ZSI_Reconnect_China/PATSTAT/patstat_analysis_pipeline_deprecated.ipynb

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   "cell_type": "code",
   "execution_count": 2,
   "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": 1,
   "outputs": [
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     },
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    }
   ],
   "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": 18,
   "outputs": [],
   "source": [
    "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
    "# tls_801.head()\n",
    "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'GB'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outdir=\"raw_files_csv\""
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   "cell_type": "code",
   "execution_count": 9,
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      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_14780\\163466245.py:1: DtypeWarning:\n",
      "\n",
      "Columns (2,6,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
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     "data": {
      "text/plain": "           appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n5420565   492443204         CN  201710924209          A        2017-09-30  \\\n4123443   476745655         CN  201510575292          A        2015-09-11   \n12669428  584371570         CN  202211359033          A        2022-11-02   \n2837134   447940178         CN  201510724424          A        2015-10-29   \n3917352   474354000         CN  201610699880          A        2016-08-19   \n7083405   510565742         CN  201910024682          A        2019-01-10   \n10061891  549184042         CN  202011496905          A        2020-12-17   \n12187346  578289051         CN  202210716624          A        2022-06-23   \n8750003   533546770         CN  202010149696          A        2020-03-06   \n2150113   439343652         CN  201410639681          A        2014-11-14   \n\n          appln_filing_year appln_nr_original ipr_type receiving_office   \n5420565                2017      201710924209       PI              NaN  \\\n4123443                2015      201510575292       PI              NaN   \n12669428               2022      202211359033       PI              NaN   \n2837134                2015     2015107244247       PI              NaN   \n3917352                2016      201610699880       PI              NaN   \n7083405                2019      201910024682       PI              NaN   \n10061891               2020      202011496905       PI              NaN   \n12187346               2022      202210716624       PI              NaN   \n8750003                2020      202010149696       PI              NaN   \n2150113                2014     2014106396816       PI              NaN   \n\n          internat_appln_id  ... earliest_pat_publn_id granted   \n5420565                   0  ...             492443205       Y  \\\n4123443                   0  ...             476745656       N   \n12669428                  0  ...             584371571       N   \n2837134                   0  ...             447940179       N   \n3917352                   0  ...             474354001       N   \n7083405                   0  ...             510565743       Y   \n10061891                  0  ...             549184043       Y   \n12187346                  0  ...             578289052       N   \n8750003                   0  ...             533546771       Y   \n2150113                   0  ...             439343653       N   \n\n         docdb_family_id inpadoc_family_id  docdb_family_size   \n5420565         61698252         492443204                  1  \\\n4123443         58346251         476745655                  1   \n12669428        84336840         584371570                  1   \n2837134         54942635         447940178                  1   \n3917352         57825252         474354000                  1   \n7083405         65916569         510565742                  1   \n10061891        75448776         549184042                  1   \n12187346        82910930         578289051                  1   \n8750003         71152278         533546770                  1   \n2150113         52882593         439343652                  1   \n\n          nb_citing_docdb_fam nb_applicants  nb_inventors  is_prior is_pct  \n5420565                     1             1             5         1      0  \n4123443                     1             4             6         1      0  \n12669428                    0             0             0         1      0  \n2837134                     0             1             4         1      0  \n3917352                     7             1             1         1      0  \n7083405                     6             1             9         1      0  \n10061891                    1             1             3         1      0  \n12187346                    0             1             1         1      0  \n8750003                     0             1             1         1      0  \n2150113                     0             1             1         1      0  \n\n[10 rows x 28 columns]",
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln = pd.read_csv(f\"{outdir}/01_patent_subset.csv\", low_memory=False)\n",
    "appln.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_kind\nA    13029613\nW      154696\nName: count, dtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[\"appln_kind\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "appln_pers = pd.read_csv(f\"{outdir}/02_persons_2011_2022.csv\",low_memory=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "(54368949, 13184309)"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln_pers[\"appln_id\"]), len(appln[\"appln_id\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
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  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "(54368949, 12646904)"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers[\"appln_id\"].isin(appln[\"appln_id\"]).sum(), appln[\"appln_id\"].isin(appln_pers[\"appln_id\"]).sum()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "           appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n1272384   407289438         CN   19595222            2             0  \\\n3185053   405545436         CN   43675023            0             1   \n46333633  572273359         CN   84986987            0             1   \n30374458  548032681         CN   64435157            0             1   \n38909435  548873284         CN   60805890            1             0   \n30940025  526314971         CN   58196262            0             1   \n33160256  532257650         CN   59633449            1             0   \n36415288  542989931         CN   64932427            1             0   \n12378533  470871991         CN   60097863            6             0   \n28095179  517589803         CN   58759116            2             0   \n\n                                                person_name person_ctry_code   \n1272384                                         LI CHAOYANG              NaN  \\\n3185053   QINGDAO CREEK NEW ENVIRONMENTAL MATERIALS CO.,...              NaN   \n46333633                         XI'AN PETROLEUM UNIVERSITY              NaN   \n30374458         SOUTH-CENTRAL UNIVERSITY FOR NATIONALITIES              NaN   \n38909435                                          LI HAIYAN              NaN   \n30940025       ANHUI JIANGHUAI AUTOMOBILE GROUP CORP., LTD.              NaN   \n33160256                                           NIE JING              NaN   \n36415288                                             LIU BO              NaN   \n12378533                                           SHENG LI              NaN   \n28095179                                       CAO JINGYUAN              NaN   \n\n             han_id                                           han_name  \n1272384   119595222                                        LI CHAOYANG  \n3185053   143675023  QINGDAO CREEK NEW ENVIRONMENTAL MATERIALS CO.,...  \n46333633  184986987                         XI'AN PETROLEUM UNIVERSITY  \n30374458  164435157         SOUTH-CENTRAL UNIVERSITY FOR NATIONALITIES  \n38909435  160805890                                          LI HAIYAN  \n30940025  158196262       ANHUI JIANGHUAI AUTOMOBILE GROUP CORP., LTD.  \n33160256  159633449                                           NIE JING  \n36415288  164932427                                             LIU BO  \n12378533  160097863                                           SHENG LI  \n28095179  158759116                                       CAO JINGYUAN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1272384</th>\n      <td>407289438</td>\n      <td>CN</td>\n      <td>19595222</td>\n      <td>2</td>\n      <td>0</td>\n      <td>LI CHAOYANG</td>\n      <td>NaN</td>\n      <td>119595222</td>\n      <td>LI CHAOYANG</td>\n    </tr>\n    <tr>\n      <th>3185053</th>\n      <td>405545436</td>\n      <td>CN</td>\n      <td>43675023</td>\n      <td>0</td>\n      <td>1</td>\n      <td>QINGDAO CREEK NEW ENVIRONMENTAL MATERIALS CO.,...</td>\n      <td>NaN</td>\n      <td>143675023</td>\n      <td>QINGDAO CREEK NEW ENVIRONMENTAL MATERIALS CO.,...</td>\n    </tr>\n    <tr>\n      <th>46333633</th>\n      <td>572273359</td>\n      <td>CN</td>\n      <td>84986987</td>\n      <td>0</td>\n      <td>1</td>\n      <td>XI'AN PETROLEUM UNIVERSITY</td>\n      <td>NaN</td>\n      <td>184986987</td>\n      <td>XI'AN PETROLEUM UNIVERSITY</td>\n    </tr>\n    <tr>\n      <th>30374458</th>\n      <td>548032681</td>\n      <td>CN</td>\n      <td>64435157</td>\n      <td>0</td>\n      <td>1</td>\n      <td>SOUTH-CENTRAL UNIVERSITY FOR NATIONALITIES</td>\n      <td>NaN</td>\n      <td>164435157</td>\n      <td>SOUTH-CENTRAL UNIVERSITY FOR NATIONALITIES</td>\n    </tr>\n    <tr>\n      <th>38909435</th>\n      <td>548873284</td>\n      <td>CN</td>\n      <td>60805890</td>\n      <td>1</td>\n      <td>0</td>\n      <td>LI HAIYAN</td>\n      <td>NaN</td>\n      <td>160805890</td>\n      <td>LI HAIYAN</td>\n    </tr>\n    <tr>\n      <th>30940025</th>\n      <td>526314971</td>\n      <td>CN</td>\n      <td>58196262</td>\n      <td>0</td>\n      <td>1</td>\n      <td>ANHUI JIANGHUAI AUTOMOBILE GROUP CORP., LTD.</td>\n      <td>NaN</td>\n      <td>158196262</td>\n      <td>ANHUI JIANGHUAI AUTOMOBILE GROUP CORP., LTD.</td>\n    </tr>\n    <tr>\n      <th>33160256</th>\n      <td>532257650</td>\n      <td>CN</td>\n      <td>59633449</td>\n      <td>1</td>\n      <td>0</td>\n      <td>NIE JING</td>\n      <td>NaN</td>\n      <td>159633449</td>\n      <td>NIE JING</td>\n    </tr>\n    <tr>\n      <th>36415288</th>\n      <td>542989931</td>\n      <td>CN</td>\n      <td>64932427</td>\n      <td>1</td>\n      <td>0</td>\n      <td>LIU BO</td>\n      <td>NaN</td>\n      <td>164932427</td>\n      <td>LIU BO</td>\n    </tr>\n    <tr>\n      <th>12378533</th>\n      <td>470871991</td>\n      <td>CN</td>\n      <td>60097863</td>\n      <td>6</td>\n      <td>0</td>\n      <td>SHENG LI</td>\n      <td>NaN</td>\n      <td>160097863</td>\n      <td>SHENG LI</td>\n    </tr>\n    <tr>\n      <th>28095179</th>\n      <td>517589803</td>\n      <td>CN</td>\n      <td>58759116</td>\n      <td>2</td>\n      <td>0</td>\n      <td>CAO JINGYUAN</td>\n      <td>NaN</td>\n      <td>158759116</td>\n      <td>CAO JINGYUAN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='appln_auth'>"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appln_pers.groupby('appln_auth')[\"person_ctry_code\"].apply(lambda x: 1-(x.isnull().sum()/len(x))).sort_values().plot(kind=\"bar\",)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[101], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery\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[38;5;124;43mIC\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmerge\u001B[49m\u001B[43m(\u001B[49m\u001B[43mappln\u001B[49m\u001B[43m[\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_filing_year\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;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[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdrop_duplicates\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mon\u001B[49m\u001B[38;5;241;43m=\u001B[39;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[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mappln_filing_year\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\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[43mapply\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mround\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misnull\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msum\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m/\u001B[39;49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m,\u001B[49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkind\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mline\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtitle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\plotting\\_core.py:975\u001B[0m, in \u001B[0;36mPlotAccessor.__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m    972\u001B[0m             label_name \u001B[38;5;241m=\u001B[39m label_kw \u001B[38;5;129;01mor\u001B[39;00m data\u001B[38;5;241m.\u001B[39mcolumns\n\u001B[0;32m    973\u001B[0m             data\u001B[38;5;241m.\u001B[39mcolumns \u001B[38;5;241m=\u001B[39m label_name\n\u001B[1;32m--> 975\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m plot_backend\u001B[38;5;241m.\u001B[39mplot(data, kind\u001B[38;5;241m=\u001B[39mkind, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py:71\u001B[0m, in \u001B[0;36mplot\u001B[1;34m(data, kind, **kwargs)\u001B[0m\n\u001B[0;32m     69\u001B[0m         kwargs[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124max\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mgetattr\u001B[39m(ax, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mleft_ax\u001B[39m\u001B[38;5;124m\"\u001B[39m, ax)\n\u001B[0;32m     70\u001B[0m plot_obj \u001B[38;5;241m=\u001B[39m PLOT_CLASSES[kind](data, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m---> 71\u001B[0m \u001B[43mplot_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgenerate\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     72\u001B[0m plot_obj\u001B[38;5;241m.\u001B[39mdraw()\n\u001B[0;32m     73\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m plot_obj\u001B[38;5;241m.\u001B[39mresult\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:446\u001B[0m, in \u001B[0;36mMPLPlot.generate\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    444\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mgenerate\u001B[39m(\u001B[38;5;28mself\u001B[39m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m    445\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_args_adjust()\n\u001B[1;32m--> 446\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_compute_plot_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    447\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_setup_subplots()\n\u001B[0;32m    448\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_plot()\n",
      "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py:632\u001B[0m, in \u001B[0;36mMPLPlot._compute_plot_data\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    630\u001B[0m \u001B[38;5;66;03m# no non-numeric frames or series allowed\u001B[39;00m\n\u001B[0;32m    631\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_empty:\n\u001B[1;32m--> 632\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mno numeric data to plot\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    634\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata \u001B[38;5;241m=\u001B[39m numeric_data\u001B[38;5;241m.\u001B[39mapply(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_convert_to_ndarray)\n",
      "\u001B[1;31mTypeError\u001B[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "appln_pers.query(\"appln_auth=='IC'\").merge(appln[[\"appln_filing_year\",\"appln_id\"]].drop_duplicates(), on='appln_id').groupby('appln_filing_year')[\"person_ctry_code\"].apply(lambda x: round(100-(x.isnull().sum()/len(x))*100,3)).plot(kind=\"line\", title=\"\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds3 = appln_pers.query(\"appln_auth=='CN'\").merge(appln[[\"appln_filing_year\",\"appln_id\"]].drop_duplicates(), on='appln_id').groupby('appln_filing_year')[\"person_ctry_code\"].apply(lambda x: (100-(x.isnull().sum()/len(x))*100)).plot(kind=\"line\", title=\"The fall of patent quality in China\\n(percent of high quality patents)\", ylim=[-0.0001,0.003])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    " # appln_pers.query(\"appln_auth=='CN'\").merge(appln[[\"appln_filing_year\",\"appln_id\"]].drop_duplicates(), on='appln_id').groupby('appln_filing_year')[\"person_ctry_code\"].apply(lambda x: (100-(x.isnull().sum()/len(x))*100))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [],
   "source": [
    "appln_pers.query(\"appln_auth=='CN'\").merge(appln[[\"appln_filing_year\",\"appln_id\"]].drop_duplicates(), on='appln_id').groupby('appln_filing_year')[\"person_ctry_code\"].apply(lambda x: 100-((x.isnull().sum()/len(x))*100)).reset_index().to_excel(\"ds3.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [],
   "source": [
    "appln.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"appln_id\"].nunique().cumsum().reset_index().to_excel(\"ds2.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds2 = appln.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"appln_id\"].nunique().cumsum().plot(kind=\"line\", title=\"Number of patents submitted in China (in TEN millions)\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAHjCAYAAAD7drHKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC96UlEQVR4nOzdd3zM9x/A8ddlT5El9gwREREURVG1R0toSxWlRs1WtQh+9q4ujdZuqVGb1p4tNaqCxBYEQZBIIrLX/f44962TRBK5uIz38/G4B3ff732/n+/nLnfv+4z3R6VWq9UIIYQQQgi9MzJ0AYQQQgghCisJtIQQQggh8ogEWkIIIYQQeUQCLSGEEEKIPGJi6AIIIYQQ+pKamkpycrKhiyGKCFNTU4yNjV+4jwRaQgghCjy1Wk1oaChRUVHIXHrxqqhUULx4cUqVKoVKpcpwHwm0hBBCFHihoaFERkZha1scc3NzIOMvPSH0R01iYiKRkVEAlC5dOsO9JNASQghRoKWmphIVpQmybG3tDF0cUYSYmVkAEBUVhYuLS4bdiDIYXgghRIGWnJyMWs3TliwhXi1zc3PUajIdGyiBlhBCiEJCuguFIbz4fSeBlhBCCCFEHpFASwghhBAij0igJYQQQhQi9+7do2HDOty7dy/Xx1qyZCGDBw/IdPvgwQNYsmRhrs9TmMmsQyGEEEJkqGfP3rz3Xg9DF6NAk0BLCCGEEBmysrIydBEKPAm0hBBCFDpqtZqE5LRXek4LU6NMs4NnJiDgLAsWzOfKlcuoVODtXZfx4ydy4sRxfv99K15etdm0aT22tsXo128A77zTBdB02dWtW4+TJ//hypXLVK9eHV/f/1GxYqV052jYsA6TJk3j119/ISTkNjVqeDBp0jRKly6TZfmWLFnI6dP+/PTTEgD+/PMgCxbMJyzsIR07vk1aWmqOrrcokkBLCCFEoaJWq+m7+gwBd6Nf6XlrlynG8p7e2Q62YmKeMGrUCHr0+JDJk6cRFhbG9OmTWbHiZ9zcqnPx4nmsrCxZsuQXLl48z5w5MylZsiQNGrwOwMqVPzNkyHB8fSewbNliPv98BL/9tinDcy1dughf3wnY2zswbtxoFi78kalTZ+To+oKDbzB+/FiGDRvB66835rffVhMQcJZ69ern6DhFjQyGF0IIUegUhIxaiYmJ9O07gH79BlC6dBm8vGrz5ptvERx8HQAjIyMmTpxGlSqudOrUmdat27J162bl+a+/3pju3XtSqVJlfH3/R1RUJCdPnsjwXD169KRevfpUqeKKj8+7XLp0Icfl3b59G97e3vTo8SEVK1biiy/G4Ozs/HIXX4RIi5YQQohCRaVSsbynd77vOnR0dKJDh46sXbuaoKArBAffICgoiFq1vAAoW7YcDg4Oyv7Vq9dgy5aNyn3tfgDW1taUK1eemzeDqVzZNd25ypUrr7NvSkpKjq4NIDg4mKpV3ZT7JiamOvdFxiTQEkIIUeioVCoszdKvO5efPHz4kL59P6R69erUr9+Qd97pwtGjf3P+/DkATEx0v6LT0lIxMvqvIyr99jRUqow7qkxMTHXuq9Xqlyrz888zNTXNZE+hJYGWEEIIYQB//XWQYsWK8fXX85XHNmxYB2iCmTt3QoiLi1Nm/l26dJEqVaoq+169ekX5f0zME+7cCcHV9b/t+la5chXOnQtQ7qelpREUdDVPz1kYyBgtIUSmXvZXr6GPnVcKYplF/mVnV5wHD+7z77//cPfuHVau/IVDhw6QlJQEQFxcHHPmzODmzWC2bt3MwYP76dr1XeX5e/fuZufO7QQH32DGjKmULFmKunXr5Vl533mnC5cvX+Lnn5dy69ZN5s//lvv3Q/PsfIWFBFoiXxo7dixubm4vvPXq1QuAXr16Kf/Xt5SUFMaOHYu3tzd16tThxImMB5o+r0WLFowdOxaAf/75Bzc3N/755x+DlOVlHThwgDFjxuTJsf39/Rk4cGCeHPtlPf8+cnNz44cffgAgKSmJmTNn8scff+TqHGPHjqVFixa5Osazjh8/zrBhw3jjjTfw8vKiTZs2zJkzh0ePHuns9+y1ZCYv/45Ext56qxVt2rRn3LjR9O37If7+JxkxYiQ3b94kOTkJF5eSODk50bfvh6xatYLJk6fj5VVbeX6bNu3YsmUjH330IfHxcXz77Q/puhP1qVy58syd+y379u2hd+8ePHoUzuuvN86z8xUW0nUo8qUhQ4bQvXt35f6PP/7IxYsX8fPzUx6zsbHJ83IcOXKELVu2MGTIEBo1akSNGjXy/Jz5pSy//PJLnh17w4YNXL9+Pc+Orw/r1q2jZMmSgGYszYoVK5g1a1aujjlkyBB69+6tj+Ixb948li5dStu2bRk/fjzFixfnypUrLFmyhL1797Jq1SpKlSqV7eNNmjRJL+US2WdsbMyYMeMYM2aczuPdu/dk+/bfUalUDB8+kuHDR2b4fBeXkkyYMDnd46VLl+bEidPK/Wf/D9Cx49t07Ph2tso4YMAnOvcbNGjImjUbsvVcoSGBlsiXypcvT/ny/82ScXBwwMzMjNq1a7/SckRFRQHg4+NDuXLlXum583NZioK8eK89+57OjR07drBkyRJ8fX356KOPlMcbNmxIs2bN6NKlCzNmzND5YZIVV9f0M9WEELknXYeiUFCr1SxZsoTmzZtTq1Yt3n//fQIDA3X2uXr1KoMGDaJOnTrUqVOHoUOHEhISkukxx44dq3T/tWzZUulWiYiIYMqUKbz55pvUrFmT+vXrM3ToUO7cufPS5U9NTWX16tV06tSJWrVq0bx5c+bNm0diYuILy/K8zZs34+bmRkBAAF26dKFWrVp06tSJ3bt36+x3584dRo8eTZMmTfDw8OD1119n9OjRREZGAppupJMnT3Ly5Emdbs+oqCgmTpxIo0aN8PT05L333uP48eM6x3Zzc2P16tWMHz+e+vXr4+3tzaeffkp4eLhyLVu2bOHu3bu4ubmxefNmMnPixAnee+89vLy8aNeuHQcOHKBVq1ZKN1hm3bLPd4MlJCTw9ddf07p1a2rWrEmdOnXo27cvly5dyvTc2u62O3fu8NZbbwHg6+tLixYt+PPPP3Fzc+Pvv//Wec6pU6dwc3PD398/w2M+33XYokUL5s+fz5w5c2jUqBG1atXi448/5ubNm5mWC2Dx4sW4urrSp0+fdNsqVqzIl19+ibe3t86YspiYGJ3XZMSIEcprklGdZfU6guZ9u3jxYjp27EitWrWoXbs23bt3z/NubaEfBw8e4M03G2d6++yzYYYuYqEgLVqiUPD39ycpKYn//e9/pKSkMHv2bAYPHsxff/2FiYkJwcHBdO/encqVKzNnzhxSUlL46aef6NGjB9u2bcPR0THdMYcMGULJkiX56aef8PPzo1KlSqjVagYNGsTjx4/54osvcHJy4sqVK3z33XdMmjSJZcuWvVT5J06cyLZt2xgwYAD16tXj4sWLLFiwgEuXLrF06dIMy/IigwYN4sMPP2TkyJFs3LiRzz77jEWLFtGsWTPi4+Pp3bs39vb2TJo0CVtbW86cOYOfnx8WFhZMnTqVSZMm8eWXXwKaLiVXV1cSExPp06cP4eHhjBw5khIlSrBp0yb69+/P0qVLef3115Xzf/vtt7Rq1YpvvvmGkJAQZs2ahbGxMd988w1DhgwhIiJC6QrOrJXnwoUL9O/fn/r16zN//nxu377NuHHjlIHCOTF69GhOnTrF559/Tvny5bl16xbff/89o0aNYseOHS/MfVSiRAn8/PwYNmwYgwcPpnXr1ri5uVGiRAm2bdtGkyZNlH23bt1KxYoVqVu3brbLtnLlSurWrcusWbN4/PgxM2bMYMyYMaxbty7D/cPCwrh8+TL9+/fPtNwffPBBhufp1KkT33//PdevX2f27NkAzJ8/P92+Wi96HUHTfbl27VpGjRqFm5sbDx48YMGCBXz66af8+eefWFpaZrsehK6suve0S+LkRsOGr7Ny5dpMt5ubW+T6HEICLVFImJmZsXjxYooXLw5AdHQ0EyZM4Nq1a1SvXh0/Pz8sLS355ZdflLFdr7/+Oi1btmT
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "ax = appln.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"appln_id\"].nunique().cumsum().plot(kind=\"line\")\n",
    "ax2 = ax.twinx()\n",
    "appln_pers.query(\"appln_auth=='CN'\").merge(appln[[\"appln_filing_year\",\"appln_id\"]].drop_duplicates(), on='appln_id').groupby('appln_filing_year')[\"person_ctry_code\"].apply(lambda x: (100-(x.isnull().sum()/len(x))*100)).plot(kind=\"line\", ylim=[-0.0001,0.003])\n",
    "# df.plot(x=\"date\", y=\"column2\", ax=ax2, legend=False, color=\"r\")\n",
    "ax.figure.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='appln_filing_year'>"
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appln.query(\"appln_auth=='WO'\").groupby(\"appln_filing_year\")[\"appln_id\"].nunique().plot(kind=\"line\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "ch_auth_pers = appln_pers.query(\"appln_auth=='CN'\")[\"person_ctry_code\"].value_counts(dropna=False).reset_index()\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "  person_ctry_code     count\n0          UNKNOWN  50476312\n1               CN        79\n2               JP         8\n3               TW         4\n4               DE         4\n5               FR         2",
      "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_ctry_code</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>UNKNOWN</td>\n      <td>50476312</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CN</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>JP</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TW</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DE</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>FR</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch_auth_pers.fillna(\"UNKNOWN\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "# appln_ch = appln_pers.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",
    "# appln_scope.sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "appln_ch_ids = appln_pers[appln_pers[\"person_ctry_code\"] == \"CN\"][\"appln_id\"].unique()\n",
    "appln_eu_ids = appln_pers[appln_pers[\"person_ctry_code\"].isin(scope_countries)][\"appln_id\"].unique()\n",
    "\n",
    "appln_scope_ids = np.intersect1d(appln_ch_ids, appln_eu_ids, assume_unique=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "appln_scope = appln_pers[((appln_pers[\"appln_id\"].isin(appln_scope_ids))&\n",
    "                          (appln_pers[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])))]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "appln_pers_haninfo = pd.read_csv(f\"first_round/tls_206_scope_v2.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "(6584667, 18789)"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "appln_pers_haninfo[\"han_id\"].nunique(), appln_scope[\"han_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [],
   "source": [
    "han_sect = appln_pers_haninfo[[\"han_id\",\"psn_sector\"]].groupby(['han_id'])['psn_sector'].agg(pd.Series.mode)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "han_sect = han_sect.reset_index()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "han_sect[\"psn_sector\"] = han_sect[\"psn_sector\"].apply(lambda y: \"UNKNOWN\" if len(y)==0 else y)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "            han_id psn_sector\n3976016  167526859    UNKNOWN\n1544707  150315846    UNKNOWN\n1031384  144076709    UNKNOWN\n251660   114916086    COMPANY\n4256551  170565063    UNKNOWN\n5436213  179983755    UNKNOWN\n2686293  159907707    UNKNOWN\n3568687  164831483    UNKNOWN\n5139105  177983993    UNKNOWN\n968579   143805259    UNKNOWN",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>han_id</th>\n      <th>psn_sector</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3976016</th>\n      <td>167526859</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>1544707</th>\n      <td>150315846</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>1031384</th>\n      <td>144076709</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>251660</th>\n      <td>114916086</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>4256551</th>\n      <td>170565063</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>5436213</th>\n      <td>179983755</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>2686293</th>\n      <td>159907707</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>3568687</th>\n      <td>164831483</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>5139105</th>\n      <td>177983993</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>968579</th>\n      <td>143805259</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "han_sect.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [],
   "source": [
    "appln_scope = appln_scope.merge(han_sect, on=\"han_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "(53909, 9)"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.shape"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n11387  507242787         WO    5262071            0             1  \\\n8226   552675100         WO    5318110            0             1   \n22721  376063059         WO   25690230            3             4   \n48806  540543104         WO   51860538            2             3   \n52709  575543924         WO   63630536            2             0   \n42582  533357722         WO   47722168            3             0   \n15378  576018852         WO   50069447            0             3   \n42176  528369260         WO   50255341            3             4   \n46235  524262213         WO   73281459            5             0   \n51521  567061143         WO   71117487            3             0   \n\n                                  person_name person_ctry_code     han_id   \n11387  Telefonaktiebolaget LM Ericsson (publ)               SE      13378  \\\n8226            Huawei Technologies Co., Ltd.               CN    1337324   \n22721                            ZOU, YANMING               CN  125690230   \n48806                             XIE, JIEWEI               CN  151860538   \n52709                           LI, Xiangyang               CN  163630536   \n42582                           YANG, WENLONG               CN  147722168   \n15378                   NOKIA TECHNOLOGIES OY               FI    2562294   \n42176                               TIAN, KAI               CN  150255341   \n46235                             FAN, Cunfei               CN  173281459   \n51521                           Lin, Shengdie               CN  171117487   \n\n                          han_name  psn_sector  \n11387  TELEFON AB LM ERICSSON PUBL     COMPANY  \n8226            HUAWEI TECH CO LTD     COMPANY  \n22721                 ZOU, YANMING  INDIVIDUAL  \n48806                  XIE, JIEWEI  INDIVIDUAL  \n52709                LI, Xiangyang  INDIVIDUAL  \n42582                YANG, WENLONG  INDIVIDUAL  \n15378               NOKIA TECH LTD     COMPANY  \n42176                    TIAN, KAI  INDIVIDUAL  \n46235                  FAN, Cunfei     UNKNOWN  \n51521                Lin, Shengdie     UNKNOWN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11387</th>\n      <td>507242787</td>\n      <td>WO</td>\n      <td>5262071</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Telefonaktiebolaget LM Ericsson (publ)</td>\n      <td>SE</td>\n      <td>13378</td>\n      <td>TELEFON AB LM ERICSSON PUBL</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>8226</th>\n      <td>552675100</td>\n      <td>WO</td>\n      <td>5318110</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n      <td>1337324</td>\n      <td>HUAWEI TECH CO LTD</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>22721</th>\n      <td>376063059</td>\n      <td>WO</td>\n      <td>25690230</td>\n      <td>3</td>\n      <td>4</td>\n      <td>ZOU, YANMING</td>\n      <td>CN</td>\n      <td>125690230</td>\n      <td>ZOU, YANMING</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n    <tr>\n      <th>48806</th>\n      <td>540543104</td>\n      <td>WO</td>\n      <td>51860538</td>\n      <td>2</td>\n      <td>3</td>\n      <td>XIE, JIEWEI</td>\n      <td>CN</td>\n      <td>151860538</td>\n      <td>XIE, JIEWEI</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n    <tr>\n      <th>52709</th>\n      <td>575543924</td>\n      <td>WO</td>\n      <td>63630536</td>\n      <td>2</td>\n      <td>0</td>\n      <td>LI, Xiangyang</td>\n      <td>CN</td>\n      <td>163630536</td>\n      <td>LI, Xiangyang</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n    <tr>\n      <th>42582</th>\n      <td>533357722</td>\n      <td>WO</td>\n      <td>47722168</td>\n      <td>3</td>\n      <td>0</td>\n      <td>YANG, WENLONG</td>\n      <td>CN</td>\n      <td>147722168</td>\n      <td>YANG, WENLONG</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n    <tr>\n      <th>15378</th>\n      <td>576018852</td>\n      <td>WO</td>\n      <td>50069447</td>\n      <td>0</td>\n      <td>3</td>\n      <td>NOKIA TECHNOLOGIES OY</td>\n      <td>FI</td>\n      <td>2562294</td>\n      <td>NOKIA TECH LTD</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>42176</th>\n      <td>528369260</td>\n      <td>WO</td>\n      <td>50255341</td>\n      <td>3</td>\n      <td>4</td>\n      <td>TIAN, KAI</td>\n      <td>CN</td>\n      <td>150255341</td>\n      <td>TIAN, KAI</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n    <tr>\n      <th>46235</th>\n      <td>524262213</td>\n      <td>WO</td>\n      <td>73281459</td>\n      <td>5</td>\n      <td>0</td>\n      <td>FAN, Cunfei</td>\n      <td>CN</td>\n      <td>173281459</td>\n      <td>FAN, Cunfei</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>51521</th>\n      <td>567061143</td>\n      <td>WO</td>\n      <td>71117487</td>\n      <td>3</td>\n      <td>0</td>\n      <td>Lin, Shengdie</td>\n      <td>CN</td>\n      <td>171117487</td>\n      <td>Lin, Shengdie</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [
    {
     "data": {
      "text/plain": "      appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n124  419052362         CN    5361204            0             1  \\\n137  419052362         CN   51988464            1             0   \n\n                           person_name person_ctry_code     han_id   \n124  Schneider Electric Industries SAS               FR     808003  \\\n137                       JUZHONG SONG               CN  151988464   \n\n                    han_name psn_sector  \n124  SCHNEIDER ELECT IND SAS    COMPANY  \n137             JUZHONG SONG    UNKNOWN  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>124</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>5361204</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Schneider Electric Industries SAS</td>\n      <td>FR</td>\n      <td>808003</td>\n      <td>SCHNEIDER ELECT IND SAS</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>51988464</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JUZHONG SONG</td>\n      <td>CN</td>\n      <td>151988464</td>\n      <td>JUZHONG SONG</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.query(\"appln_auth=='CN'\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n1568550  419052362         CN  201210551425          A        2012-12-18  \\\n\n         appln_filing_year appln_nr_original ipr_type receiving_office   \n1568550               2012     2012105514252       PI              NaN  \\\n\n         internat_appln_id  ... earliest_pat_publn_id granted docdb_family_id   \n1568550                  0  ...             419052363       Y        49886800  \\\n\n        inpadoc_family_id  docdb_family_size  nb_citing_docdb_fam   \n1568550         413446088                  4                   15  \\\n\n        nb_applicants  nb_inventors  is_prior is_pct  \n1568550             1             1         1      0  \n\n[1 rows x 28 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1568550</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>201210551425</td>\n      <td>A</td>\n      <td>2012-12-18</td>\n      <td>2012</td>\n      <td>2012105514252</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>419052363</td>\n      <td>Y</td>\n      <td>49886800</td>\n      <td>413446088</td>\n      <td>4</td>\n      <td>15</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 28 columns</p>\n</div>"
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.query('appln_id==419052362')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n11387  507242787         WO    5262071            0             1  \\\n22810  507242787         WO   43700357            1             2   \n\n                                  person_name person_ctry_code     han_id   \n11387  Telefonaktiebolaget LM Ericsson (publ)               SE      13378  \\\n22810                                WEN, GAN               CN  143700357   \n\n                          han_name  psn_sector  \n11387  TELEFON AB LM ERICSSON PUBL     COMPANY  \n22810                     WEN, GAN  INDIVIDUAL  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>11387</th>\n      <td>507242787</td>\n      <td>WO</td>\n      <td>5262071</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Telefonaktiebolaget LM Ericsson (publ)</td>\n      <td>SE</td>\n      <td>13378</td>\n      <td>TELEFON AB LM ERICSSON PUBL</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>22810</th>\n      <td>507242787</td>\n      <td>WO</td>\n      <td>43700357</td>\n      <td>1</td>\n      <td>2</td>\n      <td>WEN, GAN</td>\n      <td>CN</td>\n      <td>143700357</td>\n      <td>WEN, GAN</td>\n      <td>INDIVIDUAL</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.query('appln_id==507242787')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: title={'center': 'Number of patents produced in EU-China collaborations\\n(by location, 2011-2022)'}, ylabel='appln_auth'>"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "appln_scope.groupby(\"appln_auth\")[\"appln_id\"].nunique().sort_values(ascending=True).plot(kind=\"barh\", logx=True, title='Number of patents produced in EU-China collaborations\\n(by location, 2011-2022)')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [],
   "source": [
    "appln_scope.groupby(\"appln_auth\")[\"appln_id\"].nunique().sort_values(ascending=True).reset_index().merge(tls_801, left_on=\"appln_auth\",right_on=\"ctry_code\").to_excel(\"ds1.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "    ctry_code iso_alpha3                          st3_name organisation_flag   \n0          AD        AND                           Andorra                    \\\n1          AE        ARE              United Arab Emirates                     \n2          AF        AFG                       Afghanistan                     \n3          AG        ATG               Antigua and Barbuda                     \n4          AI        AIA                          Anguilla                     \n..        ...        ...                               ...               ...   \n235        YE        YEM                             Yemen                     \n236        YU        YUG  Yugoslavia/Serbia and Montenegro                     \n237        ZA        ZAF                      South Africa                     \n238        ZM        ZMB                            Zambia                     \n239        ZW        ZWE                          Zimbabwe                     \n\n         continent eu_member epo_member oecd_member discontinued  \n0           Europe                                                \n1             Asia                                                \n2             Asia                                                \n3    North America                                                \n4    North America                                                \n..             ...       ...        ...         ...          ...  \n235           Asia                                                \n236         Europe                                             Y  \n237         Africa                                                \n238         Africa                                                \n239         Africa                                                \n\n[240 rows x 9 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ctry_code</th>\n      <th>iso_alpha3</th>\n      <th>st3_name</th>\n      <th>organisation_flag</th>\n      <th>continent</th>\n      <th>eu_member</th>\n      <th>epo_member</th>\n      <th>oecd_member</th>\n      <th>discontinued</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>AD</td>\n      <td>AND</td>\n      <td>Andorra</td>\n      <td></td>\n      <td>Europe</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>AE</td>\n      <td>ARE</td>\n      <td>United Arab Emirates</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>AF</td>\n      <td>AFG</td>\n      <td>Afghanistan</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AG</td>\n      <td>ATG</td>\n      <td>Antigua and Barbuda</td>\n      <td></td>\n      <td>North America</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>AI</td>\n      <td>AIA</td>\n      <td>Anguilla</td>\n      <td></td>\n      <td>North America</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>YE</td>\n      <td>YEM</td>\n      <td>Yemen</td>\n      <td></td>\n      <td>Asia</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>YU</td>\n      <td>YUG</td>\n      <td>Yugoslavia/Serbia and Montenegro</td>\n      <td></td>\n      <td>Europe</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td>Y</td>\n    </tr>\n    <tr>\n      <th>237</th>\n      <td>ZA</td>\n      <td>ZAF</td>\n      <td>South Africa</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>ZM</td>\n      <td>ZMB</td>\n      <td>Zambia</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>ZW</td>\n      <td>ZWE</td>\n      <td>Zimbabwe</td>\n      <td></td>\n      <td>Africa</td>\n      <td></td>\n      <td></td>\n      <td></td>\n      <td></td>\n    </tr>\n  </tbody>\n</table>\n<p>240 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tls_801"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_auth\nWO    43113\nEP     4340\nDE     3189\nGB     1375\nFR     1042\nNL      167\nES      165\nLU       98\nFI       69\nRO       53\nPL       49\nMA       42\nCZ       40\nCH       38\nSE       33\nAT       27\nDK       21\nBE       19\nLT       15\nPT        9\nIE        3\nCN        2\nName: count, dtype: int64"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope[\"appln_auth\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "24638"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n59726  568635720         DE  102021125268         A         2021-09-29  \\\n12204  421520760         WO    2013071985         W         2013-02-28   \n41470  519271319         US  201729627257         F         2017-11-24   \n21565  456717830         TW     103144389         A         2014-12-18   \n39245  513507702         TW     107123773         A         2018-07-10   \n15067  437950387         CA       2882927         A         2013-09-11   \n2331   365201373         US  201113309201         A         2011-12-01   \n42558  521483818         WO    2019079482         W         2019-10-29   \n54938  552149434         WO    2021065120         W         2021-06-07   \n63211  580342075         CO    2022012975         A         2022-09-13   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n59726               2021      102021125268       PI                   \\\n12204               2013     CN2013/071985       PI               CN   \n41470               2017          29627257       DP                    \n21565               2014         103144389       PI                    \n39245               2018         107123773       PI                    \n15067               2013           2882927       PI                    \n2331                2011          13309201       PI                    \n42558               2019     EP2019/079482       PI               EP   \n54938               2021     EP2021/065120       PI               EP   \n63211               2022    NC2022/0012975       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n59726                  0  ...          2022-03-31                2022  \\\n12204                  0  ...          2014-09-04                2014   \n41470                  0  ...          2019-09-17                2019   \n21565                  0  ...          2016-03-16                2016   \n39245                  0  ...          2019-03-01                2019   \n15067          410236729  ...          2014-03-20                2014   \n2331           332837342  ...          2012-07-19                2012   \n42558                  0  ...          2021-05-06                2021   \n54938                  0  ...          2022-12-15                2022   \n63211          901633858  ...          2022-09-30                2022   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n59726             568635721       N         80624731          568635720  \\\n12204             421520761       N         51427475          421502712   \n41470             519271320       Y         67875052          519271319   \n21565             456717831       Y         52302387          425594478   \n39245             513507703       N         59501519          481695409   \n15067             437950388       Y         49165740          378704661   \n2331              365309255       Y         46490250          332836199   \n42558             550107980       N         68468669          521483818   \n54938             584644443       N         76355503          552149434   \n63211             580342076       N         74870863          547347540   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n59726                 3                    0              1            6  \n12204                 5                    0              2            1  \n41470                 1                    0              1            2  \n21565                46                    6              1            7  \n39245                 2                    0              1            6  \n15067                35                    2              2            8  \n2331                  1                    3              7            6  \n42558                 1                    0              2            2  \n54938                 1                    0              2            2  \n63211                14 
      "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>59726</th>\n      <td>568635720</td>\n      <td>DE</td>\n      <td>102021125268</td>\n      <td>A</td>\n      <td>2021-09-29</td>\n      <td>2021</td>\n      <td>102021125268</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2022-03-31</td>\n      <td>2022</td>\n      <td>568635721</td>\n      <td>N</td>\n      <td>80624731</td>\n      <td>568635720</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>12204</th>\n      <td>421520760</td>\n      <td>WO</td>\n      <td>2013071985</td>\n      <td>W</td>\n      <td>2013-02-28</td>\n      <td>2013</td>\n      <td>CN2013/071985</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2014-09-04</td>\n      <td>2014</td>\n      <td>421520761</td>\n      <td>N</td>\n      <td>51427475</td>\n      <td>421502712</td>\n      <td>5</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>41470</th>\n      <td>519271319</td>\n      <td>US</td>\n      <td>201729627257</td>\n      <td>F</td>\n      <td>2017-11-24</td>\n      <td>2017</td>\n      <td>29627257</td>\n      <td>DP</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-09-17</td>\n      <td>2019</td>\n      <td>519271320</td>\n      <td>Y</td>\n      <td>67875052</td>\n      <td>519271319</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21565</th>\n      <td>456717830</td>\n      <td>TW</td>\n      <td>103144389</td>\n      <td>A</td>\n      <td>2014-12-18</td>\n      <td>2014</td>\n      <td>103144389</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-03-16</td>\n      <td>2016</td>\n      <td>456717831</td>\n      <td>Y</td>\n      <td>52302387</td>\n      <td>425594478</td>\n      <td>46</td>\n      <td>6</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>39245</th>\n      <td>513507702</td>\n      <td>TW</td>\n      <td>107123773</td>\n      <td>A</td>\n      <td>2018-07-10</td>\n      <td>2018</td>\n      <td>107123773</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-03-01</td>\n      <td>2019</td>\n      <td>513507703</td>\n      <td>N</td>\n      <td>59501519</td>\n      <td>481695409</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>15067</th>\n      <td>437950387</td>\n      <td>CA</td>\n      <td>2882927</td>\n      <td>A</td>\n      <td>2013-09-11</td>\n      <td>2013</td>\n      <td>2882927</td>\n      <td>PI</td>\n      <td></td>\n      <td>410236729</td>\n      <td>...</td>\n      <td>2014-03-20</td>\n      <td>2014</td>\n      <td>437950388</td>\n      <td>Y</td>\n      <td>49165740</td>\n      <td>378704661</td>\n      <td>35</td>\n      <td>2</td>\n      <td>2</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2331</th>\n      <td>3652
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "        appln_id appln_auth    appln_nr appln_kind appln_filing_date   \n24368  472782127         EP    16808728         A         2016-11-15  \\\n21272  456135686         EP    14905672         A         2014-11-15   \n21690  456935297         CA     2932236         A         2014-12-17   \n23267  469974134         TW   104128490         A         2015-08-28   \n24227  472426897         TW   105101423         A         2016-01-18   \n...          ...        ...         ...        ...               ...   \n5372   405824062         TW   101108520         A         2012-03-13   \n15441  438564193         EP    14847669         A         2014-09-18   \n27761  481859764         TW   105133993         A         2016-10-21   \n23898  471377037         CL  2015002557         A         2015-09-09   \n20776  451149018         TW   104112868         A         2015-04-22   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n24368               2016          16808728       PI                   \\\n21272               2014          14905672       PI                    \n21690               2014           2932236       PI                    \n23267               2015         104128490       PI                    \n24227               2016         105101423       PI                    \n...                  ...               ...      ...              ...   \n5372                2012         101108520       PI                    \n15441               2014          14847669       PI                    \n27761               2016         105133993       PI                    \n23898               2015        2015002557       PI                    \n20776               2015         104112868       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n24368          471904726  ...          2018-09-26                2018  \\\n21272          456128773  ...          2017-09-20                2017   \n21690          424542770  ...          2015-07-09                2015   \n23267                  0  ...          2016-07-16                2016   \n24227                  0  ...          2016-09-01                2016   \n...                  ...  ...                 ...                 ...   \n5372                   0  ...          2013-01-01                2013   \n15441          438547499  ...          2016-08-03                2016   \n27761                  0  ...          2017-05-01                2017   \n23898                  0  ...          2016-08-26                2016   \n20776                  0  ...          2016-01-16                2016   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n24368             498806476       N         57517914          471904726  \\\n21272             482714919       Y         55953638          456128773   \n21690             456935298       N         49876509          424542770   \n23267             469974135       Y         54238507          444673843   \n24227             472426898       Y         52358602          447591949   \n...                     ...     ...              ...                ...   \n5372              405856720       N         45937460          352022356   \n15441             457602632       N         52742141          438547499   \n27761             481859765       Y         58562247          471178388   \n23898             471377038       N         51537508          421989017   \n20776             451149019       N         54147239          444402812   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n24368                 3                    7              1            4  \n21272                17                   35              1            6  \n21690                 5                    6              2            4  \n23267                49                   51              1           13  \n24227                 8                   11              1            7  \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>24368</th>\n      <td>472782127</td>\n      <td>EP</td>\n      <td>16808728</td>\n      <td>A</td>\n      <td>2016-11-15</td>\n      <td>2016</td>\n      <td>16808728</td>\n      <td>PI</td>\n      <td></td>\n      <td>471904726</td>\n      <td>...</td>\n      <td>2018-09-26</td>\n      <td>2018</td>\n      <td>498806476</td>\n      <td>N</td>\n      <td>57517914</td>\n      <td>471904726</td>\n      <td>3</td>\n      <td>7</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>21272</th>\n      <td>456135686</td>\n      <td>EP</td>\n      <td>14905672</td>\n      <td>A</td>\n      <td>2014-11-15</td>\n      <td>2014</td>\n      <td>14905672</td>\n      <td>PI</td>\n      <td></td>\n      <td>456128773</td>\n      <td>...</td>\n      <td>2017-09-20</td>\n      <td>2017</td>\n      <td>482714919</td>\n      <td>Y</td>\n      <td>55953638</td>\n      <td>456128773</td>\n      <td>17</td>\n      <td>35</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>21690</th>\n      <td>456935297</td>\n      <td>CA</td>\n      <td>2932236</td>\n      <td>A</td>\n      <td>2014-12-17</td>\n      <td>2014</td>\n      <td>2932236</td>\n      <td>PI</td>\n      <td></td>\n      <td>424542770</td>\n      <td>...</td>\n      <td>2015-07-09</td>\n      <td>2015</td>\n      <td>456935298</td>\n      <td>N</td>\n      <td>49876509</td>\n      <td>424542770</td>\n      <td>5</td>\n      <td>6</td>\n      <td>2</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>23267</th>\n      <td>469974134</td>\n      <td>TW</td>\n      <td>104128490</td>\n      <td>A</td>\n      <td>2015-08-28</td>\n      <td>2015</td>\n      <td>104128490</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-07-16</td>\n      <td>2016</td>\n      <td>469974135</td>\n      <td>Y</td>\n      <td>54238507</td>\n      <td>444673843</td>\n      <td>49</td>\n      <td>51</td>\n      <td>1</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>24227</th>\n      <td>472426897</td>\n      <td>TW</td>\n      <td>105101423</td>\n      <td>A</td>\n      <td>2016-01-18</td>\n      <td>2016</td>\n      <td>105101423</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-09-01</td>\n      <td>2016</td>\n      <td>472426898</td>\n      <td>Y</td>\n      <td>52358602</td>\n      <td>447591949</td>\n      <td>8</td>\n      <td>11</td>\n      <td>1</td>\n      <td>7</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>5372</th>\n      <td>405824062</td>\n      <td>TW</td>\n  
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]].sample(100)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "        person_id           person_name   person_name_orig_lg   \n70690    54633600          LONG, HUOJUN          LONG, HUOJUN  \\\n67675    53995974          ACCOU, BRUNO          ACCOU, BRUNO   \n116777   81591947           RAMOS, Rita           RAMOS, Rita   \n94905    71114319       VERSALIS S.P.A.       VERSALIS S.P.A.   \n104848   74737285       ISAKSSON, Björn       ISAKSSON, Björn   \n123893   83424775             LIU, Qian             LIU, Qian   \n29588    40825214    MELEY, JEAN-PIERRE    MELEY, JEAN-PIERRE   \n106912   76993952  PENG-POEHLER, Xuyuan  PENG-POEHLER, Xuyuan   \n78798    56657638        TONG, Zhaoyang        TONG, Zhaoyang   \n10578    11967242        Dai, Chenglong        Dai, Chenglong   \n\n                                           person_address person_ctry_code   \n70690                                            SHANGHAI               CN  \\\n67675            Sint Truidensestwg 263A B-3350 Linter BE               BE   \n116777    Novartis Pharma AG, Postfach, 4002 Basel, SUIZA               PT   \n94905                                                  IT               IT   \n104848                                            Mölndal               SE   \n123893                                         Otelfingen               CH   \n29588                                                 NaN               FR   \n106912                                                NaN               DE   \n78798   Tyco Electronics (Shanghai) Co., Ltd.,F/G Sect...               CN   \n10578                                            Shenzhen               CN   \n\n       nuts  nuts_level  doc_std_name_id         doc_std_name     psn_id   \n70690   NaN           9          1937752             SUN XIAO   19885311  \\\n67675    BE           0          2145736          ACCOU BRUNO     245616   \n116777   PT           0          1921587           RAMOS RITA   26605720   \n94905    IT           0           255948         VERSALIS SPA   34028198   \n104848   SE           0         21757791       ISAKSSON BJÖRN   14166564   \n123893   CH           0         18631517            QIAO NING  183424775   \n29588    FR           0           178196    MELEY JEAN-PIERRE   21635951   \n106912   DE           0         34255238  PENG-POEHLER XUYUAN   25270057   \n78798   NaN           9          2067106        TONG ZHAOYANG   32942098   \n10578   NaN           9          4323331        DAI CHENGLONG    6018020   \n\n                    psn_name  psn_level  psn_sector     han_id   \n70690           LONG, HUOJUN          0     UNKNOWN  154633600  \\\n67675           ACCOU, BRUNO          0     UNKNOWN  153995974   \n116777           RAMOS, RITA          0     UNKNOWN  181591947   \n94905               VERSALIS          1     COMPANY    3508224   \n104848       ISAKSSON, BJÖRN          0     UNKNOWN  174737285   \n123893             LIU, Qian          0     UNKNOWN  183424775   \n29588     MELEY, JEAN-PIERRE          0     UNKNOWN  140825214   \n106912  PENG-POEHLER, XUYUAN          0     UNKNOWN  176993952   \n78798         TONG, ZHAOYANG          0     UNKNOWN  156657638   \n10578         DAI, CHENGLONG          1  INDIVIDUAL  111967242   \n\n                    han_name  han_harmonized  \n70690           LONG, HUOJUN               0  \n67675           ACCOU, BRUNO               0  \n116777           RAMOS, Rita               0  \n94905           VERSALIS SPA               2  \n104848       ISAKSSON, Björn               0  \n123893             LIU, Qian               0  \n29588     MELEY, JEAN-PIERRE               0  \n106912  PENG-POEHLER, Xuyuan               0  \n78798         TONG, Zhaoyang               0  \n10578         Dai, Chenglong               0  ",
      "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>70690</th>\n      <td>54633600</td>\n      <td>LONG, HUOJUN</td>\n      <td>LONG, HUOJUN</td>\n      <td>SHANGHAI</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>1937752</td>\n      <td>SUN XIAO</td>\n      <td>19885311</td>\n      <td>LONG, HUOJUN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>154633600</td>\n      <td>LONG, HUOJUN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>67675</th>\n      <td>53995974</td>\n      <td>ACCOU, BRUNO</td>\n      <td>ACCOU, BRUNO</td>\n      <td>Sint Truidensestwg 263A B-3350 Linter BE</td>\n      <td>BE</td>\n      <td>BE</td>\n      <td>0</td>\n      <td>2145736</td>\n      <td>ACCOU BRUNO</td>\n      <td>245616</td>\n      <td>ACCOU, BRUNO</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>153995974</td>\n      <td>ACCOU, BRUNO</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>116777</th>\n      <td>81591947</td>\n      <td>RAMOS, Rita</td>\n      <td>RAMOS, Rita</td>\n      <td>Novartis Pharma AG, Postfach, 4002 Basel, SUIZA</td>\n      <td>PT</td>\n      <td>PT</td>\n      <td>0</td>\n      <td>1921587</td>\n      <td>RAMOS RITA</td>\n      <td>26605720</td>\n      <td>RAMOS, RITA</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>181591947</td>\n      <td>RAMOS, Rita</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>94905</th>\n      <td>71114319</td>\n      <td>VERSALIS S.P.A.</td>\n      <td>VERSALIS S.P.A.</td>\n      <td>IT</td>\n      <td>IT</td>\n      <td>IT</td>\n      <td>0</td>\n      <td>255948</td>\n      <td>VERSALIS SPA</td>\n      <td>34028198</td>\n      <td>VERSALIS</td>\n      <td>1</td>\n      <td>COMPANY</td>\n      <td>3508224</td>\n      <td>VERSALIS SPA</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>104848</th>\n      <td>74737285</td>\n      <td>ISAKSSON, Björn</td>\n      <td>ISAKSSON, Björn</td>\n      <td>Mölndal</td>\n      <td>SE</td>\n      <td>SE</td>\n      <td>0</td>\n      <td>21757791</td>\n      <td>ISAKSSON BJÖRN</td>\n      <td>14166564</td>\n      <td>ISAKSSON, BJÖRN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>174737285</td>\n      <td>ISAKSSON, Björn</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>123893</th>\n      <td>83424775</td>\n      <td>LIU, Qian</td>\n      <td>LIU, Qian</td>\n      <td>Otelfingen</td>\n      <td>CH</td>\n      <td>CH</td>\n      <td>0</td>\n      <td>18631517</td>\n      <td>QIAO NING</td>\n      <td>183424775</td>\n      <td>LIU, Qian</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>183424775</td>\n      <td>LIU, Qian</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>29588</th>\n      <td>40825214</td>\n      <td>MELEY, JEAN-PIERRE</td>\n      <td>MELEY, JEAN-PIERRE</td>\n      <td>NaN</td>\n      <td>FR</td>\n      <td>FR</td>\n      <td>0</td>\n      <td>178196</td>\n      <td>MELEY JEAN-PIERRE</td>\n      <td>21635951</td>\n      <td>MELEY, JEAN-PIERRE</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>140825214</td>\n      <td>MELEY, JEAN-PIERRE</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>106912</th>\n      <td>76993952</td>\n      <td>PENG-POEHLER, Xuyuan</td>\n      <td>PENG-POEHLER, Xuyuan</td>\n      <td>NaN</td>
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pers.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpc_def"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "         appln_id cpc_class_symbol\n48451   408996326    B01J  35/1019\n361399  521927284     H04M  15/785\n410341  535434338    H04L   5/0048\n447944  546323648     H04L   5/005\n128095  441829683   H01L2924/12041\n183546  470030298    H04L   1/0005\n74962   416447093    B65H2404/1119\n382818  527400811      A61P  21/00\n411680  535860118     A61K   8/365\n289902  500408460      A61K   8/87",
      "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>48451</th>\n      <td>408996326</td>\n      <td>B01J  35/1019</td>\n    </tr>\n    <tr>\n      <th>361399</th>\n      <td>521927284</td>\n      <td>H04M  15/785</td>\n    </tr>\n    <tr>\n      <th>410341</th>\n      <td>535434338</td>\n      <td>H04L   5/0048</td>\n    </tr>\n    <tr>\n      <th>447944</th>\n      <td>546323648</td>\n      <td>H04L   5/005</td>\n    </tr>\n    <tr>\n      <th>128095</th>\n      <td>441829683</td>\n      <td>H01L2924/12041</td>\n    </tr>\n    <tr>\n      <th>183546</th>\n      <td>470030298</td>\n      <td>H04L   1/0005</td>\n    </tr>\n    <tr>\n      <th>74962</th>\n      <td>416447093</td>\n      <td>B65H2404/1119</td>\n    </tr>\n    <tr>\n      <th>382818</th>\n      <td>527400811</td>\n      <td>A61P  21/00</td>\n    </tr>\n    <tr>\n      <th>411680</th>\n      <td>535860118</td>\n      <td>A61K   8/365</td>\n    </tr>\n    <tr>\n      <th>289902</th>\n      <td>500408460</td>\n      <td>A61K   8/87</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_cpc.sample(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "325243"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln_pers)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "65136"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appln)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# Number of collaborative applications\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "record_col = \"appln_id\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "fromdir=\"first_round\"\n",
    "\n",
    "os.makedirs(fr'{fromdir}/plots', exist_ok=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "                0   1  2     3  4   5\n0        43250057  SE  A  1964  1   2\n1        43265240  SE  A  1969  1   2\n2       365294032  DE  A  2009  1   1\n3       353426804  DE  A  2009  1   1\n4       335924788  DE  U  2010  1   0\n...           ...  .. ..   ... ..  ..\n773112  577173906  CN  A  2022  2  11\n773113  580290535  CN  A  2022  1   1\n773114  573161360  CN  A  2022  1   2\n773115  576722128  CN  A  2022  2   9\n773116  574827324  CN  A  2022  1   9\n\n[773117 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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>43250057</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1964</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>43265240</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1969</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>365294032</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2009</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>353426804</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2009</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>335924788</td>\n      <td>DE</td>\n      <td>U</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>773112</th>\n      <td>577173906</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>2</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>773113</th>\n      <td>580290535</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>773114</th>\n      <td>573161360</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>773115</th>\n      <td>576722128</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>773116</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>773117 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_misc = pd.read_csv(f\"{fromdir}/first-filings-raw.csv\", header=None)#,\n",
    "                      #names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
    "appln_misc"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "                 0   1  2     3  4  5             6  7\n0         43250057  SE  A  1964  1  2   B21B  37/16  A\n1         43250057  SE  A  1964  1  2   B21B  37/58  A\n2         43250057  SE  A  1964  1  2   B21B  37/64  A\n3         43265240  SE  A  1969  1  2   B65D  50/04  A\n4        365294032  DE  A  2009  1  1   C10B  49/06  A\n...            ...  .. ..   ... .. ..           ... ..\n1719607  574827324  CN  A  2022  1  9  B01J  27/138  A\n1719608  574827324  CN  A  2022  1  9   B01J  37/00  A\n1719609  574827324  CN  A  2022  1  9   B01J  37/02  A\n1719610  574827324  CN  A  2022  1  9   B01J  37/08  A\n1719611  574827324  CN  A  2022  1  9   C01G  13/04  A\n\n[1719612 rows x 8 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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>43250057</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1964</td>\n      <td>1</td>\n      <td>2</td>\n      <td>B21B  37/16</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>43250057</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1964</td>\n      <td>1</td>\n      <td>2</td>\n      <td>B21B  37/58</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>43250057</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1964</td>\n      <td>1</td>\n      <td>2</td>\n      <td>B21B  37/64</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43265240</td>\n      <td>SE</td>\n      <td>A</td>\n      <td>1969</td>\n      <td>1</td>\n      <td>2</td>\n      <td>B65D  50/04</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>365294032</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2009</td>\n      <td>1</td>\n      <td>1</td>\n      <td>C10B  49/06</td>\n      <td>A</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    </tr>\n    <tr>\n      <th>1719607</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n      <td>B01J  27/138</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1719608</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n      <td>B01J  37/00</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1719609</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n      <td>B01J  37/02</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1719610</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n      <td>B01J  37/08</td>\n      <td>A</td>\n    </tr>\n    <tr>\n      <th>1719611</th>\n      <td>574827324</td>\n      <td>CN</td>\n      <td>A</td>\n      <td>2022</td>\n      <td>1</td>\n      <td>9</td>\n      <td>C01G  13/04</td>\n      <td>A</td>\n    </tr>\n  </tbody>\n</table>\n<p>1719612 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_misc = pd.read_csv(f\"{fromdir}/first-filings-with-IPC-raw.csv\", header=None)#,\n",
    "                      #names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
    "appln_misc"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "2\nA    1643149\nU      76458\nD          5\nName: count, dtype: int64"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_misc[2].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n25       375269300         CN   41022193            0             1  \\\n26       375269300         CN   43986391            1             0   \n64       336321508         CN   13960267            4             0   \n65       336321508         CN   20214523            3             0   \n66       336321508         CN   20440543            1             0   \n...            ...        ...        ...          ...           ...   \n2418562  563162779         CN   65016826            1             0   \n2418563  563162779         CN   65604326            3             0   \n2418564  563162779         CN   69335326            4             0   \n2418565  563162779         CN   78241111            5             0   \n2418566  563162779         CN   79926327            7             0   \n\n                   person_name person_ctry_code  \n25       LU WATA ESPOO COMPANY              NaN  \n26                 DENG WENJIA              NaN  \n64                  LI JIANGUO              NaN  \n65                  YU YUEMING              NaN  \n66                 CHEN XUEMIN              NaN  \n...                        ...              ...  \n2418562             GUO HONGYU              NaN  \n2418563           ZHAO SHUFENG              NaN  \n2418564                LI BING              NaN  \n2418565             XU XIAOKAI              NaN  \n2418566            LYU JINGHUI              NaN  \n\n[1295204 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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>25</th>\n      <td>375269300</td>\n      <td>CN</td>\n      <td>41022193</td>\n      <td>0</td>\n      <td>1</td>\n      <td>LU WATA ESPOO COMPANY</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>375269300</td>\n      <td>CN</td>\n      <td>43986391</td>\n      <td>1</td>\n      <td>0</td>\n      <td>DENG WENJIA</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>336321508</td>\n      <td>CN</td>\n      <td>13960267</td>\n      <td>4</td>\n      <td>0</td>\n      <td>LI JIANGUO</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>336321508</td>\n      <td>CN</td>\n      <td>20214523</td>\n      <td>3</td>\n      <td>0</td>\n      <td>YU YUEMING</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>336321508</td>\n      <td>CN</td>\n      <td>20440543</td>\n      <td>1</td>\n      <td>0</td>\n      <td>CHEN XUEMIN</td>\n      <td>NaN</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>2418562</th>\n      <td>563162779</td>\n      <td>CN</td>\n      <td>65016826</td>\n      <td>1</td>\n      <td>0</td>\n      <td>GUO HONGYU</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2418563</th>\n      <td>563162779</td>\n      <td>CN</td>\n      <td>65604326</td>\n      <td>3</td>\n      <td>0</td>\n      <td>ZHAO SHUFENG</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2418564</th>\n      <td>563162779</td>\n      <td>CN</td>\n      <td>69335326</td>\n      <td>4</td>\n      <td>0</td>\n      <td>LI BING</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2418565</th>\n      <td>563162779</td>\n      <td>CN</td>\n      <td>78241111</td>\n      <td>5</td>\n      <td>0</td>\n      <td>XU XIAOKAI</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2418566</th>\n      <td>563162779</td>\n      <td>CN</td>\n      <td>79926327</td>\n      <td>7</td>\n      <td>0</td>\n      <td>LYU JINGHUI</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>1295204 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_colab = 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",
    "\n",
    "# appln_colab[appln_colab[\"appln_auth\"]==\"CN\"]#.merge(appln_pers_haninfo, on=\"person_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n1354     333078581         EP    4914457            1             0  \\\n1355     333078581         EP   49302649            0             1   \n1659     365282689         DE    5567000            0             1   \n1660     365282689         DE   40342643            2             0   \n1661     365282689         DE   43139037            1             0   \n...            ...        ...        ...          ...           ...   \n2396509  557244566         EP   83213913            1             0   \n2396510  557244566         EP   83239836            2             0   \n2396534  557244564         EP   83170367            0             1   \n2396535  557244564         EP   83213913            2             0   \n2396536  557244564         EP   83239836            1             0   \n\n                                         person_name person_ctry_code  \n1354                                    Collins, Tom               BE  \n1355                   Huawei Technologies Co., Ltd.               CN  \n1659     Bayerische Motoren Werke Aktiengesellschaft               DE  \n1660                                MOELLMANN, JOERG               DE  \n1661                                 JOHN, CHRISTOPH               DE  \n...                                              ...              ...  \n2396509                          LASSERRE, SEBASTIEN               FR  \n2396510                             TAQUET, JONATHAN               FR  \n2396534     Beijing Xiaomi Mobile Software Co., Ltd.               CN  \n2396535                          LASSERRE, SEBASTIEN               FR  \n2396536                             TAQUET, JONATHAN               FR  \n\n[4963 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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1354</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n    </tr>\n    <tr>\n      <th>1355</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>49302649</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>1659</th>\n      <td>365282689</td>\n      <td>DE</td>\n      <td>5567000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Bayerische Motoren Werke Aktiengesellschaft</td>\n      <td>DE</td>\n    </tr>\n    <tr>\n      <th>1660</th>\n      <td>365282689</td>\n      <td>DE</td>\n      <td>40342643</td>\n      <td>2</td>\n      <td>0</td>\n      <td>MOELLMANN, JOERG</td>\n      <td>DE</td>\n    </tr>\n    <tr>\n      <th>1661</th>\n      <td>365282689</td>\n      <td>DE</td>\n      <td>43139037</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JOHN, CHRISTOPH</td>\n      <td>DE</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>2396509</th>\n      <td>557244566</td>\n      <td>EP</td>\n      <td>83213913</td>\n      <td>1</td>\n      <td>0</td>\n      <td>LASSERRE, SEBASTIEN</td>\n      <td>FR</td>\n    </tr>\n    <tr>\n      <th>2396510</th>\n      <td>557244566</td>\n      <td>EP</td>\n      <td>83239836</td>\n      <td>2</td>\n      <td>0</td>\n      <td>TAQUET, JONATHAN</td>\n      <td>FR</td>\n    </tr>\n    <tr>\n      <th>2396534</th>\n      <td>557244564</td>\n      <td>EP</td>\n      <td>83170367</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Beijing Xiaomi Mobile Software Co., Ltd.</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>2396535</th>\n      <td>557244564</td>\n      <td>EP</td>\n      <td>83213913</td>\n      <td>2</td>\n      <td>0</td>\n      <td>LASSERRE, SEBASTIEN</td>\n      <td>FR</td>\n    </tr>\n    <tr>\n      <th>2396536</th>\n      <td>557244564</td>\n      <td>EP</td>\n      <td>83239836</td>\n      <td>1</td>\n      <td>0</td>\n      <td>TAQUET, JONATHAN</td>\n      <td>FR</td>\n    </tr>\n  </tbody>\n</table>\n<p>4963 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ch = appln_colab.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",
    "appln_scope"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "data": {
      "text/plain": "       appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n0     333078581         EP    4914457            1             0  \\\n1     365149571         EP    4914457            1             0   \n2     379191760         EP    4914457            1             0   \n3     333078581         EP   49302649            0             1   \n4     339456534         EP   49302649            0             1   \n...         ...        ...        ...          ...           ...   \n4436  549690891         EP   85841201            1             0   \n4437  549690895         EP   85841201            1             0   \n4438  549690901         EP   85841201            1             0   \n4439  549690897         EP   85841201            1             0   \n4440  549788919         EP   85774895            2             0   \n\n                        person_name person_ctry_code psn_sector     han_id   \n0                      Collins, Tom               BE        NaN  104914457  \\\n1                      Collins, Tom               BE        NaN  104914457   \n2                      Collins, Tom               BE        NaN  104914457   \n3     Huawei Technologies Co., Ltd.               CN    COMPANY    1337324   \n4     Huawei Technologies Co., Ltd.               CN    COMPANY    1337324   \n...                             ...              ...        ...        ...   \n4436                  JONSSON, Tony               SE    UNKNOWN  185841201   \n4437                  JONSSON, Tony               SE    UNKNOWN  185841201   \n4438                  JONSSON, Tony               SE    UNKNOWN  185841201   \n4439                  JONSSON, Tony               SE    UNKNOWN  185841201   \n4440            TRAN NGUYEN, Johnny               SE    UNKNOWN  185774895   \n\n                 han_name  \n0            Collins, Tom  \n1            Collins, Tom  \n2            Collins, Tom  \n3      HUAWEI TECH CO LTD  \n4      HUAWEI TECH CO LTD  \n...                   ...  \n4436        JONSSON, Tony  \n4437        JONSSON, Tony  \n4438        JONSSON, Tony  \n4439        JONSSON, Tony  \n4440  TRAN NGUYEN, Johnny  \n\n[4441 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>0</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>NaN</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>365149571</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>NaN</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>379191760</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>NaN</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>49302649</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>1337324</td>\n      <td>HUAWEI TECH CO LTD</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>339456534</td>\n      <td>EP</td>\n      <td>49302649</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n      <td>COMPANY</td>\n      <td>1337324</td>\n      <td>HUAWEI TECH CO LTD</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>4436</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>4437</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>4438</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>4439</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>4440</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>4441 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope.merge(appln_pers_haninfo, on=\"person_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [
    {
     "data": {
      "text/plain": "1155"
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope[\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: [appln_id_x, appln_auth_x, person_id, invt_seq_nr_x, applt_seq_nr_x, person_name_x, person_ctry_code_x, appln_id_y, appln_auth_y, invt_seq_nr_y, applt_seq_nr_y, person_name_y, person_ctry_code_y, psn_sector, han_id, han_name, psn_sector_primary, person_ctry_code_fullname, appln_auth_fullname, person_ctry_ISO3, person_ctry_type, auth_ISO3, auth_ctry_type]\nIndex: []\n\n[0 rows x 23 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_x</th>\n      <th>appln_auth_x</th>\n      <th>person_id</th>\n      <th>invt_seq_nr_x</th>\n      <th>applt_seq_nr_x</th>\n      <th>person_name_x</th>\n      <th>person_ctry_code_x</th>\n      <th>appln_id_y</th>\n      <th>appln_auth_y</th>\n      <th>invt_seq_nr_y</th>\n      <th>...</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_primary</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>auth_ISO3</th>\n      <th>auth_ctry_type</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n<p>0 rows × 23 columns</p>\n</div>"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_scope[appln_scope[\"person_ctry_code\"]==\"GB\"].merge(appln_pers_f, on=\"person_id\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n6810     333031060         GB   42380482            1             0  \\\n27291    334469711         GB   42552873            0             1   \n31935    336159242         GB    5318110            0             1   \n55322    333031012         GB   42013453            1             1   \n128151   337007702         GB    5318110            0             1   \n...            ...        ...        ...          ...           ...   \n2130509  546501618         GB   87463236            1             0   \n2135530  546434937         GB   20149035            3             0   \n2135533  546434937         GB   88227411            0             2   \n2210955  550995729         GB   17999648            2             0   \n2210971  550995769         GB   17999648            2             0   \n\n                              person_name person_ctry_code  \n6810                KA-SHUN CARRISON TONG               CN  \n27291       LE GROUP OF COMPANIES LIMITED               CN  \n31935       Huawei Technologies Co., Ltd.               CN  \n55322              BENJAMINO KAH HUNG LEE               CN  \n128151      Huawei Technologies Co., Ltd.               CN  \n...                                   ...              ...  \n2130509  Santiago Ramon Alvarez Fernandez               CN  \n2135530                           YU HUAI               CN  \n2135533      PICOCOM (HANGZHOU) CO., LTD.               CN  \n2210955                         ZHANG LIN               CN  \n2210971                         ZHANG LIN               CN  \n\n[278 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>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6810</th>\n      <td>333031060</td>\n      <td>GB</td>\n      <td>42380482</td>\n      <td>1</td>\n      <td>0</td>\n      <td>KA-SHUN CARRISON TONG</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>27291</th>\n      <td>334469711</td>\n      <td>GB</td>\n      <td>42552873</td>\n      <td>0</td>\n      <td>1</td>\n      <td>LE GROUP OF COMPANIES LIMITED</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>31935</th>\n      <td>336159242</td>\n      <td>GB</td>\n      <td>5318110</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>55322</th>\n      <td>333031012</td>\n      <td>GB</td>\n      <td>42013453</td>\n      <td>1</td>\n      <td>1</td>\n      <td>BENJAMINO KAH HUNG LEE</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>128151</th>\n      <td>337007702</td>\n      <td>GB</td>\n      <td>5318110</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2130509</th>\n      <td>546501618</td>\n      <td>GB</td>\n      <td>87463236</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Santiago Ramon Alvarez Fernandez</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>2135530</th>\n      <td>546434937</td>\n      <td>GB</td>\n      <td>20149035</td>\n      <td>3</td>\n      <td>0</td>\n      <td>YU HUAI</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>2135533</th>\n      <td>546434937</td>\n      <td>GB</td>\n      <td>88227411</td>\n      <td>0</td>\n      <td>2</td>\n      <td>PICOCOM (HANGZHOU) CO., LTD.</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>2210955</th>\n      <td>550995729</td>\n      <td>GB</td>\n      <td>17999648</td>\n      <td>2</td>\n      <td>0</td>\n      <td>ZHANG LIN</td>\n      <td>CN</td>\n    </tr>\n    <tr>\n      <th>2210971</th>\n      <td>550995769</td>\n      <td>GB</td>\n      <td>17999648</td>\n      <td>2</td>\n      <td>0</td>\n      <td>ZHANG LIN</td>\n      <td>CN</td>\n    </tr>\n  </tbody>\n</table>\n<p>278 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_colab[((appln_colab[\"appln_auth\"]==\"GB\")&(appln_colab[\"person_ctry_code\"]==\"CN\"))]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155\n"
     ]
    }
   ],
   "source": [
    "appln_ch = appln_colab.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())\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155\n"
     ]
    }
   ],
   "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",
    "\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": 243,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155\n"
     ]
    }
   ],
   "source": [
    "appln_pers_haninfo = pd.read_csv(f\"{fromdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
    "\n",
    "appln_pers_haninfo[\"han_name\"] = appln_pers_haninfo[\"han_name\"].str.replace(\"Zhejiang Geely Holding Group Co., Ltd.\",\n",
    "                                                                            \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\")\n",
    "\n",
    "\n",
    "appln_pers_haninfo.loc[appln_pers_haninfo[\"han_name\"]==\"ZHEJIANG GEELY HOLDINGGROUP CO LTD\",\"han_id\"] = 3345915\n",
    "\n",
    "appln_pers_f = appln_scope.merge(appln_pers_haninfo[[\"person_id\",\"han_id\",\"han_name\"]].drop_duplicates(), on=\"person_id\")\n",
    "print(appln_pers_f[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155\n"
     ]
    }
   ],
   "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",
    "appln_pers_f = appln_pers_f.merge(pers_sector_primary, on='han_id', how=\"left\")\n",
    "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_primary\"].fillna(\"UNKNOWN\")\n",
    "print(appln_pers_f[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155 1155\n"
     ]
    }
   ],
   "source": [
    "print(appln_pers_f[\"appln_id\"].nunique(), appln_scope[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "outputs": [],
   "source": [
    "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
    "# tls_801.head()\n",
    "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
    "                      (tls_801.ctry_code == 'NO')|\n",
    "                      (tls_801.ctry_code == 'CH')|\n",
    "                      (tls_801.ctry_code == 'GB'))]\n",
    "scope_countries = scope_df[\"ctry_code\"].unique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155 1155\n"
     ]
    }
   ],
   "source": [
    "appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])]\n",
    "print(appln_pers_f[\"appln_id\"].nunique(), appln_scope[\"appln_id\"].nunique())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "outputs": [
    {
     "data": {
      "text/plain": "['AT',\n 'BE',\n 'BG',\n 'CH',\n 'CY',\n 'CZ',\n 'DE',\n 'DK',\n 'EE',\n 'ES',\n 'FI',\n 'FR',\n 'GB',\n 'GR',\n 'HR',\n 'HU',\n 'IE',\n 'IT',\n 'LT',\n 'LU',\n 'LV',\n 'MT',\n 'NL',\n 'NO',\n 'PL',\n 'PT',\n 'RO',\n 'SE',\n 'SI',\n 'SK',\n 'CN']"
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(scope_countries)+[\"CN\"]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "outputs": [
    {
     "data": {
      "text/plain": "       appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n0     333078581         EP    4914457            1             0  \\\n1     365149571         EP    4914457            1             0   \n2     379191760         EP    4914457            1             0   \n3     333078581         EP   49302649            0             1   \n4     339456534         EP   49302649            0             1   \n...         ...        ...        ...          ...           ...   \n4958  549690891         EP   85841201            1             0   \n4959  549690895         EP   85841201            1             0   \n4960  549690901         EP   85841201            1             0   \n4961  549690897         EP   85841201            1             0   \n4962  549788919         EP   85774895            2             0   \n\n                        person_name person_ctry_code     han_id   \n0                      Collins, Tom               BE  104914457  \\\n1                      Collins, Tom               BE  104914457   \n2                      Collins, Tom               BE  104914457   \n3     Huawei Technologies Co., Ltd.               CN    1337324   \n4     Huawei Technologies Co., Ltd.               CN    1337324   \n...                             ...              ...        ...   \n4958                  JONSSON, Tony               SE  185841201   \n4959                  JONSSON, Tony               SE  185841201   \n4960                  JONSSON, Tony               SE  185841201   \n4961                  JONSSON, Tony               SE  185841201   \n4962            TRAN NGUYEN, Johnny               SE  185774895   \n\n                 han_name psn_sector_primary  \n0            Collins, Tom            UNKNOWN  \n1            Collins, Tom            UNKNOWN  \n2            Collins, Tom            UNKNOWN  \n3      HUAWEI TECH CO LTD            COMPANY  \n4      HUAWEI TECH CO LTD            COMPANY  \n...                   ...                ...  \n4958        JONSSON, Tony            UNKNOWN  \n4959        JONSSON, Tony            UNKNOWN  \n4960        JONSSON, Tony            UNKNOWN  \n4961        JONSSON, Tony            UNKNOWN  \n4962  TRAN NGUYEN, Johnny            UNKNOWN  \n\n[4814 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>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_primary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>365149571</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>379191760</td>\n      <td>EP</td>\n      <td>4914457</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Collins, Tom</td>\n      <td>BE</td>\n      <td>104914457</td>\n      <td>Collins, Tom</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>333078581</td>\n      <td>EP</td>\n      <td>49302649</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n      <td>1337324</td>\n      <td>HUAWEI TECH CO LTD</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>339456534</td>\n      <td>EP</td>\n      <td>49302649</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Huawei Technologies Co., Ltd.</td>\n      <td>CN</td>\n      <td>1337324</td>\n      <td>HUAWEI TECH CO LTD</td>\n      <td>COMPANY</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>4958</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>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>4959</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>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>4960</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>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>4961</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>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>4962</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>185774895</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n<p>4814 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_pers_f"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155\n"
     ]
    },
    {
     "data": {
      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n3246    331546756         EP          A         2011              1  \\\n194962  448860230         DE          A         2014              1   \n588485  513966940         EP          A         2019              1   \n206034  416859711         EP          A         2014              1   \n670021  532224185         EP          A         2020              2   \n...           ...        ...        ...          ...            ...   \n460742  491633486         EP          A         2018              1   \n275339  446738785         FR          A         2015              1   \n505075  504869364         EP          A         2018              1   \n474988  524089197         DE          A         2018              1   \n567305  509177273         EP          A         2019              1   \n\n        nb_inventors  \n3246               1  \n194962             3  \n588485             1  \n206034             2  \n670021             2  \n...              ...  \n460742             3  \n275339             3  \n505075             4  \n474988             6  \n567305             1  \n\n[100 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_kind</th>\n      <th>filing_year</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3246</th>\n      <td>331546756</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2011</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>194962</th>\n      <td>448860230</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2014</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>588485</th>\n      <td>513966940</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>206034</th>\n      <td>416859711</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2014</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>670021</th>\n      <td>532224185</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>2</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    </tr>\n    <tr>\n      <th>460742</th>\n      <td>491633486</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>275339</th>\n      <td>446738785</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>505075</th>\n      <td>504869364</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>474988</th>\n      <td>524089197</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2018</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>567305</th>\n      <td>509177273</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 250,
     "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": 250,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "outputs": [
    {
     "data": {
      "text/plain": "appln_kind\nA    1164\nU       7\nName: count, dtype: int64"
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_f[\"appln_kind\"].value_counts()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "outputs": [],
   "source": [
    "# Co-patents per year"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "outputs": [
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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),height=800,width=1600,scale = 4)\n",
    "\n",
    "figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\",height=900,width=900,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n"
     ]
    },
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    "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",
    "\n",
    "\n",
    "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
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    "              labels={\n",
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    "                 },\n",
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    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n",
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    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)"
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   "execution_count": 254,
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   ],
   "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=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
    "              markers=True,\n",
    "              labels={\n",
    "                    record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n",
    "                 },\n",
    "                title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n",
    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
    "fig.update_yaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "    ticks=\"outside\")\n",
    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
    "fig.update_yaxes(title='')\n",
    "fig.update_xaxes(title='')\n",
    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt = go.Figure(fig)\n",
    "\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 22\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP not found in ISO2\n",
      "European Patent Office not found in regex\n"
     ]
    },
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   "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[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
    "\n",
    "appln_pers_f[\"auth_ISO3\"] = 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",
    "# top 25\n",
    "# - inventor\n",
    "# - applicant\n",
    "# in china, EU, EUextended\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#9F2B68'}\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col: 'Number of co-patents',\n",
    "                     \"person_ctry_type\": \"Country type\",\n",
    "    \"psn_sector_primary\":\"Sector\",\n",
    "                                  \"han_label\": \"Name\",\n",
    "                                  \"Country_Type\":\"Country type\",\n",
    "                                  \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "for trait in  [\"person_ctry_type\",\"psn_sector_primary\"]:\n",
    "    for ctry in [\"China\",'EU']:\n",
    "        ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
    "        # for role in [\"invt\",\"applt\"]:\n",
    "        if True:\n",
    "            role = \"applt\"\n",
    "            pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
    "            data = (pers_sub\n",
    "                     .groupby([\"han_id\",'han_name'],as_index=False)\n",
    "                     .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n",
    "                     .sort_values(by=\"appln_id\", ascending=False)\n",
    "                    .head(15))\n",
    "            data[\"han_label\"] = (data[\"han_name\"]\n",
    "                                 .str.upper()\n",
    "                                 .str.strip(\".\")\n",
    "                                 .str.replace(\"&\",\"<br>\")\n",
    "                                 + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n",
    "\n",
    "            fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n",
    "                         color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
    "            fig.update_layout(font_family=\"Montserrat\",\n",
    "                          yaxis={'categoryorder':'total ascending'})\n",
    "\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=12\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "\n",
    "            figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "            figsuper.write_image(f\"{fromdir}/plots/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "outputs": [
    {
     "name": "stderr",
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   ],
   "source": [
    "applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n",
    "                          (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n",
    "invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n",
    "\n",
    "eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
    "\n",
    "#\n",
    "# patstat_applt_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "# patstat_applt_dipol.sample(100)\n",
    "\n",
    "patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
    "\n",
    "# co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
    "#                             values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
    "                            values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
    "\n",
    "\n",
    "co_occur = co_occur.T\n",
    "co_occur[\"sum\"] = co_occur.sum(axis=1)\n",
    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
    "\n",
    "co_occur = (co_occur\n",
    "            .sort_values(by=\"sum\", ascending=False)\n",
    "            .head(20)\n",
    "            .drop(columns=\"sum\")\n",
    "            .replace(0,np.nan)\n",
    "            .dropna(axis=1,how=\"all\")\n",
    "            .replace(np.nan,0)\n",
    "            .astype(int))\n",
    "\n",
    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
    "                labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
    "                x=list(co_occur.columns),\n",
    "                text_auto=True,\n",
    "                y=list(co_occur.index))\n",
    "\n",
    "\n",
    "fig.update_layout(title_x=0.5,\n",
    "                   width=1000, height=1000,\n",
    "                   xaxis_showgrid=False,\n",
    "                   yaxis_showgrid=False,\n",
    "                   yaxis_autorange='reversed',\n",
    "                  font_family=\"Montserrat\",\n",
    "                  template='plotly_white',\n",
    "                  coloraxis_colorbar=dict(\n",
    "                            thicknessmode=\"pixels\", thickness=25,\n",
    "                            ticks=\"outside\", ticksuffix=\" \",\n",
    "                            dtick=20,outlinewidth=1,\n",
    "                        ))\n",
    "\n",
    "# fig.update_traces(texttemplate='%{z}')\n",
    "# fig.update_traces(z=co_occur.values)\n",
    "# fig.update(data=[{'customdata': co_occur}])\n",
    "# fig.update(data=[{'customdata': co_occur,\n",
    "#     'hovertemplate': \"(x) %{x} product: %{customdata[0]}<br>(y) %{y} product: %{customdata[1]}<br>%{z}\"}])\n",
    "# fig.update_traces(texttemplate='%{customdata}')\n",
    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "fig.update_xaxes(tickangle= -45)\n",
    "fig.update_yaxes(\n",
    "    ticks=\"outside\")\n",
    "fig.update_xaxes(\n",
    "    ticks=\"outside\")\n",
    "\n",
    "figsuper_ppt = fig\n",
    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 16\n",
    "\n",
    "s=16\n",
    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
    "\n",
    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
    "\n",
    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)"
   ],
   "metadata": {
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  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "outputs": [],
   "source": [
    "patstat_applt_dipol_2 = (patstat_applt_dipol\n",
    "                         .groupby([\"han_name_ch\",\"han_name_eu\"], as_index=False)\n",
    "                         .agg({\"psn_sector_primary_ch\":\"first\",\n",
    "                               \"psn_sector_primary_eu\":\"first\",\n",
    "                               \"person_ctry_ISO3_eu\":\"first\",\n",
    "                               record_col:\"nunique\"})\n",
    "                         .sort_values(by=record_col, ascending=False)\n",
    "                         .head(-1))\n",
    "patstat_applt_dipol_2[\"applt_eu_label\"] = (patstat_applt_dipol_2[\"han_name_eu\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"person_ctry_ISO3_eu\"]\n",
    "                                           +\"; \"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_eu\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[\"applt_ch_label\"] = (patstat_applt_dipol_2[\"han_name_ch\"]\n",
    "                                           +\" (\"\n",
    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_ch\"]\n",
    "                                           +\")\")\n",
    "\n",
    "patstat_applt_dipol_2[[\"applt_ch_label\",\"applt_eu_label\",\"appln_id\"]].to_excel(rf\"{fromdir}/plots/applt_table.xlsx\", index=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [
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           },
           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
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           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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          "yaxis": {
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            "standoff": 15
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         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0.0,
          1.0
         ],
         "title": {
          "text": "Percent of related co-patents"
         },
         "tickformat": ".1%",
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
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        "yaxis": {
         "anchor": "x",
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         "title": {
          "text": "Application authority"
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         "categoryorder": "total ascending",
         "showgrid": true,
         "showline": true,
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         "mirror": true,
         "ticks": "outside",
         "tickfont": {
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        "legend": {
         "title": {
          "text": "Authority type"
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        "barmode": "relative",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# percent of publications related to\n",
    "# -authority country\n",
    "# -inventor country\n",
    "# -applicant country\n",
    "\n",
    "color_discrete_map= {'China': '#EF553B',\n",
    "                    'EU': '#636EFA',\n",
    "                    'Non-EU associate': '#00CC96',\n",
    "                     'EPO':'#7F00FF'}#CC8899\n",
    "\n",
    "\n",
    "labeld_dict = dict({\n",
    "                     record_col+\"_percent\": 'Percent of related co-patents',\n",
    "                    \"person_ctry_code_fullname\": \"Country\",\n",
    "                    \"person_ctry_type\": \"Country type\",\n",
    "                    \"appln_auth_fullname\": \"Application authority\",\n",
    "                    \"auth_ctry_type\": \"Authority type\",\n",
    "                    \"Country_Type\":\"Country type\",\n",
    "                    \"Eurovoc_Class\":\"Region\"\n",
    "                 })\n",
    "\n",
    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
    "    pers_sub = appln_pers_f\n",
    "    if c == \"person_ctry_code\":\n",
    "        for role in [\"invt\",\"applt\"]:\n",
    "            pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
    "            role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n",
    "            labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n",
    "\n",
    "\n",
    "            data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
    "            data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "            fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "            fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "            figsuper = fig\n",
    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "            figsuper.update_yaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_xaxes(\n",
    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "                ticks=\"outside\")\n",
    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "            figsuper['layout'][\"font\"][\"size\"]=14\n",
    "            for a in figsuper['layout'][\"annotations\"]:\n",
    "                a[\"font\"][\"size\"] = 14\n",
    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "            figsuper.update_layout(uniformtext_minsize=10)\n",
    "            figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "            figsuper.show()\n",
    "    else:\n",
    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
    "        data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
    "        fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
    "                         labels = labeld_dict)\n",
    "        fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
    "                      yaxis={'categoryorder':'total ascending'},)\n",
    "        figsuper = fig\n",
    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
    "        figsuper.update_yaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_xaxes(\n",
    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
    "            ticks=\"outside\")\n",
    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
    "\n",
    "        figsuper['layout'][\"font\"][\"size\"]=14\n",
    "        for a in figsuper['layout'][\"annotations\"]:\n",
    "            a[\"font\"][\"size\"] = 14\n",
    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
    "\n",
    "        figsuper.update_layout(uniformtext_minsize=10)\n",
    "        figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
    "        figsuper.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "outputs": [],
   "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\"]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "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": 262,
   "outputs": [],
   "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"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "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": 264,
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "alignmentgroup": "True",
         "cliponaxis": true,
         "hovertemplate": "%{x:d}",
         "legendgroup": "A-Human necessities",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "A-Human necessities",
         "offsetgroup": "A-Human necessities",
         "orientation": "h",
         "showlegend": false,
         "textangle": 0,
         "textfont": {
          "size": 12
         },
         "textposition": "outside",
         "texttemplate": "%{x}",
         "x": [
          132
         ],
         "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",
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         "domain": [
          0.0,
          0.375
         ],
         "showgrid": true,
         "showline": true,
         "linewidth": 1,
         "linecolor": "black",
         "mirror": true,
         "ticks": "outside",
         "zeroline": true,
         "zerolinewidth": 0.5,
         "zerolinecolor": "grey",
         "tickformat": ".0%"
        },
        "annotations": [
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Distribution of IPC classes",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 1.0,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Cumulative sum of related co-patents",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 1.0,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Related co-patents per year",
          "x": 0.225,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         },
         {
          "font": {
           "size": 14
          },
          "showarrow": false,
          "text": "Relative growth of co-patents",
          "x": 0.775,
          "xanchor": "center",
          "xref": "paper",
          "y": 0.375,
          "yanchor": "bottom",
          "yref": "paper"
         }
        ],
        "barmode": "relative",
        "font": {
         "family": "Montserrat",
         "size": 14
        }
       },
       "config": {
        "displayModeBar": false,
        "responsive": true,
        "plotlyServerURL": "https://plotly.com"
       }
      },
      "text/html": "<div>                            <div id=\"0c9621d9-5671-4103-85ca-d0218df89384\" 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(\"0c9621d9-5671-4103-85ca-d0218df89384\")) {                    Plotly.newPlot(                        \"0c9621d9-5671-4103-85ca-d0218df89384\",                        [{\"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\":[132],\"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\":[363],\"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\":[153],\"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\":[14],\"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\":[51],\"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\":[157],\"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\":[263],\"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",
    "    trace.showlegend=False\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\"]=14\n",
    "for a in figsuper['layout'][\"annotations\"]:\n",
    "    a[\"font\"][\"size\"] = 14\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "figsuper.write_image(rf\"{fromdir}/plots/ipc_classes.png\",height=900,width=1600,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\openpyxl\\reader\\workbook.py:118: UserWarning:\n",
      "\n",
      "Print area cannot be set to Defined name: Sheet1!$A:$B.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                                 TOPIC                 IPC   \n2                                          Solid fuels  C10L5/00,5/40-5/48  \\\n3                              Torrefaction of biomass           C10B53/02   \n5                                         Liquid fuels  C10L1/00,1/02,1/14   \n6                                       Vegetable oils       C10L1/02,1/19   \n7                                            Biodiesel     C07C67/00,69/00   \n..                                                 ...                 ...   \n287                                Nuclear engineering                 G21   \n288                                    Fusion reactors                G21B   \n289                         Nuclear (fission) reactors                G21C   \n290                                Nuclear power plant                G21D   \n291  Gas turbine power plants using heat source of ...            F02C1/05   \n\n         IPC_f  \n2     C10L5/00  \n3    C10B53/02  \n5     C10L1/00  \n6     C10L1/02  \n7    C07C67/00  \n..         ...  \n287        G21  \n288       G21B  \n289       G21C  \n290       G21D  \n291   F02C1/05  \n\n[186 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TOPIC</th>\n      <th>IPC</th>\n      <th>IPC_f</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>Solid fuels</td>\n      <td>C10L5/00,5/40-5/48</td>\n      <td>C10L5/00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Torrefaction of biomass</td>\n      <td>C10B53/02</td>\n      <td>C10B53/02</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Liquid fuels</td>\n      <td>C10L1/00,1/02,1/14</td>\n      <td>C10L1/00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Vegetable oils</td>\n      <td>C10L1/02,1/19</td>\n      <td>C10L1/02</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Biodiesel</td>\n      <td>C07C67/00,69/00</td>\n      <td>C07C67/00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>287</th>\n      <td>Nuclear engineering</td>\n      <td>G21</td>\n      <td>G21</td>\n    </tr>\n    <tr>\n      <th>288</th>\n      <td>Fusion reactors</td>\n      <td>G21B</td>\n      <td>G21B</td>\n    </tr>\n    <tr>\n      <th>289</th>\n      <td>Nuclear (fission) reactors</td>\n      <td>G21C</td>\n      <td>G21C</td>\n    </tr>\n    <tr>\n      <th>290</th>\n      <td>Nuclear power plant</td>\n      <td>G21D</td>\n      <td>G21D</td>\n    </tr>\n    <tr>\n      <th>291</th>\n      <td>Gas turbine power plants using heat source of ...</td>\n      <td>F02C1/05</td>\n      <td>F02C1/05</td>\n    </tr>\n  </tbody>\n</table>\n<p>186 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "#\n",
    "# https://gitlab.com/area-science-park-sustainability/green-patents/-/tree/main\n",
    "#\n",
    "clean_codes = pd.read_excel(f'{fromdir}/2IPCGreenInventoryList_2022WIPO.xlsx')\n",
    "ipc_green = clean_codes[[\"TOPIC\",\"IPC\"]].dropna()\n",
    "ipc_green[\"IPC\"] = ipc_green[\"IPC\"].apply(lambda x: re.sub(r\"\\s+\", '', x))\n",
    "ipc_green[\"IPC_f\"]  = ipc_green[\"IPC\"].str.split(\",\").str[0].str.split(\"-\").str[0]\n",
    "ipc_green"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "outputs": [
    {
     "data": {
      "text/plain": "'C10L5/00,5/40-5/48; C10B53/02; C10L1/00,1/02,1/14; C10L1/02,1/19; C07C67/00,69/00; C10L1/02,1/182; C02F3/28,11/04; C12N1/13,1/15,1/21,5/10,15/00; C10L3/00; H01M4/86-4/98,8/00-8/24,12/00-12/08; H01M4/86-4/98; H01M8/00-8/24,50/00-50/171; H01M12/00-12/08; C10B53/00; C10L5/00; C10L5/42,5/44; F23G7/00,7/10; C10J3/02,3/46; B09B3/00; C10L5/48; C21B5/06; D21C11/00; A62D3/02; B09B; B01D53/02,53/04,53/047,53/14,53/22,53/24; C10L5/46; E02B9/00-9/06; E02B9/08; F03B; F03B13/12-13/26; F03B15/00-15/22; B63H19/02,19/04; F03G7/05; F03D; H02K7/18; B63B35/00; B60K16/00; B60L8/00; B63H13/00; F24S; H01L27/142,31/00-31/078; H01L27/30,51/42-51/48; H01L25/00,25/03,25/16,25/18,31/042; C01B33/02; G05F1/67; F21L4/00; H02J7/35; H01G9/20; F24D17/00,18/00; F24D3/00,5/00,11/00,19/00; F24S90/00; F03D1/04,9/00,13/20; C02F1/14; F02C1/05; H01L31/0525; F03G6/00-6/06; E04D13/00,13/18; F22B1/00; F25B27/00; F26B3/00,3/28; F24S23/00; F24S10/10; F24T; F01K; F03G4/00-4/06,7/04; F24T10/00-50/00; F24D11/02; F24D15/04; F24D17/02,18/00; F24H4/00; F25B30/00; F01K27/00; F01K23/06-23/10; F01K17/00,23/04; F02C6/18; F25B27/02; C02F1/16; D21F5/20; F22B1/02; F23G5/46; F24F12/00; F27D17/00; F28D17/00-20/00; C10J3/86; F03G5/00-5/08; B60K6/00,6/20; B60W20/00; F16H3/00-3/78,48/00-48/30; H02K29/08; H02K49/10; B60L7/10-7/22; B60L9/00; B60L50/50-58/40; F02B43/00; H02J7/00; B62D35/00,35/02; B62K; B61; B61D17/02; B63H9/00; B63H16/00; B63H21/18; B64G1/44; B60K6/28; H02J; H02J9/00; B60L3/00; C09K5/00; F21K99/00; E04B1/62,1/74-1/80,1/88,1/90; E04C1/40,1/41,2/284-2/296; E06B3/263; E04B2/00; E04B5/00; E04B7/00; E04B9/00; F03G7/08; B60K6/10,6/30; A61L11/00; A62D3/00,101/00; G21F9/00; B03B9/06; B09C; D21B1/08,1/32; F23G; A43B1/12,21/14; B22F8/00; C04B7/24-7/30; C04B18/04-18/10; C05F; C08J11/00-11/28; B29B17/00; B62D67/00; C08J11/04-11/28; C10G1/10; C10L5/46,5/48; C22B7/00-7/04,19/30,25/06; D01G11/00; D21C5/02; H01J9/50,9/52; H01M6/52,10/54; B01D53/14,53/22,53/62; B01D53/00-53/96; F01N3/00-3/38; B01D53/92; C21C5/38; C10B21/18; F23G7/06; F01N9/00; B01D45/00-51/00; C21B7/22; C10L10/02,10/06; F23J15/00; C09K3/22; G08B21/12; B63J4/00; C05F7/00; C09K3/32; B63B35/32; E03C1/12; C02F1/00,3/00,9/00; G21C13/10; A01G23/00; A01G25/00; A01N25/00-65/00; C09K17/00; G06Q; E04H1/00; G21; G21B; G21C; G21D'"
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipc_codes = \"; \".join(ipc_green[\"IPC\"].drop_duplicates())\n",
    "ipc_codes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "outputs": [],
   "source": [
    "chat_gpt_convert = \"\"\"C10L5/00; C10L5/40; C10L5/41; C10L5/42; C10L5/43; C10L5/44; C10L5/45; C10L5/46; C10L5/47; C10L5/48; C10B53/02; C10L1/00; C10L1/02; C10L1/14; C10L1/02; C10L1/19; C07C67/00; C07C69/00; C10L1/02; C10L1/182; C02F3/28; C02F3/11; C02F11/04; C12N1/13; C12N1/15; C12N1/21; C12N5/10; C12N15/00; C10L3/00; H01M4/86; H01M4/87; H01M4/88; H01M4/89; H01M4/90; H01M4/91; H01M4/92; H01M4/93; H01M4/94; H01M4/95; H01M4/96; H01M4/97; H01M4/98; H01M8/00; H01M8/01; H01M8/02; H01M8/03; H01M8/04; H01M8/05; H01M8/06; H01M8/07; H01M8/08; H01M8/09; H01M8/10; H01M8/11; H01M8/12; H01M8/13; H01M8/14; H01M8/15; H01M8/16; H01M8/17; H01M8/18; H01M8/19; H01M8/50; H01M8/51; H01M8/52; H01M8/53; H01M8/54; H01M8/55; H01M8/56; H01M8/57; H01M8/58; H01M12/00; H01M12/01; H01M12/02; H01M12/03; H01M12/04; H01M12/05; H01M12/06; H01M12/07; H01M12/08; C10B53/00; C10L5/00; C10L5/42; C10L5/44; F23G7/00; F23G7/10; C10J3/02; C10J3/03; B09B3/00; C10L5/48; C21B5/06; D21C11/00; A62D3/02; F23G7/00; F23G7/10; B09B3/00; B09B; B01D53/02; B01D53/04; B01D53/047; B01D53/14; B01D53/22; B01D53/24; C10L5/46; E02B9/00; E02B9/01; E02B9/02; E02B9/03; E02B9/04; E02B9/05; E02B9/06; E02B9/08; F03B; F03B13/12; F03B13/13; F03B13/14; F03B13/15; F03B13/16; F03B13/17; F03B13/18; F03B13/19; F03B13/20; F03B13/21; F03B13/22; F03B13/23; F03B13/24; F03B13/25; F03B13/26; F03B15/00; F03B15/01; F03B15/02; F03B15/03; F03B15/04; F03B15/05; F03B15/06; F03B15/07; F03B15/08; F03B15/09; F03B15/10; F03B15/11; F03B15/12; F03B15/13; F03B15/14; F03B15/15; F03B15/16; F03B15/17; F03B15/18; F03B15/19; F03B15/20; F03B15/21; B63H19/02; B63H19/04; F03G7/05; F03D; H02K7/18; B63B35/00; B60K16/00; B60L8/00; B63H13/00; F24S; H01L27/142; H01L27/31; H01L27/31; H01L27/32; H01L27/33; H01L27/34; H01L27/35; H01L27/36; H01L27/37; H01L27/38; H01L27/39; H01L27/40; H01L27/41; H01L27/42; H01L27/43; H01L27/44; H01L27/45; H01L27/46; H01L27/47; H01L27/48; C01B33/02; G05F1/67; F21L4/00; H02J7/35; H01G9/20; F24S; F24D17/00; F24D18/00; F24D3/00; F24D5/00; F24D11/00; F24D19/00; F24S90/00; F03D1/04; F03D1/09; F03D1/13; F03D1/20; C02F1/14; F02C1/05; H01L31/0525; B60K16/00; B60L8/00; F03G6/00; F03G6/01; F03G6/02; F03G6/03; F03G6/04; F03G6/05; F03G6/06; E04D13/00; E04D13/18; F22B1/00; F25B27/00; F26B3/00; F26B3/28; F24S23/00; F24S10/10; F24T; F01K; F03G4/00; F03G4/01; F03G4/02; F03G4/03; F03G4/04; F03G4/05; F03G4/06; F24T10/00; F24T50/00; F24T50/10; F24T50/20; F24T50/30; F24T50/40; F24T50/50; F24D11/02; F24D15/04; F24D17/02; F24D18/00; F24H4/00; F25B30/00; F01K27/00; F01K23/06; F01K23/07; F01K23/08; F01K23/09; F01K23/10; F01K17/00; F01K23/04; F02C6/18; F25B27/02; C02F1/16; D21F5/20; F22B1/02; F23G5/46; F24F12/00; F27D17/00; F28D17/00; F28D18/00; F28D19/00; F28D20/00; C10J3/86; F03G5/00; F03G5/01; F03G5/02; F03G5/03; F03G5/04; F03G5/05; B60K6/00; B60K6/20; B60W20/00; F16H3/00; F16H3/01; F16H3/02; F16H3/03; F16H3/04; F16H3/05; F16H3/06; F16H3/07; F16H3/08; F16H3/09; F16H3/10; F16H3/11; F16H3/12; F16H3/13; F16H3/14; F16H3/15; F16H3/16; F16H3/17; F16H3/18; F16H3/19; F16H3/20; F16H3/21; F16H3/22; F16H3/23; F16H3/24; F16H3/25; F16H3/26; F16H3/27; F16H3/28; F16H3/29; F16H3/30; F16H3/31; F16H3/32; F16H3/33; F16H3/34; F16H3/35; F16H3/36; F16H3/37; F16H3/38; F16H3/48; F16H3/49; F16H3/50; F16H3/51; F16H3/52; F16H3/53; F16H3/54; F16H3/55; F16H3/56; F16H3/57; F16H3/58; H02K29/08; H02K49/10; B60L7/10; B60L7/11; B60L7/12; B60L7/13; B60L7/14; B60L7/15; B60L7/16; B60L7/17; B60L7/18; B60L7/19; B60L7/20; B60L7/21; B60L7/22; B60L8/00; B60L9/00; B60L50/50; B60L50/51; B60L50/52; B60L50/53; B60L50/54; B60L50/55; B60L50/56; B60L50/57; B60L50/58; F02B43/00; B60K16/00; H02J7/00; B62D35/00; B62D35/02; B62K; B61; B61D17/02; B63H9/00; B63H13/00; B63H19/02; B63H19/04; B63H16/00; B63H21/18; B64G1/44; B60K6/28; H02J; H02J9/00; B60L3/00; C09K5/00; F21K99/00; E04B1/62; E04B1/74; E04B1/75; E04B1/76; E04B1/77; E04B1/78; E04B1/79; E04B1/80; E04B1/88; E04B1/90; E06B3/263; E04B2/00; E04B5/00; E04B7/00; E04B9/00; F03G7/08; B60K6/10; B60K6/30; B09B; A61L11/00; A62D3/00; A62D101/00; G21F9/00; B03B9/06; B09C; D21B1/08; D21B1/32; F23G; A43B1/12; A43B21/14; B
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "outputs": [
    {
     "data": {
      "text/plain": "'C10L5/00|C10L5/40|C10L5/41|C10L5/42|C10L5/43|C10L5/44|C10L5/45|C10L5/46|C10L5/47|C10L5/48|C10B53/02|C10L1/00|C10L1/02|C10L1/14|C10L1/02|C10L1/19|C07C67/00|C07C69/00|C10L1/02|C10L1/182|C02F3/28|C02F3/11|C02F11/04|C12N1/13|C12N1/15|C12N1/21|C12N5/10|C12N15/00|C10L3/00|H01M4/86|H01M4/87|H01M4/88|H01M4/89|H01M4/90|H01M4/91|H01M4/92|H01M4/93|H01M4/94|H01M4/95|H01M4/96|H01M4/97|H01M4/98|H01M8/00|H01M8/01|H01M8/02|H01M8/03|H01M8/04|H01M8/05|H01M8/06|H01M8/07|H01M8/08|H01M8/09|H01M8/10|H01M8/11|H01M8/12|H01M8/13|H01M8/14|H01M8/15|H01M8/16|H01M8/17|H01M8/18|H01M8/19|H01M8/50|H01M8/51|H01M8/52|H01M8/53|H01M8/54|H01M8/55|H01M8/56|H01M8/57|H01M8/58|H01M12/00|H01M12/01|H01M12/02|H01M12/03|H01M12/04|H01M12/05|H01M12/06|H01M12/07|H01M12/08|C10B53/00|C10L5/00|C10L5/42|C10L5/44|F23G7/00|F23G7/10|C10J3/02|C10J3/03|B09B3/00|C10L5/48|C21B5/06|D21C11/00|A62D3/02|F23G7/00|F23G7/10|B09B3/00|B09B|B01D53/02|B01D53/04|B01D53/047|B01D53/14|B01D53/22|B01D53/24|C10L5/46|E02B9/00|E02B9/01|E02B9/02|E02B9/03|E02B9/04|E02B9/05|E02B9/06|E02B9/08|F03B|F03B13/12|F03B13/13|F03B13/14|F03B13/15|F03B13/16|F03B13/17|F03B13/18|F03B13/19|F03B13/20|F03B13/21|F03B13/22|F03B13/23|F03B13/24|F03B13/25|F03B13/26|F03B15/00|F03B15/01|F03B15/02|F03B15/03|F03B15/04|F03B15/05|F03B15/06|F03B15/07|F03B15/08|F03B15/09|F03B15/10|F03B15/11|F03B15/12|F03B15/13|F03B15/14|F03B15/15|F03B15/16|F03B15/17|F03B15/18|F03B15/19|F03B15/20|F03B15/21|B63H19/02|B63H19/04|F03G7/05|F03D|H02K7/18|B63B35/00|B60K16/00|B60L8/00|B63H13/00|F24S|H01L27/142|H01L27/31|H01L27/31|H01L27/32|H01L27/33|H01L27/34|H01L27/35|H01L27/36|H01L27/37|H01L27/38|H01L27/39|H01L27/40|H01L27/41|H01L27/42|H01L27/43|H01L27/44|H01L27/45|H01L27/46|H01L27/47|H01L27/48|C01B33/02|G05F1/67|F21L4/00|H02J7/35|H01G9/20|F24S|F24D17/00|F24D18/00|F24D3/00|F24D5/00|F24D11/00|F24D19/00|F24S90/00|F03D1/04|F03D1/09|F03D1/13|F03D1/20|C02F1/14|F02C1/05|H01L31/0525|B60K16/00|B60L8/00|F03G6/00|F03G6/01|F03G6/02|F03G6/03|F03G6/04|F03G6/05|F03G6/06|E04D13/00|E04D13/18|F22B1/00|F25B27/00|F26B3/00|F26B3/28|F24S23/00|F24S10/10|F24T|F01K|F03G4/00|F03G4/01|F03G4/02|F03G4/03|F03G4/04|F03G4/05|F03G4/06|F24T10/00|F24T50/00|F24T50/10|F24T50/20|F24T50/30|F24T50/40|F24T50/50|F24D11/02|F24D15/04|F24D17/02|F24D18/00|F24H4/00|F25B30/00|F01K27/00|F01K23/06|F01K23/07|F01K23/08|F01K23/09|F01K23/10|F01K17/00|F01K23/04|F02C6/18|F25B27/02|C02F1/16|D21F5/20|F22B1/02|F23G5/46|F24F12/00|F27D17/00|F28D17/00|F28D18/00|F28D19/00|F28D20/00|C10J3/86|F03G5/00|F03G5/01|F03G5/02|F03G5/03|F03G5/04|F03G5/05|B60K6/00|B60K6/20|B60W20/00|F16H3/00|F16H3/01|F16H3/02|F16H3/03|F16H3/04|F16H3/05|F16H3/06|F16H3/07|F16H3/08|F16H3/09|F16H3/10|F16H3/11|F16H3/12|F16H3/13|F16H3/14|F16H3/15|F16H3/16|F16H3/17|F16H3/18|F16H3/19|F16H3/20|F16H3/21|F16H3/22|F16H3/23|F16H3/24|F16H3/25|F16H3/26|F16H3/27|F16H3/28|F16H3/29|F16H3/30|F16H3/31|F16H3/32|F16H3/33|F16H3/34|F16H3/35|F16H3/36|F16H3/37|F16H3/38|F16H3/48|F16H3/49|F16H3/50|F16H3/51|F16H3/52|F16H3/53|F16H3/54|F16H3/55|F16H3/56|F16H3/57|F16H3/58|H02K29/08|H02K49/10|B60L7/10|B60L7/11|B60L7/12|B60L7/13|B60L7/14|B60L7/15|B60L7/16|B60L7/17|B60L7/18|B60L7/19|B60L7/20|B60L7/21|B60L7/22|B60L8/00|B60L9/00|B60L50/50|B60L50/51|B60L50/52|B60L50/53|B60L50/54|B60L50/55|B60L50/56|B60L50/57|B60L50/58|F02B43/00|B60K16/00|H02J7/00|B62D35/00|B62D35/02|B62K|B61|B61D17/02|B63H9/00|B63H13/00|B63H19/02|B63H19/04|B63H16/00|B63H21/18|B64G1/44|B60K6/28|H02J|H02J9/00|B60L3/00|C09K5/00|F21K99/00|E04B1/62|E04B1/74|E04B1/75|E04B1/76|E04B1/77|E04B1/78|E04B1/79|E04B1/80|E04B1/88|E04B1/90|E06B3/263|E04B2/00|E04B5/00|E04B7/00|E04B9/00|F03G7/08|B60K6/10|B60K6/30|B09B|A61L11/00|A62D3/00|A62D101/00|G21F9/00|B03B9/06|B09C|D21B1/08|D21B1/32|F23G|A43B1/12|A43B21/14|B22F8/00|C04B7/24|C04B7/25|C04B7/26|C04B7/27|C04B7/28|C04B7/29|C04B7/30|C04B18/04|C04B18/05|C04B18/06|C04B18/07|C04B18/08|C04B18/09|C05F|C08J11/00|C08J11/01|C08J11/02|C08J11/03|C08J11/04|C08J11/05|C08J11/06|C08J11/07|C08J11/08|C08J11/09|C08J11/10|C08J11/11|C08J11/12|C08J11/13|C08J11/14|C08J11/15|C08J11/16|C08J11/17|C08J11/18|C08J11/19|C08J11/20|C08J11/21|C08J11/22|C08J11/23|C08J11/24|C08J11/25|C08J11/2
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = \"|\".join(chat_gpt_convert.split('; '))\n",
    "tokens"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "outputs": [],
   "source": [
    "ipc_green_codes_csv = pd.read_excel(f'{fromdir}/IPC_green_codes.xlsx')\n",
    "ipc_green_codes_csv.columns=[\"IPC_code\",\"name\"]\n",
    "ipc_green_codes_csv\n",
    "\n",
    "eu_tokens=\"solar energy|wind|hydrogen|biofuel|biogas|bioethanol|carbon capture|heat pump\"\n",
    "eu_ipc_green_codes_csv = ipc_green_codes_csv[ipc_green_codes_csv[\"name\"].str.lower().str.contains(eu_tokens)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eu_tokens_csv = \"|\".join(eu_ipc_green_codes_csv[\"IPC_code\"].str.strip(\" \").drop_duplicates())\n",
    "tokens_csv = \"|\".join(ipc_green_codes_csv[\"IPC_code\"].str.strip(\" \").drop_duplicates())\n",
    "tokens_csv"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "outputs": [
    {
     "data": {
      "text/plain": "(801, 1214)"
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokens.split(\"|\")),len(tokens_csv.split(\"|\"))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "outputs": [
    {
     "data": {
      "text/plain": "337"
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(tokens.split(\"|\")).intersection(set(tokens_csv.split(\"|\"))))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "outputs": [
    {
     "data": {
      "text/plain": "          appln_id appln_auth appln_kind  filing_year  nb_applicants   \n5162     375545706         DE          A         2011              1  \\\n108346   408613474         DE          A         2012              2   \n121711   411146713         AT          A         2012              1   \n122555   410825570         DE          A         2012              1   \n125815   364242878         FR          A         2012              1   \n...            ...        ...        ...          ...            ...   \n1640264  546368267         EP          A         2021              2   \n1711712  577493769         DE          A         2021              1   \n1711714  577493769         DE          A         2021              1   \n1712528  544509389         EP          A         2021              1   \n1719335  546393918         EP          A         2021              1   \n\n         nb_inventors ipc_class_symbol ipc_class_level       cpc_id   \n5162                3      H01L  33/60               A    H01L33/60  \\\n108346              6     H01M  50/159               A   H01M50/159   \n121711              5      G01R  31/36               A    G01R31/36   \n122555              4    H01L  31/0224               A  H01L31/0224   \n125815              4      H01L  51/46               A    H01L51/46   \n...               ...              ...             ...          ...   \n1640264             2      F16H  48/24               A    F16H48/24   \n1711712             3    H01L  31/0224               A  H01L31/0224   \n1711714             3      H01L  33/42               A    H01L33/42   \n1712528             2      B01D  53/58               A    B01D53/58   \n1719335            13      A01N  25/28               A    A01N25/28   \n\n                          ipc_class             ipc_classname       ipc_id  \n5162                  H-Electricity               Electricity    H01L33/60  \n108346                H-Electricity               Electricity   H01M50/159  \n121711                    G-Physics                   Physics    G01R31/36  \n122555                H-Electricity               Electricity  H01L31/0224  \n125815                H-Electricity               Electricity    H01L51/46  \n...                             ...                       ...          ...  \n1640264               F-Engineering               Engineering    F16H48/24  \n1711712               H-Electricity               Electricity  H01L31/0224  \n1711714               H-Electricity               Electricity    H01L33/42  \n1712528  B-Operations; transporting  Operations; transporting    B01D53/58  \n1719335         A-Human necessities         Human necessities    A01N25/28  \n\n[333 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>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      <th>ipc_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5162</th>\n      <td>375545706</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2011</td>\n      <td>1</td>\n      <td>3</td>\n      <td>H01L  33/60</td>\n      <td>A</td>\n      <td>H01L33/60</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01L33/60</td>\n    </tr>\n    <tr>\n      <th>108346</th>\n      <td>408613474</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>2</td>\n      <td>6</td>\n      <td>H01M  50/159</td>\n      <td>A</td>\n      <td>H01M50/159</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01M50/159</td>\n    </tr>\n    <tr>\n      <th>121711</th>\n      <td>411146713</td>\n      <td>AT</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>5</td>\n      <td>G01R  31/36</td>\n      <td>A</td>\n      <td>G01R31/36</td>\n      <td>G-Physics</td>\n      <td>Physics</td>\n      <td>G01R31/36</td>\n    </tr>\n    <tr>\n      <th>122555</th>\n      <td>410825570</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>4</td>\n      <td>H01L  31/0224</td>\n      <td>A</td>\n      <td>H01L31/0224</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01L31/0224</td>\n    </tr>\n    <tr>\n      <th>125815</th>\n      <td>364242878</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2012</td>\n      <td>1</td>\n      <td>4</td>\n      <td>H01L  51/46</td>\n      <td>A</td>\n      <td>H01L51/46</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01L51/46</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1640264</th>\n      <td>546368267</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>2</td>\n      <td>2</td>\n      <td>F16H  48/24</td>\n      <td>A</td>\n      <td>F16H48/24</td>\n      <td>F-Engineering</td>\n      <td>Engineering</td>\n      <td>F16H48/24</td>\n    </tr>\n    <tr>\n      <th>1711712</th>\n      <td>577493769</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>3</td>\n      <td>H01L  31/0224</td>\n      <td>A</td>\n      <td>H01L31/0224</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01L31/0224</td>\n    </tr>\n    <tr>\n      <th>1711714</th>\n      <td>577493769</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>3</td>\n      <td>H01L  33/42</td>\n      <td>A</td>\n      <td>H01L33/42</td>\n      <td>H-Electricity</td>\n      <td>Electricity</td>\n      <td>H01L33/42</td>\n    </tr>\n    <tr>\n      <th>1712528</th>\n      <td>544509389</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>2</td>\n      <td>B01D  53/58</td>\n      <td>A</td>\n      <td>B01D53/58</td>\n      <td>B-Operations; transporting</td>\n      <td>Operations; transporting</td>\n      <td>B01D53/58</td>\n    </tr>\n    <tr>\n      <th>1719335</th>\n      <td>546393918</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>1</td>\n      <td>13</td>\
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f[\"ipc_id\"] = appln_ipc_f[\"cpc_id\"]\n",
    "appln_ipc_f[appln_ipc_f[\"ipc_id\"].str.contains(tokens_csv, regex=True)]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "outputs": [
    {
     "data": {
      "text/plain": "174"
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appln_ipc_f[appln_ipc_f[\"ipc_id\"].str.contains(tokens_csv, regex=True)][\"appln_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "outputs": [
    {
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      "text/html": "<div>                            <div id=\"293170a4-5ce6-49b0-8cae-e3d21275f2d9\" 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(\"293170a4-5ce6-49b0-8cae-e3d21275f2d9\")) {                    Plotly.newPlot(                        \"293170a4-5ce6-49b0-8cae-e3d21275f2d9\",                        [{\"hovertemplate\":\"Year:%{x:d}<br>Number of co-publications:%{y:d}\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+text+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[1.0,8.0,8.0,7.0,7.0,12.0,29.0,28.0,34.0,32.0,8.0],\"textfont\":{\"size\":12},\"textposition\":\"top center\",\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x\",\"y\":[1,8,8,7,7,12,29,28,34,32,8],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"hovertemplate\":\"Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+text+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[0.0,7.0,7.0,6.0,6.0,11.0,28.0,27.0,33.0,31.0,7.0],\"textfont\":{\"size\":12},\"textposition\":\"top center\",\"texttemplate\":\"%{y:.0%}\",\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x2\",\"y\":[0.0,7.0,7.0,6.0,6.0,11.0,28.0,27.0,33.0,31.0,7.0],\"yaxis\":\"y2\",\"type\":\"scatter\"},{\"hovertemplate\":\"Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\",\"dash\":\"solid\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"markers+text+lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"text\":[0.018518518518518517,0.16326530612244897,0.11594202898550725,0.109375,0.07446808510638298,0.11650485436893204,0.18354430379746836,0.15217391304347827,0.2,0.2191780821917808,0.125],\"textfont\":{\"size\":12},\"textposition\":\"top center\",\"texttemplate\":\"%{y:.0%}\",\"x\":[2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021],\"xaxis\":\"x3\",\"y\":[0.018518518518518517,0.16326530612244897,0.11594202898550725,0.109375,0.07446808510638298,0.11650485436893204,0.18354430379746836,0.15217391304347827,0.2,0.2191780821917808,0.125],\"yaxis\":\"y3\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_applns = appln_ipc_f[appln_ipc_f[\"ipc_id\"].str.contains(tokens_csv, regex=True)][\"appln_id\"].unique()\n",
    "appln_f_greenset = appln_f[appln_f[\"appln_id\"].isin(green_applns)]\n",
    "\n",
    "datafull = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n",
    "        .reset_index()\n",
    "        .rename(columns={0:record_col}))\n",
    "datafull[record_col+\"_relative_growth_full\"] = datafull[datafull[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n",
    "datafull[record_col+\"_relative_growth_full\"] = (datafull[record_col]-datafull[record_col+\"_relative_growth_full\"])/datafull[record_col+\"_relative_growth_full\"]\n",
    "datafull = datafull.rename(columns={record_col:f\"{record_col}_full\"})\n",
    "\n",
    "\n",
    "data = (appln_f_greenset.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",
    "data = data.merge(datafull, on=\"filing_year\")\n",
    "data[\"green_ratio\"] = data[record_col]/data[f\"{record_col}_full\"]\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",
    "year_ratio_output = px.line(data,x=\"filing_year\", y=\"green_ratio\", markers=True, text=\"green_ratio\")\n",
    "year_ratio_output.update_traces(textposition='top center',textfont_size=12, texttemplate='%{y:.0%}')\n",
    "year_ratio_output.update_traces(hovertemplate='Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}')\n",
    "\n",
    "figsuper = make_subplots(rows=3, cols=1, subplot_titles=[\"IPC green inventory co-patents per year\",\"Relative growth of IPC green inventory co-patents\", \"Ratio of IPC green inventory co-patents to all co-patents\"],\n",
    "                         specs=[\n",
    "                            [{\"type\": \"xy\"}],\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",
    "for trace in list(year_ratio_output.select_traces()):\n",
    "    figsuper.add_trace(trace,\n",
    "        row=3, 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",
    "figsuper['layout']['yaxis3'].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\"] = [0,45]\n",
    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,40]\n",
    "figsuper['layout'][\"yaxis3\"][\"range\"] = [0,0.3]\n",
    "#\n",
    "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
    "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
    "\n",
    "figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
    "\n",
    "figsuper.write_image(f\"{fromdir}/plots/green_trends.png\",height=900,width=900,scale = 4)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "outputs": [
    {
     "data": {
      "text/plain": "    filing_year  appln_id  appln_id_relative_growth  appln_id_full   \n0          2011         1                       0.0             54  \\\n1          2012         8                       7.0             49   \n2          2013         8                       7.0             69   \n3          2014         7                       6.0             64   \n4          2015         7                       6.0             94   \n5          2016        12                      11.0            103   \n6          2017        29                      28.0            158   \n7          2018        28                      27.0            184   \n8          2019        34                      33.0            170   \n9          2020        32                      31.0            146   \n10         2021         8                       7.0             64   \n\n    appln_id_relative_growth_full  green_ratio  \n0                        0.000000     0.018519  \n1                       -0.092593     0.163265  \n2                        0.277778     0.115942  \n3                        0.185185     0.109375  \n4                        0.740741     0.074468  \n5                        0.907407     0.116505  \n6                        1.925926     0.183544  \n7                        2.407407     0.152174  \n8                        2.148148     0.200000  \n9                        1.703704     0.219178  \n10                       0.185185     0.125000  ",
      "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_id</th>\n      <th>appln_id_relative_growth</th>\n      <th>appln_id_full</th>\n      <th>appln_id_relative_growth_full</th>\n      <th>green_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>54</td>\n      <td>0.000000</td>\n      <td>0.018519</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2012</td>\n      <td>8</td>\n      <td>7.0</td>\n      <td>49</td>\n      <td>-0.092593</td>\n      <td>0.163265</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013</td>\n      <td>8</td>\n      <td>7.0</td>\n      <td>69</td>\n      <td>0.277778</td>\n      <td>0.115942</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014</td>\n      <td>7</td>\n      <td>6.0</td>\n      <td>64</td>\n      <td>0.185185</td>\n      <td>0.109375</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2015</td>\n      <td>7</td>\n      <td>6.0</td>\n      <td>94</td>\n      <td>0.740741</td>\n      <td>0.074468</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2016</td>\n      <td>12</td>\n      <td>11.0</td>\n      <td>103</td>\n      <td>0.907407</td>\n      <td>0.116505</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2017</td>\n      <td>29</td>\n      <td>28.0</td>\n      <td>158</td>\n      <td>1.925926</td>\n      <td>0.183544</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2018</td>\n      <td>28</td>\n      <td>27.0</td>\n      <td>184</td>\n      <td>2.407407</td>\n      <td>0.152174</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2019</td>\n      <td>34</td>\n      <td>33.0</td>\n      <td>170</td>\n      <td>2.148148</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2020</td>\n      <td>32</td>\n      <td>31.0</td>\n      <td>146</td>\n      <td>1.703704</td>\n      <td>0.219178</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2021</td>\n      <td>8</td>\n      <td>7.0</td>\n      <td>64</td>\n      <td>0.185185</td>\n      <td>0.125000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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