ZSI_Reconnect_China/PATSTAT/patstat_analysis_pipeline.ipynb

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  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "outputs": [
    {
     "data": {
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     },
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     "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": 35,
   "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": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 36,
     "metadata": {},
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    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "24638"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n8712   413791676         KR   20127034385         A         2011-06-03  \\\n15588  439148012         EP      15714777         A         2015-03-26   \n48628  536723783         RU    2019104609         A         2017-07-18   \n4458   380638095         US  201113218876         A         2011-08-26   \n43221  522842130         US  201829659634         F         2018-08-10   \n3836   378587773         WO    2011074635         W         2011-05-25   \n9237   414823192         EP      12882000         A         2012-08-03   \n6777   409424323         US  201113638187         A         2011-03-22   \n18826  446137103         WO    2015057861         W         2015-10-14   \n52265  545763074         BR  112020021641         A         2018-04-23   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n8712                2011     1020127034385       PI                   \\\n15588               2015          15714777       PI                    \n48628               2017        2019104609       PI                    \n4458                2011          13218876       PI                    \n43221               2018               NaN       DP                    \n3836                2011     CN2011/074635       PI               CN   \n9237                2012          12882000       PI                    \n6777                2011          13638187       PI                    \n18826               2015     IB2015/057861       PI               IB   \n52265               2018      112020021641       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n8712           339637678  ...          2013-11-04                2013  \\\n15588          438355565  ...          2017-02-15                2017   \n48628          488987246  ...          2020-08-20                2020   \n4458                   0  ...          2013-01-31                2013   \n43221                  0  ...          2019-12-03                2019   \n3836                   0  ...          2012-11-29                2012   \n9237           414790439  ...          2015-06-10                2015   \n6777           333698631  ...          2013-08-08                2013   \n18826                  0  ...          2016-04-21                2016   \n52265          521276805  ...          2021-01-26                2021   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n8712              413791677       Y         45067306          339616929  \\\n15588             473888077       Y         52814965          438355565   \n48628             536723784       Y         60991950          488987246   \n4458              380922724       N         47598050          380638095   \n43221             522842131       Y         68653880          522842130   \n3836              378816884       N         47216526          378587773   \n9237              439934604       N         50027133          414790439   \n6777              409424324       Y         43020401          319321099   \n18826             451530410       N         52014243          446137103   \n52265             545763075       N         68293428          521276805   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n8712                  6                   10              1            7  \n15588                 5                    1              1            2  \n48628                39                    3              1           19  \n4458                  2                    3              8            7  \n43221                 2                    0              1            1  \n3836                  3                    4              3            2  \n9237                  2                    0              1            2  \n6777                  6                    4              6            5  \n18826                 9                    3              2            1  \n52265                 6 
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8712</th>\n      <td>413791676</td>\n      <td>KR</td>\n      <td>20127034385</td>\n      <td>A</td>\n      <td>2011-06-03</td>\n      <td>2011</td>\n      <td>1020127034385</td>\n      <td>PI</td>\n      <td></td>\n      <td>339637678</td>\n      <td>...</td>\n      <td>2013-11-04</td>\n      <td>2013</td>\n      <td>413791677</td>\n      <td>Y</td>\n      <td>45067306</td>\n      <td>339616929</td>\n      <td>6</td>\n      <td>10</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>15588</th>\n      <td>439148012</td>\n      <td>EP</td>\n      <td>15714777</td>\n      <td>A</td>\n      <td>2015-03-26</td>\n      <td>2015</td>\n      <td>15714777</td>\n      <td>PI</td>\n      <td></td>\n      <td>438355565</td>\n      <td>...</td>\n      <td>2017-02-15</td>\n      <td>2017</td>\n      <td>473888077</td>\n      <td>Y</td>\n      <td>52814965</td>\n      <td>438355565</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>48628</th>\n      <td>536723783</td>\n      <td>RU</td>\n      <td>2019104609</td>\n      <td>A</td>\n      <td>2017-07-18</td>\n      <td>2017</td>\n      <td>2019104609</td>\n      <td>PI</td>\n      <td></td>\n      <td>488987246</td>\n      <td>...</td>\n      <td>2020-08-20</td>\n      <td>2020</td>\n      <td>536723784</td>\n      <td>Y</td>\n      <td>60991950</td>\n      <td>488987246</td>\n      <td>39</td>\n      <td>3</td>\n      <td>1</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>4458</th>\n      <td>380638095</td>\n      <td>US</td>\n      <td>201113218876</td>\n      <td>A</td>\n      <td>2011-08-26</td>\n      <td>2011</td>\n      <td>13218876</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2013-01-31</td>\n      <td>2013</td>\n      <td>380922724</td>\n      <td>N</td>\n      <td>47598050</td>\n      <td>380638095</td>\n      <td>2</td>\n      <td>3</td>\n      <td>8</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>43221</th>\n      <td>522842130</td>\n      <td>US</td>\n      <td>201829659634</td>\n      <td>F</td>\n      <td>2018-08-10</td>\n      <td>2018</td>\n      <td>NaN</td>\n      <td>DP</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-12-03</td>\n      <td>2019</td>\n      <td>522842131</td>\n      <td>Y</td>\n      <td>68653880</td>\n      <td>522842130</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3836</th>\n      <td>378587773</td>\n      <td>WO</td>\n      <td>2011074635</td>\n      <td>W</td>\n      <td>2011-05-25</td>\n      <td>2011</td>\n      <td>CN2011/074635</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-11-29</td>\n      <td>2012</td>\n      <td>378816884</td>\n      <td>N</td>\n      <td>47216526</td>\n      <td>378587773</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9237</th>\
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n56447  556331792         PE    2021000402         A         2019-09-26  \\\n41201  518451341         TW     107131654         A         2018-09-10   \n54094  550225934         TW     109119471         A         2017-07-06   \n9133   414556231         EP      12881920         A         2012-07-27   \n43037  522510076         EP      19306441         A         2019-11-07   \n...          ...        ...           ...        ...               ...   \n60095  569415925         TW     110116177         A         2021-05-05   \n61281  573442692         DE  102021213653         A         2021-12-02   \n41189  518448790         TW     108109981         A         2014-03-07   \n64973  587185640         CR      20220470         A         2021-03-18   \n8491   413374202         EP      13197783         A         2011-04-28   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n56447               2019        2021000402       PI                   \\\n41201               2018         107131654       PI                    \n54094               2017         109119471       PI                    \n9133                2012          12881920       PI                    \n43037               2019          19306441       PI                    \n...                  ...               ...      ...              ...   \n60095               2021         110116177       PI                    \n61281               2021      102021213653       PI                    \n41189               2014         108109981       PI                    \n64973               2021          20220470       PI                    \n8491                2011          13197783       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n56447          520193774  ...          2021-07-19                2021  \\\n41201                  0  ...          2019-06-16                2019   \n54094                  0  ...          2021-02-01                2021   \n9133           414544093  ...          2015-06-03                2015   \n43037                  0  ...          2021-05-12                2021   \n...                  ...  ...                 ...                 ...   \n60095                  0  ...          2021-12-16                2021   \n61281                  0  ...          2022-06-15                2022   \n41189                  0  ...          2019-06-16                2019   \n64973          549449130  ...          2022-11-14                2022   \n8491           376727956  ...          2014-08-20                2014   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n56447             556331793       N         68542579          520193774  \\\n41201             518451342       N         66100215          511344799   \n54094             550225935       N         59337896          481645536   \n9133              439682012       Y         49996511          414544093   \n43037             548841071       N         68655464          522510076   \n...                     ...     ...              ...                ...   \n60095             568265190       N         74845079          546468249   \n61281             573442693       N         81749879          573442692   \n41189             518448791       Y         51500341          421859342   \n64973             587185641       N         75660301          549449130   \n8491              420445397       N         45896256          352844875   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n56447                10                    0              1            3  \n41201                 2                    0              1            2  \n54094                 8                    3              1            7  \n9133                  4                    5              1            3  \n43037                 3                    0              1            2  \n...                 ...       
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>56447</th>\n      <td>556331792</td>\n      <td>PE</td>\n      <td>2021000402</td>\n      <td>A</td>\n      <td>2019-09-26</td>\n      <td>2019</td>\n      <td>2021000402</td>\n      <td>PI</td>\n      <td></td>\n      <td>520193774</td>\n      <td>...</td>\n      <td>2021-07-19</td>\n      <td>2021</td>\n      <td>556331793</td>\n      <td>N</td>\n      <td>68542579</td>\n      <td>520193774</td>\n      <td>10</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>41201</th>\n      <td>518451341</td>\n      <td>TW</td>\n      <td>107131654</td>\n      <td>A</td>\n      <td>2018-09-10</td>\n      <td>2018</td>\n      <td>107131654</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-06-16</td>\n      <td>2019</td>\n      <td>518451342</td>\n      <td>N</td>\n      <td>66100215</td>\n      <td>511344799</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>54094</th>\n      <td>550225934</td>\n      <td>TW</td>\n      <td>109119471</td>\n      <td>A</td>\n      <td>2017-07-06</td>\n      <td>2017</td>\n      <td>109119471</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-02-01</td>\n      <td>2021</td>\n      <td>550225935</td>\n      <td>N</td>\n      <td>59337896</td>\n      <td>481645536</td>\n      <td>8</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9133</th>\n      <td>414556231</td>\n      <td>EP</td>\n      <td>12881920</td>\n      <td>A</td>\n      <td>2012-07-27</td>\n      <td>2012</td>\n      <td>12881920</td>\n      <td>PI</td>\n      <td></td>\n      <td>414544093</td>\n      <td>...</td>\n      <td>2015-06-03</td>\n      <td>2015</td>\n      <td>439682012</td>\n      <td>Y</td>\n      <td>49996511</td>\n      <td>414544093</td>\n      <td>4</td>\n      <td>5</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>43037</th>\n      <td>522510076</td>\n      <td>EP</td>\n      <td>19306441</td>\n      <td>A</td>\n      <td>2019-11-07</td>\n      <td>2019</td>\n      <td>19306441</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-05-12</td>\n      <td>2021</td>\n      <td>548841071</td>\n      <td>N</td>\n      <td>68655464</td>\n      <td>522510076</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>60095</th>\n      <td>569415925</td>\n      <td>TW</td>\n      <td
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]].sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "        person_id                   person_name           person_name_orig_lg   \n99304    72271503                   SHE, HUIMIN                   SHE, HUIMIN  \\\n4389      6211563                    Zhou, Jian                    Zhou, Jian   \n59373    52078604              KIRCHNER, NICOLE              KIRCHNER, NICOLE   \n19688    16957554              BLAWERT, CARSTEN              BLAWERT, CARSTEN   \n25828    25701586                KNIESEL, SIMON                KNIESEL, SIMON   \n73679    55544020  WHITING, Tien Yi Theresa Hsu  WHITING, Tien Yi Theresa Hsu   \n39905    45320991                Zhang, Michael                Zhang, Michael   \n65003    53390230                 DONG, Hailong                 DONG, Hailong   \n113369   79456511                    LING, Jing                    LING, Jing   \n58383    51832619                  WEN JU ZHANG                  WEN JU ZHANG   \n\n                                           person_address person_ctry_code   \n99304                                            Shanghai               CN  \\\n4389                                              Beijing               CN   \n59373             SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE               DE   \n19688                                                 NaN               DE   \n25828                                                 NaN               DE   \n73679                                                 NaN               US   \n39905                                             Beijing               CN   \n65003                     Molenstraat 148,5701 KK Helmond               NL   \n113369  No. 6 North Of Binjiang Road, Taixing Economic...               CN   \n58383                                                  CN               CN   \n\n         nuts  nuts_level  doc_std_name_id                 doc_std_name   \n99304     NaN           9         32084338                   SHE HUIMIN  \\\n4389      NaN           9            33759                    ZHOU JIAN   \n59373      DE           0          2139104              KIRCHNER NICOLE   \n19688      DE           0           311260              BLAWERT CARSTEN   \n25828      DE           0           255684                KNIESEL SIMON   \n73679     NaN           9         21758228  WHITING TIEN YI THERESA HSU   \n39905     NaN           9           725806                ZHANG MICHAEL   \n65003   NL414           3          7196768                 DONG HAILONG   \n113369    NaN           9           429470                    LING JING   \n58383     NaN           9         23069059                 ZHANG WEN JU   \n\n          psn_id                      psn_name  psn_level  psn_sector   \n99304   29505777                   SHE, HUIMIN          0     UNKNOWN  \\\n4389    37354342                     ZHOU JIAN          1  INDIVIDUAL   \n59373   17194552              KIRCHNER, NICOLE          0     UNKNOWN   \n19688    2923498              BLAWERT, CARSTEN          1  INDIVIDUAL   \n25828   17406789                KNIESEL, SIMON          1  INDIVIDUAL   \n73679   35065052  WHITING, TIEN YI THERESA HSU          0     UNKNOWN   \n39905   37097378                ZHANG, MICHAEL          1  INDIVIDUAL   \n65003    7077131                 DONG, HAILONG          0     UNKNOWN   \n113369  19626776                    LING, JING          0     UNKNOWN   \n58383   34908417                  WEN JU ZHANG          0     UNKNOWN   \n\n           han_id                      han_name  han_harmonized  \n99304   172271503                   SHE, HUIMIN               0  \n4389    106211563                    Zhou, Jian               0  \n59373   152078604              KIRCHNER, NICOLE               0  \n19688   116957554              BLAWERT, CARSTEN               0  \n25828   125701586                KNIESEL, SIMON               0  \n73679   155544020  WHITING, Tien Yi Theresa Hsu               0  \n39905   145320991                Zhang, Michael               0  \n65003   153390230                 DONG, Hailong               0  \n113369  179456511                    
      "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>person_name</th>\n      <th>person_name_orig_lg</th>\n      <th>person_address</th>\n      <th>person_ctry_code</th>\n      <th>nuts</th>\n      <th>nuts_level</th>\n      <th>doc_std_name_id</th>\n      <th>doc_std_name</th>\n      <th>psn_id</th>\n      <th>psn_name</th>\n      <th>psn_level</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>han_harmonized</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>99304</th>\n      <td>72271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>SHE, HUIMIN</td>\n      <td>Shanghai</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>32084338</td>\n      <td>SHE HUIMIN</td>\n      <td>29505777</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>172271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4389</th>\n      <td>6211563</td>\n      <td>Zhou, Jian</td>\n      <td>Zhou, Jian</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>33759</td>\n      <td>ZHOU JIAN</td>\n      <td>37354342</td>\n      <td>ZHOU JIAN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>106211563</td>\n      <td>Zhou, Jian</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>59373</th>\n      <td>52078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>2139104</td>\n      <td>KIRCHNER NICOLE</td>\n      <td>17194552</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>152078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19688</th>\n      <td>16957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>311260</td>\n      <td>BLAWERT CARSTEN</td>\n      <td>2923498</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>116957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25828</th>\n      <td>25701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>KNIESEL, SIMON</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>255684</td>\n      <td>KNIESEL SIMON</td>\n      <td>17406789</td>\n      <td>KNIESEL, SIMON</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>125701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73679</th>\n      <td>55544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>NaN</td>\n      <td>US</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>21758228</td>\n      <td>WHITING TIEN YI THERESA HSU</td>\n      <td>35065052</td>\n      <td>WHITING, TIEN YI THERESA HSU</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>155544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>39905</th>\n      <td>45320991</td>\n      <td>Zhang, Michael</td>\n      <td>Zhang, Michael</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>725806</td>\n      <td>ZHANG MICHAEL</td>\n      <td>37097378</td>\n      <td>ZHANG, MICHAEL</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>145320991</td>\n      <td>Zhang, Michael</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>65003</th>\n      <td>53390230</td>\n      <td>DONG, Hailong</td>\n      <t
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pers.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc_def"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id cpc_class_symbol\n490979  560314579      B32B   7/12\n139994  445201240    C08G2261/3241\n505928  567683420     C25D   7/123\n130069  442410454      Y02D  10/00\n467350  551851575    H04B   7/0486\n318101  509884390    H01L  21/0228\n350171  519161420      G06F  21/42\n129829  442384375      H04W  88/04\n277164  496690078      H04L  27/26\n240979  487005701    F16F2230/0064",
      "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>490979</th>\n      <td>560314579</td>\n      <td>B32B   7/12</td>\n    </tr>\n    <tr>\n      <th>139994</th>\n      <td>445201240</td>\n      <td>C08G2261/3241</td>\n    </tr>\n    <tr>\n      <th>505928</th>\n      <td>567683420</td>\n      <td>C25D   7/123</td>\n    </tr>\n    <tr>\n      <th>130069</th>\n      <td>442410454</td>\n      <td>Y02D  10/00</td>\n    </tr>\n    <tr>\n      <th>467350</th>\n      <td>551851575</td>\n      <td>H04B   7/0486</td>\n    </tr>\n    <tr>\n      <th>318101</th>\n      <td>509884390</td>\n      <td>H01L  21/0228</td>\n    </tr>\n    <tr>\n      <th>350171</th>\n      <td>519161420</td>\n      <td>G06F  21/42</td>\n    </tr>\n    <tr>\n      <th>129829</th>\n      <td>442384375</td>\n      <td>H04W  88/04</td>\n    </tr>\n    <tr>\n      <th>277164</th>\n      <td>496690078</td>\n      <td>H04L  27/26</td>\n    </tr>\n    <tr>\n      <th>240979</th>\n      <td>487005701</td>\n      <td>F16F2230/0064</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_cpc.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "325243"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln_pers)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "# Number of collaborative applications\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [],
   "source": [
    "record_col = \"appln_id\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "fromdir=\"first_round\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [],
   "source": [
    "appln_pers_f = pd.read_csv(f\"{fromdir}/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\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1116\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 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(scope_countries)).any()|\n",
    "#                                                               (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
    "\n",
    "appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n",
    "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n",
    "\n",
    "print(appln_scope[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1116\n"
     ]
    }
   ],
   "source": [
    "appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n",
    "print(appln_pers_f[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n76      410448968         DE    7067540            0             1  \\\n87      420386087         DE    7067540            0             1   \n88      420386089         DE    7067540            0             1   \n828     410512383         EP        220            0             1   \n1454    333687134         EP      10329            0             1   \n...           ...        ...        ...          ...           ...   \n147073  549690891         EP   85841201            1             0   \n147074  549690895         EP   85841201            1             0   \n147075  549690901         EP   85841201            1             0   \n147076  549690897         EP   85841201            1             0   \n147077  549788919         EP   85774895            2             0   \n\n                       person_name person_ctry_code psn_sector     han_id   \n76             Evonik Degussa GmbH               DE    COMPANY     840063  \\\n87             Evonik Degussa GmbH               DE    COMPANY     840063   \n88             Evonik Degussa GmbH               DE    COMPANY     840063   \n828     SIEMENS AKTIENGESELLSCHAFT               DE    COMPANY    2709504   \n1454                Alcatel Lucent               FR    COMPANY      68848   \n...                            ...              ...        ...        ...   \n147073               JONSSON, Tony               SE    UNKNOWN  185841201   \n147074               JONSSON, Tony               SE    UNKNOWN  185841201   \n147075               JONSSON, Tony               SE    UNKNOWN  185841201   \n147076               JONSSON, Tony               SE    UNKNOWN  185841201   \n147077         TRAN NGUYEN, Johnny               SE    UNKNOWN  185774895   \n\n                   han_name  \n76      EVONIK DEGUSSA GMBH  \n87      EVONIK DEGUSSA GMBH  \n88      EVONIK DEGUSSA GMBH  \n828              SIEMENS AG  \n1454         ALCATEL LUCENT  \n...                     ...  \n147073        JONSSON, Tony  \n147074        JONSSON, Tony  \n147075        JONSSON, Tony  \n147076        JONSSON, Tony  \n147077  TRAN NGUYEN, Johnny  \n\n[4434 rows x 10 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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>76</th>\n      <td>410448968</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>420386087</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>420386089</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n    </tr>\n    <tr>\n      <th>828</th>\n      <td>410512383</td>\n      <td>EP</td>\n      <td>220</td>\n      <td>0</td>\n      <td>1</td>\n      <td>SIEMENS AKTIENGESELLSCHAFT</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>2709504</td>\n      <td>SIEMENS AG</td>\n    </tr>\n    <tr>\n      <th>1454</th>\n      <td>333687134</td>\n      <td>EP</td>\n      <td>10329</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Alcatel Lucent</td>\n      <td>FR</td>\n      <td>COMPANY</td>\n      <td>68848</td>\n      <td>ALCATEL LUCENT</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    </tr>\n    <tr>\n      <th>147073</th>\n      <td>549690891</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147074</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147075</th>\n      <td>549690901</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147076</th>\n      <td>549690897</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n    </tr>\n    <tr>\n      <th>147077</th>\n      <td>549788919</td>\n      <td>EP</td>\n      <td>85774895</td>\n      <td>2</td>\n      <td>0</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185774895</td>\n      <td>TRAN NGUYEN, Johnny</td>\n    </tr>\n  </tbody>\n</table>\n<p>4434 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1116\n"
     ]
    },
    {
     "data": {
      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n51083   407234637         FI          A         2011              1  \\\n240847  442945435         FR          A         2015              1   \n685164  531137359         NL          A         2020              1   \n235203  438685261         EP          A         2015              1   \n430561  497548590         DE          A         2017              2   \n...           ...        ...        ...          ...            ...   \n721473  579480994         DE          A         2021              1   \n619328  523950441         EP          A         2019              2   \n484715  490602095         EP          A         2018              1   \n196502  450101287         DE          A         2014              1   \n367660  477378741         GB          A         2017              1   \n\n        nb_inventors  \n51083              2  \n240847             5  \n685164             3  \n235203             4  \n430561             8  \n...              ...  \n721473             3  \n619328             3  \n484715             1  \n196502             6  \n367660             1  \n\n[100 rows x 6 columns]",
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     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f = pd.read_csv(f\"{fromdir}/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 = appln_f[appln_f[\"appln_id\"].isin(appln_scope['appln_id'])]\n",
    "print(appln_f[\"appln_id\"].nunique())\n",
    "\n",
    "appln_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_kind\nA    1114\nU      13\nName: count, dtype: int64"
     },
     "execution_count": 57,
     "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": 65,
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "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\"] = [0,250]\n",
    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,3.5]\n",
    "#\n",
    "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
    "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
    "\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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       0.0                         2012  \\\n1           2012         AT       1.0                         2012   \n2           2013         AT       4.0                         2012   \n3           2014         AT       0.0                         2012   \n4           2015         AT       1.0                         2012   \n..           ...        ...       ...                          ...   \n160         2017         SE       3.0                         2015   \n161         2018         SE       1.0                         2015   \n162         2019         SE       0.0                         2015   \n163         2020         SE       1.0                         2015   \n164         2021         SE       0.0                         2015   \n\n     appln_id_relative_growth  appln_id_cumsum  Country  \n0                        -1.0              0.0  Austria  \n1                         0.0              1.0  Austria  \n2                         3.0              5.0  Austria  \n3                        -1.0              5.0  Austria  \n4                         0.0              6.0  Austria  \n..                        ...              ...      ...  \n160                       2.0              5.0   Sweden  \n161                       0.0              6.0   Sweden  \n162                      -1.0              6.0   Sweden  \n163                       0.0              7.0   Sweden  \n164                      -1.0              7.0   Sweden  \n\n[165 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>0.0</td>\n      <td>2012</td>\n      <td>-1.0</td>\n      <td>0.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>AT</td>\n      <td>1.0</td>\n      <td>2012</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>AT</td>\n      <td>4.0</td>\n      <td>2012</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>AT</td>\n      <td>0.0</td>\n      <td>2012</td>\n      <td>-1.0</td>\n      <td>5.0</td>\n      <td>Austria</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>AT</td>\n      <td>1.0</td>\n      <td>2012</td>\n      <td>0.0</td>\n      <td>6.0</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>160</th>\n      <td>2017</td>\n      <td>SE</td>\n      <td>3.0</td>\n      <td>2015</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>161</th>\n      <td>2018</td>\n      <td>SE</td>\n      <td>1.0</td>\n      <td>2015</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>162</th>\n      <td>2019</td>\n      <td>SE</td>\n      <td>0.0</td>\n      <td>2015</td>\n      <td>-1.0</td>\n      <td>6.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>163</th>\n      <td>2020</td>\n      <td>SE</td>\n      <td>1.0</td>\n      <td>2015</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>Sweden</td>\n    </tr>\n    <tr>\n      <th>164</th>\n      <td>2021</td>\n      <td>SE</td>\n      <td>0.0</td>\n      <td>2015</td>\n      <td>-1.0</td>\n      <td>7.0</td>\n      <td>Sweden</td>\n    </tr>\n  </tbody>\n</table>\n<p>165 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 66,
     "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"
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   "metadata": {
    "collapsed": false
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   "execution_count": 67,
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      "text/html": "<div>                            <div id=\"21bc843b-bdcd-4416-b694-7e1a28f01620\" 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(\"21bc843b-bdcd-4416-b694-7e1a28f01620\")) {                    Plotly.newPlot(                        \"21bc843b-bdcd-4416-b694-7e1a28f01620\",                        [{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x13\",\"y\":[0.0,1.0,4.0,0.0,1.0,0.0,0.0,0.0,2.0,3.0,0.0],\"yaxis\":\"y13\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x14\",\"y\":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0],\"yaxis\":\"y14\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x15\",\"y\":[0.0,0.0,1.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],\"yaxis\":\"y15\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x16\",\"y\":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,2.0,1.0,0.0],\"yaxis\":\"y16\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x17\",\"y\":[0.0,0.0,0.0,0.0,2.0,1.0,0.0,0.0,2.0,0.0,0.0],\"yaxis\":\"y17\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name
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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,\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": 68,
   "outputs": [
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      },
      "text/html": "<div>                            <div id=\"343a61ac-7b2f-48e5-9c3b-bbeca1c7e03e\" 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(\"343a61ac-7b2f-48e5-9c3b-bbeca1c7e03e\")) {                    Plotly.newPlot(                        \"343a61ac-7b2f-48e5-9c3b-bbeca1c7e03e\",                        [{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\",\"Austria\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x13\",\"y\":[-1.0,0.0,3.0,-1.0,0.0,-1.0,-1.0,-1.0,1.0,2.0,-1.0],\"yaxis\":\"y13\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\",\"Belgium\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x14\",\"y\":[-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,0.0,0.0],\"yaxis\":\"y14\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\",\"China\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x15\",\"y\":[-1.0,-1.0,0.0,1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0],\"yaxis\":\"y15\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\",\"Czech Republic\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x16\",\"y\":[-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,-1.0,0.0,1.0,0.0,-1.0],\"yaxis\":\"y16\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\",\"Denmark\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x17\",\"y\":[-1.0,-1.0,-1.0,-1.0,0.0,-0.5,-1.0,-1.0,0.0,-1.0,-1.0],\"yaxis\":\"y17\",\"type\":\"scatter\"},{\"hovertemplate\":\"<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}\",\"hovertext\":[\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\",\"European Patent Office\"],\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"ci
     },
     "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": 131,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n",
      "European Patent Office not found in regex\n"
     ]
    },
    {
     "data": {
      "text/plain": "         appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n136092  512548123         GB   55086687            0             1  \\\n140959  523940692         EP   69747486            0             1   \n145846  517778054         EP   77709457            3             0   \n111339  352908327         FR   46503468            4             0   \n102245  336208056         EP    5019334            1             0   \n...           ...        ...        ...          ...           ...   \n140952  513966940         EP   69747486            0             1   \n143598  546020617         DE   81337072            1             0   \n141356  512140061         NL   71325506            1             0   \n141273  488981411         EP   69749947            2             0   \n124034  407239903         EP   47903112            4             0   \n\n                                              person_name person_ctry_code   \n136092                          TCL Communication Limited               CN  \\\n140959  Ningbo Geely Automobile Research & Development...               CN   \n145846                                FORSBERG, Christian               SE   \n111339                                     GILIS, FABRICE               CN   \n102245                                      Carcone, Luca               IT   \n...                                                   ...              ...   \n140952  Ningbo Geely Automobile Research & Development...               CN   \n143598                                       Zhou, Hulian               CN   \n141356                                  JAN VAN DER HORST               NL   \n141273                                           Zuo, Fei               CN   \n124034                              Wichmann, Daniel, Dr.               DE   \n\n       psn_sector     han_id                            han_name   \n136092    COMPANY    3635036                        TCL COMM LTD  \\\n140959    COMPANY    3697072  NINGBO GEELY AUTOMOBILE R&D CO LTD   \n145846        NaN  177709457                 FORSBERG, Christian   \n111339        NaN  146503468                      GILIS, FABRICE   \n102245        NaN  105019334                       Carcone, Luca   \n...           ...        ...                                 ...   \n140952    COMPANY    3697072  NINGBO GEELY AUTOMOBILE R&D CO LTD   \n143598        NaN  181337072                        Zhou, Hulian   \n141356        NaN  171325506                   JAN VAN DER HORST   \n141273        NaN  169749947                            Zuo, Fei   \n124034        NaN  147903112               Wichmann, Daniel, Dr.   \n\n       person_ctry_code_fullname     appln_auth_fullname person_ctry_ISO3   \n136092                     China          United Kingdom              CHN  \\\n140959                     China  European Patent Office              CHN   \n145846                    Sweden  European Patent Office              SWE   \n111339                     China                  France              CHN   \n102245                     Italy  European Patent Office              ITA   \n...                          ...                     ...              ...   \n140952                     China  European Patent Office              CHN   \n143598                     China                 Germany              CHN   \n141356               Netherlands             Netherlands              NLD   \n141273                     China  European Patent Office              CHN   \n124034                   Germany  European Patent Office              DEU   \n\n       person_ctry_type ISO3  auth_ISO3    auth_ctry_type  \n136092            China  CHN        GBR  Non-EU associate  \n140959            China  CHN  not found               EPO  \n145846               EU  SWE  not found               EPO  \n111339            China  CHN        FRA                EU  \n102245               EU  ITA  not found               EPO  \n...                 ...  ...        ...               ...  \n140952            China  CHN  not found               EPO  \n143598    
      "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      <th>person_ctry_ISO3</th>\n      <th>person_ctry_type</th>\n      <th>ISO3</th>\n      <th>auth_ISO3</th>\n      <th>auth_ctry_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136092</th>\n      <td>512548123</td>\n      <td>GB</td>\n      <td>55086687</td>\n      <td>0</td>\n      <td>1</td>\n      <td>TCL Communication Limited</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3635036</td>\n      <td>TCL COMM LTD</td>\n      <td>China</td>\n      <td>United Kingdom</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>GBR</td>\n      <td>Non-EU associate</td>\n    </tr>\n    <tr>\n      <th>140959</th>\n      <td>523940692</td>\n      <td>EP</td>\n      <td>69747486</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Ningbo Geely Automobile Research &amp; Development...</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3697072</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>China</td>\n      <td>European Patent Office</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>145846</th>\n      <td>517778054</td>\n      <td>EP</td>\n      <td>77709457</td>\n      <td>3</td>\n      <td>0</td>\n      <td>FORSBERG, Christian</td>\n      <td>SE</td>\n      <td>NaN</td>\n      <td>177709457</td>\n      <td>FORSBERG, Christian</td>\n      <td>Sweden</td>\n      <td>European Patent Office</td>\n      <td>SWE</td>\n      <td>EU</td>\n      <td>SWE</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>111339</th>\n      <td>352908327</td>\n      <td>FR</td>\n      <td>46503468</td>\n      <td>4</td>\n      <td>0</td>\n      <td>GILIS, FABRICE</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>146503468</td>\n      <td>GILIS, FABRICE</td>\n      <td>China</td>\n      <td>France</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>FRA</td>\n      <td>EU</td>\n    </tr>\n    <tr>\n      <th>102245</th>\n      <td>336208056</td>\n      <td>EP</td>\n      <td>5019334</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Carcone, Luca</td>\n      <td>IT</td>\n      <td>NaN</td>\n      <td>105019334</td>\n      <td>Carcone, Luca</td>\n      <td>Italy</td>\n      <td>European Patent Office</td>\n      <td>ITA</td>\n      <td>EU</td>\n      <td>ITA</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>140952</th>\n      <td>513966940</td>\n      <td>EP</td>\n      <td>69747486</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Ningbo Geely Automobile Research &amp; Development...</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>3697072</td>\n      <td>NINGBO GEELY AUTOMOBILE R&amp;D CO LTD</td>\n      <td>China</td>\n      <td>European Patent Office</td>\n      <td>CHN</td>\n      <td>China</td>\n      <td>CHN</td>\n      <td>not found</td>\n      <td>EPO</td>\n    </tr>\n    <tr>\n    
     },
     "execution_count": 131,
     "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",
    "appln_pers_f[\"ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
    "\n",
    "appln_pers_f[\"auth_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"appln_auth_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\" if x!='not found' else \"EPO\")\n",
    "\n",
    "appln_pers_f.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['DEU', 'FRA', 'SWE', 'ESP', 'BEL', 'CHN', 'CHE', 'AUT', 'FIN',\n       'NLD', 'GRC', 'ITA', 'CZE', 'HUN', 'DNK', 'IRL', 'POL', 'LUX',\n       'CYP', 'PRT', 'NOR', 'ROU'], dtype=object)"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f[\"person_ctry_ISO3\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [
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        "xaxis": {
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     "metadata": {},
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   ],
   "source": [
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#9F2B68'}\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col: 'Number of co-patents',\n",
    "                     \"person_ctry_type\": \"Country type\",\n",
    "                                  \"han_label\": \"Name\",\n",
    "                                  \"Country_Type\":\"Country type\",\n",
    "                                  \"Eurovoc_Class\":\"Region\"\n",
    "                 },**c_dict)\n",
    "\n",
    "for ctry in [\"China\",'EU']:\n",
    "    ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
    "    for role in [\"invt\",\"applt\"]:\n",
    "        pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
    "        data = (pers_sub\n",
    "                 .groupby([\"han_id\",'han_name'],as_index=False)\n",
    "                 .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"})\n",
    "                 .sort_values(by=\"appln_id\", ascending=False)\n",
    "                .head(15))\n",
    "        data[\"han_label\"] = data[\"han_name\"].str.upper().str.strip(\".\")+ \" (\"+data[\"person_ctry_ISO3\"]+\")\"\n",
    "\n",
    "        fig = px.bar(data, x=record_col, y=\"han_label\", color=\"person_ctry_type\",\n",
    "                     color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
    "        fig.update_layout(font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'})\n",
    "\n",
    "        figsuper = fig\n",
    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "        figsuper.update_yaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_xaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "        figsuper['layout'][\"font\"][\"size\"]=12\n",
    "        for a in figsuper['layout'][\"annotations\"]:\n",
    "            a[\"font\"][\"size\"] = 14\n",
    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
    "\n",
    "        figsuper.update_layout(uniformtext_minsize=10)\n",
    "\n",
    "        figsuper.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "        figsuper.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [
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    "# percent of publications related to\n",
    "# -authority country\n",
    "# -inventor country\n",
    "# -applicant country\n",
    "\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#7F00FF'}#CC8899\n",
    "\n",
    "c_dict = {\"count\":\"Number of co-publications\",\n",
    "          \"percent_of_copubs\":\"Percent of co-publications\",\n",
    "          \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col+\"_percent\": 'Percent of related co-patents',\n",
    "                     \"person_ctry_code_fullname\": \"Country\",\n",
    "    \"person_ctry_type\": \"Country type\",\n",
    "                                  \"appln_auth_fullname\": \"Authority\",\n",
    "                                    \"auth_ctry_type\": \"Authority type\",\n",
    "                                  \"Country_Type\":\"Country type\",\n",
    "                                  \"Eurovoc_Class\":\"Region\"\n",
    "                 },**c_dict)\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    pers_sub = appln_pers_f\n",
    "    if c == \"person_ctry_code\":\n",
    "        for role in [\"invt\",\"applt\"]:\n",
    "            pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
    "\n",
    "            data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
    "            data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "            fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "            fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=12\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "            figsuper.show()\n",
    "    else:\n",
    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
    "        data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "        fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "        fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "        figsuper = fig\n",
    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "        figsuper.update_yaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_xaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "        figsuper['layout'][\"font\"][\"size\"]=12\n",
    "        for a in figsuper['layout'][\"annotations\"]:\n",
    "            a[\"font\"][\"size\"] = 14\n",
    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
    "\n",
    "        figsuper.update_layout(uniformtext_minsize=10)\n",
    "        figsuper.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": [],
   "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": null,
   "outputs": [],
   "source": [],
   "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": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth appln_kind  filing_year  nb_applicants   \n1322402  523940692         EP          A         2019              2  \\\n765767   476494118         EP          A         2017              1   \n34130    378211797         DE          U         2011              1   \n789796   480385078         GB          A         2017              1   \n701348   451567585         NL          A         2016              1   \n...            ...        ...        ...          ...            ...   \n1361812  536054394         EP          A         2020              1   \n242632   424885398         DE          A         2013              1   \n1108772  503145148         EP          A         2018              1   \n538788   447597102         EP          A         2015              1   \n272481   412013115         EP          A         2013              1   \n\n         nb_inventors ipc_class_symbol ipc_class_level      cpc_id   \n1322402             2      B60W  50/14               A   B60W50/14  \\\n765767              4      B60H   1/34               A    B60H1/34   \n34130               0      F04B  17/04               A   F04B17/04   \n789796              2      H04W  76/14               A   H04W76/14   \n701348              3     E04F  13/077               A  E04F13/077   \n...               ...              ...             ...         ...   \n1361812             4      G05B  23/02               A   G05B23/02   \n242632              1      F02M  51/06               A   F02M51/06   \n1108772             2      G08G   1/01               A    G08G1/01   \n538788              6      H01F  38/30               A   H01F38/30   \n272481              3      C04B  28/04               A   C04B28/04   \n\n                          ipc_class             ipc_classname  \n1322402  B-Operations; transporting  Operations; transporting  \n765767   B-Operations; transporting  Operations; transporting  \n34130                 F-Engineering               Engineering  \n789796                H-Electricity               Electricity  \n701348        E-Fixed constructions       Fixed constructions  \n...                             ...                       ...  \n1361812                   G-Physics                   Physics  \n242632                F-Engineering               Engineering  \n1108772                   G-Physics                   Physics  \n538788                H-Electricity               Electricity  \n272481      C-Chemistry; metallurgy     Chemistry; metallurgy  \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>1322402</th>\n      <td>523940692</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>2</td>\n      <td>2</td>\n      <td>B60W  50/14</td>\n      <td>A</td>\n      <td>B60W50/14</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n    </tr>\n    <tr>\n      <th>765767</th>\n      <td>476494118</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>4</td>\n      <td>B60H   1/34</td>\n      <td>A</td>\n      <td>B60H1/34</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n    </tr>\n    <tr>\n      <th>34130</th>\n      <td>378211797</td>\n      <td>DE</td>\n      <td>U</td>\n      <td>2011</td>\n      <td>1</td>\n      <td>0</td>\n      <td>F04B  17/04</td>\n      <td>A</td>\n      <td>F04B17/04</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n    </tr>\n    <tr>\n      <th>789796</th>\n      <td>480385078</td>\n      <td>GB</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n      <td>H04W  76/14</td>\n      <td>A</td>\n      <td>H04W76/14</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>701348</th>\n      <td>451567585</td>\n      <td>NL</td>\n      <td>A</td>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n      <td>E04F  13/077</td>\n      <td>A</td>\n      <td>E04F13/077</td>\n      <td>E-Fixed constructions</td>\n      <td>Fixed constructions</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>1361812</th>\n      <td>536054394</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>1</td>\n      <td>4</td>\n      <td>G05B  23/02</td>\n      <td>A</td>\n      <td>G05B23/02</td>\n      <td>G-Physics</td>\n      <td>Physics</td>\n    </tr>\n    <tr>\n      <th>242632</th>\n      <td>424885398</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>1</td>\n      <td>F02M  51/06</td>\n      <td>A</td>\n      <td>F02M51/06</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n    </tr>\n    <tr>\n      <th>1108772</th>\n      <td>503145148</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>2</td>\n      <td>G08G   1/01</td>\n      <td>A</td>\n      <td>G08G1/01</td>\n      <td>G-Physics</td>\n      <td>Physics</td>\n    </tr>\n    <tr>\n      <th>538788</th>\n      <td>447597102</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>6</td>\n      <td>H01F  38/30</td>\n      <td>A</td>\n      <td>H01F38/30</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n    </tr>\n    <tr>\n      <th>272481</th>\n      <td>412013115</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>3</td>\n      <td>C04B  28/04</td>\n      <td>A</td>\n      <td>C04B28/04</td>\n      <td>C-Chemistry; metallurgy</td>\n      <td>Chemistry; metallurgy</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f = pd.read_csv(f\"{fromdir}/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 = appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])]\n",
    "\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": 81,
   "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": 82,
   "outputs": [
    {
     "data": {
      "text/plain": "    filing_year            ipc_class  appln_id  filing_year_relative_growth   \n0          2011  A-Human necessities       7.0                         2011  \\\n1          2012  A-Human necessities       5.0                         2011   \n2          2013  A-Human necessities      18.0                         2011   \n3          2014  A-Human necessities       9.0                         2011   \n4          2015  A-Human necessities      10.0                         2011   \n..          ...                  ...       ...                          ...   \n83         2017        H-Electricity      71.0                         2011   \n84         2018        H-Electricity      65.0                         2011   \n85         2019        H-Electricity      27.0                         2011   \n86         2020        H-Electricity      27.0                         2011   \n87         2021        H-Electricity      11.0                         2011   \n\n    appln_id_relative_growth  appln_id_cumsum  \n0                   0.000000              7.0  \n1                  -0.285714             12.0  \n2                   1.571429             30.0  \n3                   0.285714             39.0  \n4                   0.428571             49.0  \n..                       ...              ...  \n83                  3.437500            213.0  \n84                  3.062500            278.0  \n85                  0.687500            305.0  \n86                  0.687500            332.0  \n87                 -0.312500            343.0  \n\n[88 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>7.0</td>\n      <td>2011</td>\n      <td>0.000000</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>A-Human necessities</td>\n      <td>5.0</td>\n      <td>2011</td>\n      <td>-0.285714</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>A-Human necessities</td>\n      <td>18.0</td>\n      <td>2011</td>\n      <td>1.571429</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>A-Human necessities</td>\n      <td>9.0</td>\n      <td>2011</td>\n      <td>0.285714</td>\n      <td>39.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>A-Human necessities</td>\n      <td>10.0</td>\n      <td>2011</td>\n      <td>0.428571</td>\n      <td>49.0</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>83</th>\n      <td>2017</td>\n      <td>H-Electricity</td>\n      <td>71.0</td>\n      <td>2011</td>\n      <td>3.437500</td>\n      <td>213.0</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>2018</td>\n      <td>H-Electricity</td>\n      <td>65.0</td>\n      <td>2011</td>\n      <td>3.062500</td>\n      <td>278.0</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>2019</td>\n      <td>H-Electricity</td>\n      <td>27.0</td>\n      <td>2011</td>\n      <td>0.687500</td>\n      <td>305.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>2020</td>\n      <td>H-Electricity</td>\n      <td>27.0</td>\n      <td>2011</td>\n      <td>0.687500</td>\n      <td>332.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>2021</td>\n      <td>H-Electricity</td>\n      <td>11.0</td>\n      <td>2011</td>\n      <td>-0.312500</td>\n      <td>343.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>88 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 82,
     "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": 83,
   "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": 87,
   "outputs": [
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          "scene": {
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          },
          "shapedefaults": {
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          },
          "title": {
           "x": 0.05
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          "xaxis": {
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           "title": {
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          "yaxis": {
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            "standoff": 15
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           "zerolinewidth": 2
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         }
        },
        "xaxis": {
         "anchor": "y",
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         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "tickformat": ".0f",
         "range": [
          0,
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         ]
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.625,
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         ],
         "categoryorder": "total ascending",
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         "showline": true,
         "linewidth": 1,
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        "xaxis2": {
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         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "range": [
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        },
        "yaxis2": {
         "anchor": "x2",
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         "showgrid": true,
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        "xaxis3": {
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         "showgrid": true,
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         "range": [
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        },
        "yaxis3": {
         "anchor": "x3",
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         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside"
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
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         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "range": [
          2010.8,
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         ]
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0.0,
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         ],
         "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",
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         },
         {
          "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=\"b5e8e03a-23f5-4562-9e4a-6952973aba81\" 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(\"b5e8e03a-23f5-4562-9e4a-6952973aba81\")) {                    Plotly.newPlot(                        \"b5e8e03a-23f5-4562-9e4a-6952973aba81\",                        [{\"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\":[108],\"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\":[357],\"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\":[150],\"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\":[15],\"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\":[50],\"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\":[155],\"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\":[248],\"xaxis\":\"x\",\"y\":[\"G-Physics\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"cliponaxis\":true,\"hovertemplate\":\"%{x:d}\",\"legendgroup\":\"H-Electricity\",\"marker\"
     },
     "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\"] = [0,400]\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(scope_countries)).any()|\n",
    "                                                              (gr[\"appln_auth\"].isin(scope_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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