{
"cells": [
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import janitor\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from matplotlib.ticker import MaxNLocator\n",
"import math\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"outputs": [
{
"data": {
"text/html": " \n "
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import janitor\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from matplotlib.ticker import MaxNLocator\n",
"import math\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"import plotly.offline as pyo\n",
"from plotly.subplots import make_subplots\n",
"import plotly.graph_objects as go\n",
"pyo.init_notebook_mode()\n",
"\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"plotly_mimetype+notebook\"\n",
"\n",
"import country_converter as coco\n",
"cc = coco.CountryConverter()\n",
"\n",
"\n",
"%matplotlib inline"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 35,
"outputs": [],
"source": [
"outdir=\"EU_CH_scope\"\n",
"\n",
"appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 36,
"outputs": [
{
"data": {
"text/plain": "65136"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(appln)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 37,
"outputs": [
{
"data": {
"text/plain": "24638"
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 38,
"outputs": [
{
"data": {
"text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n8712 413791676 KR 20127034385 A 2011-06-03 \\\n15588 439148012 EP 15714777 A 2015-03-26 \n48628 536723783 RU 2019104609 A 2017-07-18 \n4458 380638095 US 201113218876 A 2011-08-26 \n43221 522842130 US 201829659634 F 2018-08-10 \n3836 378587773 WO 2011074635 W 2011-05-25 \n9237 414823192 EP 12882000 A 2012-08-03 \n6777 409424323 US 201113638187 A 2011-03-22 \n18826 446137103 WO 2015057861 W 2015-10-14 \n52265 545763074 BR 112020021641 A 2018-04-23 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n8712 2011 1020127034385 PI \\\n15588 2015 15714777 PI \n48628 2017 2019104609 PI \n4458 2011 13218876 PI \n43221 2018 NaN DP \n3836 2011 CN2011/074635 PI CN \n9237 2012 12882000 PI \n6777 2011 13638187 PI \n18826 2015 IB2015/057861 PI IB \n52265 2018 112020021641 PI \n\n internat_appln_id ... earliest_publn_date earliest_publn_year \n8712 339637678 ... 2013-11-04 2013 \\\n15588 438355565 ... 2017-02-15 2017 \n48628 488987246 ... 2020-08-20 2020 \n4458 0 ... 2013-01-31 2013 \n43221 0 ... 2019-12-03 2019 \n3836 0 ... 2012-11-29 2012 \n9237 414790439 ... 2015-06-10 2015 \n6777 333698631 ... 2013-08-08 2013 \n18826 0 ... 2016-04-21 2016 \n52265 521276805 ... 2021-01-26 2021 \n\n earliest_pat_publn_id granted docdb_family_id inpadoc_family_id \n8712 413791677 Y 45067306 339616929 \\\n15588 473888077 Y 52814965 438355565 \n48628 536723784 Y 60991950 488987246 \n4458 380922724 N 47598050 380638095 \n43221 522842131 Y 68653880 522842130 \n3836 378816884 N 47216526 378587773 \n9237 439934604 N 50027133 414790439 \n6777 409424324 Y 43020401 319321099 \n18826 451530410 N 52014243 446137103 \n52265 545763075 N 68293428 521276805 \n\n docdb_family_size nb_citing_docdb_fam nb_applicants nb_inventors \n8712 6 10 1 7 \n15588 5 1 1 2 \n48628 39 3 1 19 \n4458 2 3 8 7 \n43221 2 0 1 1 \n3836 3 4 3 2 \n9237 2 0 1 2 \n6777 6 4 6 5 \n18826 9 3 2 1 \n52265 6 0 3 5 \n\n[10 rows x 26 columns]",
"text/html": "
\n\n
\n \n \n | \n appln_id | \n appln_auth | \n appln_nr | \n appln_kind | \n appln_filing_date | \n appln_filing_year | \n appln_nr_original | \n ipr_type | \n receiving_office | \n internat_appln_id | \n ... | \n earliest_publn_date | \n earliest_publn_year | \n earliest_pat_publn_id | \n granted | \n docdb_family_id | \n inpadoc_family_id | \n docdb_family_size | \n nb_citing_docdb_fam | \n nb_applicants | \n nb_inventors | \n
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\n \n 4458 | \n 380638095 | \n US | \n 201113218876 | \n A | \n 2011-08-26 | \n 2011 | \n 13218876 | \n PI | \n | \n 0 | \n ... | \n 2013-01-31 | \n 2013 | \n 380922724 | \n N | \n 47598050 | \n 380638095 | \n 2 | \n 3 | \n 8 | \n 7 | \n
\n \n 43221 | \n 522842130 | \n US | \n 201829659634 | \n F | \n 2018-08-10 | \n 2018 | \n NaN | \n DP | \n | \n 0 | \n ... | \n 2019-12-03 | \n 2019 | \n 522842131 | \n Y | \n 68653880 | \n 522842130 | \n 2 | \n 0 | \n 1 | \n 1 | \n
\n \n 3836 | \n 378587773 | \n WO | \n 2011074635 | \n W | \n 2011-05-25 | \n 2011 | \n CN2011/074635 | \n PI | \n CN | \n 0 | \n ... | \n 2012-11-29 | \n 2012 | \n 378816884 | \n N | \n 47216526 | \n 378587773 | \n 3 | \n 4 | \n 3 | \n 2 | \n
\n \n 9237 | \n 414823192 | \n EP | \n 12882000 | \n A | \n 2012-08-03 | \n 2012 | \n 12882000 | \n PI | \n | \n 414790439 | \n ... | \n 2015-06-10 | \n 2015 | \n 439934604 | \n N | \n 50027133 | \n 414790439 | \n 2 | \n 0 | \n 1 | \n 2 | \n
\n \n 6777 | \n 409424323 | \n US | \n 201113638187 | \n A | \n 2011-03-22 | \n 2011 | \n 13638187 | \n PI | \n | \n 333698631 | \n ... | \n 2013-08-08 | \n 2013 | \n 409424324 | \n Y | \n 43020401 | \n 319321099 | \n 6 | \n 4 | \n 6 | \n 5 | \n
\n \n 18826 | \n 446137103 | \n WO | \n 2015057861 | \n W | \n 2015-10-14 | \n 2015 | \n IB2015/057861 | \n PI | \n IB | \n 0 | \n ... | \n 2016-04-21 | \n 2016 | \n 451530410 | \n N | \n 52014243 | \n 446137103 | \n 9 | \n 3 | \n 2 | \n 1 | \n
\n \n 52265 | \n 545763074 | \n BR | \n 112020021641 | \n A | \n 2018-04-23 | \n 2018 | \n 112020021641 | \n PI | \n | \n 521276805 | \n ... | \n 2021-01-26 | \n 2021 | \n 545763075 | \n N | \n 68293428 | \n 521276805 | \n 6 | \n 0 | \n 3 | \n 5 | \n
\n \n
\n
10 rows × 26 columns
\n
"
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln.sample(10)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 39,
"outputs": [
{
"data": {
"text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n56447 556331792 PE 2021000402 A 2019-09-26 \\\n41201 518451341 TW 107131654 A 2018-09-10 \n54094 550225934 TW 109119471 A 2017-07-06 \n9133 414556231 EP 12881920 A 2012-07-27 \n43037 522510076 EP 19306441 A 2019-11-07 \n... ... ... ... ... ... \n60095 569415925 TW 110116177 A 2021-05-05 \n61281 573442692 DE 102021213653 A 2021-12-02 \n41189 518448790 TW 108109981 A 2014-03-07 \n64973 587185640 CR 20220470 A 2021-03-18 \n8491 413374202 EP 13197783 A 2011-04-28 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n56447 2019 2021000402 PI \\\n41201 2018 107131654 PI \n54094 2017 109119471 PI \n9133 2012 12881920 PI \n43037 2019 19306441 PI \n... ... ... ... ... \n60095 2021 110116177 PI \n61281 2021 102021213653 PI \n41189 2014 108109981 PI \n64973 2021 20220470 PI \n8491 2011 13197783 PI \n\n internat_appln_id ... earliest_publn_date earliest_publn_year \n56447 520193774 ... 2021-07-19 2021 \\\n41201 0 ... 2019-06-16 2019 \n54094 0 ... 2021-02-01 2021 \n9133 414544093 ... 2015-06-03 2015 \n43037 0 ... 2021-05-12 2021 \n... ... ... ... ... \n60095 0 ... 2021-12-16 2021 \n61281 0 ... 2022-06-15 2022 \n41189 0 ... 2019-06-16 2019 \n64973 549449130 ... 2022-11-14 2022 \n8491 376727956 ... 2014-08-20 2014 \n\n earliest_pat_publn_id granted docdb_family_id inpadoc_family_id \n56447 556331793 N 68542579 520193774 \\\n41201 518451342 N 66100215 511344799 \n54094 550225935 N 59337896 481645536 \n9133 439682012 Y 49996511 414544093 \n43037 548841071 N 68655464 522510076 \n... ... ... ... ... \n60095 568265190 N 74845079 546468249 \n61281 573442693 N 81749879 573442692 \n41189 518448791 Y 51500341 421859342 \n64973 587185641 N 75660301 549449130 \n8491 420445397 N 45896256 352844875 \n\n docdb_family_size nb_citing_docdb_fam nb_applicants nb_inventors \n56447 10 0 1 3 \n41201 2 0 1 2 \n54094 8 3 1 7 \n9133 4 5 1 3 \n43037 3 0 1 2 \n... ... ... ... ... \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": "\n\n
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\n \n \n \n 56447 | \n 556331792 | \n PE | \n 2021000402 | \n A | \n 2019-09-26 | \n 2019 | \n 2021000402 | \n PI | \n | \n 520193774 | \n ... | \n 2021-07-19 | \n 2021 | \n 556331793 | \n N | \n 68542579 | \n 520193774 | \n 10 | \n 0 | \n 1 | \n 3 | \n
\n \n 41201 | \n 518451341 | \n TW | \n 107131654 | \n A | \n 2018-09-10 | \n 2018 | \n 107131654 | \n PI | \n | \n 0 | \n ... | \n 2019-06-16 | \n 2019 | \n 518451342 | \n N | \n 66100215 | \n 511344799 | \n 2 | \n 0 | \n 1 | \n 2 | \n
\n \n 54094 | \n 550225934 | \n TW | \n 109119471 | \n A | \n 2017-07-06 | \n 2017 | \n 109119471 | \n PI | \n | \n 0 | \n ... | \n 2021-02-01 | \n 2021 | \n 550225935 | \n N | \n 59337896 | \n 481645536 | \n 8 | \n 3 | \n 1 | \n 7 | \n
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\n \n 64973 | \n 587185640 | \n CR | \n 20220470 | \n A | \n 2021-03-18 | \n 2021 | \n 20220470 | \n PI | \n | \n 549449130 | \n ... | \n 2022-11-14 | \n 2022 | \n 587185641 | \n N | \n 75660301 | \n 549449130 | \n 14 | \n 4 | \n 1 | \n 25 | \n
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\n
100 rows × 26 columns
\n
"
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln[appln[\"appln_nr\"]==appln[\"appln_nr_original\"]].sample(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 40,
"outputs": [],
"source": [
"\n",
"appln_title = pd.read_csv(f\"{outdir}/tls_202_scope.csv\")\n",
"\n",
"pers = pd.read_csv(f\"{outdir}/tls_206_scope.csv\")\n",
"pers['psn_sector'] = pers['psn_sector'].fillna(\"UNKNOWN\")\n",
"\n",
"appln_pers = pd.read_csv(f\"{outdir}/tls_207_scope.csv\")\n",
"\n",
"appln_cpc = pd.read_csv(f\"{outdir}/tls_224_scope.csv\")\n",
"\n",
"cpc_def = pd.read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 41,
"outputs": [
{
"data": {
"text/plain": " person_id person_name person_name_orig_lg \n99304 72271503 SHE, HUIMIN SHE, HUIMIN \\\n4389 6211563 Zhou, Jian Zhou, Jian \n59373 52078604 KIRCHNER, NICOLE KIRCHNER, NICOLE \n19688 16957554 BLAWERT, CARSTEN BLAWERT, CARSTEN \n25828 25701586 KNIESEL, SIMON KNIESEL, SIMON \n73679 55544020 WHITING, Tien Yi Theresa Hsu WHITING, Tien Yi Theresa Hsu \n39905 45320991 Zhang, Michael Zhang, Michael \n65003 53390230 DONG, Hailong DONG, Hailong \n113369 79456511 LING, Jing LING, Jing \n58383 51832619 WEN JU ZHANG WEN JU ZHANG \n\n person_address person_ctry_code \n99304 Shanghai CN \\\n4389 Beijing CN \n59373 SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE DE \n19688 NaN DE \n25828 NaN DE \n73679 NaN US \n39905 Beijing CN \n65003 Molenstraat 148,5701 KK Helmond NL \n113369 No. 6 North Of Binjiang Road, Taixing Economic... CN \n58383 CN CN \n\n nuts nuts_level doc_std_name_id doc_std_name \n99304 NaN 9 32084338 SHE HUIMIN \\\n4389 NaN 9 33759 ZHOU JIAN \n59373 DE 0 2139104 KIRCHNER NICOLE \n19688 DE 0 311260 BLAWERT CARSTEN \n25828 DE 0 255684 KNIESEL SIMON \n73679 NaN 9 21758228 WHITING TIEN YI THERESA HSU \n39905 NaN 9 725806 ZHANG MICHAEL \n65003 NL414 3 7196768 DONG HAILONG \n113369 NaN 9 429470 LING JING \n58383 NaN 9 23069059 ZHANG WEN JU \n\n psn_id psn_name psn_level psn_sector \n99304 29505777 SHE, HUIMIN 0 UNKNOWN \\\n4389 37354342 ZHOU JIAN 1 INDIVIDUAL \n59373 17194552 KIRCHNER, NICOLE 0 UNKNOWN \n19688 2923498 BLAWERT, CARSTEN 1 INDIVIDUAL \n25828 17406789 KNIESEL, SIMON 1 INDIVIDUAL \n73679 35065052 WHITING, TIEN YI THERESA HSU 0 UNKNOWN \n39905 37097378 ZHANG, MICHAEL 1 INDIVIDUAL \n65003 7077131 DONG, HAILONG 0 UNKNOWN \n113369 19626776 LING, JING 0 UNKNOWN \n58383 34908417 WEN JU ZHANG 0 UNKNOWN \n\n han_id han_name han_harmonized \n99304 172271503 SHE, HUIMIN 0 \n4389 106211563 Zhou, Jian 0 \n59373 152078604 KIRCHNER, NICOLE 0 \n19688 116957554 BLAWERT, CARSTEN 0 \n25828 125701586 KNIESEL, SIMON 0 \n73679 155544020 WHITING, Tien Yi Theresa Hsu 0 \n39905 145320991 Zhang, Michael 0 \n65003 153390230 DONG, Hailong 0 \n113369 179456511 LING, Jing 0 \n58383 151832619 WEN JU ZHANG 0 ",
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\n \n 59373 | \n 52078604 | \n KIRCHNER, NICOLE | \n KIRCHNER, NICOLE | \n SACHSENSTRAßE 2845665 RECKLINGHAUSEN DE | \n DE | \n DE | \n 0 | \n 2139104 | \n KIRCHNER NICOLE | \n 17194552 | \n KIRCHNER, NICOLE | \n 0 | \n UNKNOWN | \n 152078604 | \n KIRCHNER, NICOLE | \n 0 | \n
\n \n 19688 | \n 16957554 | \n BLAWERT, CARSTEN | \n BLAWERT, CARSTEN | \n NaN | \n DE | \n DE | \n 0 | \n 311260 | \n BLAWERT CARSTEN | \n 2923498 | \n BLAWERT, CARSTEN | \n 1 | \n INDIVIDUAL | \n 116957554 | \n BLAWERT, CARSTEN | \n 0 | \n
\n \n 25828 | \n 25701586 | \n KNIESEL, SIMON | \n KNIESEL, SIMON | \n NaN | \n DE | \n DE | \n 0 | \n 255684 | \n KNIESEL SIMON | \n 17406789 | \n KNIESEL, SIMON | \n 1 | \n INDIVIDUAL | \n 125701586 | \n KNIESEL, SIMON | \n 0 | \n
\n \n 73679 | \n 55544020 | \n WHITING, Tien Yi Theresa Hsu | \n WHITING, Tien Yi Theresa Hsu | \n NaN | \n US | \n NaN | \n 9 | \n 21758228 | \n WHITING TIEN YI THERESA HSU | \n 35065052 | \n WHITING, TIEN YI THERESA HSU | \n 0 | \n UNKNOWN | \n 155544020 | \n WHITING, Tien Yi Theresa Hsu | \n 0 | \n
\n \n 39905 | \n 45320991 | \n Zhang, Michael | \n Zhang, Michael | \n Beijing | \n CN | \n NaN | \n 9 | \n 725806 | \n ZHANG MICHAEL | \n 37097378 | \n ZHANG, MICHAEL | \n 1 | \n INDIVIDUAL | \n 145320991 | \n Zhang, Michael | \n 0 | \n
\n \n 65003 | \n 53390230 | \n DONG, Hailong | \n DONG, Hailong | \n Molenstraat 148,5701 KK Helmond | \n NL | \n NL414 | \n 3 | \n 7196768 | \n DONG HAILONG | \n 7077131 | \n DONG, HAILONG | \n 0 | \n UNKNOWN | \n 153390230 | \n DONG, Hailong | \n 0 | \n
\n \n 113369 | \n 79456511 | \n LING, Jing | \n LING, Jing | \n No. 6 North Of Binjiang Road, Taixing Economic... | \n CN | \n NaN | \n 9 | \n 429470 | \n LING JING | \n 19626776 | \n LING, JING | \n 0 | \n UNKNOWN | \n 179456511 | \n LING, Jing | \n 0 | \n
\n \n 58383 | \n 51832619 | \n WEN JU ZHANG | \n WEN JU ZHANG | \n CN | \n CN | \n NaN | \n 9 | \n 23069059 | \n ZHANG WEN JU | \n 34908417 | \n WEN JU ZHANG | \n 0 | \n UNKNOWN | \n 151832619 | \n WEN JU ZHANG | \n 0 | \n
\n \n
\n
"
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pers.sample(10)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 42,
"outputs": [
{
"data": {
"text/plain": " cpc_id cpc_name \n0 A HUMAN NECESSITIES \\\n1 A01 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n2 A01B SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n3 A01B1/00 Hand tools (edge trimmers for lawns A01G3/06 ... \n4 A01B1/02 Spades; Shovels {(hand-operated dredgers E02F3... \n... ... ... \n260486 Y10T483/1873 Indexing matrix \n260487 Y10T483/1882 Rotary disc \n260488 Y10T483/1891 Chain or belt \n260489 Y10T483/19 Miscellaneous \n260490 NaN NaN \n\n section class subclass group main_group cpc_version \n0 A NaN NaN NaN NaN 2023 \\\n1 A 1.0 NaN NaN NaN 2023 \n2 A 1.0 B NaN NaN 2023 \n3 A 1.0 B 1.0 0.0 2023 \n4 A 1.0 B 1.0 2.0 2023 \n... ... ... ... ... ... ... \n260486 Y 10.0 T 483.0 1873.0 2023 \n260487 Y 10.0 T 483.0 1882.0 2023 \n260488 Y 10.0 T 483.0 1891.0 2023 \n260489 Y 10.0 T 483.0 19.0 2023 \n260490 NaN NaN NaN NaN NaN 2022 \n\n version https://git-lfs.github.com/spec/v1 \n0 NaN \\\n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n260486 NaN \n260487 NaN \n260488 NaN \n260489 NaN \n260490 oid sha256:f138d6bdf2939ba576b96b633d81366123b... \n\n cpc_taxonomy \n0 [('A', 'HUMAN NECESSITIES')] \\\n1 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n2 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n3 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n4 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n... ... \n260486 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260487 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260488 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260489 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260490 [] \n\n cpc_fullname \n0 HUMAN NECESSITIES \\\n1 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n2 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n3 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n4 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n... ... \n260486 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260487 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260488 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260489 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260490 NaN \n\n tax_level_0 \n0 HUMAN 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": "\n\n
\n \n \n | \n cpc_id | \n cpc_name | \n section | \n class | \n subclass | \n group | \n main_group | \n cpc_version | \n version https://git-lfs.github.com/spec/v1 | \n cpc_taxonomy | \n cpc_fullname | \n tax_level_0 | \n tax_level_1 | \n tax_level_2 | \n tax_level_3 | \n tax_level_4 | \n tax_level_5 | \n tax_level_6 | \n tax_level_7 | \n data_scope | \n
\n \n \n \n 0 | \n A | \n HUMAN NECESSITIES | \n A | \n NaN | \n NaN | \n NaN | \n NaN | \n 2023 | \n NaN | \n [('A', 'HUMAN NECESSITIES')] | \n HUMAN NECESSITIES | \n HUMAN NECESSITIES | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 1 | \n A01 | \n AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... | \n A | \n 1.0 | \n NaN | \n NaN | \n NaN | \n 2023 | \n NaN | \n [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... | \n HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... | \n HUMAN NECESSITIES | \n AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 2 | \n A01B | \n SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... | \n A | \n 1.0 | \n B | \n NaN | \n NaN | \n 2023 | \n NaN | \n [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... | \n HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... | \n HUMAN NECESSITIES | \n AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... | \n SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 3 | \n A01B1/00 | \n Hand tools (edge trimmers for lawns A01G3/06 ... | \n A | \n 1.0 | \n B | \n 1.0 | \n 0.0 | \n 2023 | \n NaN | \n [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... | \n HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... | \n HUMAN NECESSITIES | \n AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... | \n SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... | \n Hand tools (edge trimmers for lawns A01G3/06 ... | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 4 | \n A01B1/02 | \n Spades; Shovels {(hand-operated dredgers E02F3... | \n A | \n 1.0 | \n B | \n 1.0 | \n 2.0 | \n 2023 | \n NaN | \n [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... | \n HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... | \n HUMAN NECESSITIES | \n AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... | \n SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... | \n Spades; Shovels {(hand-operated dredgers E02F3... | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 260486 | \n Y10T483/1873 | \n Indexing matrix | \n Y | \n 10.0 | \n T | \n 483.0 | \n 1873.0 | \n 2023 | \n NaN | \n [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n TECHNICAL SUBJECTS COVERED BY FORMER USPC | \n TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... | \n Tool transfer to or from matrix | \n Indexing matrix | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 260487 | \n Y10T483/1882 | \n Rotary disc | \n Y | \n 10.0 | \n T | \n 483.0 | \n 1882.0 | \n 2023 | \n NaN | \n [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n TECHNICAL SUBJECTS COVERED BY FORMER USPC | \n TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... | \n Tool transfer to or from matrix | \n Rotary disc | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 260488 | \n Y10T483/1891 | \n Chain or belt | \n Y | \n 10.0 | \n T | \n 483.0 | \n 1891.0 | \n 2023 | \n NaN | \n [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n TECHNICAL SUBJECTS COVERED BY FORMER USPC | \n TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... | \n Tool transfer to or from matrix | \n Chain or belt | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 260489 | \n Y10T483/19 | \n Miscellaneous | \n Y | \n 10.0 | \n T | \n 483.0 | \n 19.0 | \n 2023 | \n NaN | \n [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... | \n TECHNICAL SUBJECTS COVERED BY FORMER USPC | \n TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... | \n Miscellaneous | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n 260490 | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n 2022 | \n oid sha256:f138d6bdf2939ba576b96b633d81366123b... | \n [] | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n NaN | \n False | \n
\n \n
\n
260491 rows × 20 columns
\n
"
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cpc_def"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 43,
"outputs": [
{
"data": {
"text/plain": " appln_id cpc_class_symbol\n490979 560314579 B32B 7/12\n139994 445201240 C08G2261/3241\n505928 567683420 C25D 7/123\n130069 442410454 Y02D 10/00\n467350 551851575 H04B 7/0486\n318101 509884390 H01L 21/0228\n350171 519161420 G06F 21/42\n129829 442384375 H04W 88/04\n277164 496690078 H04L 27/26\n240979 487005701 F16F2230/0064",
"text/html": "\n\n
\n \n \n | \n appln_id | \n cpc_class_symbol | \n
\n \n \n \n 490979 | \n 560314579 | \n B32B 7/12 | \n
\n \n 139994 | \n 445201240 | \n C08G2261/3241 | \n
\n \n 505928 | \n 567683420 | \n C25D 7/123 | \n
\n \n 130069 | \n 442410454 | \n Y02D 10/00 | \n
\n \n 467350 | \n 551851575 | \n H04B 7/0486 | \n
\n \n 318101 | \n 509884390 | \n H01L 21/0228 | \n
\n \n 350171 | \n 519161420 | \n G06F 21/42 | \n
\n \n 129829 | \n 442384375 | \n H04W 88/04 | \n
\n \n 277164 | \n 496690078 | \n H04L 27/26 | \n
\n \n 240979 | \n 487005701 | \n F16F2230/0064 | \n
\n \n
\n
"
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_cpc.sample(10)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 44,
"outputs": [
{
"data": {
"text/plain": "325243"
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(appln_pers)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 45,
"outputs": [
{
"data": {
"text/plain": "65136"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(appln)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 46,
"outputs": [],
"source": [
"# Number of collaborative applications\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 47,
"outputs": [],
"source": [
"record_col = \"appln_id\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 297,
"outputs": [
{
"data": {
"text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 429,
"outputs": [],
"source": [
"fromdir=\"first_round\"\n",
"\n",
"os.makedirs(fr'{fromdir}/plots')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 392,
"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\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 393,
"outputs": [
{
"data": {
"text/plain": "han_id\n3345915 6\nName: count, dtype: int64"
},
"execution_count": 393,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_pers_haninfo[appln_pers_haninfo[\"han_name\"]==\"ZHEJIANG GEELY HOLDINGGROUP CO LTD\"][\"han_id\"].value_counts()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 394,
"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')"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 395,
"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": 396,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1055\n"
]
}
],
"source": [
"\n",
"\n",
"# appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
"# (gr[\"appln_auth\"]==\"CN\")).any())\n",
"# appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n",
"# (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
"\n",
"appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n",
"appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n",
"\n",
"print(appln_scope[\"appln_id\"].nunique())"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 397,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1055\n"
]
}
],
"source": [
"appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n",
"print(appln_pers_f[\"appln_id\"].nunique())"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 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": "\n\n
\n \n \n | \n appln_id | \n appln_auth | \n person_id | \n invt_seq_nr | \n applt_seq_nr | \n person_name | \n person_ctry_code | \n psn_sector | \n han_id | \n han_name | \n psn_sector_primary | \n
\n \n \n \n 76 | \n 410448968 | \n DE | \n 7067540 | \n 0 | \n 1 | \n Evonik Degussa GmbH | \n DE | \n COMPANY | \n 840063 | \n EVONIK DEGUSSA GMBH | \n COMPANY | \n
\n \n 87 | \n 420386087 | \n DE | \n 7067540 | \n 0 | \n 1 | \n Evonik Degussa GmbH | \n DE | \n COMPANY | \n 840063 | \n EVONIK DEGUSSA GMBH | \n COMPANY | \n
\n \n 88 | \n 420386089 | \n DE | \n 7067540 | \n 0 | \n 1 | \n Evonik Degussa GmbH | \n DE | \n COMPANY | \n 840063 | \n EVONIK DEGUSSA GMBH | \n COMPANY | \n
\n \n 173 | \n 337088926 | \n EP | \n 21790 | \n 0 | \n 1 | \n Evonik Degussa GmbH | \n DE | \n COMPANY | \n 840063 | \n EVONIK DEGUSSA GMBH | \n COMPANY | \n
\n \n 974 | \n 410512383 | \n EP | \n 220 | \n 0 | \n 1 | \n SIEMENS AKTIENGESELLSCHAFT | \n DE | \n COMPANY | \n 2709504 | \n SIEMENS AG | \n COMPANY | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 147073 | \n 549690891 | \n EP | \n 85841201 | \n 1 | \n 0 | \n JONSSON, Tony | \n SE | \n UNKNOWN | \n 185841201 | \n JONSSON, Tony | \n UNKNOWN | \n
\n \n 147074 | \n 549690895 | \n EP | \n 85841201 | \n 1 | \n 0 | \n JONSSON, Tony | \n SE | \n UNKNOWN | \n 185841201 | \n JONSSON, Tony | \n UNKNOWN | \n
\n \n 147075 | \n 549690901 | \n EP | \n 85841201 | \n 1 | \n 0 | \n JONSSON, Tony | \n SE | \n UNKNOWN | \n 185841201 | \n JONSSON, Tony | \n UNKNOWN | \n
\n \n 147076 | \n 549690897 | \n EP | \n 85841201 | \n 1 | \n 0 | \n JONSSON, Tony | \n SE | \n UNKNOWN | \n 185841201 | \n JONSSON, Tony | \n UNKNOWN | \n
\n \n 147077 | \n 549788919 | \n EP | \n 85774895 | \n 2 | \n 0 | \n TRAN NGUYEN, Johnny | \n SE | \n UNKNOWN | \n 185774895 | \n TRAN NGUYEN, Johnny | \n UNKNOWN | \n
\n \n
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4367 rows × 11 columns
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},
"execution_count": 398,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"appln_pers_f"
],
"metadata": {
"collapsed": false
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},
{
"cell_type": "code",
"execution_count": 399,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1055\n"
]
},
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"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]",
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\n \n \n | \n appln_id | \n appln_auth | \n appln_kind | \n filing_year | \n nb_applicants | \n nb_inventors | \n
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\n \n 568605 | \n 520224479 | \n EP | \n A | \n 2019 | \n 1 | \n 2 | \n
\n \n 441101 | \n 483520394 | \n FR | \n A | \n 2017 | \n 1 | \n 2 | \n
\n \n 687085 | \n 554756101 | \n DE | \n A | \n 2020 | \n 1 | \n 3 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 235723 | \n 443704166 | \n FR | \n A | \n 2015 | \n 3 | \n 3 | \n
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\n \n 415863 | \n 486019755 | \n EP | \n A | \n 2017 | \n 1 | \n 2 | \n
\n \n 608249 | \n 520115105 | \n EP | \n A | \n 2019 | \n 1 | \n 4 | \n
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100 rows × 6 columns
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},
"execution_count": 399,
"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": 399,
"outputs": [],
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"metadata": {
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},
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"execution_count": 399,
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"metadata": {
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}
},
{
"cell_type": "code",
"execution_count": 400,
"outputs": [
{
"data": {
"text/plain": "appln_kind\nA 1059\nU 7\nName: count, dtype: int64"
},
"execution_count": 400,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_f[\"appln_kind\"].value_counts()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 401,
"outputs": [],
"source": [
"# Co-patents per year"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 430,
"outputs": [
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"data = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
"data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"\n",
"data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n",
"data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n",
"\n",
"year_output = px.line(data,x=\"filing_year\", y=record_col, markers=True, text=record_col)\n",
"year_output.update_traces(textposition='top center',textfont_size=12)\n",
"year_output.update_traces(hovertemplate='Year:%{x:d}
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"\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}
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"\n",
"figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\",\"Relative growth of co-patents\"],\n",
" specs=[\n",
" [{\"type\": \"xy\"}],\n",
" [{\"type\": \"xy\"}]\n",
" ])\n",
"\n",
"for trace in list(year_output.select_traces()):\n",
" figsuper.add_trace(trace,\n",
" row=1, col=1\n",
" )\n",
"\n",
"for trace in list(year_rel_output.select_traces()):\n",
" figsuper.add_trace(trace,\n",
" row=2, col=1\n",
" )\n",
"\n",
"figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n",
"figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n",
"# for i in[\"xaxis\",\"xaxis2\"]:\n",
"# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n",
"#\n",
"figsuper['layout'][\"yaxis\"][\"range\"] = [0,250]\n",
"figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,3.5]\n",
"#\n",
"# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
"# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
"\n",
"figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n",
"\n",
"figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\",height=900,width=900,scale = 4)"
],
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"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 440,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"EP not found in ISO2\n"
]
},
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"data = (appln_f.groupby(['filing_year',\"appln_auth\"])[record_col]\n",
" .nunique(dropna=False).unstack()\n",
" .fillna(0)\n",
" .stack()\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
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"data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n",
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" on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"data = data.sort_values(by =[\"appln_auth\",\"filing_year\"], ascending=[True,True])\n",
"data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
"data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n",
"data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n",
"\n",
"\n",
"fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
" y=record_col,\n",
" x='filing_year'\n",
" ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
" markers=True,\n",
" labels={\n",
" record_col: 'Number of co-patents',\n",
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" title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n",
"fig.update_traces(hovertemplate='%{hovertext}
%{x}
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"fig.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n",
"fig.update_yaxes(title='')\n",
"fig.update_xaxes(title='')\n",
"fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
"# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
"figsuper_ppt = go.Figure(fig)\n",
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"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",
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"s=16\n",
"figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
"figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
"figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
"figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)"
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"fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
" y=record_col+\"_relative_growth\",\n",
" x='filing_year'\n",
" ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
" markers=True,\n",
" labels={\n",
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" title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n",
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%{x}
Co-publications: %{y}')\n",
"fig.update_yaxes(\n",
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" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
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"fig.update_yaxes(title='')\n",
"fig.update_xaxes(title='')\n",
"fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
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"\n",
"figsuper_ppt = go.Figure(fig)\n",
"\n",
"figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
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" a[\"font\"][\"size\"] = 22\n",
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"figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
"figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
"figsuper_ppt.show(height=800,width=1600,scale = 4)\n",
"figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)"
],
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},
{
"cell_type": "code",
"execution_count": 405,
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},
{
"cell_type": "code",
"execution_count": 446,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"EP not found in ISO2\n",
"European Patent Office not found in regex\n"
]
},
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"# appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n",
"# names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
"\n",
"# appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
"#\n",
"# appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n",
"#\n",
"# tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
"# # tls_801.head()\n",
"# scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
"# (tls_801.ctry_code == 'CN')|\n",
"# (tls_801.ctry_code == 'NO')|\n",
"# (tls_801.ctry_code == 'CH')|\n",
"# (tls_801.ctry_code == 'UK'))]\n",
"# scope_countries = scope_df[\"ctry_code\"].unique()\n",
"#\n",
"# appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n",
"\n",
"for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
" appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n",
"\n",
"appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
"appln_pers_f[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
"appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
"\n",
"appln_pers_f[\"auth_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"appln_auth_fullname\"], to='ISO3')\n",
"appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\" if x!='not found' else \"EPO\")\n",
"\n",
"# top 25\n",
"# - inventor\n",
"# - applicant\n",
"# in china, EU, EUextended\n",
"color_discrete_map= {'China': '#EF553B',\n",
" 'EU': '#636EFA',\n",
" 'Non-EU associate': '#00CC96',\n",
" 'EPO':'#9F2B68'}\n",
"\n",
"labeld_dict = dict({\n",
" record_col: 'Number of co-patents',\n",
" \"person_ctry_type\": \"Country type\",\n",
" \"psn_sector_primary\":\"Sector\",\n",
" \"han_label\": \"Name\",\n",
" \"Country_Type\":\"Country type\",\n",
" \"Eurovoc_Class\":\"Region\"\n",
" })\n",
"for trait in [\"person_ctry_type\",\"psn_sector_primary\"]:\n",
" for ctry in [\"China\",'EU']:\n",
" ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
" # for role in [\"invt\",\"applt\"]:\n",
" if True:\n",
" role = \"applt\"\n",
" pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
" data = (pers_sub\n",
" .groupby([\"han_id\",'han_name'],as_index=False)\n",
" .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n",
" .sort_values(by=\"appln_id\", ascending=False)\n",
" .head(15))\n",
" data[\"han_label\"] = (data[\"han_name\"]\n",
" .str.upper()\n",
" .str.strip(\".\")\n",
" .str.replace(\"&\",\"
\")\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)"
],
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"outputs": [
{
"name": "stderr",
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"text": [
"C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n",
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"divide by zero encountered in log10\n",
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"applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n",
" (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n",
"invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n",
"\n",
"eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
"ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
"\n",
"eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
"ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n",
"\n",
"#\n",
"# patstat_applt_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
"# patstat_applt_dipol.sample(100)\n",
"\n",
"patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
"patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n",
"\n",
"# co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n",
"# values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
"co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n",
" values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n",
"\n",
"\n",
"co_occur = co_occur.T\n",
"co_occur[\"sum\"] = co_occur.sum(axis=1)\n",
"co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n",
"\n",
"co_occur = (co_occur\n",
" .sort_values(by=\"sum\", ascending=False)\n",
" .head(20)\n",
" .drop(columns=\"sum\")\n",
" .replace(0,np.nan)\n",
" .dropna(axis=1,how=\"all\")\n",
" .replace(np.nan,0)\n",
" .astype(int))\n",
"\n",
"fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n",
" labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n",
" x=list(co_occur.columns),\n",
" text_auto=True,\n",
" y=list(co_occur.index))\n",
"\n",
"\n",
"fig.update_layout(title_x=0.5,\n",
" width=1000, height=1000,\n",
" xaxis_showgrid=False,\n",
" yaxis_showgrid=False,\n",
" yaxis_autorange='reversed',\n",
" font_family=\"Montserrat\",\n",
" template='plotly_white',\n",
" coloraxis_colorbar=dict(\n",
" thicknessmode=\"pixels\", thickness=25,\n",
" ticks=\"outside\", ticksuffix=\" \",\n",
" dtick=20,outlinewidth=1,\n",
" ))\n",
"\n",
"# fig.update_traces(texttemplate='%{z}')\n",
"# fig.update_traces(z=co_occur.values)\n",
"# fig.update(data=[{'customdata': co_occur}])\n",
"# fig.update(data=[{'customdata': co_occur,\n",
"# 'hovertemplate': \"(x) %{x} product: %{customdata[0]}
(y) %{y} product: %{customdata[1]}
%{z}\"}])\n",
"# fig.update_traces(texttemplate='%{customdata}')\n",
"fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n",
"fig.update(layout_coloraxis_showscale=False)\n",
"fig.update_xaxes(tickangle= -45)\n",
"fig.update_yaxes(\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" ticks=\"outside\")\n",
"\n",
"figsuper_ppt = fig\n",
"figsuper_ppt['layout'][\"font\"][\"size\"]=16\n",
"for a in figsuper_ppt['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 16\n",
"\n",
"s=16\n",
"figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
"figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
"\n",
"figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n",
"\n",
"figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)"
],
"metadata": {
"collapsed": false
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},
{
"cell_type": "code",
"execution_count": 453,
"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": {
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}
},
{
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"execution_count": 456,
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"source": [
"# percent of publications related to\n",
"# -authority country\n",
"# -inventor country\n",
"# -applicant country\n",
"\n",
"color_discrete_map= {'China': '#EF553B',\n",
" 'EU': '#636EFA',\n",
" 'Non-EU associate': '#00CC96',\n",
" 'EPO':'#7F00FF'}#CC8899\n",
"\n",
"\n",
"labeld_dict = dict({\n",
" record_col+\"_percent\": 'Percent of related co-patents',\n",
" \"person_ctry_code_fullname\": \"Country\",\n",
" \"person_ctry_type\": \"Country type\",\n",
" \"appln_auth_fullname\": \"Application authority\",\n",
" \"auth_ctry_type\": \"Authority type\",\n",
" \"Country_Type\":\"Country type\",\n",
" \"Eurovoc_Class\":\"Region\"\n",
" })\n",
"\n",
"for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
" pers_sub = appln_pers_f\n",
" if c == \"person_ctry_code\":\n",
" for role in [\"invt\",\"applt\"]:\n",
" pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
" role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n",
" labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n",
"\n",
"\n",
" data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
" data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
" fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
" labels = labeld_dict)\n",
" fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
" yaxis={'categoryorder':'total ascending'},)\n",
" figsuper = fig\n",
" figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
" figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"\n",
" figsuper['layout'][\"font\"][\"size\"]=14\n",
" for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
" figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
"\n",
" figsuper.update_layout(uniformtext_minsize=10)\n",
" figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
" figsuper.show()\n",
" else:\n",
" data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
" data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
" fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
" labels = labeld_dict)\n",
" fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
" yaxis={'categoryorder':'total ascending'},)\n",
" figsuper = fig\n",
" figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
" figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"\n",
" figsuper['layout'][\"font\"][\"size\"]=14\n",
" for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
" figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n",
"\n",
" figsuper.update_layout(uniformtext_minsize=10)\n",
" figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n",
" figsuper.show()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 420,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 420,
"outputs": [],
"source": [],
"metadata": {
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{
"cell_type": "code",
"execution_count": 420,
"outputs": [],
"source": [],
"metadata": {
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},
{
"cell_type": "code",
"execution_count": 457,
"outputs": [],
"source": [
"appln_ipc_f = pd.read_csv(f\"{fromdir}/first-filings-with-IPC-raw.csv\", header=None,\n",
" names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n",
" 'ipc_class_symbol','ipc_class_level'])\n",
"\n",
"appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n",
"appln_ipc_f = appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])]\n",
"\n",
"appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n",
"appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n",
"appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n",
" \"B\":\"Performing operations; transporting\",\n",
" \"B\":\"Operations; transporting\",\n",
" \"C\":\"Chemistry; metallurgy\",\n",
" \"D\":\"Textiles; paper\",\n",
" \"E\":\"Fixed constructions\",\n",
" \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n",
" \"F\":\"Engineering\",\n",
" \"G\":\"Physics\",\n",
" \"H\":\"Electricity\"})\n",
"\n",
"appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 458,
"outputs": [],
"source": [
"group = 'ipc_class'\n",
"data = appln_ipc_f.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
"\n",
"fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',text_auto=True,\n",
" labels={\n",
" record_col: 'Number of co-publications',\n",
" group: \"\",\n",
" },\n",
" title=\"Distribution of Domains\", template='plotly')\n",
"fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n",
"fig.update_traces(hovertemplate='%{x:d}')\n",
"fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=True)\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_layout(yaxis={'categoryorder':'total ascending'})\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"dom_distr = go.Figure(fig)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 459,
"outputs": [],
"source": [
"data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n",
" .fillna(0)\n",
" .stack()\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
"data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n",
" on='ipc_class', suffixes=[None,\"_relative_growth\"])\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"\n",
"data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n",
"data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 460,
"outputs": [],
"source": [
"group = ['filing_year','ipc_class']\n",
"fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True, labels={\n",
" record_col: 'Number of co-publications',\n",
" group[-1]: \"Domain\",\n",
" },\n",
" title=\"Yearly output of co-publications\", template='plotly')\n",
"fig.update_traces(hovertemplate='%{y:d}')\n",
"fig.update_layout(hovermode='x unified')\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"\n",
"year_output_by_domain = go.Figure(fig)\n",
"\n",
"fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True, labels={\n",
" record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n",
" group[-1]: \"Domain\",\n",
" },\n",
" title=\"Relative growth in the output of co-publications\", template='plotly')\n",
"\n",
"fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n",
"fig.update_traces(hovertemplate='%{y:.0f}00%')\n",
"fig.add_shape(\n",
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" type=\"rect\",\n",
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" )\n",
" )\n",
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"\n",
"rel_output_by_domain = go.Figure(fig)\n",
"\n",
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" line=dict(\n",
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" width=0.5,\n",
" )\n",
" )\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"\n",
"cumsum_by_domain = go.Figure(fig)"
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{
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"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": 425,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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