diff --git a/.gitignore b/.gitignore
index d820630..71e1c92 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,3 +9,18 @@
 /PATSTAT/first_round/first-filings-with-persons-raw.csv
 /PATSTAT/first_round/first-filings-with-IPC-raw.csv
 /PATSTAT/first_round/first-filings-raw.csv
+/PATSTAT/raw_files_csv/01_patent_subset.csv
+/PATSTAT/raw_files_csv/04_persons_2011_2022_inv_pairs.csv
+/PATSTAT/raw_files_csv/03_persons_2011_2022_collected.csv
+/PATSTAT/raw_files_csv/02_persons_2011_2022.csv
+/PATSTAT/first_round/2IPCGreenInventoryList_2022WIPO.xlsx
+/PATSTAT/DS/ds1.png
+/PATSTAT/ds1.xlsx
+/PATSTAT/DS/ds2.png
+/PATSTAT/ds2.xlsx
+/PATSTAT/ds3.xlsx
+/PATSTAT/DS/ds3.png
+/PATSTAT/first_round/IPC_green_codes.csv
+/PATSTAT/first_round/IPC_green_codes.xlsx
+/ZSI_previous_analyses/ReConnect China_preliminary_report.docx
+/PATSTAT/first_round/tls_206_scope_v2.csv
diff --git a/PATSTAT/first_round/plots/CN_applt_ctry.png b/PATSTAT/first_round/plots/CN_applt_ctry.png
index 636f0d6..cca5ab4 100644
Binary files a/PATSTAT/first_round/plots/CN_applt_ctry.png and b/PATSTAT/first_round/plots/CN_applt_ctry.png differ
diff --git a/PATSTAT/first_round/plots/applt_loc_ctry.png b/PATSTAT/first_round/plots/applt_loc_ctry.png
index d157295..78d30ea 100644
Binary files a/PATSTAT/first_round/plots/applt_loc_ctry.png and b/PATSTAT/first_round/plots/applt_loc_ctry.png differ
diff --git a/PATSTAT/first_round/plots/applt_table.xlsx b/PATSTAT/first_round/plots/applt_table.xlsx
index f9bc211..8b7f9c2 100644
Binary files a/PATSTAT/first_round/plots/applt_table.xlsx and b/PATSTAT/first_round/plots/applt_table.xlsx differ
diff --git a/PATSTAT/first_round/plots/auth_loc_ctry.png b/PATSTAT/first_round/plots/auth_loc_ctry.png
index ceaec9f..3102ad8 100644
Binary files a/PATSTAT/first_round/plots/auth_loc_ctry.png and b/PATSTAT/first_round/plots/auth_loc_ctry.png differ
diff --git a/PATSTAT/first_round/plots/green_trends.png b/PATSTAT/first_round/plots/green_trends.png
new file mode 100644
index 0000000..aca6e77
Binary files /dev/null and b/PATSTAT/first_round/plots/green_trends.png differ
diff --git a/PATSTAT/first_round/plots/invt_loc_ctry.png b/PATSTAT/first_round/plots/invt_loc_ctry.png
index efcd501..05c327a 100644
Binary files a/PATSTAT/first_round/plots/invt_loc_ctry.png and b/PATSTAT/first_round/plots/invt_loc_ctry.png differ
diff --git a/PATSTAT/first_round/plots/ipc_classes.png b/PATSTAT/first_round/plots/ipc_classes.png
index 75fc114..d26cf63 100644
Binary files a/PATSTAT/first_round/plots/ipc_classes.png and b/PATSTAT/first_round/plots/ipc_classes.png differ
diff --git a/PATSTAT/first_round/plots/overall_distr&trends.png b/PATSTAT/first_round/plots/overall_distr&trends.png
index 3119b66..3ad2d20 100644
Binary files a/PATSTAT/first_round/plots/overall_distr&trends.png and b/PATSTAT/first_round/plots/overall_distr&trends.png differ
diff --git a/PATSTAT/first_round/plots/top15_applt_person_ctry_type_China.png b/PATSTAT/first_round/plots/top15_applt_person_ctry_type_China.png
index 677ac1e..4dd5e3d 100644
Binary files a/PATSTAT/first_round/plots/top15_applt_person_ctry_type_China.png and b/PATSTAT/first_round/plots/top15_applt_person_ctry_type_China.png differ
diff --git a/PATSTAT/first_round/plots/top15_applt_person_ctry_type_EU.png b/PATSTAT/first_round/plots/top15_applt_person_ctry_type_EU.png
index 0cb1490..3a36c1b 100644
Binary files a/PATSTAT/first_round/plots/top15_applt_person_ctry_type_EU.png and b/PATSTAT/first_round/plots/top15_applt_person_ctry_type_EU.png differ
diff --git a/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_China.png b/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_China.png
index 04ca0be..5e74e4b 100644
Binary files a/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_China.png and b/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_China.png differ
diff --git a/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_EU.png b/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_EU.png
index 28a55af..a288469 100644
Binary files a/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_EU.png and b/PATSTAT/first_round/plots/top15_applt_psn_sector_primary_EU.png differ
diff --git a/PATSTAT/first_round/plots/yearly_outputs.png b/PATSTAT/first_round/plots/yearly_outputs.png
index 0425cec..14d6a5b 100644
Binary files a/PATSTAT/first_round/plots/yearly_outputs.png and b/PATSTAT/first_round/plots/yearly_outputs.png differ
diff --git a/PATSTAT/first_round/plots/yearly_rel_outputs.png b/PATSTAT/first_round/plots/yearly_rel_outputs.png
index 1481eb3..3e25d1d 100644
Binary files a/PATSTAT/first_round/plots/yearly_rel_outputs.png and b/PATSTAT/first_round/plots/yearly_rel_outputs.png differ
diff --git a/PATSTAT/patstat_analyses.ipynb b/PATSTAT/patstat_analyses.ipynb
index b62e474..8c40f84 100644
--- a/PATSTAT/patstat_analyses.ipynb
+++ b/PATSTAT/patstat_analyses.ipynb
@@ -3,9 +3,6 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
    "outputs": [
     {
      "data": {
@@ -39,14 +36,46 @@
     "\n",
     "\n",
     "%matplotlib inline"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "outputs": [],
    "source": [
-    "os.makedirs('plot_html/PPT_plots',exist_ok=True)"
+    "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
+    "# tls_801.head()\n",
+    "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
+    "                      (tls_801.ctry_code == 'NO')|\n",
+    "                      (tls_801.ctry_code == 'CH')|\n",
+    "                      (tls_801.ctry_code == 'GB'))]\n",
+    "scope_countries = scope_df[\"ctry_code\"].unique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    ctry_code iso_alpha3 st3_name organisation_flag continent eu_member   \n",
+      "137        MA        MAR  Morocco                      Africa            \\\n",
+      "\n",
+      "    epo_member oecd_member discontinued  \n",
+      "137                                      \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tls_801[tls_801[\"ctry_code\"]==\"MA\"])"
    ],
    "metadata": {
     "collapsed": false
@@ -57,7 +86,50 @@
    "execution_count": 3,
    "outputs": [],
    "source": [
-    "agg_ds = 'aggregated_data'"
+    "outdir=\"raw_files_csv\""
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "           appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n8641708   531710630         CN  201811380088          A        2018-11-20  \\\n5246513   489770910         CN  201711004155          A        2017-10-24   \n6383260   503205376         CN  201810447617          A        2018-05-11   \n1437288   417129813         GR   20120100519          A        2012-10-12   \n4304665   479561975         CN  201510807721          A        2015-11-19   \n8099995   524172492         CN  201911055629          A        2019-10-31   \n2496404   443580404         CN  201510312381          A        2015-06-08   \n2862771   448112136         CN  201510736913          A        2015-11-03   \n8746800   533540106         CN  202010109483          A        2020-02-22   \n12407625  580940603         CN  202210840515          A        2022-07-18   \n\n          appln_filing_year appln_nr_original ipr_type receiving_office   \n8641708                2018      201811380088       PI              NaN  \\\n5246513                2017      201711004155       PI              NaN   \n6383260                2018      201810447617       PI              NaN   \n1437288                2012       20120100519       PI              NaN   \n4304665                2015      201510807721       PI              NaN   \n8099995                2019      201911055629       PI              NaN   \n2496404                2015     2015103123811       PI              NaN   \n2862771                2015     2015107369134       PI              NaN   \n8746800                2020      202010109483       PI              NaN   \n12407625               2022      202210840515       PI              NaN   \n\n          internat_appln_id  ... earliest_pat_publn_id granted   \n8641708                   0  ...             531710631       N  \\\n5246513                   0  ...             489770911       N   \n6383260                   0  ...             503205377       N   \n1437288                   0  ...             417129814       Y   \n4304665                   0  ...             479561976       N   \n8099995                   0  ...             524172493       N   \n2496404                   0  ...             443580405       N   \n2862771                   0  ...             448112137       N   \n8746800                   0  ...             533540107       Y   \n12407625                  0  ...             580940604       N   \n\n         docdb_family_id inpadoc_family_id  docdb_family_size   \n8641708         70744210         531710630                  1  \\\n5246513         61141626         489770910                  1   \n6383260         64213024         503205376                  1   \n1437288         50483672         417129813                  1   \n4304665         58885008         479561975                  1   \n8099995         68978631         524172492                  1   \n2496404         53812114         443580404                  1   \n2862771         54991770         448112136                  1   \n8746800         71149074         533540106                  1   \n12407625        83518531         580940603                  1   \n\n          nb_citing_docdb_fam nb_applicants  nb_inventors  is_prior is_pct  \n8641708                     0             1             1         1      0  \n5246513                     0             1             5         1      0  \n6383260                     0             1             3         1      0  \n1437288                     0             1             1         1      0  \n4304665                     0             1             1         1      0  \n8099995                     0             1             3         1      0  \n2496404                     3             2             3         1      0  \n2862771                     2             1             5         1      0  \n8746800                     0             1             5         1      0  \n12407625                    0             1             6         1      0  \n\n[10 rows x 28 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8641708</th>\n      <td>531710630</td>\n      <td>CN</td>\n      <td>201811380088</td>\n      <td>A</td>\n      <td>2018-11-20</td>\n      <td>2018</td>\n      <td>201811380088</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>531710631</td>\n      <td>N</td>\n      <td>70744210</td>\n      <td>531710630</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5246513</th>\n      <td>489770910</td>\n      <td>CN</td>\n      <td>201711004155</td>\n      <td>A</td>\n      <td>2017-10-24</td>\n      <td>2017</td>\n      <td>201711004155</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>489770911</td>\n      <td>N</td>\n      <td>61141626</td>\n      <td>489770910</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6383260</th>\n      <td>503205376</td>\n      <td>CN</td>\n      <td>201810447617</td>\n      <td>A</td>\n      <td>2018-05-11</td>\n      <td>2018</td>\n      <td>201810447617</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>503205377</td>\n      <td>N</td>\n      <td>64213024</td>\n      <td>503205376</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1437288</th>\n      <td>417129813</td>\n      <td>GR</td>\n      <td>20120100519</td>\n      <td>A</td>\n      <td>2012-10-12</td>\n      <td>2012</td>\n      <td>20120100519</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>417129814</td>\n      <td>Y</td>\n      <td>50483672</td>\n      <td>417129813</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4304665</th>\n      <td>479561975</td>\n      <td>CN</td>\n      <td>201510807721</td>\n      <td>A</td>\n      <td>2015-11-19</td>\n      <td>2015</td>\n      <td>201510807721</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>479561976</td>\n      <td>N</td>\n      <td>58885008</td>\n      <td>479561975</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8099995</th>\n      <td>524172492</td>\n      <td>CN</td>\n      <td>201911055629</td>\n      <td>A</td>\n      <td>2019-10-31</td>\n      <td>2019</td>\n      <td>201911055629</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>524172493</td>\n      <td>N</td>\n      <td>68978631</td>\n      <td>524172492</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2496404</th>\n      <td>443580404</td>\n      <td>CN</td>\n      <td>201510312381</td>\n      <td>A</td>\n      <td>2015-06-08</td>\n      <td>2015</td>\n      <td>2015103123811</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>443580405</td>\n      <td>N</td>\n      <td>53812114</td>\n      <td>443580404</td>\n      <td>1</td>\n      <td>3</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2862771</th>\n      <td>448112136</td>\n      <td>CN</td>\n      <td>201510736913</td>\n      <td>A</td>\n      <td>2015-11-03</td>\n      <td>2015</td>\n      <td>2015107369134</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>448112137</td>\n      <td>N</td>\n      <td>54991770</td>\n      <td>448112136</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8746800</th>\n      <td>533540106</td>\n      <td>CN</td>\n      <td>202010109483</td>\n      <td>A</td>\n      <td>2020-02-22</td>\n      <td>2020</td>\n      <td>202010109483</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>533540107</td>\n      <td>Y</td>\n      <td>71149074</td>\n      <td>533540106</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12407625</th>\n      <td>580940603</td>\n      <td>CN</td>\n      <td>202210840515</td>\n      <td>A</td>\n      <td>2022-07-18</td>\n      <td>2022</td>\n      <td>202210840515</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>580940604</td>\n      <td>N</td>\n      <td>83518531</td>\n      <td>580940603</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 28 columns</p>\n</div>"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln = pd.read_csv(f\"{outdir}/01_patent_subset.csv\", low_memory=False)\n",
+    "appln.sample(10)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(54368949, 13184309)"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_pers = pd.read_csv(f\"{outdir}/02_persons_2011_2022.csv\",low_memory=False)\n",
+    "len(appln_pers[\"appln_id\"]), len(appln[\"appln_id\"])"
    ],
    "metadata": {
     "collapsed": false
@@ -66,10 +138,18 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(54368949, 12646904)"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "cn = pd.read_csv(f\"{agg_ds}/01_counts_cty_yr_CN.csv\", header=None)\n",
-    "eur = pd.read_csv(f\"{agg_ds}/01_counts_cty_yr_eu_27_GB.csv\", header=None)"
+    "appln_pers[\"appln_id\"].isin(appln[\"appln_id\"]).sum(), appln[\"appln_id\"].isin(appln_pers[\"appln_id\"]).sum()"
    ],
    "metadata": {
     "collapsed": false
@@ -81,8 +161,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "       0   1        2\n0   2011  AT     2395\n1   2011  BE      646\n2   2011  BG      330\n3   2011  CY     1256\n4   2011  CZ     2246\n..   ...  ..      ...\n7   2018  CN  3242424\n8   2019  CN  3547407\n9   2020  CN  4367505\n10  2021  CN  4012952\n11  2022  CN  2613602\n\n[359 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>0</th>\n      <th>1</th>\n      <th>2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2011</td>\n      <td>AT</td>\n      <td>2395</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2011</td>\n      <td>BE</td>\n      <td>646</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2011</td>\n      <td>BG</td>\n      <td>330</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2011</td>\n      <td>CY</td>\n      <td>1256</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2011</td>\n      <td>CZ</td>\n      <td>2246</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2018</td>\n      <td>CN</td>\n      <td>3242424</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2019</td>\n      <td>CN</td>\n      <td>3547407</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2020</td>\n      <td>CN</td>\n      <td>4367505</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2021</td>\n      <td>CN</td>\n      <td>4012952</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2022</td>\n      <td>CN</td>\n      <td>2613602</td>\n    </tr>\n  </tbody>\n</table>\n<p>359 rows × 3 columns</p>\n</div>"
+      "text/plain": "12415"
      },
      "execution_count": 7,
      "metadata": {},
@@ -90,7 +169,20155 @@
     }
    ],
    "source": [
-    "pd.concat([eur,cn])"
+    "appln_ch_ids = appln_pers[appln_pers[\"person_ctry_code\"] == \"CN\"][\"appln_id\"].unique()\n",
+    "appln_eu_ids = appln_pers[appln_pers[\"person_ctry_code\"].isin(scope_countries)][\"appln_id\"].unique()\n",
+    "\n",
+    "appln_scope_ids = np.intersect1d(appln_ch_ids, appln_eu_ids, assume_unique=False)\n",
+    "appln_scope = appln_pers[((appln_pers[\"appln_id\"].isin(appln_scope_ids)) &\n",
+    "                          (appln_pers[\"person_ctry_code\"].isin(list(scope_countries) + [\"CN\"])))]\n",
+    "\n",
+    "appln_scope[\"appln_id\"].nunique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(6584667, 18789)"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_pers_haninfo = pd.read_csv(f\"first_round/tls_206_scope_v2.csv\",\n",
+    "                                 usecols=[\"person_id\", \"han_id\", \"han_name\", \"psn_sector\"])\n",
+    "\n",
+    "appln_pers_haninfo[\"han_id\"].nunique(), appln_scope[\"han_id\"].nunique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "pers_sector_primary = appln_pers_haninfo.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
+    "    lambda x: \"UNKNOWN\" if len(pd.Series.mode(x))==0 else pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n       'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n       'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n       'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type',\n       'psn_sector_primary_x', 'psn_sector_primary_y'],\n      dtype='object')"
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_pers_f.columns"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12415\n"
+     ]
+    }
+   ],
+   "source": [
+    "appln_pers_f = appln_scope.merge(pers_sector_primary, on='han_id', how=\"left\").rename(columns={\"psn_sector_primary_y\":\"psn_sector_primary\"})\n",
+    "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_primary\"].fillna(\"UNKNOWN\")\n",
+    "print(appln_pers_f[\"appln_id\"].nunique())"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "outputs": [],
+   "source": [
+    "appln_pers_f[\"han_name\"] = appln_pers_f[\"han_name\"].str.replace(\"Zhejiang Geely Holding Group Co., Ltd.\",\n",
+    "                                                                            \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "outputs": [],
+   "source": [
+    "g = appln_pers.groupby('appln_id')[\"person_ctry_code\"]\n",
+    "missing_ctry_data = g.count().rsub(g.size(), axis=0).ge(1).astype(int).reset_index()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n",
+      "21408  556911606         MA   42012433            2             0  \\\n",
+      "\n",
+      "            person_name person_ctry_code     han_id          han_name   \n",
+      "21408  TURTINEN, SAMULI               FI  142012433  TURTINEN, SAMULI  \\\n",
+      "\n",
+      "      psn_sector_x psn_sector_y person_ctry_code_fullname appln_auth_fullname   \n",
+      "21408   INDIVIDUAL   INDIVIDUAL                   Finland             Morocco  \\\n",
+      "\n",
+      "      person_ctry_ISO3 person_ctry_type auth_ISO3 auth_ctry_type   \n",
+      "21408              FIN               EU       MAR             EU  \\\n",
+      "\n",
+      "      psn_sector_primary_x psn_sector_primary  \n",
+      "21408           INDIVIDUAL         INDIVIDUAL  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(appln_pers_f.sample())"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(10810, 10084)"
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appl_eu_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n",
+    "                              (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n",
+    "\n",
+    "appl_ch_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n",
+    "                              (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n",
+    "\n",
+    "len(appl_eu_applt),len(appl_ch_applt)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(7638, 8788)"
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appl_eu_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n",
+    "                              (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n",
+    "\n",
+    "appl_ch_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n",
+    "                              (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n",
+    "\n",
+    "len(appl_eu_invt),len(appl_ch_invt)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "12197"
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0))][\"appln_id\"].nunique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "outputs": [],
+   "source": [
+    "# missing_ctry_data[missing_ctry_data[\"person_ctry_code\"]>0]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "outputs": [],
+   "source": [
+    "appln_ana = appln.merge(missing_ctry_data,on=\"appln_id\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(12646904, 13184309)"
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_ana[\"appln_id\"].nunique(),appln[\"appln_id\"].nunique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Axes: xlabel='appln_auth'>"
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ctry_ana = appln_ana.groupby(\"appln_auth\")[\"person_ctry_code\"]\n",
+    "(ctry_ana.sum()/ctry_ana.size()).sort_values().plot(kind=\"bar\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Axes: xlabel='appln_filing_year'>"
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ch_ana = appln_ana.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"person_ctry_code\"]\n",
+    "((1-(ch_ana.sum()/ch_ana.size()))*100).plot(kind=\"line\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "# That one Chinese auth co-patent"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "      appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n124  419052362         CN    5361204            0             1  \\\n137  419052362         CN   51988464            1             0   \n\n                           person_name person_ctry_code     han_id   \n124  Schneider Electric Industries SAS               FR     808003  \\\n137                       JUZHONG SONG               CN  151988464   \n\n                    han_name psn_sector_x psn_sector_y  \n124  SCHNEIDER ELECT IND SAS      COMPANY      COMPANY  \n137             JUZHONG SONG      UNKNOWN      UNKNOWN  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_x</th>\n      <th>psn_sector_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>124</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>5361204</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Schneider Electric Industries SAS</td>\n      <td>FR</td>\n      <td>808003</td>\n      <td>SCHNEIDER ELECT IND SAS</td>\n      <td>COMPANY</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>51988464</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JUZHONG SONG</td>\n      <td>CN</td>\n      <td>151988464</td>\n      <td>JUZHONG SONG</td>\n      <td>UNKNOWN</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_scope.query(\"appln_auth=='CN'\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "          appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n1568550  419052362         CN  201210551425          A        2012-12-18  \\\n\n         appln_filing_year appln_nr_original ipr_type receiving_office   \n1568550               2012     2012105514252       PI              NaN  \\\n\n         internat_appln_id  ... earliest_pat_publn_id granted docdb_family_id   \n1568550                  0  ...             419052363       Y        49886800  \\\n\n        inpadoc_family_id  docdb_family_size  nb_citing_docdb_fam   \n1568550         413446088                  4                   15  \\\n\n        nb_applicants  nb_inventors  is_prior is_pct  \n1568550             1             1         1      0  \n\n[1 rows x 28 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n      <th>is_prior</th>\n      <th>is_pct</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1568550</th>\n      <td>419052362</td>\n      <td>CN</td>\n      <td>201210551425</td>\n      <td>A</td>\n      <td>2012-12-18</td>\n      <td>2012</td>\n      <td>2012105514252</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>419052363</td>\n      <td>Y</td>\n      <td>49886800</td>\n      <td>413446088</td>\n      <td>4</td>\n      <td>15</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 28 columns</p>\n</div>"
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln.query('appln_id==419052362')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12415\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "           appln_id appln_auth    appln_nr appln_kind appln_filing_date   \n863506    407017979         WO  2011083302          W        2011-12-01  \\\n7100916   510736006         WO  2019058105          W        2019-03-29   \n1603837   419656375         WO  2012087307          W        2012-12-24   \n7183441   511620156         WO  2017107161          W        2017-10-20   \n5588243   494153147         EP    18171845          A        2018-05-11   \n...             ...        ...         ...        ...               ...   \n2295082   440740099         WO  2013088240          W        2013-11-29   \n12569735  582929150         WO  2021092424          W        2021-05-08   \n8050187   523796882         WO  2018091667          W        2018-06-15   \n5582748   494089044         WO  2016104438          W        2016-11-03   \n716562    405080010         WO  2013056669          W        2013-03-28   \n\n          appln_filing_year appln_nr_original ipr_type receiving_office   \n863506                 2011     CN2011/083302       PI               CN  \\\n7100916                2019     EP2019/058105       PI               EP   \n1603837                2012     CN2012/087307       PI               CN   \n7183441                2017     CN2017/107161       PI               CN   \n5588243                2018          18171845       PI              NaN   \n...                     ...               ...      ...              ...   \n2295082                2013     CN2013/088240       PI               CN   \n12569735               2021     CN2021/092424       PI               CN   \n8050187                2018     CN2018/091667       PI               CN   \n5582748                2016     CN2016/104438       PI               CN   \n716562                 2013     EP2013/056669       PI               EP   \n\n          internat_appln_id  ... earliest_pat_publn_id granted   \n863506                    0  ...             407072959       N  \\\n7100916                   0  ...             538911303       N   \n1603837                   0  ...             419656376       N   \n7183441                   0  ...             511620157       N   \n5588243                   0  ...             520464429       N   \n...                     ...  ...                   ...     ...   \n2295082                   0  ...             440740100       N   \n12569735                  0  ...             582929151       N   \n8050187                   0  ...             523796883       N   \n5582748                   0  ...             494089045       N   \n716562                    0  ...             422430381       N   \n\n         docdb_family_id inpadoc_family_id  docdb_family_size   \n863506          48534637         407017979                  2  \\\n7100916         66001221         510736006                  4   \n1603837         51019632         419626782                  4   \n7183441         66173145         511620156                  4   \n5588243         62152456         494153147                  3   \n...                  ...               ...                ...   \n2295082         53198253         440740099                  4   \n12569735        84027865         582929150                  1   \n8050187         68841789         523796882                  4   \n5582748         62076012         494089044                  3   \n716562          48045501         405080010                  5   \n\n          nb_citing_docdb_fam nb_applicants  nb_inventors  is_prior is_pct  \n863506                      1             6             5         1      1  \n7100916                     0             2             2         1      1  \n1603837                    12             2             5         1      1  \n7183441                     1             2             3         1      1  \n5588243                     3             1             2         1      0  \n...                       ...           ...           ...       ...    ...  \n2295082                     2             5             4         1      1  \n12569735                    0             2             1         1      1  \n8050187                     2             2             3         1      1  \n5582748                     1             2             4         1      1  \n716562                      9             3             2         1      1  \n\n[100 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>863506</th>\n      <td>407017979</td>\n      <td>WO</td>\n      <td>2011083302</td>\n      <td>W</td>\n      <td>2011-12-01</td>\n      <td>2011</td>\n      <td>CN2011/083302</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>407072959</td>\n      <td>N</td>\n      <td>48534637</td>\n      <td>407017979</td>\n      <td>2</td>\n      <td>1</td>\n      <td>6</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7100916</th>\n      <td>510736006</td>\n      <td>WO</td>\n      <td>2019058105</td>\n      <td>W</td>\n      <td>2019-03-29</td>\n      <td>2019</td>\n      <td>EP2019/058105</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>538911303</td>\n      <td>N</td>\n      <td>66001221</td>\n      <td>510736006</td>\n      <td>4</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1603837</th>\n      <td>419656375</td>\n      <td>WO</td>\n      <td>2012087307</td>\n      <td>W</td>\n      <td>2012-12-24</td>\n      <td>2012</td>\n      <td>CN2012/087307</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>419656376</td>\n      <td>N</td>\n      <td>51019632</td>\n      <td>419626782</td>\n      <td>4</td>\n      <td>12</td>\n      <td>2</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7183441</th>\n      <td>511620156</td>\n      <td>WO</td>\n      <td>2017107161</td>\n      <td>W</td>\n      <td>2017-10-20</td>\n      <td>2017</td>\n      <td>CN2017/107161</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>511620157</td>\n      <td>N</td>\n      <td>66173145</td>\n      <td>511620156</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5588243</th>\n      <td>494153147</td>\n      <td>EP</td>\n      <td>18171845</td>\n      <td>A</td>\n      <td>2018-05-11</td>\n      <td>2018</td>\n      <td>18171845</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>520464429</td>\n      <td>N</td>\n      <td>62152456</td>\n      <td>494153147</td>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2295082</th>\n      <td>440740099</td>\n      <td>WO</td>\n      <td>2013088240</td>\n      <td>W</td>\n      <td>2013-11-29</td>\n      <td>2013</td>\n      <td>CN2013/088240</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>440740100</td>\n      <td>N</td>\n      <td>53198253</td>\n      <td>440740099</td>\n      <td>4</td>\n      <td>2</td>\n      <td>5</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12569735</th>\n      <td>582929150</td>\n      <td>WO</td>\n      <td>2021092424</td>\n      <td>W</td>\n      <td>2021-05-08</td>\n      <td>2021</td>\n      <td>CN2021/092424</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>582929151</td>\n      <td>N</td>\n      <td>84027865</td>\n      <td>582929150</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8050187</th>\n      <td>523796882</td>\n      <td>WO</td>\n      <td>2018091667</td>\n      <td>W</td>\n      <td>2018-06-15</td>\n      <td>2018</td>\n      <td>CN2018/091667</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>523796883</td>\n      <td>N</td>\n      <td>68841789</td>\n      <td>523796882</td>\n      <td>4</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5582748</th>\n      <td>494089044</td>\n      <td>WO</td>\n      <td>2016104438</td>\n      <td>W</td>\n      <td>2016-11-03</td>\n      <td>2016</td>\n      <td>CN2016/104438</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>494089045</td>\n      <td>N</td>\n      <td>62076012</td>\n      <td>494089044</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>716562</th>\n      <td>405080010</td>\n      <td>WO</td>\n      <td>2013056669</td>\n      <td>W</td>\n      <td>2013-03-28</td>\n      <td>2013</td>\n      <td>EP2013/056669</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>422430381</td>\n      <td>N</td>\n      <td>48045501</td>\n      <td>405080010</td>\n      <td>5</td>\n      <td>9</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 28 columns</p>\n</div>"
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_f = appln[appln[\"appln_id\"].isin(appln_scope['appln_id'])].copy()\n",
+    "print(appln_f[\"appln_id\"].nunique())\n",
+    "\n",
+    "appln_f.sample(100)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:1: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:2: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:3: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:4: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "appln_f[\"ch_applt\"] = appln_f[\"appln_id\"].isin(appl_ch_applt)\n",
+    "appln_f[\"eu_applt\"] = appln_f[\"appln_id\"].isin(appl_eu_applt)\n",
+    "appln_f[\"ch_invt\"] = appln_f[\"appln_id\"].isin(appl_ch_invt)\n",
+    "appln_f[\"eu_invt\"] = appln_f[\"appln_id\"].isin(appl_eu_invt)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eu_applt  False  True \n",
+      "ch_applt              \n",
+      "False       218   2113\n",
+      "True       1387   8697\n",
+      "eu_invt  False  True \n",
+      "ch_invt              \n",
+      "False       74   3553\n",
+      "True      4703   4085\n"
+     ]
+    }
+   ],
+   "source": [
+    "applt_co_occur = pd.crosstab(appln_f[\"ch_applt\"], appln_f[\"eu_applt\"])\n",
+    "print(applt_co_occur)\n",
+    "invt_co_occur = pd.crosstab(appln_f[\"ch_invt\"], appln_f[\"eu_invt\"])\n",
+    "print(invt_co_occur)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:11: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:12: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def bool_calc(eu,ch):\n",
+    "    if eu==True and ch==False:\n",
+    "        return \"European \"\n",
+    "    if ch==True and eu==False:\n",
+    "        return \"Chinese \"\n",
+    "    if ch==False and eu==False:\n",
+    "        return \"Third-party \"\n",
+    "    if ch==True and eu==True:\n",
+    "        return \"Co-\"\n",
+    "\n",
+    "appln_f[\"ownership\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_applt\"],x[\"ch_applt\"])+\"ownership\" ,axis=1)\n",
+    "appln_f[\"invention\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_invt\"],x[\"ch_invt\"])+\"invention\" ,axis=1)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "invention              Chinese invention  Co-invention  European invention   \n",
+      "ownership                                                                    \n",
+      "Chinese ownership                      0           465                 922  \\\n",
+      "Co-ownership                        3994          1998                2631   \n",
+      "European ownership                   709          1404                   0   \n",
+      "Third-party ownership                  0           218                   0   \n",
+      "\n",
+      "invention              Third-party invention  \n",
+      "ownership                                     \n",
+      "Chinese ownership                          0  \n",
+      "Co-ownership                              74  \n",
+      "European ownership                         0  \n",
+      "Third-party ownership                      0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "ship_co_occur = pd.crosstab(appln_f[\"ownership\"], appln_f[\"invention\"])\n",
+    "print(ship_co_occur)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "outputs": [],
+   "source": [
+    "record_col=\"appln_id\"\n",
+    "fromdir =outdir"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "outputs": [],
+   "source": [
+    "ship_co_occur.to_excel(f\"{fromdir}/plots/_ships.xlsx\",index=True)"
+   ],
+   "metadata": {
+    "collapsed": false
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gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Co-patents per year\n",
+    "data = (appln_f.groupby(['appln_filing_year'])[record_col].nunique(dropna=False)\n",
+    "        .reset_index()\n",
+    "        .rename(columns={0: record_col}))\n",
+    "data[record_col + \"_relative_growth\"] = data[data[record_col] > 0].sort_values(by=[\"appln_filing_year\"], ascending=True)[record_col][0]\n",
+    "data[record_col + \"_relative_growth\"] = (data[record_col] - data[record_col + \"_relative_growth\"]) / data[\n",
+    "    record_col + \"_relative_growth\"]\n",
+    "\n",
+    "data = data.sort_values(by=[\"appln_filing_year\"], ascending=[True])\n",
+    "data[record_col + \"_cumsum\"] = (data[record_col].cumsum())\n",
+    "\n",
+    "year_output = px.line(data, x=\"appln_filing_year\", y=record_col, markers=True, text=record_col)\n",
+    "year_output.update_traces(textposition='top center', textfont_size=12)\n",
+    "year_output.update_traces(hovertemplate='Year:%{x:d}<br>Number of co-publications:%{y:d}')\n",
+    "\n",
+    "year_rel_output = px.line(data, x=\"appln_filing_year\", y=record_col + \"_relative_growth\", markers=True,\n",
+    "                          text=record_col + \"_relative_growth\")\n",
+    "year_rel_output.update_traces(textposition='top center', textfont_size=12, texttemplate='%{y:.0%}')\n",
+    "year_rel_output.update_traces(hovertemplate='Year:%{x:d}<br>Rel.growth in co-publications:%{y:.0%}')\n",
+    "\n",
+    "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\", \"Relative growth of co-patents\"],\n",
+    "                         specs=[\n",
+    "                             [{\"type\": \"xy\"}],\n",
+    "                             [{\"type\": \"xy\"}]\n",
+    "                         ])\n",
+    "\n",
+    "for trace in list(year_output.select_traces()):\n",
+    "    figsuper.add_trace(trace,\n",
+    "                       row=1, col=1\n",
+    "                       )\n",
+    "\n",
+    "for trace in list(year_rel_output.select_traces()):\n",
+    "    figsuper.add_trace(trace,\n",
+    "                       row=2, col=1\n",
+    "                       )\n",
+    "\n",
+    "figsuper.update_layout(yaxis={'categoryorder': 'total ascending'}, barmode='relative')\n",
+    "figsuper.update_yaxes(\n",
+    "    showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "figsuper.update_xaxes(\n",
+    "    showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "figsuper.update_layout({'template': \"plotly\", \"font_family\": \"Montserrat\"})\n",
+    "figsuper['layout']['yaxis2'].update(zerolinecolor='grey', tickformat=\".0%\")\n",
+    "# for i in[\"xaxis\",\"xaxis2\"]:\n",
+    "#     figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n",
+    "#\n",
+    "figsuper['layout'][\"yaxis\"][\"range\"] = [0, 2500]\n",
+    "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1, 3.5]\n",
+    "#\n",
+    "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
+    "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
+    "\n",
+    "figsuper.show(config=dict(displayModeBar=False, responsive=True), height=800, width=1600, scale=4)\n",
+    "\n",
+    "figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\", height=900, width=900, scale=4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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+        {
+         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
+         "hovertext": [
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria"
+         ],
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "markers+lines",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "x": [
+          2011,
+          2012,
+          2013,
+          2014,
+          2015,
+          2016,
+          2017,
+          2018,
+          2019,
+          2020,
+          2021,
+          2022
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+          1.0,
+          0.0,
+          0.0
+         ],
+         "yaxis": "y21",
+         "type": "scatter"
+        },
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
+         "hovertext": [
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium"
+         ],
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "markers+lines",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "x": [
+          2011,
+          2012,
+          2013,
+          2014,
+          2015,
+          2016,
+          2017,
+          2018,
+          2019,
+          2020,
+          2021,
+          2022
+         ],
+         "xaxis": "x22",
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+          0.0,
+          1.0,
+          0.0,
+          0.0,
+          1.0,
+          0.0,
+          0.0,
+          0.0,
+          2.0,
+          1.0,
+          1.0,
+          0.0
+         ],
+         "yaxis": "y22",
+         "type": "scatter"
+        },
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
+         "hovertext": [
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authority\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,0.18400000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,0.144],\"linecolor\":\"black\",\"linewidth\":1,\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis2\":{\"anchor\":\"y2\",\"domain\":[0.20400000000000001,0.388],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis2\":{\"anchor\":\"x2\",\"domain\":[0.0,0.144],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis3\":{\"anchor\":\"y3\",\"domain\":[0.40800000000000003,0.5920000000000001],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis3\":{\"anchor\":\"x3\",\"domain\":[0.0,0.144],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis4\":{\"anchor\":\"y4\",\"domain\":[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis4\":{\"anchor\":\"x4\",\"domain\":[0.0,0.144],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis5\":{\"anchor\":\"y5\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis5\":{\"anchor\":\"x5\",\"domain\":[0.0,0.144],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis6\":{\"anchor\":\"y6\",\"domain\":[0.0,0.18400000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis6\":{\"anchor\":\"x6\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis7\":{\"anchor\":\"y7\",\"domain\":[0.20400000000000001,0.388],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis7\":{\"anchor\":\"x7\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis8\":{\"anchor\":\"y8\",\"domain\":[0.40800000000000003,0.5920000000000001],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis8\":{\"anchor\":\"x8\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis9\":{\"anchor\":\"y9\",\"domain\":[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis9\":{\"anchor\":\"x9\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16}},\"xaxis10\":{\"anchor\":\"y10\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidt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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = (appln_f.groupby(['appln_filing_year',\"appln_auth\"])[record_col]\n",
+    "        .nunique(dropna=False).unstack()\n",
+    "        .fillna(0)\n",
+    "        .stack()\n",
+    "        .reset_index()\n",
+    "        .rename(columns={0:record_col}))\n",
+    "\n",
+    "# data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n",
+    "\n",
+    "data = data.merge(data[data[record_col]>0].sort_values(by=[\"appln_filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n",
+    "                  on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n",
+    "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
+    "data = data.sort_values(by =[\"appln_auth\",\"appln_filing_year\"], ascending=[True,True])\n",
+    "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
+    "\n",
+    "data = data.merge(tls_801,left_on='appln_auth', right_on =\"ctry_code\")\n",
+    "data[\"Country\"] = data[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")\n",
+    "\n",
+    "\n",
+    "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n",
+    "              y=record_col,\n",
+    "              x='appln_filing_year'\n",
+    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
+    "              markers=True,\n",
+    "              labels={\n",
+    "                     record_col: 'Number of co-patents',\n",
+    "                 },\n",
+    "                title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n",
+    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
+    "fig.update_yaxes(\n",
+    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_xaxes(\n",
+    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
+    "fig.update_yaxes(title='')\n",
+    "fig.update_xaxes(title='')\n",
+    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
+    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
+    "figsuper_ppt = go.Figure(fig)\n",
+    "\n",
+    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
+    "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n",
+    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
+    "    a[\"font\"][\"size\"] = 22\n",
+    "\n",
+    "s=16\n",
+    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
+    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
+    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
+    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "data": [
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
+         "hovertext": [
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria",
+          "Austria"
+         ],
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
+         },
+         "mode": "markers+lines",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "x": [
+          2011,
+          2012,
+          2013,
+          2014,
+          2015,
+          2016,
+          2017,
+          2018,
+          2019,
+          2020,
+          2021,
+          2022
+         ],
+         "xaxis": "x21",
+         "y": [
+          -1.0,
+          0.0,
+          -1.0,
+          0.0,
+          0.0,
+          0.0,
+          1.0,
+          -1.0,
+          -1.0,
+          0.0,
+          -1.0,
+          -1.0
+         ],
+         "yaxis": "y21",
+         "type": "scatter"
+        },
+        {
+         "hovertemplate": "<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}",
+         "hovertext": [
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium",
+          "Belgium"
+         ],
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
+         "marker": {
+          "symbol": "circle"
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"tickfont\":{\"size\":16}},\"xaxis9\":{\"anchor\":\"y9\",\"domain\":[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis9\":{\"anchor\":\"x9\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis10\":{\"anchor\":\"y10\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis10\":{\"anchor\":\"x10\",\"domain\":[0.214,0.358],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis11\":{\"anchor\":\"y11\",\"domain\":[0.0,0.18400000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis11\":{\"anchor\":\"x11\",\"domain\":[0.428,0.572],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis12\":{\"anchor\":\"y12\",\"domain\":[0.20400000000000001,0.388],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis12\":{\"anchor\":\"x12\",\"domain\":[0.428,0.572],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis13\":{\"anchor\":\"y13\",\"domain\":[0.40800000000000003,0.5920000000000001],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis13\":{\"anchor\":\"x13\",\"domain\":[0.428,0.572],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis14\":{\"anchor\":\"y14\",\"domain\":[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis14\":{\"anchor\":\"x14\",\"domain\":[0.428,0.572],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis15\":{\"anchor\":\"y15\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis15\":{\"anchor\":\"x15\",\"domain\":[0.428,0.572],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis16\":{\"anchor\":\"y16\",\"domain\":[0.0,0.18400000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis16\":{\"anchor\":\"x16\",\"domain\":[0.6419999999999999,0.7859999999999999],\"lineco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[0.6120000000000001,0.7960000000000002],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis24\":{\"anchor\":\"x24\",\"domain\":[0.856,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}},\"xaxis25\":{\"anchor\":\"y25\",\"domain\":[0.8160000000000001,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"x\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"tickfont\":{\"size\":16},\"tickangle\":45},\"yaxis25\":{\"anchor\":\"x25\",\"domain\":[0.856,1.0],\"linecolor\":\"black\",\"linewidth\":1,\"matches\":\"y\",\"mirror\":true,\"showgrid\":true,\"showline\":true,\"showticklabels\":false,\"ticks\":\"outside\",\"title\":{\"text\":\"\"},\"zeroline\":true,\"zerolinewidth\":2,\"zerolinecolor\":\"grey\",\"tickformat\":\".0%\",\"tickfont\":{\"size\":16}}},                        {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('6b738783-0d4f-420e-a03c-92b6dc08ab4f');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n",
+    "              y=record_col+\"_relative_growth\",\n",
+    "              x='appln_filing_year'\n",
+    "              ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
+    "              markers=True,\n",
+    "              labels={\n",
+    "                    record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n",
+    "                 },\n",
+    "                title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n",
+    "fig.update_traces(hovertemplate='<b>%{hovertext}</b><br>%{x}<br>Co-publications: %{y}')\n",
+    "fig.update_yaxes(\n",
+    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_xaxes(\n",
+    "    showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
+    "fig.update_yaxes(title='')\n",
+    "fig.update_xaxes(title='')\n",
+    "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
+    "# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
+    "\n",
+    "figsuper_ppt = go.Figure(fig)\n",
+    "\n",
+    "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
+    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
+    "    a[\"font\"][\"size\"] = 22\n",
+    "\n",
+    "s=16\n",
+    "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n",
+    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
+    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
+    "figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
+    "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "outputs": [],
+   "source": [
+    "appln_pers_f = appln_scope\n",
+    "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_x\"]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n       'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n       'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n       'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type'],\n      dtype='object')"
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "appln_scope.columns"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    ctry_code iso_alpha3                          st3_name organisation_flag   \n",
+      "0          AD        AND                           Andorra                    \\\n",
+      "1          AE        ARE              United Arab Emirates                     \n",
+      "2          AF        AFG                       Afghanistan                     \n",
+      "3          AG        ATG               Antigua and Barbuda                     \n",
+      "4          AI        AIA                          Anguilla                     \n",
+      "..        ...        ...                               ...               ...   \n",
+      "235        YE        YEM                             Yemen                     \n",
+      "236        YU        YUG  Yugoslavia/Serbia and Montenegro                     \n",
+      "237        ZA        ZAF                      South Africa                     \n",
+      "238        ZM        ZMB                            Zambia                     \n",
+      "239        ZW        ZWE                          Zimbabwe                     \n",
+      "\n",
+      "         continent eu_member epo_member oecd_member discontinued  \n",
+      "0           Europe                                                \n",
+      "1             Asia                                                \n",
+      "2             Asia                                                \n",
+      "3    North America                                                \n",
+      "4    North America                                                \n",
+      "..             ...       ...        ...         ...          ...  \n",
+      "235           Asia                                                \n",
+      "236         Europe                                             Y  \n",
+      "237         Africa                                                \n",
+      "238         Africa                                                \n",
+      "239         Africa                                                \n",
+      "\n",
+      "[240 rows x 9 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tls_801)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "outputs": [],
+   "source": [
+    "iso2_to_namedict = dict(zip(tls_801[\"ctry_code\"],tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")))\n",
+    "name_to_iso3 = dict(zip(tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\"),tls_801[\"iso_alpha3\"]))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "{'Andorra': 'AND',\n 'United Arab Emirates': 'ARE',\n 'Afghanistan': 'AFG',\n 'Antigua and Barbuda': 'ATG',\n 'Anguilla': 'AIA',\n 'Albania': 'ALB',\n 'Armenia': 'ARM',\n 'Netherlands Antilles ': 'ANT',\n 'Angola': 'AGO',\n 'ARIPO': '   ',\n 'Argentina': 'ARG',\n 'Austria': 'AUT',\n 'Australia': 'AUS',\n 'Aruba': 'ABW',\n 'Azerbaijan': 'AZE',\n 'Bosnia and Herzegovina': 'BIH',\n 'Barbados': 'BRB',\n 'Bangladesh': 'BGD',\n 'Belgium': 'BEL',\n 'Burkina Faso': 'BFA',\n 'Bulgaria': 'BGR',\n 'Bahrain': 'BHR',\n 'Burundi': 'BDI',\n 'Benin': 'BEN',\n 'Bermuda': 'BMU',\n 'Brunei Darussalam': 'BRN',\n 'Plurinational State of': 'BOL',\n 'Bonaire, Sint Eustatius and Saba': 'BES',\n 'Brazil': 'BRA',\n 'Bahamas': 'BHS',\n 'Bhutan': 'BTN',\n 'Burma': 'BUR',\n 'Bouvet Island': 'BVT',\n 'Botswana': 'BWA',\n 'BOIP': '   ',\n 'Belarus': 'BLR',\n 'Belize': 'BLZ',\n 'Canada': 'CAN',\n 'Democratic Republic of the Congo': 'COD',\n 'Central African Republic': 'CAF',\n 'Congo': 'COG',\n 'Switzerland': 'CHE',\n \"Côte d'Ivoire\": 'CIV',\n 'Cook Islands': 'COK',\n 'Chile': 'CHL',\n 'Cameroon': 'CMR',\n 'China': 'CHN',\n 'Colombia': 'COL',\n 'Costa Rica': 'CRI',\n 'Czechoslovakia': 'CSK',\n 'Cuba': 'CUB',\n 'Cabo Verde': 'CPV',\n 'Curaçao': 'CUW',\n 'Cyprus': 'CYP',\n 'Czechia': 'CZE',\n 'German Democratic Republic': 'DDR',\n 'Germany': 'DEU',\n 'Djibouti': 'DJI',\n 'Denmark': 'DNK',\n 'Dominica': 'DMA',\n 'Dominican Republic': 'DOM',\n 'Algeria': 'DZA',\n 'EAPO': '   ',\n 'Ecuador': 'ECU',\n 'Estonia': 'EST',\n 'Egypt': 'EGY',\n 'Western Sahara': 'ESH',\n 'EUIPO': '   ',\n 'EPO': '   ',\n 'Eritrea': 'ERI',\n 'Spain': 'ESP',\n 'Ethiopia': 'ETH',\n 'European Union': '   ',\n 'Finland': 'FIN',\n 'Fiji': 'FJI',\n 'Malvinas': 'FLK',\n 'Faroe Islands': 'FRO',\n 'France': 'FRA',\n 'Gabon': 'GAB',\n 'United Kingdom': 'GBR',\n 'GCC Patent Office': '   ',\n 'Grenada': 'GRD',\n 'Georgia': 'GEO',\n 'Guernsey': 'GGY',\n 'Ghana': 'GHA',\n 'Gibraltar': 'GIB',\n 'Greenland': 'GRL',\n 'Gambia': 'GMB',\n 'Guinea': 'GIN',\n 'Equatorial Guinea': 'GNQ',\n 'Greece': 'GRC',\n 'South Georgia and the South Sandwich Islands': 'SGS',\n 'Guatemala': 'GTM',\n 'Guinea-Bissau': 'GNB',\n 'Guyana': 'GUY',\n 'Hong Kong, China': 'HKG',\n 'Honduras': 'HND',\n 'Croatia': 'HRV',\n 'Haiti': 'HTI',\n 'Hungary': 'HUN',\n 'WIPO': '   ',\n 'Indonesia': 'IDN',\n 'Ireland': 'IRL',\n 'Israel': 'ISR',\n 'Isle of Man': 'IMN',\n 'India': 'IND',\n 'Iraq': 'IRQ',\n 'Islamic Republic of': 'IRN',\n 'Iceland': 'ISL',\n 'Italy': 'ITA',\n 'Jersey': 'JEY',\n 'Jamaica': 'JAM',\n 'Jordan': 'JOR',\n 'Japan': 'JPN',\n 'Kenya': 'KEN',\n 'Kyrgyzstan': 'KGZ',\n 'Cambodia': 'KHM',\n 'Kiribati': 'KIR',\n 'Comoros': 'COM',\n 'Saint Kitts and Nevis': 'KNA',\n \"Democratic People's Republic of Korea\": 'PRK',\n 'Republic of Korea': 'KOR',\n 'Kuwait': 'KWT',\n 'Cayman Islands': 'CYM',\n 'Kazakhstan': 'KAZ',\n \"Lao People's Democratic Republic\": 'LAO',\n 'Lebanon': 'LBN',\n 'Saint Lucia': 'LCA',\n 'Liechtenstein': 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+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "name_to_iso3"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
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+   "execution_count": 126,
+   "outputs": [
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
+    "    appln_pers_f[c+\"_fullname\"] = appln_pers_f[c].map(iso2_to_namedict)\n",
+    "\n",
+    "# appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
+    "appln_pers_f[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
+    "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
+    "\n",
+    "appln_pers_f[\"auth_ISO3\"] = appln_pers_f[\"appln_auth_fullname\"].map(name_to_iso3)\n",
+    "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"Patent Office\" if x==\"   \" else \"EU\")\n",
+    "\n",
+    "# top 25\n",
+    "# - inventor\n",
+    "# - applicant\n",
+    "# in china, EU, EUextended\n",
+    "color_discrete_map= {'China': '#EF553B',\n",
+    "                    'EU': '#636EFA',\n",
+    "                    'Non-EU associate': '#00CC96',\n",
+    "                     'EPO':'#9F2B68'}\n",
+    "\n",
+    "labeld_dict = dict({\n",
+    "                     record_col: 'Number of co-patents',\n",
+    "                     \"person_ctry_type\": \"Country type\",\n",
+    "    \"psn_sector_primary\":\"Sector\",\n",
+    "                                  \"han_label\": \"Name\",\n",
+    "                                  \"Country_Type\":\"Country type\",\n",
+    "                                  \"Eurovoc_Class\":\"Region\"\n",
+    "                 })\n",
+    "for trait in  [\"person_ctry_type\",\"psn_sector_primary\"]:\n",
+    "    for ctry in [\"China\",'EU']:\n",
+    "        ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
+    "        # for role in [\"invt\",\"applt\"]:\n",
+    "        if True:\n",
+    "            role = \"applt\"\n",
+    "            pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
+    "            data = (pers_sub\n",
+    "                     .groupby([\"han_id\",'han_name'],as_index=False)\n",
+    "                     .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n",
+    "                     .sort_values(by=\"appln_id\", ascending=False)\n",
+    "                    .head(15))\n",
+    "            data[\"han_label\"] = (data[\"han_name\"]\n",
+    "                                 .str.upper()\n",
+    "                                 .str.strip(\".\")\n",
+    "                                 .str.replace(\"&\",\"<br>\")\n",
+    "                                 + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n",
+    "\n",
+    "            fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n",
+    "                         color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
+    "            fig.update_layout(font_family=\"Montserrat\",\n",
+    "                          yaxis={'categoryorder':'total ascending'})\n",
+    "\n",
+    "            figsuper = fig\n",
+    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
+    "            figsuper.update_yaxes(\n",
+    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "                ticks=\"outside\")\n",
+    "            figsuper.update_xaxes(\n",
+    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "                ticks=\"outside\")\n",
+    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
+    "\n",
+    "            figsuper['layout'][\"font\"][\"size\"]=12\n",
+    "            for a in figsuper['layout'][\"annotations\"]:\n",
+    "                a[\"font\"][\"size\"] = 14\n",
+    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n",
+    "\n",
+    "            figsuper.update_layout(uniformtext_minsize=10)\n",
+    "\n",
+    "            figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
+    "            figsuper.write_image(f\"{fromdir}/plots/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
+      "\n",
+      "divide by zero encountered in log10\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "data": [
+        {
+         "coloraxis": "coloraxis",
+         "name": "0",
+         "texttemplate": "%{text}",
+         "x": [
+          "Germany",
+          "Sweden",
+          "Finland",
+          "United Kingdom",
+          "France",
+          "Denmark",
+          "Switzerland",
+          "Italy",
+          "Poland",
+          "Netherlands",
+          "Belgium",
+          "Spain",
+          "Austria",
+          "Ireland",
+          "Slovakia",
+          "Czechia",
+          "Hungary",
+          "Greece"
+         ],
+         "y": [
+          "HUAWEI TECH CO LTD",
+          "NOKIA SHANGHAI BELL CO LTD",
+          "NINGBO GEELY AUTOMOBILE R&D CO LTD",
+          "TCL COMM LTD",
+          "ZHEJIANG GEELY HOLDINGGROUP CO LTD",
+          "LENOVO BEIJING CO LTD",
+          "ALCATEL LUCENT SHANGHAI BELL CO LTD",
+          "NOKIA CHINA INVESTMENT CO LTD",
+          "CHINA TRIUMPH INT ENGINEERING CO LTD",
+          "NOKIA TECH BEIJING CO LTD",
+          "SCHOTT GLASS TECH SUZHOU CO LTD",
+          "SIEMENS LTD CHINA",
+          "DETROIT ELECT EV LTD",
+          "BEIJING XIAOMI MOBILE SOFTWARE CO LTD",
+          "CHANGZHOU GLOBE CO LTD",
+          "SICHUAN GOLDEN RIDGE INTELLIGENCE SCIENCE & TECH CO LTD",
+          "ZF DRIVETECH SUZHOU CO LTD",
+          "CRRC QINGDAO SIFANG CO LTD",
+          "GOODIX TECH HK CO LTD",
+          "SHENZHEN GOODIX TECH CO LTD"
+         ],
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n",
+    "                          (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n",
+    "invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n",
+    "\n",
+    "eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
+    "ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
+    "\n",
+    "eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
+    "ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
+    "\n",
+    "#\n",
+    "# patstat_applt_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
+    "# patstat_applt_dipol.sample(100)\n",
+    "\n",
+    "patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
+    "patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
+    "\n",
+    "# co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
+    "#                             values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
+    "co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
+    "                            values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "co_occur = co_occur.T\n",
+    "co_occur[\"sum\"] = co_occur.sum(axis=1)\n",
+    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
+    "\n",
+    "co_occur = (co_occur\n",
+    "            .sort_values(by=\"sum\", ascending=False)\n",
+    "            .head(20)\n",
+    "            .drop(columns=\"sum\")\n",
+    "            .replace(0,np.nan)\n",
+    "            .dropna(axis=1,how=\"all\")\n",
+    "            .replace(np.nan,0)\n",
+    "            .astype(int))\n",
+    "\n",
+    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
+    "                labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
+    "                x=list(co_occur.columns),\n",
+    "                text_auto=True,\n",
+    "                y=list(co_occur.index))\n",
+    "\n",
+    "\n",
+    "fig.update_layout(title_x=0.5,\n",
+    "                   width=1000, height=1000,\n",
+    "                   xaxis_showgrid=False,\n",
+    "                   yaxis_showgrid=False,\n",
+    "                   yaxis_autorange='reversed',\n",
+    "                  font_family=\"Montserrat\",\n",
+    "                  template='plotly_white',\n",
+    "                  coloraxis_colorbar=dict(\n",
+    "                            thicknessmode=\"pixels\", thickness=25,\n",
+    "                            ticks=\"outside\", ticksuffix=\" \",\n",
+    "                            dtick=20,outlinewidth=1,\n",
+    "                        ))\n",
+    "\n",
+    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
+    "fig.update(layout_coloraxis_showscale=False)\n",
+    "fig.update_xaxes(tickangle= -45)\n",
+    "fig.update_yaxes(\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_xaxes(\n",
+    "    ticks=\"outside\")\n",
+    "\n",
+    "figsuper_ppt = fig\n",
+    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
+    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
+    "    a[\"font\"][\"size\"] = 16\n",
+    "\n",
+    "s=16\n",
+    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
+    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
+    "\n",
+    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
+    "\n",
+    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "outputs": [],
+   "source": [
+    "patstat_applt_dipol_2 = (patstat_applt_dipol\n",
+    "                         .groupby([\"han_name_ch\",\"han_name_eu\"], as_index=False)\n",
+    "                         .agg({\"psn_sector_primary_ch\":\"first\",\n",
+    "                               \"psn_sector_primary_eu\":\"first\",\n",
+    "                               \"person_ctry_ISO3_eu\":\"first\",\n",
+    "                               record_col:\"nunique\"})\n",
+    "                         .sort_values(by=record_col, ascending=False)\n",
+    "                         .head(-1))\n",
+    "patstat_applt_dipol_2[\"applt_eu_label\"] = (patstat_applt_dipol_2[\"han_name_eu\"]\n",
+    "                                           +\" (\"\n",
+    "                                           +patstat_applt_dipol_2[\"person_ctry_ISO3_eu\"]\n",
+    "                                           +\"; \"\n",
+    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_eu\"]\n",
+    "                                           +\")\")\n",
+    "\n",
+    "patstat_applt_dipol_2[\"applt_ch_label\"] = (patstat_applt_dipol_2[\"han_name_ch\"]\n",
+    "                                           +\" (\"\n",
+    "                                           +patstat_applt_dipol_2[\"psn_sector_primary_ch\"]\n",
+    "                                           +\")\")\n",
+    "\n",
+    "patstat_applt_dipol_2[[\"applt_ch_label\",\"applt_eu_label\",\"appln_id\"]].to_excel(rf\"{fromdir}/plots/applt_table.xlsx\", index=False)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
+      "\n",
+      "divide by zero encountered in log10\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
+    "                            values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
+    "# co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
+    "#                             values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
+    "\n",
+    "\n",
+    "co_occur = co_occur.T\n",
+    "co_occur[\"SUM\"] = co_occur.sum(axis=1)\n",
+    "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
+    "\n",
+    "co_occur = co_occur.drop(columns = co_occur.columns[~co_occur.columns.str.isupper()])\n",
+    "co_occur = co_occur.drop(index = co_occur.index[~co_occur.index.str.isupper()])\n",
+    "\n",
+    "co_occur.columns = co_occur.columns.str.split(\"&\").str[0:2].str.join(\"&\")\n",
+    "\n",
+    "top=20\n",
+    "co_occur = (co_occur\n",
+    "            .sort_values(by=\"SUM\", ascending=False)\n",
+    "            .head(20)\n",
+    "            .drop(columns=\"SUM\")\n",
+    "            .replace(0,np.nan)\n",
+    "            .dropna(axis=1,how=\"all\")\n",
+    "            .replace(np.nan,0)\n",
+    "            .astype(int)\n",
+    "            .iloc[0:top,0:top]).copy()\n",
+    "\n",
+    "co_occur = co_occur.sort_index(axis=1).sort_index(axis=0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
+    "                labels=dict(x=\"Applicant (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
+    "                x=list(co_occur.columns),\n",
+    "                text_auto=True,\n",
+    "                y=list(co_occur.index))\n",
+    "\n",
+    "\n",
+    "fig.update_layout(title_x=0.5,\n",
+    "                   width=1000, height=1000,\n",
+    "                   xaxis_showgrid=False,\n",
+    "                   yaxis_showgrid=False,\n",
+    "                   yaxis_autorange='reversed',\n",
+    "                  font_family=\"Montserrat\",\n",
+    "                  template='plotly_white',\n",
+    "                  coloraxis_colorbar=dict(\n",
+    "                            thicknessmode=\"pixels\", thickness=25,\n",
+    "                            ticks=\"outside\", ticksuffix=\" \",\n",
+    "                            dtick=20,outlinewidth=1,\n",
+    "                        ))\n",
+    "\n",
+    "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
+    "fig.update(layout_coloraxis_showscale=False)\n",
+    "fig.update_xaxes(tickangle= -45)\n",
+    "fig.update_yaxes(\n",
+    "    ticks=\"outside\")\n",
+    "fig.update_xaxes(\n",
+    "    ticks=\"outside\")\n",
+    "\n",
+    "figsuper_ppt = fig\n",
+    "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
+    "for a in figsuper_ppt['layout'][\"annotations\"]:\n",
+    "    a[\"font\"][\"size\"] = 16\n",
+    "\n",
+    "s=12\n",
+    "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
+    "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=30)\n",
+    "\n",
+    "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
+    "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_EU_coapplt.png\",height=900,width=1200,scale = 4)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index([], dtype='object', name='han_name_eu')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(co_occur.columns[~co_occur.columns.str.isupper()])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "346"
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len((co_occur.columns.str.isupper()))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "outputs": [
+    {
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+          "pattern": {
+           "shape": ""
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+         },
+         "name": "EU",
+         "offsetgroup": "EU",
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+          "Croatia",
+          "Czechia",
+          "Denmark",
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+          "France",
+          "Germany",
+          "Greece",
+          "Hungary",
+          "Ireland",
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+          "Luxembourg",
+          "Netherlands",
+          "Poland",
+          "Portugal",
+          "Romania",
+          "Slovakia",
+          "Slovenia",
+          "Spain",
+          "Sweden"
+         ],
+         "yaxis": "y",
+         "type": "bar"
+        },
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Country type=China<br>Percent of related co-patents=%{x}<br>Inventor's country=%{y}<extra></extra>",
+         "legendgroup": "China",
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+         "name": "China",
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defaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[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\"]],\"sequentialminus\":[[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\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Percent 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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",
+    "                     'Patent Office':'#7F00FF'}#CC8899\n",
+    "\n",
+    "\n",
+    "labeld_dict = dict({\n",
+    "                     record_col+\"_percent\": 'Percent of related co-patents',\n",
+    "                    \"person_ctry_code_fullname\": \"Country\",\n",
+    "                    \"person_ctry_type\": \"Country type\",\n",
+    "                    \"appln_auth_fullname\": \"Application authority\",\n",
+    "                    \"auth_ctry_type\": \"Authority type\",\n",
+    "                    \"Country_Type\":\"Country type\",\n",
+    "                    \"Eurovoc_Class\":\"Region\"\n",
+    "                 })\n",
+    "\n",
+    "for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
+    "    pers_sub = appln_pers_f\n",
+    "    if c == \"person_ctry_code\":\n",
+    "        for role in [\"invt\",\"applt\"]:\n",
+    "            pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
+    "            role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n",
+    "            labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n",
+    "\n",
+    "\n",
+    "            data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
+    "            data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
+    "            fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
+    "                         labels = labeld_dict)\n",
+    "            fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
+    "                      yaxis={'categoryorder':'total ascending'},)\n",
+    "            figsuper = fig\n",
+    "            figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
+    "            figsuper.update_yaxes(\n",
+    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "                ticks=\"outside\")\n",
+    "            figsuper.update_xaxes(\n",
+    "                showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "                ticks=\"outside\")\n",
+    "            figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
+    "\n",
+    "            figsuper['layout'][\"font\"][\"size\"]=14\n",
+    "            for a in figsuper['layout'][\"annotations\"]:\n",
+    "                a[\"font\"][\"size\"] = 14\n",
+    "            figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
+    "\n",
+    "            figsuper.update_layout(uniformtext_minsize=10)\n",
+    "            figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
+    "            figsuper.show()\n",
+    "    else:\n",
+    "        data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
+    "        data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
+    "        fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
+    "                         labels = labeld_dict)\n",
+    "        fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
+    "                      yaxis={'categoryorder':'total ascending'},)\n",
+    "        figsuper = fig\n",
+    "        figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
+    "        figsuper.update_yaxes(\n",
+    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "            ticks=\"outside\")\n",
+    "        figsuper.update_xaxes(\n",
+    "            showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
+    "            ticks=\"outside\")\n",
+    "        figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
+    "\n",
+    "        figsuper['layout'][\"font\"][\"size\"]=14\n",
+    "        for a in figsuper['layout'][\"annotations\"]:\n",
+    "            a[\"font\"][\"size\"] = 14\n",
+    "        figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
+    "\n",
+    "        figsuper.update_layout(uniformtext_minsize=10)\n",
+    "        figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
+    "        figsuper.show()"
    ],
    "metadata": {
     "collapsed": false
diff --git a/PATSTAT/patstat_analysis_pipeline.ipynb b/PATSTAT/patstat_analysis_pipeline_deprecated.ipynb
similarity index 87%
rename from PATSTAT/patstat_analysis_pipeline.ipynb
rename to PATSTAT/patstat_analysis_pipeline_deprecated.ipynb
index b5508af..06681cb 100644
--- a/PATSTAT/patstat_analysis_pipeline.ipynb
+++ b/PATSTAT/patstat_analysis_pipeline_deprecated.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 1,
    "outputs": [
     {
      "data": {
@@ -60,12 +60,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "outputs": [],
    "source": [
-    "outdir=\"EU_CH_scope\"\n",
-    "\n",
-    "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")"
+    "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
@@ -73,19 +77,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "65136"
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "outputs": [],
    "source": [
-    "len(appln)"
+    "outdir=\"raw_files_csv\""
    ],
    "metadata": {
     "collapsed": false
@@ -93,19 +88,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 9,
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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"
+     ]
+    },
     {
      "data": {
-      "text/plain": "24638"
+      "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]",
+      "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>5420565</th>\n      <td>492443204</td>\n      <td>CN</td>\n      <td>201710924209</td>\n      <td>A</td>\n      <td>2017-09-30</td>\n      <td>2017</td>\n      <td>201710924209</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>492443205</td>\n      <td>Y</td>\n      <td>61698252</td>\n      <td>492443204</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4123443</th>\n      <td>476745655</td>\n      <td>CN</td>\n      <td>201510575292</td>\n      <td>A</td>\n      <td>2015-09-11</td>\n      <td>2015</td>\n      <td>201510575292</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>476745656</td>\n      <td>N</td>\n      <td>58346251</td>\n      <td>476745655</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>6</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12669428</th>\n      <td>584371570</td>\n      <td>CN</td>\n      <td>202211359033</td>\n      <td>A</td>\n      <td>2022-11-02</td>\n      <td>2022</td>\n      <td>202211359033</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>584371571</td>\n      <td>N</td>\n      <td>84336840</td>\n      <td>584371570</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2837134</th>\n      <td>447940178</td>\n      <td>CN</td>\n      <td>201510724424</td>\n      <td>A</td>\n      <td>2015-10-29</td>\n      <td>2015</td>\n      <td>2015107244247</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>447940179</td>\n      <td>N</td>\n      <td>54942635</td>\n      <td>447940178</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3917352</th>\n      <td>474354000</td>\n      <td>CN</td>\n      <td>201610699880</td>\n      <td>A</td>\n      <td>2016-08-19</td>\n      <td>2016</td>\n      <td>201610699880</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>474354001</td>\n      <td>N</td>\n      <td>57825252</td>\n      <td>474354000</td>\n      <td>1</td>\n      <td>7</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7083405</th>\n      <td>510565742</td>\n      <td>CN</td>\n      <td>201910024682</td>\n      <td>A</td>\n      <td>2019-01-10</td>\n      <td>2019</td>\n      <td>201910024682</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>510565743</td>\n      <td>Y</td>\n      <td>65916569</td>\n      <td>510565742</td>\n      <td>1</td>\n      <td>6</td>\n      <td>1</td>\n      <td>9</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10061891</th>\n      <td>549184042</td>\n      <td>CN</td>\n      <td>202011496905</td>\n      <td>A</td>\n      <td>2020-12-17</td>\n      <td>2020</td>\n      <td>202011496905</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>549184043</td>\n      <td>Y</td>\n      <td>75448776</td>\n      <td>549184042</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12187346</th>\n      <td>578289051</td>\n      <td>CN</td>\n      <td>202210716624</td>\n      <td>A</td>\n      <td>2022-06-23</td>\n      <td>2022</td>\n      <td>202210716624</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>578289052</td>\n      <td>N</td>\n      <td>82910930</td>\n      <td>578289051</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8750003</th>\n      <td>533546770</td>\n      <td>CN</td>\n      <td>202010149696</td>\n      <td>A</td>\n      <td>2020-03-06</td>\n      <td>2020</td>\n      <td>202010149696</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>533546771</td>\n      <td>Y</td>\n      <td>71152278</td>\n      <td>533546770</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2150113</th>\n      <td>439343652</td>\n      <td>CN</td>\n      <td>201410639681</td>\n      <td>A</td>\n      <td>2014-11-14</td>\n      <td>2014</td>\n      <td>2014106396816</td>\n      <td>PI</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>439343653</td>\n      <td>N</td>\n      <td>52882593</td>\n      <td>439343652</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 28 columns</p>\n</div>"
      },
-     "execution_count": 37,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]])"
+    "appln = pd.read_csv(f\"{outdir}/01_patent_subset.csv\", low_memory=False)\n",
+    "appln.sample(10)"
    ],
    "metadata": {
     "collapsed": false
@@ -113,20 +120,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 11,
    "outputs": [
     {
      "data": {
-      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n8712   413791676         KR   20127034385         A         2011-06-03  \\\n15588  439148012         EP      15714777         A         2015-03-26   \n48628  536723783         RU    2019104609         A         2017-07-18   \n4458   380638095         US  201113218876         A         2011-08-26   \n43221  522842130         US  201829659634         F         2018-08-10   \n3836   378587773         WO    2011074635         W         2011-05-25   \n9237   414823192         EP      12882000         A         2012-08-03   \n6777   409424323         US  201113638187         A         2011-03-22   \n18826  446137103         WO    2015057861         W         2015-10-14   \n52265  545763074         BR  112020021641         A         2018-04-23   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n8712                2011     1020127034385       PI                   \\\n15588               2015          15714777       PI                    \n48628               2017        2019104609       PI                    \n4458                2011          13218876       PI                    \n43221               2018               NaN       DP                    \n3836                2011     CN2011/074635       PI               CN   \n9237                2012          12882000       PI                    \n6777                2011          13638187       PI                    \n18826               2015     IB2015/057861       PI               IB   \n52265               2018      112020021641       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n8712           339637678  ...          2013-11-04                2013  \\\n15588          438355565  ...          2017-02-15                2017   \n48628          488987246  ...          2020-08-20                2020   \n4458                   0  ...          2013-01-31                2013   \n43221                  0  ...          2019-12-03                2019   \n3836                   0  ...          2012-11-29                2012   \n9237           414790439  ...          2015-06-10                2015   \n6777           333698631  ...          2013-08-08                2013   \n18826                  0  ...          2016-04-21                2016   \n52265          521276805  ...          2021-01-26                2021   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n8712              413791677       Y         45067306          339616929  \\\n15588             473888077       Y         52814965          438355565   \n48628             536723784       Y         60991950          488987246   \n4458              380922724       N         47598050          380638095   \n43221             522842131       Y         68653880          522842130   \n3836              378816884       N         47216526          378587773   \n9237              439934604       N         50027133          414790439   \n6777              409424324       Y         43020401          319321099   \n18826             451530410       N         52014243          446137103   \n52265             545763075       N         68293428          521276805   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n8712                  6                   10              1            7  \n15588                 5                    1              1            2  \n48628                39                    3              1           19  \n4458                  2                    3              8            7  \n43221                 2                    0              1            1  \n3836                  3                    4              3            2  \n9237                  2                    0              1            2  \n6777                  6                    4              6            5  \n18826                 9                    3              2            1  \n52265                 6                    0              3            5  \n\n[10 rows x 26 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8712</th>\n      <td>413791676</td>\n      <td>KR</td>\n      <td>20127034385</td>\n      <td>A</td>\n      <td>2011-06-03</td>\n      <td>2011</td>\n      <td>1020127034385</td>\n      <td>PI</td>\n      <td></td>\n      <td>339637678</td>\n      <td>...</td>\n      <td>2013-11-04</td>\n      <td>2013</td>\n      <td>413791677</td>\n      <td>Y</td>\n      <td>45067306</td>\n      <td>339616929</td>\n      <td>6</td>\n      <td>10</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>15588</th>\n      <td>439148012</td>\n      <td>EP</td>\n      <td>15714777</td>\n      <td>A</td>\n      <td>2015-03-26</td>\n      <td>2015</td>\n      <td>15714777</td>\n      <td>PI</td>\n      <td></td>\n      <td>438355565</td>\n      <td>...</td>\n      <td>2017-02-15</td>\n      <td>2017</td>\n      <td>473888077</td>\n      <td>Y</td>\n      <td>52814965</td>\n      <td>438355565</td>\n      <td>5</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>48628</th>\n      <td>536723783</td>\n      <td>RU</td>\n      <td>2019104609</td>\n      <td>A</td>\n      <td>2017-07-18</td>\n      <td>2017</td>\n      <td>2019104609</td>\n      <td>PI</td>\n      <td></td>\n      <td>488987246</td>\n      <td>...</td>\n      <td>2020-08-20</td>\n      <td>2020</td>\n      <td>536723784</td>\n      <td>Y</td>\n      <td>60991950</td>\n      <td>488987246</td>\n      <td>39</td>\n      <td>3</td>\n      <td>1</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>4458</th>\n      <td>380638095</td>\n      <td>US</td>\n      <td>201113218876</td>\n      <td>A</td>\n      <td>2011-08-26</td>\n      <td>2011</td>\n      <td>13218876</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2013-01-31</td>\n      <td>2013</td>\n      <td>380922724</td>\n      <td>N</td>\n      <td>47598050</td>\n      <td>380638095</td>\n      <td>2</td>\n      <td>3</td>\n      <td>8</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>43221</th>\n      <td>522842130</td>\n      <td>US</td>\n      <td>201829659634</td>\n      <td>F</td>\n      <td>2018-08-10</td>\n      <td>2018</td>\n      <td>NaN</td>\n      <td>DP</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-12-03</td>\n      <td>2019</td>\n      <td>522842131</td>\n      <td>Y</td>\n      <td>68653880</td>\n      <td>522842130</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3836</th>\n      <td>378587773</td>\n      <td>WO</td>\n      <td>2011074635</td>\n      <td>W</td>\n      <td>2011-05-25</td>\n      <td>2011</td>\n      <td>CN2011/074635</td>\n      <td>PI</td>\n      <td>CN</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2012-11-29</td>\n      <td>2012</td>\n      <td>378816884</td>\n      <td>N</td>\n      <td>47216526</td>\n      <td>378587773</td>\n      <td>3</td>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9237</th>\n      <td>414823192</td>\n      <td>EP</td>\n      <td>12882000</td>\n      <td>A</td>\n      <td>2012-08-03</td>\n      <td>2012</td>\n      <td>12882000</td>\n      <td>PI</td>\n      <td></td>\n      <td>414790439</td>\n      <td>...</td>\n      <td>2015-06-10</td>\n      <td>2015</td>\n      <td>439934604</td>\n      <td>N</td>\n      <td>50027133</td>\n      <td>414790439</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6777</th>\n      <td>409424323</td>\n      <td>US</td>\n      <td>201113638187</td>\n      <td>A</td>\n      <td>2011-03-22</td>\n      <td>2011</td>\n      <td>13638187</td>\n      <td>PI</td>\n      <td></td>\n      <td>333698631</td>\n      <td>...</td>\n      <td>2013-08-08</td>\n      <td>2013</td>\n      <td>409424324</td>\n      <td>Y</td>\n      <td>43020401</td>\n      <td>319321099</td>\n      <td>6</td>\n      <td>4</td>\n      <td>6</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>18826</th>\n      <td>446137103</td>\n      <td>WO</td>\n      <td>2015057861</td>\n      <td>W</td>\n      <td>2015-10-14</td>\n      <td>2015</td>\n      <td>IB2015/057861</td>\n      <td>PI</td>\n      <td>IB</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-04-21</td>\n      <td>2016</td>\n      <td>451530410</td>\n      <td>N</td>\n      <td>52014243</td>\n      <td>446137103</td>\n      <td>9</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>52265</th>\n      <td>545763074</td>\n      <td>BR</td>\n      <td>112020021641</td>\n      <td>A</td>\n      <td>2018-04-23</td>\n      <td>2018</td>\n      <td>112020021641</td>\n      <td>PI</td>\n      <td></td>\n      <td>521276805</td>\n      <td>...</td>\n      <td>2021-01-26</td>\n      <td>2021</td>\n      <td>545763075</td>\n      <td>N</td>\n      <td>68293428</td>\n      <td>521276805</td>\n      <td>6</td>\n      <td>0</td>\n      <td>3</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 26 columns</p>\n</div>"
+      "text/plain": "appln_kind\nA    13029613\nW      154696\nName: count, dtype: int64"
      },
-     "execution_count": 38,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "appln.sample(10)"
+    "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
@@ -134,20 +160,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 15,
    "outputs": [
     {
      "data": {
-      "text/plain": "        appln_id appln_auth      appln_nr appln_kind appln_filing_date   \n56447  556331792         PE    2021000402         A         2019-09-26  \\\n41201  518451341         TW     107131654         A         2018-09-10   \n54094  550225934         TW     109119471         A         2017-07-06   \n9133   414556231         EP      12881920         A         2012-07-27   \n43037  522510076         EP      19306441         A         2019-11-07   \n...          ...        ...           ...        ...               ...   \n60095  569415925         TW     110116177         A         2021-05-05   \n61281  573442692         DE  102021213653         A         2021-12-02   \n41189  518448790         TW     108109981         A         2014-03-07   \n64973  587185640         CR      20220470         A         2021-03-18   \n8491   413374202         EP      13197783         A         2011-04-28   \n\n       appln_filing_year appln_nr_original ipr_type receiving_office   \n56447               2019        2021000402       PI                   \\\n41201               2018         107131654       PI                    \n54094               2017         109119471       PI                    \n9133                2012          12881920       PI                    \n43037               2019          19306441       PI                    \n...                  ...               ...      ...              ...   \n60095               2021         110116177       PI                    \n61281               2021      102021213653       PI                    \n41189               2014         108109981       PI                    \n64973               2021          20220470       PI                    \n8491                2011          13197783       PI                    \n\n       internat_appln_id  ... earliest_publn_date earliest_publn_year   \n56447          520193774  ...          2021-07-19                2021  \\\n41201                  0  ...          2019-06-16                2019   \n54094                  0  ...          2021-02-01                2021   \n9133           414544093  ...          2015-06-03                2015   \n43037                  0  ...          2021-05-12                2021   \n...                  ...  ...                 ...                 ...   \n60095                  0  ...          2021-12-16                2021   \n61281                  0  ...          2022-06-15                2022   \n41189                  0  ...          2019-06-16                2019   \n64973          549449130  ...          2022-11-14                2022   \n8491           376727956  ...          2014-08-20                2014   \n\n      earliest_pat_publn_id granted  docdb_family_id  inpadoc_family_id   \n56447             556331793       N         68542579          520193774  \\\n41201             518451342       N         66100215          511344799   \n54094             550225935       N         59337896          481645536   \n9133              439682012       Y         49996511          414544093   \n43037             548841071       N         68655464          522510076   \n...                     ...     ...              ...                ...   \n60095             568265190       N         74845079          546468249   \n61281             573442693       N         81749879          573442692   \n41189             518448791       Y         51500341          421859342   \n64973             587185641       N         75660301          549449130   \n8491              420445397       N         45896256          352844875   \n\n      docdb_family_size  nb_citing_docdb_fam  nb_applicants nb_inventors  \n56447                10                    0              1            3  \n41201                 2                    0              1            2  \n54094                 8                    3              1            7  \n9133                  4                    5              1            3  \n43037                 3                    0              1            2  \n...                 ...                  ...            ...          ...  \n60095                 4                    0              1            8  \n61281                 2                    0              1            1  \n41189                22                  290              1            3  \n64973                14                    4              1           25  \n8491                 10                    0              1            6  \n\n[100 rows x 26 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>56447</th>\n      <td>556331792</td>\n      <td>PE</td>\n      <td>2021000402</td>\n      <td>A</td>\n      <td>2019-09-26</td>\n      <td>2019</td>\n      <td>2021000402</td>\n      <td>PI</td>\n      <td></td>\n      <td>520193774</td>\n      <td>...</td>\n      <td>2021-07-19</td>\n      <td>2021</td>\n      <td>556331793</td>\n      <td>N</td>\n      <td>68542579</td>\n      <td>520193774</td>\n      <td>10</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>41201</th>\n      <td>518451341</td>\n      <td>TW</td>\n      <td>107131654</td>\n      <td>A</td>\n      <td>2018-09-10</td>\n      <td>2018</td>\n      <td>107131654</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-06-16</td>\n      <td>2019</td>\n      <td>518451342</td>\n      <td>N</td>\n      <td>66100215</td>\n      <td>511344799</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>54094</th>\n      <td>550225934</td>\n      <td>TW</td>\n      <td>109119471</td>\n      <td>A</td>\n      <td>2017-07-06</td>\n      <td>2017</td>\n      <td>109119471</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-02-01</td>\n      <td>2021</td>\n      <td>550225935</td>\n      <td>N</td>\n      <td>59337896</td>\n      <td>481645536</td>\n      <td>8</td>\n      <td>3</td>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9133</th>\n      <td>414556231</td>\n      <td>EP</td>\n      <td>12881920</td>\n      <td>A</td>\n      <td>2012-07-27</td>\n      <td>2012</td>\n      <td>12881920</td>\n      <td>PI</td>\n      <td></td>\n      <td>414544093</td>\n      <td>...</td>\n      <td>2015-06-03</td>\n      <td>2015</td>\n      <td>439682012</td>\n      <td>Y</td>\n      <td>49996511</td>\n      <td>414544093</td>\n      <td>4</td>\n      <td>5</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>43037</th>\n      <td>522510076</td>\n      <td>EP</td>\n      <td>19306441</td>\n      <td>A</td>\n      <td>2019-11-07</td>\n      <td>2019</td>\n      <td>19306441</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-05-12</td>\n      <td>2021</td>\n      <td>548841071</td>\n      <td>N</td>\n      <td>68655464</td>\n      <td>522510076</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>60095</th>\n      <td>569415925</td>\n      <td>TW</td>\n      <td>110116177</td>\n      <td>A</td>\n      <td>2021-05-05</td>\n      <td>2021</td>\n      <td>110116177</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-12-16</td>\n      <td>2021</td>\n      <td>568265190</td>\n      <td>N</td>\n      <td>74845079</td>\n      <td>546468249</td>\n      <td>4</td>\n      <td>0</td>\n      <td>1</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>61281</th>\n      <td>573442692</td>\n      <td>DE</td>\n      <td>102021213653</td>\n      <td>A</td>\n      <td>2021-12-02</td>\n      <td>2021</td>\n      <td>102021213653</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2022-06-15</td>\n      <td>2022</td>\n      <td>573442693</td>\n      <td>N</td>\n      <td>81749879</td>\n      <td>573442692</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>41189</th>\n      <td>518448790</td>\n      <td>TW</td>\n      <td>108109981</td>\n      <td>A</td>\n      <td>2014-03-07</td>\n      <td>2014</td>\n      <td>108109981</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2019-06-16</td>\n      <td>2019</td>\n      <td>518448791</td>\n      <td>Y</td>\n      <td>51500341</td>\n      <td>421859342</td>\n      <td>22</td>\n      <td>290</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>64973</th>\n      <td>587185640</td>\n      <td>CR</td>\n      <td>20220470</td>\n      <td>A</td>\n      <td>2021-03-18</td>\n      <td>2021</td>\n      <td>20220470</td>\n      <td>PI</td>\n      <td></td>\n      <td>549449130</td>\n      <td>...</td>\n      <td>2022-11-14</td>\n      <td>2022</td>\n      <td>587185641</td>\n      <td>N</td>\n      <td>75660301</td>\n      <td>549449130</td>\n      <td>14</td>\n      <td>4</td>\n      <td>1</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>8491</th>\n      <td>413374202</td>\n      <td>EP</td>\n      <td>13197783</td>\n      <td>A</td>\n      <td>2011-04-28</td>\n      <td>2011</td>\n      <td>13197783</td>\n      <td>PI</td>\n      <td></td>\n      <td>376727956</td>\n      <td>...</td>\n      <td>2014-08-20</td>\n      <td>2014</td>\n      <td>420445397</td>\n      <td>N</td>\n      <td>45896256</td>\n      <td>352844875</td>\n      <td>10</td>\n      <td>0</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 26 columns</p>\n</div>"
+      "text/plain": "(54368949, 13184309)"
      },
-     "execution_count": 39,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]].sample(100)"
+    "len(appln_pers[\"appln_id\"]), len(appln[\"appln_id\"])"
    ],
    "metadata": {
     "collapsed": false
@@ -155,41 +180,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "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)"
-   ],
+   "source": [],
    "metadata": {
     "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 16,
    "outputs": [
     {
      "data": {
-      "text/plain": "        person_id                   person_name           person_name_orig_lg   \n99304    72271503                   SHE, HUIMIN                   SHE, HUIMIN  \\\n4389      6211563                    Zhou, Jian                    Zhou, Jian   \n59373    52078604              KIRCHNER, NICOLE              KIRCHNER, NICOLE   \n19688    16957554              BLAWERT, CARSTEN              BLAWERT, CARSTEN   \n25828    25701586                KNIESEL, SIMON                KNIESEL, SIMON   \n73679    55544020  WHITING, Tien Yi Theresa Hsu  WHITING, Tien Yi Theresa Hsu   \n39905    45320991                Zhang, Michael                Zhang, Michael   \n65003    53390230                 DONG, Hailong                 DONG, Hailong   \n113369   79456511                    LING, Jing                    LING, Jing   \n58383    51832619                  WEN JU ZHANG                  WEN JU ZHANG   \n\n                                           person_address person_ctry_code   \n99304                                            Shanghai               CN  \\\n4389                                              Beijing               CN   \n59373             SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE               DE   \n19688                                                 NaN               DE   \n25828                                                 NaN               DE   \n73679                                                 NaN               US   \n39905                                             Beijing               CN   \n65003                     Molenstraat 148,5701 KK Helmond               NL   \n113369  No. 6 North Of Binjiang Road, Taixing Economic...               CN   \n58383                                                  CN               CN   \n\n         nuts  nuts_level  doc_std_name_id                 doc_std_name   \n99304     NaN           9         32084338                   SHE HUIMIN  \\\n4389      NaN           9            33759                    ZHOU JIAN   \n59373      DE           0          2139104              KIRCHNER NICOLE   \n19688      DE           0           311260              BLAWERT CARSTEN   \n25828      DE           0           255684                KNIESEL SIMON   \n73679     NaN           9         21758228  WHITING TIEN YI THERESA HSU   \n39905     NaN           9           725806                ZHANG MICHAEL   \n65003   NL414           3          7196768                 DONG HAILONG   \n113369    NaN           9           429470                    LING JING   \n58383     NaN           9         23069059                 ZHANG WEN JU   \n\n          psn_id                      psn_name  psn_level  psn_sector   \n99304   29505777                   SHE, HUIMIN          0     UNKNOWN  \\\n4389    37354342                     ZHOU JIAN          1  INDIVIDUAL   \n59373   17194552              KIRCHNER, NICOLE          0     UNKNOWN   \n19688    2923498              BLAWERT, CARSTEN          1  INDIVIDUAL   \n25828   17406789                KNIESEL, SIMON          1  INDIVIDUAL   \n73679   35065052  WHITING, TIEN YI THERESA HSU          0     UNKNOWN   \n39905   37097378                ZHANG, MICHAEL          1  INDIVIDUAL   \n65003    7077131                 DONG, HAILONG          0     UNKNOWN   \n113369  19626776                    LING, JING          0     UNKNOWN   \n58383   34908417                  WEN JU ZHANG          0     UNKNOWN   \n\n           han_id                      han_name  han_harmonized  \n99304   172271503                   SHE, HUIMIN               0  \n4389    106211563                    Zhou, Jian               0  \n59373   152078604              KIRCHNER, NICOLE               0  \n19688   116957554              BLAWERT, CARSTEN               0  \n25828   125701586                KNIESEL, SIMON               0  \n73679   155544020  WHITING, Tien Yi Theresa Hsu               0  \n39905   145320991                Zhang, Michael               0  \n65003   153390230                 DONG, Hailong               0  \n113369  179456511                    LING, Jing               0  \n58383   151832619                  WEN JU ZHANG               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>99304</th>\n      <td>72271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>SHE, HUIMIN</td>\n      <td>Shanghai</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>32084338</td>\n      <td>SHE HUIMIN</td>\n      <td>29505777</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>172271503</td>\n      <td>SHE, HUIMIN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4389</th>\n      <td>6211563</td>\n      <td>Zhou, Jian</td>\n      <td>Zhou, Jian</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>33759</td>\n      <td>ZHOU JIAN</td>\n      <td>37354342</td>\n      <td>ZHOU JIAN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>106211563</td>\n      <td>Zhou, Jian</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>59373</th>\n      <td>52078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>2139104</td>\n      <td>KIRCHNER NICOLE</td>\n      <td>17194552</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>152078604</td>\n      <td>KIRCHNER, NICOLE</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19688</th>\n      <td>16957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>311260</td>\n      <td>BLAWERT CARSTEN</td>\n      <td>2923498</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>116957554</td>\n      <td>BLAWERT, CARSTEN</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25828</th>\n      <td>25701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>KNIESEL, SIMON</td>\n      <td>NaN</td>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>255684</td>\n      <td>KNIESEL SIMON</td>\n      <td>17406789</td>\n      <td>KNIESEL, SIMON</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>125701586</td>\n      <td>KNIESEL, SIMON</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73679</th>\n      <td>55544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>NaN</td>\n      <td>US</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>21758228</td>\n      <td>WHITING TIEN YI THERESA HSU</td>\n      <td>35065052</td>\n      <td>WHITING, TIEN YI THERESA HSU</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>155544020</td>\n      <td>WHITING, Tien Yi Theresa Hsu</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>39905</th>\n      <td>45320991</td>\n      <td>Zhang, Michael</td>\n      <td>Zhang, Michael</td>\n      <td>Beijing</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>725806</td>\n      <td>ZHANG MICHAEL</td>\n      <td>37097378</td>\n      <td>ZHANG, MICHAEL</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>145320991</td>\n      <td>Zhang, Michael</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>65003</th>\n      <td>53390230</td>\n      <td>DONG, Hailong</td>\n      <td>DONG, Hailong</td>\n      <td>Molenstraat 148,5701 KK Helmond</td>\n      <td>NL</td>\n      <td>NL414</td>\n      <td>3</td>\n      <td>7196768</td>\n      <td>DONG HAILONG</td>\n      <td>7077131</td>\n      <td>DONG, HAILONG</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>153390230</td>\n      <td>DONG, Hailong</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>113369</th>\n      <td>79456511</td>\n      <td>LING, Jing</td>\n      <td>LING, Jing</td>\n      <td>No. 6 North Of Binjiang Road, Taixing Economic...</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>429470</td>\n      <td>LING JING</td>\n      <td>19626776</td>\n      <td>LING, JING</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>179456511</td>\n      <td>LING, Jing</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>58383</th>\n      <td>51832619</td>\n      <td>WEN JU ZHANG</td>\n      <td>WEN JU ZHANG</td>\n      <td>CN</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>23069059</td>\n      <td>ZHANG WEN JU</td>\n      <td>34908417</td>\n      <td>WEN JU ZHANG</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>151832619</td>\n      <td>WEN JU ZHANG</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "(54368949, 12646904)"
      },
-     "execution_count": 41,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pers.sample(10)"
+    "appln_pers[\"appln_id\"].isin(appln[\"appln_id\"]).sum(), appln[\"appln_id\"].isin(appln_pers[\"appln_id\"]).sum()"
    ],
    "metadata": {
     "collapsed": false
@@ -197,20 +209,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "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 NECESSITIES  \\\n1                                       HUMAN NECESSITIES   \n2                                       HUMAN NECESSITIES   \n3                                       HUMAN NECESSITIES   \n4                                       HUMAN NECESSITIES   \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_1   \n0                                                     NaN  \\\n1       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n2       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n3       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n4       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n...                                                   ...   \n260486          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260487          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260488          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260489          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260490                                                NaN   \n\n                                              tax_level_2   \n0                                                     NaN  \\\n1                                                     NaN   \n2       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n3       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n4       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n...                                                   ...   \n260486  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260487  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260488  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260489  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260490                                                NaN   \n\n                                              tax_level_3      tax_level_4   \n0                                                     NaN              NaN  \\\n1                                                     NaN              NaN   \n2                                                     NaN              NaN   \n3       Hand tools (edge trimmers for lawns A01G3/06  ...              NaN   \n4       Spades; Shovels {(hand-operated dredgers E02F3...              NaN   \n...                                                   ...              ...   \n260486                    Tool transfer to or from matrix  Indexing matrix   \n260487                    Tool transfer to or from matrix      Rotary disc   \n260488                    Tool transfer to or from matrix    Chain or belt   \n260489                                      Miscellaneous              NaN   \n260490                                                NaN              NaN   \n\n       tax_level_5 tax_level_6 tax_level_7  data_scope  \n0              NaN         NaN         NaN       False  \n1              NaN         NaN         NaN       False  \n2              NaN         NaN         NaN       False  \n3              NaN         NaN         NaN       False  \n4              NaN         NaN         NaN       False  \n...            ...         ...         ...         ...  \n260486         NaN         NaN         NaN       False  \n260487         NaN         NaN         NaN       False  \n260488         NaN         NaN         NaN       False  \n260489         NaN         NaN         NaN       False  \n260490         NaN         NaN         NaN       False  \n\n[260491 rows x 20 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>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 FORESTRY; PARTS...</td>\n      <td>Spades; Shovels {(hand-operated dredgers E02F3...</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>...</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    </tr>\n    <tr>\n      <th>260486</th>\n      <td>Y10T483/1873</td>\n      <td>Indexing matrix</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1873.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Indexing matrix</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260487</th>\n      <td>Y10T483/1882</td>\n      <td>Rotary disc</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1882.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Rotary disc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260488</th>\n      <td>Y10T483/1891</td>\n      <td>Chain or belt</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1891.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Chain or belt</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260489</th>\n      <td>Y10T483/19</td>\n      <td>Miscellaneous</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>19.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Miscellaneous</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>260490</th>\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>2022</td>\n      <td>oid sha256:f138d6bdf2939ba576b96b633d81366123b...</td>\n      <td>[]</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>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>260491 rows × 20 columns</p>\n</div>"
+      "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": 42,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "cpc_def"
+    "appln_pers.sample(10)"
    ],
    "metadata": {
     "collapsed": false
@@ -218,20 +239,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 98,
    "outputs": [
     {
      "data": {
-      "text/plain": "         appln_id cpc_class_symbol\n490979  560314579      B32B   7/12\n139994  445201240    C08G2261/3241\n505928  567683420     C25D   7/123\n130069  442410454      Y02D  10/00\n467350  551851575    H04B   7/0486\n318101  509884390    H01L  21/0228\n350171  519161420      G06F  21/42\n129829  442384375      H04W  88/04\n277164  496690078      H04L  27/26\n240979  487005701    F16F2230/0064",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>cpc_class_symbol</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>490979</th>\n      <td>560314579</td>\n      <td>B32B   7/12</td>\n    </tr>\n    <tr>\n      <th>139994</th>\n      <td>445201240</td>\n      <td>C08G2261/3241</td>\n    </tr>\n    <tr>\n      <th>505928</th>\n      <td>567683420</td>\n      <td>C25D   7/123</td>\n    </tr>\n    <tr>\n      <th>130069</th>\n      <td>442410454</td>\n      <td>Y02D  10/00</td>\n    </tr>\n    <tr>\n      <th>467350</th>\n      <td>551851575</td>\n      <td>H04B   7/0486</td>\n    </tr>\n    <tr>\n      <th>318101</th>\n      <td>509884390</td>\n      <td>H01L  21/0228</td>\n    </tr>\n    <tr>\n      <th>350171</th>\n      <td>519161420</td>\n      <td>G06F  21/42</td>\n    </tr>\n    <tr>\n      <th>129829</th>\n      <td>442384375</td>\n      <td>H04W  88/04</td>\n    </tr>\n    <tr>\n      <th>277164</th>\n      <td>496690078</td>\n      <td>H04L  27/26</td>\n    </tr>\n    <tr>\n      <th>240979</th>\n      <td>487005701</td>\n      <td>F16F2230/0064</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "<Axes: xlabel='appln_auth'>"
      },
-     "execution_count": 43,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "appln_cpc.sample(10)"
+    "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
@@ -239,19 +267,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 101,
    "outputs": [
     {
-     "data": {
-      "text/plain": "325243"
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
+     "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": [
-    "len(appln_pers)"
+    "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
@@ -259,19 +294,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 115,
    "outputs": [
     {
      "data": {
-      "text/plain": "65136"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
      },
-     "execution_count": 45,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "len(appln)"
+    "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
@@ -279,10 +314,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "outputs": [],
    "source": [
-    "# Number of collaborative applications\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))"
    ],
    "metadata": {
     "collapsed": false
@@ -290,10 +325,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 125,
    "outputs": [],
    "source": [
-    "record_col = \"appln_id\""
+    "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
@@ -301,19 +347,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 113,
    "outputs": [
     {
      "data": {
-      "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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\n"
      },
-     "execution_count": 297,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "os.getcwd()"
+    "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
@@ -321,12 +367,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 429,
-   "outputs": [],
+   "execution_count": 117,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 640x480 with 2 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "fromdir=\"first_round\"\n",
+    "import seaborn as sns\n",
     "\n",
-    "os.makedirs(fr'{fromdir}/plots')"
+    "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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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "appln.query(\"appln_auth=='WO'\").groupby(\"appln_filing_year\")[\"appln_id\"].nunique().plot(kind=\"line\")"
    ],
    "metadata": {
     "collapsed": false
@@ -334,22 +422,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": 54,
    "outputs": [],
    "source": [
-    "appln_pers_f = pd.read_csv(f\"{fromdir}/first-filings-with-persons-raw.csv\", header=None,\n",
-    "                      names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
-    "\n",
-    "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
-    "\n",
-    "appln_pers_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",
-    "# appln_pers_haninfo[\"psn_sector\"] = appln_pers_haninfo[\"psn_sector\"].fillna(\"UNKNOWN\")\n",
-    "\n",
-    "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")"
+    "ch_auth_pers = appln_pers.query(\"appln_auth=='CN'\")[\"person_ctry_code\"].value_counts(dropna=False).reset_index()\n"
    ],
    "metadata": {
     "collapsed": false
@@ -357,19 +433,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 393,
+   "execution_count": 56,
    "outputs": [
     {
      "data": {
-      "text/plain": "han_id\n3345915    6\nName: count, dtype: int64"
+      "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": 393,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "appln_pers_haninfo[appln_pers_haninfo[\"han_name\"]==\"ZHEJIANG GEELY HOLDINGGROUP CO LTD\"][\"han_id\"].value_counts()"
+    "ch_auth_pers.fillna(\"UNKNOWN\")"
    ],
    "metadata": {
     "collapsed": false
@@ -377,12 +454,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 394,
+   "execution_count": 49,
    "outputs": [],
    "source": [
-    "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
-    "    lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n",
-    "appln_pers_f = appln_pers_f.merge(pers_sector_primary, on='han_id')"
+    "# 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
@@ -390,16 +467,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": 20,
    "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()"
+    "import numpy as np"
    ],
    "metadata": {
     "collapsed": false
@@ -407,28 +478,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 396,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1055\n"
-     ]
-    }
-   ],
+   "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",
-    "\n",
-    "# appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
-    "#                                                              (gr[\"appln_auth\"]==\"CN\")).any())\n",
-    "# appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n",
-    "#                                                               (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
-    "\n",
-    "appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n",
-    "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n",
-    "\n",
-    "print(appln_scope[\"appln_id\"].nunique())"
+    "appln_scope_ids = np.intersect1d(appln_ch_ids, appln_eu_ids, assume_unique=False)"
    ],
    "metadata": {
     "collapsed": false
@@ -436,19 +492,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1055\n"
-     ]
-    }
-   ],
+   "execution_count": 37,
+   "outputs": [],
    "source": [
-    "appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n",
-    "print(appln_pers_f[\"appln_id\"].nunique())"
+    "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
@@ -456,20 +504,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "         appln_id appln_auth  person_id  invt_seq_nr  applt_seq_nr   \n76      410448968         DE    7067540            0             1  \\\n87      420386087         DE    7067540            0             1   \n88      420386089         DE    7067540            0             1   \n173     337088926         EP      21790            0             1   \n974     410512383         EP        220            0             1   \n...           ...        ...        ...          ...           ...   \n147073  549690891         EP   85841201            1             0   \n147074  549690895         EP   85841201            1             0   \n147075  549690901         EP   85841201            1             0   \n147076  549690897         EP   85841201            1             0   \n147077  549788919         EP   85774895            2             0   \n\n                       person_name person_ctry_code psn_sector     han_id   \n76             Evonik Degussa GmbH               DE    COMPANY     840063  \\\n87             Evonik Degussa GmbH               DE    COMPANY     840063   \n88             Evonik Degussa GmbH               DE    COMPANY     840063   \n173            Evonik Degussa GmbH               DE    COMPANY     840063   \n974     SIEMENS AKTIENGESELLSCHAFT               DE    COMPANY    2709504   \n...                            ...              ...        ...        ...   \n147073               JONSSON, Tony               SE    UNKNOWN  185841201   \n147074               JONSSON, Tony               SE    UNKNOWN  185841201   \n147075               JONSSON, Tony               SE    UNKNOWN  185841201   \n147076               JONSSON, Tony               SE    UNKNOWN  185841201   \n147077         TRAN NGUYEN, Johnny               SE    UNKNOWN  185774895   \n\n                   han_name psn_sector_primary  \n76      EVONIK DEGUSSA GMBH            COMPANY  \n87      EVONIK DEGUSSA GMBH            COMPANY  \n88      EVONIK DEGUSSA GMBH            COMPANY  \n173     EVONIK DEGUSSA GMBH            COMPANY  \n974              SIEMENS AG            COMPANY  \n...                     ...                ...  \n147073        JONSSON, Tony            UNKNOWN  \n147074        JONSSON, Tony            UNKNOWN  \n147075        JONSSON, Tony            UNKNOWN  \n147076        JONSSON, Tony            UNKNOWN  \n147077  TRAN NGUYEN, Johnny            UNKNOWN  \n\n[4367 rows x 11 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>person_id</th>\n      <th>invt_seq_nr</th>\n      <th>applt_seq_nr</th>\n      <th>person_name</th>\n      <th>person_ctry_code</th>\n      <th>psn_sector</th>\n      <th>han_id</th>\n      <th>han_name</th>\n      <th>psn_sector_primary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>76</th>\n      <td>410448968</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>420386087</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>420386089</td>\n      <td>DE</td>\n      <td>7067540</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>337088926</td>\n      <td>EP</td>\n      <td>21790</td>\n      <td>0</td>\n      <td>1</td>\n      <td>Evonik Degussa GmbH</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>840063</td>\n      <td>EVONIK DEGUSSA GMBH</td>\n      <td>COMPANY</td>\n    </tr>\n    <tr>\n      <th>974</th>\n      <td>410512383</td>\n      <td>EP</td>\n      <td>220</td>\n      <td>0</td>\n      <td>1</td>\n      <td>SIEMENS AKTIENGESELLSCHAFT</td>\n      <td>DE</td>\n      <td>COMPANY</td>\n      <td>2709504</td>\n      <td>SIEMENS AG</td>\n      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>147073</th>\n      <td>549690891</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147074</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147075</th>\n      <td>549690901</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147076</th>\n      <td>549690897</td>\n      <td>EP</td>\n      <td>85841201</td>\n      <td>1</td>\n      <td>0</td>\n      <td>JONSSON, Tony</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185841201</td>\n      <td>JONSSON, Tony</td>\n      <td>UNKNOWN</td>\n    </tr>\n    <tr>\n      <th>147077</th>\n      <td>549788919</td>\n      <td>EP</td>\n      <td>85774895</td>\n      <td>2</td>\n      <td>0</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>SE</td>\n      <td>UNKNOWN</td>\n      <td>185774895</td>\n      <td>TRAN NGUYEN, Johnny</td>\n      <td>UNKNOWN</td>\n    </tr>\n  </tbody>\n</table>\n<p>4367 rows × 11 columns</p>\n</div>"
-     },
-     "execution_count": 398,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": 45,
+   "outputs": [],
    "source": [
-    "appln_pers_f"
+    "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
@@ -477,39 +515,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 399,
+   "execution_count": 46,
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1055\n"
-     ]
-    },
     {
      "data": {
-      "text/plain": "         appln_id appln_auth appln_kind  filing_year  nb_applicants   \n114926  405478585         EP          A         2013              1  \\\n670680  532677899         EP          A         2020              2   \n568605  520224479         EP          A         2019              1   \n441101  483520394         FR          A         2017              1   \n687085  554756101         DE          A         2020              1   \n...           ...        ...        ...          ...            ...   \n235723  443704166         FR          A         2015              3   \n670840  532706785         EP          A         2020              2   \n415863  486019755         EP          A         2017              1   \n608249  520115105         EP          A         2019              1   \n753188  549690895         EP          A         2021              2   \n\n        nb_inventors  \n114926             4  \n670680             2  \n568605             2  \n441101             2  \n687085             3  \n...              ...  \n235723             3  \n670840             2  \n415863             2  \n608249             4  \n753188             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>114926</th>\n      <td>405478585</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2013</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>670680</th>\n      <td>532677899</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>568605</th>\n      <td>520224479</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>441101</th>\n      <td>483520394</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>687085</th>\n      <td>554756101</td>\n      <td>DE</td>\n      <td>A</td>\n      <td>2020</td>\n      <td>1</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>235723</th>\n      <td>443704166</td>\n      <td>FR</td>\n      <td>A</td>\n      <td>2015</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>670840</th>\n      <td>532706785</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>415863</th>\n      <td>486019755</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2017</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>608249</th>\n      <td>520115105</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2019</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>753188</th>\n      <td>549690895</td>\n      <td>EP</td>\n      <td>A</td>\n      <td>2021</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 6 columns</p>\n</div>"
+      "text/plain": "(6584667, 18789)"
      },
-     "execution_count": 399,
+     "execution_count": 46,
      "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)"
+    "appln_pers_haninfo[\"han_id\"].nunique(), appln_scope[\"han_id\"].nunique()"
    ],
    "metadata": {
     "collapsed": false
@@ -517,37 +536,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 399,
+   "execution_count": 53,
    "outputs": [],
-   "source": [],
+   "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": 399,
+   "execution_count": 59,
    "outputs": [],
-   "source": [],
+   "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": 400,
+   "execution_count": 63,
    "outputs": [
     {
      "data": {
-      "text/plain": "appln_kind\nA    1059\nU       7\nName: count, dtype: int64"
+      "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": 400,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "appln_f[\"appln_kind\"].value_counts()"
+    "han_sect.sample(10)"
    ],
    "metadata": {
     "collapsed": false
@@ -555,10 +590,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 401,
+   "execution_count": 64,
    "outputs": [],
    "source": [
-    "# Co-patents per year"
+    "appln_scope = appln_scope.merge(han_sect, on=\"han_id\")"
    ],
    "metadata": {
     "collapsed": false
@@ -566,19 +601,1013 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 430,
+   "execution_count": 38,
    "outputs": [
     {
      "data": {
-      "application/vnd.plotly.v1+json": {
-       "data": [
-        {
-         "hovertemplate": "Year:%{x:d}<br>Number of co-publications:%{y:d}",
-         "legendgroup": "",
-         "line": {
-          "color": "#636efa",
-          "dash": "solid"
-         },
+      "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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\n"
+     },
+     "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                    0              1           19  \n\n[10 rows x 26 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>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>365201373</td>\n      <td>US</td>\n      <td>201113309201</td>\n      <td>A</td>\n      <td>2011-12-01</td>\n      <td>2011</td>\n      <td>13309201</td>\n      <td>PI</td>\n      <td></td>\n      <td>332837342</td>\n      <td>...</td>\n      <td>2012-07-19</td>\n      <td>2012</td>\n      <td>365309255</td>\n      <td>Y</td>\n      <td>46490250</td>\n      <td>332836199</td>\n      <td>1</td>\n      <td>3</td>\n      <td>7</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>42558</th>\n      <td>521483818</td>\n      <td>WO</td>\n      <td>2019079482</td>\n      <td>W</td>\n      <td>2019-10-29</td>\n      <td>2019</td>\n      <td>EP2019/079482</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2021-05-06</td>\n      <td>2021</td>\n      <td>550107980</td>\n      <td>N</td>\n      <td>68468669</td>\n      <td>521483818</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>54938</th>\n      <td>552149434</td>\n      <td>WO</td>\n      <td>2021065120</td>\n      <td>W</td>\n      <td>2021-06-07</td>\n      <td>2021</td>\n      <td>EP2021/065120</td>\n      <td>PI</td>\n      <td>EP</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2022-12-15</td>\n      <td>2022</td>\n      <td>584644443</td>\n      <td>N</td>\n      <td>76355503</td>\n      <td>552149434</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>63211</th>\n      <td>580342075</td>\n      <td>CO</td>\n      <td>2022012975</td>\n      <td>A</td>\n      <td>2022-09-13</td>\n      <td>2022</td>\n      <td>NC2022/0012975</td>\n      <td>PI</td>\n      <td></td>\n      <td>901633858</td>\n      <td>...</td>\n      <td>2022-09-30</td>\n      <td>2022</td>\n      <td>580342076</td>\n      <td>N</td>\n      <td>74870863</td>\n      <td>547347540</td>\n      <td>14</td>\n      <td>0</td>\n      <td>1</td>\n      <td>19</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 26 columns</p>\n</div>"
+     },
+     "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...                 ...                  ...            ...          ...  \n5372                 12                   13              1            5  \n15441                 4                    1              1            4  \n27761                22                   24              1            4  \n23898                22                    9              3           10  \n20776                 5                    6              1            2  \n\n[100 rows x 26 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>appln_id</th>\n      <th>appln_auth</th>\n      <th>appln_nr</th>\n      <th>appln_kind</th>\n      <th>appln_filing_date</th>\n      <th>appln_filing_year</th>\n      <th>appln_nr_original</th>\n      <th>ipr_type</th>\n      <th>receiving_office</th>\n      <th>internat_appln_id</th>\n      <th>...</th>\n      <th>earliest_publn_date</th>\n      <th>earliest_publn_year</th>\n      <th>earliest_pat_publn_id</th>\n      <th>granted</th>\n      <th>docdb_family_id</th>\n      <th>inpadoc_family_id</th>\n      <th>docdb_family_size</th>\n      <th>nb_citing_docdb_fam</th>\n      <th>nb_applicants</th>\n      <th>nb_inventors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>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      <td>101108520</td>\n      <td>A</td>\n      <td>2012-03-13</td>\n      <td>2012</td>\n      <td>101108520</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2013-01-01</td>\n      <td>2013</td>\n      <td>405856720</td>\n      <td>N</td>\n      <td>45937460</td>\n      <td>352022356</td>\n      <td>12</td>\n      <td>13</td>\n      <td>1</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>15441</th>\n      <td>438564193</td>\n      <td>EP</td>\n      <td>14847669</td>\n      <td>A</td>\n      <td>2014-09-18</td>\n      <td>2014</td>\n      <td>14847669</td>\n      <td>PI</td>\n      <td></td>\n      <td>438547499</td>\n      <td>...</td>\n      <td>2016-08-03</td>\n      <td>2016</td>\n      <td>457602632</td>\n      <td>N</td>\n      <td>52742141</td>\n      <td>438547499</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>27761</th>\n      <td>481859764</td>\n      <td>TW</td>\n      <td>105133993</td>\n      <td>A</td>\n      <td>2016-10-21</td>\n      <td>2016</td>\n      <td>105133993</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2017-05-01</td>\n      <td>2017</td>\n      <td>481859765</td>\n      <td>Y</td>\n      <td>58562247</td>\n      <td>471178388</td>\n      <td>22</td>\n      <td>24</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>23898</th>\n      <td>471377037</td>\n      <td>CL</td>\n      <td>2015002557</td>\n      <td>A</td>\n      <td>2015-09-09</td>\n      <td>2015</td>\n      <td>2015002557</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-08-26</td>\n      <td>2016</td>\n      <td>471377038</td>\n      <td>N</td>\n      <td>51537508</td>\n      <td>421989017</td>\n      <td>22</td>\n      <td>9</td>\n      <td>3</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>20776</th>\n      <td>451149018</td>\n      <td>TW</td>\n      <td>104112868</td>\n      <td>A</td>\n      <td>2015-04-22</td>\n      <td>2015</td>\n      <td>104112868</td>\n      <td>PI</td>\n      <td></td>\n      <td>0</td>\n      <td>...</td>\n      <td>2016-01-16</td>\n      <td>2016</td>\n      <td>451149019</td>\n      <td>N</td>\n      <td>54147239</td>\n      <td>444402812</td>\n      <td>5</td>\n      <td>6</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 26 columns</p>\n</div>"
+     },
+     "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>\n      <td>DE</td>\n      <td>DE</td>\n      <td>0</td>\n      <td>34255238</td>\n      <td>PENG-POEHLER XUYUAN</td>\n      <td>25270057</td>\n      <td>PENG-POEHLER, XUYUAN</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>176993952</td>\n      <td>PENG-POEHLER, Xuyuan</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>78798</th>\n      <td>56657638</td>\n      <td>TONG, Zhaoyang</td>\n      <td>TONG, Zhaoyang</td>\n      <td>Tyco Electronics (Shanghai) Co., Ltd.,F/G Sect...</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>2067106</td>\n      <td>TONG ZHAOYANG</td>\n      <td>32942098</td>\n      <td>TONG, ZHAOYANG</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>156657638</td>\n      <td>TONG, Zhaoyang</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10578</th>\n      <td>11967242</td>\n      <td>Dai, Chenglong</td>\n      <td>Dai, Chenglong</td>\n      <td>Shenzhen</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>9</td>\n      <td>4323331</td>\n      <td>DAI CHENGLONG</td>\n      <td>6018020</td>\n      <td>DAI, CHENGLONG</td>\n      <td>1</td>\n      <td>INDIVIDUAL</td>\n      <td>111967242</td>\n      <td>Dai, Chenglong</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "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 NECESSITIES  \\\n1                                       HUMAN NECESSITIES   \n2                                       HUMAN NECESSITIES   \n3                                       HUMAN NECESSITIES   \n4                                       HUMAN NECESSITIES   \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_1   \n0                                                     NaN  \\\n1       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n2       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n3       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n4       AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...   \n...                                                   ...   \n260486          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260487          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260488          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260489          TECHNICAL SUBJECTS COVERED BY FORMER USPC   \n260490                                                NaN   \n\n                                              tax_level_2   \n0                                                     NaN  \\\n1                                                     NaN   \n2       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n3       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n4       SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...   \n...                                                   ...   \n260486  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260487  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260488  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260489  TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...   \n260490                                                NaN   \n\n                                              tax_level_3      tax_level_4   \n0                                                     NaN              NaN  \\\n1                                                     NaN              NaN   \n2                                                     NaN              NaN   \n3       Hand tools (edge trimmers for lawns A01G3/06  ...              NaN   \n4       Spades; Shovels {(hand-operated dredgers E02F3...              NaN   \n...                                                   ...              ...   \n260486                    Tool transfer to or from matrix  Indexing matrix   \n260487                    Tool transfer to or from matrix      Rotary disc   \n260488                    Tool transfer to or from matrix    Chain or belt   \n260489                                      Miscellaneous              NaN   \n260490                                                NaN              NaN   \n\n       tax_level_5 tax_level_6 tax_level_7  data_scope  \n0              NaN         NaN         NaN       False  \n1              NaN         NaN         NaN       False  \n2              NaN         NaN         NaN       False  \n3              NaN         NaN         NaN       False  \n4              NaN         NaN         NaN       False  \n...            ...         ...         ...         ...  \n260486         NaN         NaN         NaN       False  \n260487         NaN         NaN         NaN       False  \n260488         NaN         NaN         NaN       False  \n260489         NaN         NaN         NaN       False  \n260490         NaN         NaN         NaN       False  \n\n[260491 rows x 20 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>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 FORESTRY; PARTS...</td>\n      <td>Spades; Shovels {(hand-operated dredgers E02F3...</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>...</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    </tr>\n    <tr>\n      <th>260486</th>\n      <td>Y10T483/1873</td>\n      <td>Indexing matrix</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1873.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Indexing matrix</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260487</th>\n      <td>Y10T483/1882</td>\n      <td>Rotary disc</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1882.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Rotary disc</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260488</th>\n      <td>Y10T483/1891</td>\n      <td>Chain or belt</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>1891.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Tool transfer to or from matrix</td>\n      <td>Chain or belt</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>260489</th>\n      <td>Y10T483/19</td>\n      <td>Miscellaneous</td>\n      <td>Y</td>\n      <td>10.0</td>\n      <td>T</td>\n      <td>483.0</td>\n      <td>19.0</td>\n      <td>2023</td>\n      <td>NaN</td>\n      <td>[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER USPC</td>\n      <td>TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...</td>\n      <td>Miscellaneous</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>260490</th>\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>2022</td>\n      <td>oid sha256:f138d6bdf2939ba576b96b633d81366123b...</td>\n      <td>[]</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>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>260491 rows × 20 columns</p>\n</div>"
+     },
+     "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": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "data": [
+        {
+         "hovertemplate": "Year:%{x:d}<br>Number of co-publications:%{y:d}",
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
+         },
          "marker": {
           "symbol": "circle"
          },
@@ -587,17 +1616,17 @@
          "orientation": "v",
          "showlegend": false,
          "text": [
-          48.0,
-          42.0,
+          54.0,
+          49.0,
+          69.0,
           64.0,
-          58.0,
-          83.0,
           94.0,
-          148.0,
-          174.0,
-          151.0,
-          135.0,
-          58.0
+          103.0,
+          158.0,
+          184.0,
+          170.0,
+          146.0,
+          64.0
          ],
          "textfont": {
           "size": 12
@@ -618,17 +1647,17 @@
          ],
          "xaxis": "x",
          "y": [
-          48,
-          42,
+          54,
+          49,
+          69,
           64,
-          58,
-          83,
           94,
-          148,
-          174,
-          151,
-          135,
-          58
+          103,
+          158,
+          184,
+          170,
+          146,
+          64
          ],
          "yaxis": "y",
          "type": "scatter"
@@ -649,16 +1678,16 @@
          "showlegend": false,
          "text": [
           0.0,
-          -0.125,
-          0.3333333333333333,
-          0.20833333333333334,
-          0.7291666666666666,
-          0.9583333333333334,
-          2.0833333333333335,
-          2.625,
-          2.1458333333333335,
-          1.8125,
-          0.20833333333333334
+          -0.09259259259259259,
+          0.2777777777777778,
+          0.18518518518518517,
+          0.7407407407407407,
+          0.9074074074074074,
+          1.9259259259259258,
+          2.4074074074074074,
+          2.1481481481481484,
+          1.7037037037037037,
+          0.18518518518518517
          ],
          "textfont": {
           "size": 12
@@ -681,16 +1710,16 @@
          "xaxis": "x2",
          "y": [
           0.0,
-          -0.125,
-          0.3333333333333333,
-          0.20833333333333334,
-          0.7291666666666666,
-          0.9583333333333334,
-          2.0833333333333335,
-          2.625,
-          2.1458333333333335,
-          1.8125,
-          0.20833333333333334
+          -0.09259259259259259,
+          0.2777777777777778,
+          0.18518518518518517,
+          0.7407407407407407,
+          0.9074074074074074,
+          1.9259259259259258,
+          2.4074074074074074,
+          2.1481481481481484,
+          1.7037037037037037,
+          0.18518518518518517
          ],
          "yaxis": "y2",
          "type": "scatter"
@@ -1615,7 +2644,7 @@
         "plotlyServerURL": "https://plotly.com"
        }
       },
-      "text/html": "<div>                            <div id=\"75ec3337-4e6d-4e58-a9c8-b65f37b2bb8e\" 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(\"75ec3337-4e6d-4e58-a9c8-b65f37b2bb8e\")) {                    Plotly.newPlot(                        \"75ec3337-4e6d-4e58-a9c8-b65f37b2bb8e\",                        [{\"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\":[48.0,42.0,64.0,58.0,83.0,94.0,148.0,174.0,151.0,135.0,58.0],\"textfont\":{\"size\":12},\"textposition\":\"top 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@@ -15470,7 +16734,7 @@
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@@ -15479,7 +16743,7 @@
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@@ -15488,7 +16752,7 @@
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+   "execution_count": 260,
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@@ -15519,7 +16783,16 @@
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   {
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@@ -15563,7 +16836,7 @@
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-   "execution_count": 459,
+   "execution_count": 262,
    "outputs": [],
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+     },
+     "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; 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; C08J11/26; C08J11/27; C08J11/28; C08J11/29; C08J11/30; C08J11/31; C08J11/32; C08J11/33; C08J11/34; C08J11/35; C08J11/36; C08J11/37; C08J11/38; C08J11/39; B29B17/00; B62D67/00; 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; C08J11/26; C08J11/27; C08J11/28; C08J11/29; C08J11/30; C08J11/31; C08J11/32; C08J11/33; C08J11/34; C08J11/35; C08J11/36; C08J11/37; C08J11/38; C08J11/39; C08J11/40; C08J11/41; C08J11/42; C08J11/43; C08J11/44; C08J11/45; C08J11/46; C08J11/47; C08J11/48; C08J11/49; C08J11/50; C08J11/51; C08J11/52; C08J11/53; C08J11/54; C08J11/55; C08J11/56; C08J11/57; C08J11/58; C08J11/59; C08J11/60; C08J11/61; C08J11/62; C08J11/63; C08J11/64; C08J11/65; C08J11/66; C08J11/67; C08J11/68; C08J11/69; C08J11/70; C08J11/71; C08J11/72; C08J11/73; C08J11/74; C08J11/75; C08J11/76; C08J11/77; C08J11/78; C08J11/79; C08J11/80; C08J11/81; C08J11/82; C08J11/83; C08J11/84; C08J11/85; C08J11/86; C08J11/87; C08J11/88; C08J11/89; C08J11/90; C08J11/91; C08J11/92; C08J11/93; C08J11/94; C08J11/95; C08J11/96; C08J11/97; C08J11/98; C08J11/99; C10G1/10; C10L5/46; C10L5/48; C22B7/00; C22B7/01; C22B7/02; C22B7/03; C22B7/04; C22B7/19; C22B7/30; C22B7/25; C22B7/06; D01G11/00; D21C5/02; H01J9/50; H01J9/52; H01M6/52; H01M10/54; B01D53/14; B01D53/22; B01D53/62; B01D53/92; C21C5/38; C10B21/18; F23G7/06; F01N9/00; B01D45/00; B01D45/01; B01D45/02; B01D45/03; B01D45/04; B01D45/05; B01D45/06; B01D45/07; B01D45/08; B01D45/09; B01D45/10; B01D45/11; B01D45/12; B01D45/13; B01D45/14; B01D45/15; B01D45/16; B01D45/17; B01D45/18; B01D45/19; B01D45/20; B01D45/21; B01D45/22; B01D45/23; B01D45/24; B01D45/25; B01D45/26; B01D45/27; B01D45/28; B01D45/29; B01D45/30; B01D45/31; B01D45/32; B01D45/33; B01D45/34; B01D45/35; B01D45/36; B01D45/37; B01D45/38; B01D45/39; B01D45/40; B01D45/41; B01D45/42; B01D45/43; B01D45/44; B01D45/45; B01D45/46; B01D45/47; B01D45/48; B01D45/49; B01D45/50; B01D45/51; B01D45/52; B01D45/53; B01D45/54; B01D45/55; B01D45/56; B01D45/57; B01D45/58; B01D45/59; B01D45/60; B01D45/61; B01D45/62; B01D45/63; B01D45/64; B01D45/65; B01D45/66; B01D45/67; B01D45/68; B01D45/69; B01D45/70; B01D45/71; B01D45/72; B01D45/73; B01D45/74; B01D45/75; B01D45/76; B01D45/77; B01D45/78; B01D45/79; B01D45/80; B01D45/81; B01D45/82; B01D45/83; B01D45/84; B01D45/85; B01D45/86; B01D45/87; B01D45/88; B01D45/89; B01D45/90; B01D45/91; B01D45/92; B01D45/93; B01D45/94; B01D45/95; B01D45/96; B01D45/97; B01D45/98; B01D45/99; C21B7/22; C10L10/02; C10L10/06; F23J15/00; C09K3/22; G08B21/12; B63J4/00; C05F7/00; C09K3/32; B63B35/32; E03C1/12; C02F1/00; C02F3/00; C02F9/00; G21C13/10; A01G23/00; A01G25/00; A01N25/00; A01N25/01; A01N25/02; A01N25/03; A01N25/04; A01N25/05; A01N25/06; A01N25/07; A01N25/08; A01N25/09; A01N25/10; A01N25/11; A01N25/12; A01N25/13; A01N25/14; A01N25/15; A01N25/16; A01N25/17; A01N25/18; A01N25/19; A01N25/20; A01N25/21; A01N25/22; A01N25/23; A01N25/24; A01N25/25; A01N25/26; A01N25/27; A01N25/28; A01N25/29; A01N25/30; A01N25/31; A01N25/32; A01N25/33; A01N25/34; A01N25/35; A01N25/36; A01N25/37; A01N25/38; A01N25/39; A01N25/40; A01N25/41; A01N25/42; A01N25/43; A01N25/44; A01N25/45; A01N25/46; A01N25/47; A01N25/48; A01N25/49; A01N25/50; A01N25/51; A01N25/52; A01N25/53; A01N25/54; A01N25/55; A01N25/56; A01N25/57; A01N25/58; A01N25/59; A01N25/60; A01N25/61; A01N25/62; A01N25/63; A01N25/64; A01N25/65; A01N25/66; A01N25/67; A01N25/68; A01N25/69; A01N25/70; A01N25/71; A01N25/72; A01N25/73; A01N25/74; A01N25/75; A01N25/76; A01N25/77; A01N25/78; A01N25/79; A01N25/80; A01N25/81; A01N25/82; A01N25/83; A01N25/84; A01N25/85; A01N25/86; A01N25/87; A01N25/88; A01N25/89; A01N25/90; A01N25/91; A01N25/92; A01N25/93; A01N25/94; A01N25/95; A01N25/96; A01N25/97; A01N25/98; A01N25/99; C09K17/00; C05F; G06Q; E04H1/00; G21; G21B; G21C; G21D; F02C1/05\"\"\""
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": 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+     },
+     "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": {
+      "text/plain": 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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]",
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+     },
+     "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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      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from plotly.subplots import make_subplots\n",
-    "import plotly.graph_objects as go\n",
+    "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",
-    "# dom_distr\n",
-    "# year_output_by_domain\n",
-    "# rel_output_by_domain\n",
-    "# cumsum_by_domain\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",
-    "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",
+    "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",
-    "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",
+    "data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n",
+    "# data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n",
     "\n",
-    "for trace in list(cumsum_by_domain.select_traces()):\n",
-    "    trace.showlegend=False\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=2\n",
+    "        row=1, col=1\n",
     "    )\n",
     "\n",
-    "for trace in list(year_output_by_domain.select_traces()):\n",
-    "    trace.showlegend=False\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(rel_output_by_domain.select_traces()):\n",
-    "    trace.showlegend=False\n",
+    "for trace in list(year_ratio_output.select_traces()):\n",
     "    figsuper.add_trace(trace,\n",
-    "        row=2, col=2\n",
+    "        row=3, col=1\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",
@@ -18084,19 +20816,22 @@
     "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['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",
-    "figsuper.write_image(rf\"{fromdir}/plots/ipc_classes.png\",height=900,width=1600,scale = 4)"
+    "\n",
+    "figsuper.write_image(f\"{fromdir}/plots/green_trends.png\",height=900,width=900,scale = 4)"
    ],
    "metadata": {
     "collapsed": false
@@ -18104,9 +20839,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 425,
-   "outputs": [],
-   "source": [],
+   "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
    }
diff --git a/PATSTAT/person_minipipe.ipynb b/PATSTAT/person_minipipe.ipynb
index a21687b..b9743f3 100644
--- a/PATSTAT/person_minipipe.ipynb
+++ b/PATSTAT/person_minipipe.ipynb
@@ -54,9 +54,9 @@
    "execution_count": 3,
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "appln_pers_f = pd.read_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\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\"])"
+    "# import pandas as pd\n",
+    "# appln_pers_f = pd.read_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\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\"])"
    ],
    "metadata": {
     "collapsed": false
@@ -67,7 +67,10 @@
    "execution_count": 4,
    "outputs": [],
    "source": [
-    "pers_id_scope =appln_pers_f[\"person_id\"].unique()"
+    "import pandas as pd\n",
+    "\n",
+    "outdir=\"raw_files_csv\"\n",
+    "appln_pers_f = pd.read_csv(r\"C:/Users/radvanyi/PycharmProjects/ZSI_analytics/PATSTAT/raw_files_csv/02_persons_2011_2022.csv\",low_memory=False)"
    ],
    "metadata": {
     "collapsed": false
@@ -77,9 +80,61 @@
    "cell_type": "code",
    "execution_count": 5,
    "outputs": [],
+   "source": [
+    "pers_id_scope = appln_pers_f[\"person_id\"].unique()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
    "source": [
     "tls_206_scope = tls_206_p[tls_206_p['person_id'].isin(pers_id_scope)]\n",
-    "tls_206_scope.compute().to_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\tls_206_scope.csv\",index=False)"
+    "tls_206_scope.compute().to_csv(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\PATSTAT\\first_round\\tls_206_scope_v2.csv\",index=False)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "df_206 = tls_206_scope.compute()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "        person_id                                        person_name   \n4023         4025  Meritor Heavy Vehicle Braking Systems (UK) Lim...  \\\n4347         4349                                     Fraser, Stuart   \n5627         5629                            Xaar Technology Limited   \n5811         5813                                        SIEMENS PLC   \n6499         6501                                    BAE Systems PLC   \n...           ...                                                ...   \n366118   88823960                                       WARD, Lauren   \n366119   88823961                              WÄRTSILÄ UK LIMITED   \n366130   88823972                                      Xavier Erdödy   \n366135   88823977                                         Zeg.AI Ltd   \n366137   88823979                                        Zhiyang Pan   \n\n                                      person_name_orig_lg   \n4023    Meritor Heavy Vehicle Braking Systems (UK) Lim...  \\\n4347                                       Fraser, Stuart   \n5627                              Xaar Technology Limited   \n5811                                          SIEMENS PLC   \n6499                                      BAE Systems PLC   \n...                                                   ...   \n366118                                       WARD, Lauren   \n366119                              WÄRTSILÄ UK LIMITED   \n366130                                      Xavier Erdödy   \n366135                                         Zeg.AI Ltd   \n366137                                        Zhiyang Pan   \n\n                                           person_address person_ctry_code   \n4023                   Grange Road Cwmbran,Gwent NP44 3XU               GB  \\\n4347    Fernhill Lees Lane,Little Neston Cheshire CH64...               GB   \n5627             Unit 316, Science Park,Cambridge CB4 0XR               GB   \n5811    Faraday House Sir William Siemens Square Friml...               GB   \n6499                    6 Carlton Gardens,London SW1Y 5AD               GB   \n...                                                   ...              ...   \n366118                                               None               GB   \n366119                                               None               GB   \n366130                                               None               GB   \n366135                                               None               GB   \n366137                                               None               GB   \n\n         nuts  nuts_level  doc_std_name_id   \n4023    UKL16           3         25273975  \\\n4347    UKD63           3             3738   \n5627    UKH12           3             4824   \n5811    UKJ25           3             4979   \n6499    UKI32           3             5583   \n...       ...         ...              ...   \n366118     UK           0         40301088   \n366119     UK           0         21929085   \n366130     UK           0         40578262   \n366135     UK           0         37017676   \n366137     UK           0         17409767   \n\n                                        doc_std_name     psn_id   \n4023    MERITOR HEAVY VEHICLE BRAKING SYSTEMS UK LTD   21718818  \\\n4347                                   FRASER STUART    9243356   \n5627                             XAAR TECHNOLOGY LTD   35706185   \n5811                                     SIEMENS PLC   30138991   \n6499                                 BAE SYSTEMS PLC    1787059   \n...                                              ...        ...   \n366118                                   WARD LAUREN  188823960   \n366119                                RTSIL UK LTD W  188823961   \n366130                                 XAVIER ERDÖDY  188823972   \n366135                                    ZEG AI LTD  188823977   \n366137                                   ZHIYANG PAN  188823979   \n\n                                          psn_name  psn_level psn_sector   \n4023    MERITOR HEAVY VEHICLE BRAKING SYSTEMS (UK)          1    COMPANY  \\\n4347                                FRASER, STUART          0       None   \n5627                               XAAR TECHNOLOGY          1    COMPANY   \n5811                                       SIEMENS          2    COMPANY   \n6499                                   BAE SYSTEMS          2    COMPANY   \n...                                            ...        ...        ...   \n366118                                WARD, Lauren          0    UNKNOWN   \n366119                       WÄRTSILÄ UK LIMITED          0    UNKNOWN   \n366130                               Xavier Erdödy          0    UNKNOWN   \n366135                                  Zeg.AI Ltd          0    UNKNOWN   \n366137                                 Zhiyang Pan          0    UNKNOWN   \n\n           han_id                                      han_name   \n4023      1940089  MERITOR HEAVY VEHICLE BRAKING SYSTEMS UK LTD  \\\n4347    100004349                                Fraser, Stuart   \n5627      3228426                                 XAAR TECH LTD   \n5811      2755905                                   SIEMENS PLC   \n6499       208539                               BAE SYSTEMS PLC   \n...           ...                                           ...   \n366118  188823960                                  WARD, Lauren   \n366119  188823961                         WÄRTSILÄ UK LIMITED   \n366130  188823972                                 Xavier Erdödy   \n366135  188823977                                    Zeg.AI Ltd   \n366137  188823979                                   Zhiyang Pan   \n\n        han_harmonized  \n4023                 2  \n4347                 0  \n5627                 2  \n5811                 2  \n6499                 2  \n...                ...  \n366118               0  \n366119               0  \n366130               0  \n366135               0  \n366137               0  \n\n[77303 rows x 16 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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>4023</th>\n      <td>4025</td>\n      <td>Meritor Heavy Vehicle Braking Systems (UK) Lim...</td>\n      <td>Meritor Heavy Vehicle Braking Systems (UK) Lim...</td>\n      <td>Grange Road Cwmbran,Gwent NP44 3XU</td>\n      <td>GB</td>\n      <td>UKL16</td>\n      <td>3</td>\n      <td>25273975</td>\n      <td>MERITOR HEAVY VEHICLE BRAKING SYSTEMS UK LTD</td>\n      <td>21718818</td>\n      <td>MERITOR HEAVY VEHICLE BRAKING SYSTEMS (UK)</td>\n      <td>1</td>\n      <td>COMPANY</td>\n      <td>1940089</td>\n      <td>MERITOR HEAVY VEHICLE BRAKING SYSTEMS UK LTD</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4347</th>\n      <td>4349</td>\n      <td>Fraser, Stuart</td>\n      <td>Fraser, Stuart</td>\n      <td>Fernhill Lees Lane,Little Neston Cheshire CH64...</td>\n      <td>GB</td>\n      <td>UKD63</td>\n      <td>3</td>\n      <td>3738</td>\n      <td>FRASER STUART</td>\n      <td>9243356</td>\n      <td>FRASER, STUART</td>\n      <td>0</td>\n      <td>None</td>\n      <td>100004349</td>\n      <td>Fraser, Stuart</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5627</th>\n      <td>5629</td>\n      <td>Xaar Technology Limited</td>\n      <td>Xaar Technology Limited</td>\n      <td>Unit 316, Science Park,Cambridge CB4 0XR</td>\n      <td>GB</td>\n      <td>UKH12</td>\n      <td>3</td>\n      <td>4824</td>\n      <td>XAAR TECHNOLOGY LTD</td>\n      <td>35706185</td>\n      <td>XAAR TECHNOLOGY</td>\n      <td>1</td>\n      <td>COMPANY</td>\n      <td>3228426</td>\n      <td>XAAR TECH LTD</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5811</th>\n      <td>5813</td>\n      <td>SIEMENS PLC</td>\n      <td>SIEMENS PLC</td>\n      <td>Faraday House Sir William Siemens Square Friml...</td>\n      <td>GB</td>\n      <td>UKJ25</td>\n      <td>3</td>\n      <td>4979</td>\n      <td>SIEMENS PLC</td>\n      <td>30138991</td>\n      <td>SIEMENS</td>\n      <td>2</td>\n      <td>COMPANY</td>\n      <td>2755905</td>\n      <td>SIEMENS PLC</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6499</th>\n      <td>6501</td>\n      <td>BAE Systems PLC</td>\n      <td>BAE Systems PLC</td>\n      <td>6 Carlton Gardens,London SW1Y 5AD</td>\n      <td>GB</td>\n      <td>UKI32</td>\n      <td>3</td>\n      <td>5583</td>\n      <td>BAE SYSTEMS PLC</td>\n      <td>1787059</td>\n      <td>BAE SYSTEMS</td>\n      <td>2</td>\n      <td>COMPANY</td>\n      <td>208539</td>\n      <td>BAE SYSTEMS PLC</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>366118</th>\n      <td>88823960</td>\n      <td>WARD, Lauren</td>\n      <td>WARD, Lauren</td>\n      <td>None</td>\n      <td>GB</td>\n      <td>UK</td>\n      <td>0</td>\n      <td>40301088</td>\n      <td>WARD LAUREN</td>\n      <td>188823960</td>\n      <td>WARD, Lauren</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>188823960</td>\n      <td>WARD, Lauren</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>366119</th>\n      <td>88823961</td>\n      <td>WÄRTSILÄ UK LIMITED</td>\n      <td>WÄRTSILÄ UK LIMITED</td>\n      <td>None</td>\n      <td>GB</td>\n      <td>UK</td>\n      <td>0</td>\n      <td>21929085</td>\n      <td>RTSIL UK LTD W</td>\n      <td>188823961</td>\n      <td>WÄRTSILÄ UK LIMITED</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>188823961</td>\n      <td>WÄRTSILÄ UK LIMITED</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>366130</th>\n      <td>88823972</td>\n      <td>Xavier Erdödy</td>\n      <td>Xavier Erdödy</td>\n      <td>None</td>\n      <td>GB</td>\n      <td>UK</td>\n      <td>0</td>\n      <td>40578262</td>\n      <td>XAVIER ERDÖDY</td>\n      <td>188823972</td>\n      <td>Xavier Erdödy</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>188823972</td>\n      <td>Xavier Erdödy</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>366135</th>\n      <td>88823977</td>\n      <td>Zeg.AI Ltd</td>\n      <td>Zeg.AI Ltd</td>\n      <td>None</td>\n      <td>GB</td>\n      <td>UK</td>\n      <td>0</td>\n      <td>37017676</td>\n      <td>ZEG AI LTD</td>\n      <td>188823977</td>\n      <td>Zeg.AI Ltd</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>188823977</td>\n      <td>Zeg.AI Ltd</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>366137</th>\n      <td>88823979</td>\n      <td>Zhiyang Pan</td>\n      <td>Zhiyang Pan</td>\n      <td>None</td>\n      <td>GB</td>\n      <td>UK</td>\n      <td>0</td>\n      <td>17409767</td>\n      <td>ZHIYANG PAN</td>\n      <td>188823979</td>\n      <td>Zhiyang Pan</td>\n      <td>0</td>\n      <td>UNKNOWN</td>\n      <td>188823979</td>\n      <td>Zhiyang Pan</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>77303 rows × 16 columns</p>\n</div>"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_206[df_206[\"person_ctry_code\"]==\"GB\"]"
    ],
    "metadata": {
     "collapsed": false
diff --git a/PATSTAT/raw_files_csv/plots/CN_EU_coapplt.png b/PATSTAT/raw_files_csv/plots/CN_EU_coapplt.png
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diff --git a/PATSTAT/raw_files_csv/plots/top15_applt_person_ctry_type_China.png b/PATSTAT/raw_files_csv/plots/top15_applt_person_ctry_type_China.png
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diff --git a/PATSTAT/raw_files_csv/plots/top15_applt_person_ctry_type_EU.png b/PATSTAT/raw_files_csv/plots/top15_applt_person_ctry_type_EU.png
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