n464708   59491689             NaN  159491689   \n169950   42286280      INDIVIDUAL    4099527   \n485679   60214508             NaN  160214508   \n542210   62188978             NaN  162188978   \n752136   81407527             NaN  181407527   \n\n                                                 han_name  \n269495                            TECHFLOW MARINE LIMITED  \n225401  FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...  \n426484                                       DING ZHIJIAO  \n50805                                   FOURNIER, THIERRY  \n112503                                      ADRIAN WALLER  \n...                                                   ...  \n464708                                         LIU YANZHI  \n169950                                     RIEMANN STEFAN  \n485679                                             WAN BO  \n542210                                            CHU WEI  \n752136                            GARCIA MONSONIS, Javier  \n\n[100 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>person_id</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>269495</th>\n      <td>48715290</td>\n      <td>COMPANY</td>\n      <td>148715290</td>\n      <td>TECHFLOW MARINE LIMITED</td>\n    </tr>\n    <tr>\n      <th>225401</th>\n      <td>45096499</td>\n      <td>GOV NON-PROFIT</td>\n      <td>986555</td>\n      <td>FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...</td>\n    </tr>\n    <tr>\n      <th>426484</th>\n      <td>58455492</td>\n      <td>NaN</td>\n      <td>158455492</td>\n      <td>DING ZHIJIAO</td>\n    </tr>\n    <tr>\n      <th>50805</th>\n      <td>15325724</td>\n      <td>INDIVIDUAL</td>\n      <td>115325724</td>\n      <td>FOURNIER, THIERRY</td>\n    </tr>\n    <tr>\n      <th>112503</th>\n      <td>28064269</td>\n      <td>NaN</td>\n      <td>128064269</td>\n      <td>ADRIAN WALLER</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>464708</th>\n      <td>59491689</td>\n      <td>NaN</td>\n      <td>159491689</td>\n      <td>LIU YANZHI</td>\n    </tr>\n    <tr>\n      <th>169950</th>\n      <td>42286280</td>\n      <td>INDIVIDUAL</td>\n      <td>4099527</td>\n      <td>RIEMANN STEFAN</td>\n    </tr>\n    <tr>\n      <th>485679</th>\n      <td>60214508</td>\n      <td>NaN</td>\n      <td>160214508</td>\n      <td>WAN BO</td>\n    </tr>\n    <tr>\n      <th>542210</th>\n      <td>62188978</td>\n      <td>NaN</td>\n      <td>162188978</td>\n      <td>CHU WEI</td>\n    </tr>\n    <tr>\n      <th>752136</th>\n      <td>81407527</td>\n      <td>NaN</td>\n      <td>181407527</td>\n      <td>GARCIA MONSONIS, Javier</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
    "appln_pers_haninfo.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth appln_kind  filing_year  nb_applicants   \n209158   404772731         CN          A         2012              1  \\\n924078   507793803         CN          A         2018              1   \n399887   425594860         FR          A         2014              1   \n521812   443742521         CN          A         2015              1   \n1279727  524830291         CN          A         2019              1   \n...            ...        ...        ...          ...            ...   \n967174   496304374         GB          A         2018              1   \n602289   492364744         SE          A         2016              1   \n229323   410162871         FR          A         2013              1   \n1051611  507623754         FR          A         2018              1   \n1050112  500224940         CN          A         2018              1   \n\n         nb_inventors ipc_class_symbol ipc_class_level     cpc_id   \n209158              7      C07C  41/16               A  C07C41/16  \\\n924078             12      B29C  35/02               A  B29C35/02   \n399887              2      A61P   7/04               A   A61P7/04   \n521812              1      H04W  48/16               A  H04W48/16   \n1279727             1      H01L  51/56               A  H01L51/56   \n...               ...              ...             ...        ...   \n967174              2      A61F  13/53               A  A61F13/53   \n602289              3      C08L   5/08               A   C08L5/08   \n229323              5      H05K  13/04               A  H05K13/04   \n1051611             4      F02C   7/32               A   F02C7/32   \n1050112             1      A61P  31/00               A  A61P31/00   \n\n                          ipc_class             ipc_classname  \n209158      C-Chemistry; metallurgy     Chemistry; metallurgy  \n924078   B-Operations; transporting  Operations; transporting  \n399887          A-Human necessities         Human necessities  \n521812                H-Electricity               Electricity  \n1279727               H-Electricity               Electricity  \n...                             ...                       ...  \n967174          A-Human necessities         Human necessities  \n602289      C-Chemistry; metallurgy     Chemistry; metallurgy  \n229323                H-Electricity               Electricity  \n1051611               F-Engineering               Engineering  \n1050112         A-Human necessities         Human necessities  \n\n[100 rows x 11 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_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n      <th>ipc_class_symbol</th>\n      <th>ipc_class_level</th>\n      <th>cpc_id</th>\n      <th>ipc_class</th>\n      <th>ipc_classname</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>209158</th>\n      <td>404772731</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>7</td>\n      <td>C07C  41/16</td>\n      <td>A</td>\n      <td>C07C41/16</td>\n      <td>C-Chemistry; metallurgy</td>\n      <td>Chemistry; metallurgy</td>\n    </tr>\n    <tr>\n      <th>924078</th>\n      <td>507793803</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>12</td>\n      <td>B29C  35/02</td>\n      <td>A</td>\n      <td>B29C35/02</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n    </tr>\n    <tr>\n      <th>399887</th>\n      <td>425594860</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2014</td>\n      <td>1</td>\n      <td>2</td>\n      <td>A61P   7/04</td>\n      <td>A</td>\n      <td>A61P7/04</td>\n      <td>A-Human necessities</td>\n      <td>Human necessities</td>\n    </tr>\n    <tr>\n      <th>521812</th>\n      <td>443742521</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>1</td>\n      <td>H04W  48/16</td>\n      <td>A</td>\n      <td>H04W48/16</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>1279727</th>\n      <td>524830291</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1</td>\n      <td>H01L  51/56</td>\n      <td>A</td>\n      <td>H01L51/56</td>\n      <td>H-Electricity</td>\n      <td>Electricity</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    </tr>\n    <tr>\n      <th>967174</th>\n      <td>496304374</td>\n      <td>GB</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>2</td>\n      <td>A61F  13/53</td>\n      <td>A</td>\n      <td>A61F13/53</td>\n      <td>A-Human necessities</td>\n      <td>Human necessities</td>\n    </tr>\n    <tr>\n      <th>602289</th>\n      <td>492364744</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n      <td>C08L   5/08</td>\n      <td>A</td>\n      <td>C08L5/08</td>\n      <td>C-Chemistry; metallurgy</td>\n      <td>Chemistry; metallurgy</td>\n    </tr>\n    <tr>\n      <th>229323</th>\n      <td>410162871</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>5</td>\n      <td>H05K  13/04</td>\n      <td>A</td>\n      <td>H05K13/04</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>1051611</th>\n      <td>507623754</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>4</td>\n      <td>F02C   7/32</td>\n      <td>A</td>\n      <td>F02C7/32</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n    </tr>\n    <tr>\n      <th>1050112</th>\n      <td>500224940</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>1</td>\n      <td>A61P  31/00</td>\n      <td>A</td>\n      <td>A61P31/00</td>\n      <td>A-Human necessities</td>\n      <td>Human necessities</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f = pd.read_csv(f\"{outdir}/first-filings-with-IPC-raw.csv\", header=None,\n",
    "                      names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n",
    "                             'ipc_class_symbol','ipc_class_level'])\n",
    "\n",
    "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n",
    "appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n",
    "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n",
    "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n",
    "                                                                 \"B\":\"Performing operations; transporting\",\n",
    "                                                                 \"B\":\"Operations; transporting\",\n",
    "                                                                 \"C\":\"Chemistry; metallurgy\",\n",
    "                                                                 \"D\":\"Textiles; paper\",\n",
    "                                                                 \"E\":\"Fixed constructions\",\n",
    "                                                                 \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n",
    "                                                                 \"F\":\"Engineering\",\n",
    "                                                                 \"G\":\"Physics\",\n",
    "                                                                 \"H\":\"Electricity\"})\n",
    "\n",
    "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]\n",
    "appln_ipc_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [],
   "source": [
    "group = 'ipc_class'\n",
    "data = appln_ipc_f.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
    "\n",
    "fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',text_auto=True,\n",
    "                              labels={\n",
    "                     record_col: 'Number of co-publications',\n",
    "                     group: \"\",\n",
    "                 },\n",
    "                title=\"Distribution of Domains\", template='plotly')\n",
    "fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n",
    "fig.update_traces(hovertemplate='%{x:d}')\n",
    "fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=True)\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_layout(yaxis={'categoryorder':'total ascending'})\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "dom_distr = go.Figure(fig)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "    filing_year            ipc_class  appln_id  filing_year_relative_growth   \n0          2011  A-Human necessities    6063.0                         2011  \\\n1          2012  A-Human necessities    5804.0                         2011   \n2          2013  A-Human necessities    6301.0                         2011   \n3          2014  A-Human necessities    6415.0                         2011   \n4          2015  A-Human necessities    7455.0                         2011   \n..          ...                  ...       ...                          ...   \n91         2018        H-Electricity   19820.0                         2011   \n92         2019        H-Electricity   21883.0                         2011   \n93         2020        H-Electricity   23436.0                         2011   \n94         2021        H-Electricity   13101.0                         2011   \n95         2022        H-Electricity      10.0                         2011   \n\n    appln_id_relative_growth  appln_id_cumsum  \n0                   0.000000           6063.0  \n1                  -0.042718          11867.0  \n2                   0.039254          18168.0  \n3                   0.058057          24583.0  \n4                   0.229589          32038.0  \n..                       ...              ...  \n91                  0.607070         121682.0  \n92                  0.774345         143565.0  \n93                  0.900268         167001.0  \n94                  0.062272         180102.0  \n95                 -0.999189         180112.0  \n\n[96 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>filing_year</th>\n      <th>ipc_class</th>\n      <th>appln_id</th>\n      <th>filing_year_relative_growth</th>\n      <th>appln_id_relative_growth</th>\n      <th>appln_id_cumsum</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>A-Human necessities</td>\n      <td>6063.0</td>\n      <td>2011</td>\n      <td>0.000000</td>\n      <td>6063.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>A-Human necessities</td>\n      <td>5804.0</td>\n      <td>2011</td>\n      <td>-0.042718</td>\n      <td>11867.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>A-Human necessities</td>\n      <td>6301.0</td>\n      <td>2011</td>\n      <td>0.039254</td>\n      <td>18168.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>A-Human necessities</td>\n      <td>6415.0</td>\n      <td>2011</td>\n      <td>0.058057</td>\n      <td>24583.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>A-Human necessities</td>\n      <td>7455.0</td>\n      <td>2011</td>\n      <td>0.229589</td>\n      <td>32038.0</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    </tr>\n    <tr>\n      <th>91</th>\n      <td>2018</td>\n      <td>H-Electricity</td>\n      <td>19820.0</td>\n      <td>2011</td>\n      <td>0.607070</td>\n      <td>121682.0</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>2019</td>\n      <td>H-Electricity</td>\n      <td>21883.0</td>\n      <td>2011</td>\n      <td>0.774345</td>\n      <td>143565.0</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>2020</td>\n      <td>H-Electricity</td>\n      <td>23436.0</td>\n      <td>2011</td>\n      <td>0.900268</td>\n      <td>167001.0</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>2021</td>\n      <td>H-Electricity</td>\n      <td>13101.0</td>\n      <td>2011</td>\n      <td>0.062272</td>\n      <td>180102.0</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>2022</td>\n      <td>H-Electricity</td>\n      <td>10.0</td>\n      <td>2011</td>\n      <td>-0.999189</td>\n      <td>180112.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>96 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n",
    "        .fillna(0)\n",
    "        .stack()\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n",
    "                  on='ipc_class', suffixes=[None,\"_relative_growth\"])\n",
    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
    "\n",
    "data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n",
    "data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n",
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "group = ['filing_year','ipc_class']\n",
    "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True,                             labels={\n",
    "                     record_col: 'Number of co-publications',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Yearly output of co-publications\", template='plotly')\n",
    "fig.update_traces(hovertemplate='%{y:d}')\n",
    "fig.update_layout(hovermode='x unified')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "year_output_by_domain = go.Figure(fig)\n",
    "\n",
    "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True,                             labels={\n",
    "                     record_col+\"_relative_growth\": 'Rel. growth<br>in co-publications (%)',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Relative growth in the output of co-publications\", template='plotly')\n",
    "\n",
    "fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n",
    "fig.update_traces(hovertemplate='%{y:.0f}00%')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "rel_output_by_domain = go.Figure(fig)\n",
    "\n",
    "\n",
    "fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n",
    "              labels={\n",
    "                     record_col+\"_cumsum\": 'Cumulative number of co-publications',\n",
    "                     group[-1]: \"Domain\",\n",
    "                 },\n",
    "                title=\"Cumulative number of co-publications\", template='plotly')\n",
    "fig.update_traces(hovertemplate='%{y:d}')\n",
    "fig.update_layout(hovermode='x unified')\n",
    "fig.add_shape(\n",
    "        # Rectangle with reference to the plot\n",
    "            type=\"rect\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x0=0,\n",
    "            y0=0,\n",
    "            x1=1.0,\n",
    "            y1=1.0,\n",
    "            line=dict(\n",
    "                color=\"black\",\n",
    "                 width=0.5,\n",
    "             )\n",
    "         )\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,\n",
    "    ticks=\"outside\")\n",
    "\n",
    "cumsum_by_domain = go.Figure(fig)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "A-Human necessities",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "A-Human necessities",
         "offsetgroup": "A-Human necessities",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          89497
         ],
         "xaxis": "x",
         "y": [
          "A-Human necessities"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "B-Operations; transporting",
         "marker": {
          "color": "#EF553B",
          "pattern": {
           "shape": ""
          }
         },
         "name": "B-Operations; transporting",
         "offsetgroup": "B-Operations; transporting",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          135217
         ],
         "xaxis": "x",
         "y": [
          "B-Operations; transporting"
         ],
         "yaxis": "y",
         "type": "bar"
        },
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "C-Chemistry; metallurgy",
         "marker": {
          "color": "#00cc96",
          "pattern": {
           "shape": ""
          }
         },
         "name": "C-Chemistry; metallurgy",
         "offsetgroup": "C-Chemistry; metallurgy",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
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         "range": [
          2010.8,
          2022.2
         ]
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.625,
          1.0
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "tickformat": ".0f"
        },
        "xaxis3": {
         "anchor": "y3",
         "domain": [
          0.0,
          0.45
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "range": [
          2010.8,
          2022.2
         ]
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0.0,
          0.375
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside"
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0.55,
          1.0
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "range": [
          2010.8,
          2022.2
         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0.0,
          0.375
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "zeroline": true,
         "zerolinewidth": 0.5,
         "zerolinecolor": "grey",
         "tickformat": ".0%"
        },
        "annotations": [
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Distribution of IPC classes",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 1.0,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Cumulative sum of related co-patents",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 1.0,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Related co-patents per year",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Relative growth of co-patents",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "barmode": "relative",
        "font": {
         "family": "Montserrat",
         "size": 12
        }
       },
       "config": {
        "displayModeBar": false,
        "responsive": true,
        "plotlyServerURL": "https://plotly.com"
       }
      },
      "text/html": "<div>                            <div id=\"6a943daa-95ea-46ec-9cd3-5913f05426cf\" 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(\"6a943daa-95ea-46ec-9cd3-5913f05426cf\")) {                    Plotly.newPlot(                        \"6a943daa-95ea-46ec-9cd3-5913f05426cf\",                        [{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"A-Human necessities\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"A-Human necessities\",\"offsetgroup\":\"A-Human necessities\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[89497],\"xaxis\":\"x\",\"y\":[\"A-Human necessities\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"B-Operations; transporting\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"B-Operations; transporting\",\"offsetgroup\":\"B-Operations; transporting\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[135217],\"xaxis\":\"x\",\"y\":[\"B-Operations; transporting\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"C-Chemistry; metallurgy\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"C-Chemistry; metallurgy\",\"offsetgroup\":\"C-Chemistry; metallurgy\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[67549],\"xaxis\":\"x\",\"y\":[\"C-Chemistry; metallurgy\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"D-Textiles; paper\",\"marker\":{\"color\":\"#ab63fa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"D-Textiles; paper\",\"offsetgroup\":\"D-Textiles; paper\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[10113],\"xaxis\":\"x\",\"y\":[\"D-Textiles; paper\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"E-Fixed constructions\",\"marker\":{\"color\":\"#FFA15A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"E-Fixed constructions\",\"offsetgroup\":\"E-Fixed constructions\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[22377],\"xaxis\":\"x\",\"y\":[\"E-Fixed constructions\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"F-Engineering\",\"marker\":{\"color\":\"#19d3f3\",\"pattern\":{\"shape\":\"\"}},\"name\":\"F-Engineering\",\"offsetgroup\":\"F-Engineering\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[79587],\"xaxis\":\"x\",\"y\":[\"F-Engineering\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"G-Physics\",\"marker\":{\"color\":\"#FF6692\",\"pattern\":{\"shape\":\"\"}},\"name\":\"G-Physics\",\"offsetgroup\":\"G-Physics\",\"orientation\":\"h\",\"showlegend\":false,\"textangle\":0,\"textfont\":{\"size\":12},\"textposition\":\"outside\",\"texttemplate\":\"%{x}\",\"x\":[173875],\"xaxis\":\"x\",\"y\":[\"G-Physics\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"H-Electr
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# dom_distr\n",
    "# year_output_by_domain\n",
    "# rel_output_by_domain\n",
    "# cumsum_by_domain\n",
    "\n",
    "figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of IPC classes\",\"Cumulative sum of related co-patents\",\n",
    "                                                         \"Related co-patents per year\",\"Relative growth of co-patents\"])\n",
    "\n",
    "\n",
    "for trace in list(dom_distr.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    # trace.barmode\n",
    "    figsuper.add_trace(trace,\n",
    "        row=1, col=1\n",
    "    )\n",
    "\n",
    "for trace in list(cumsum_by_domain.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "        row=1, col=2\n",
    "    )\n",
    "\n",
    "for trace in list(year_output_by_domain.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    figsuper.add_trace(trace,\n",
    "        row=2, col=1\n",
    "    )\n",
    "\n",
    "for trace in list(rel_output_by_domain.select_traces()):\n",
    "    trace.showlegend=False\n",
    "    figsuper.add_trace(trace,\n",
    "        row=2, col=2\n",
    "    )\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",
    "\n",
    "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n",
    "figsuper['layout']['yaxis2'].update(tickformat=\".0f\")\n",
    "figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n",
    "figsuper['layout'][f'xaxis'][\"range\"] = [0,210000]\n",
    "for i in range(2,5):\n",
    "    figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n",
    "figsuper['layout'][\"font\"][\"size\"]=12\n",
    "for a in figsuper['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 14\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "1964"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f[\"filing_year\"].min()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "615464"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "591442"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f.merge(cpc_def, on=\"cpc_id\")[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['A', 'S', 'C'], dtype=object)"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f[\"ipc_class_level\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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 == 'CN')|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'UK'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI',\n       'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL',\n       'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'], dtype=object)"
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scope_countries"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "    person_id   appln_id  applt_seq_nr  invt_seq_nr\n0           1  413601768             1            0\n1          21  332015605             1            0\n2          21  333490084             1            0\n3          21  335903805             1            0\n4          76  352908776             1            0\n..        ...        ...           ...          ...\n95        220  417147567             1            0\n96        220  417932952             1            0\n97        220  420712526             1            0\n98        220  420927947             1            0\n99        220  420947631             1            0\n\n[100 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>person_id</th>\n      <th>appln_id</th>\n      <th>applt_seq_nr</th>\n      <th>invt_seq_nr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>413601768</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>332015605</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21</td>\n      <td>333490084</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>21</td>\n      <td>335903805</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>76</td>\n      <td>352908776</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    </tr>\n    <tr>\n      <th>95</th>\n      <td>220</td>\n      <td>417147567</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>220</td>\n      <td>417932952</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>220</td>\n      <td>420712526</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>220</td>\n      <td>420927947</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>220</td>\n      <td>420947631</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers.head(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'person_ctry_code'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgr\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\n\u001B[0;32m      2\u001B[0m \u001B[43m                                                             \u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_auth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      4\u001B[0m                                                               (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\groupby\\generic.py:1741\u001B[0m, in \u001B[0;36mDataFrameGroupBy.filter\u001B[1;34m(self, func, dropna, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1738\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m name, group \u001B[38;5;129;01min\u001B[39;00m gen:\n\u001B[0;32m   1739\u001B[0m     \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__setattr__\u001B[39m(group, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m\"\u001B[39m, name)\n\u001B[1;32m-> 1741\u001B[0m     res \u001B[38;5;241m=\u001B[39m func(group, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1743\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1744\u001B[0m         res \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39msqueeze()\n",
      "Cell \u001B[1;32mIn[26], line 1\u001B[0m, in \u001B[0;36m<lambda>\u001B[1;34m(gr)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m appln_pers\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      2\u001B[0m                                                              (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m      4\u001B[0m                                                               (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m      5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m   3744\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m   3747\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m   3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m   3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m   3653\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m   3654\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m   3655\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[0;32m   3656\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
      "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'"
     ]
    }
   ],
   "source": [
    "appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
    "                                                             (gr[\"appln_auth\"]==\"CN\")).any())\n",
    "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(eu_countries)).any()|\n",
    "                                                              (gr[\"appln_auth\"].isin(eu_countries))).any())\n",
    "appln_scope[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_scope"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_scope[appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_scope[~appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_cpc[\"cpc_id\"] = appln_cpc[\"cpc_class_symbol\"].str.replace(\" \",\"\")\n",
    "\n",
    "appln_cpc_merge = appln_cpc.merge(cpc_def,on='cpc_id')\n",
    "print (f\"CPC Merge loss: {(1-len(appln_cpc_merge)/len(appln_cpc))*100}%\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "len(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")[\"appln_nr\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\")[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "cpc_def[cpc_def[\"data_scope\"]==True]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_title.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_data = appln.merge(appln_title, on=\"appln_id\")\n",
    "appln_data.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_data[\"appln_auth\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_pers"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
    "    lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n",
    "persn = pers.merge(pers_sector_primary, on='han_id')\n",
    "persn"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pers_sector_primary"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n",
    "appln_merge.to_excel(\"appln_data.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "person_merge = appln_pers.merge(pers,on=\"person_id\")\n",
    "person_merge.to_excel(\"person_data.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "pers[\"person_ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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