{
"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": 48,
"outputs": [
{
"data": {
"text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'"
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 49,
"outputs": [],
"source": [
"fromdir=\"first_round\""
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 50,
"outputs": [],
"source": [
"appln_pers_f = pd.read_csv(f\"{fromdir}/first-filings-with-persons-raw.csv\", header=None,\n",
" names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
"\n",
"appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
"\n",
"appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 51,
"outputs": [],
"source": [
"tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
"# tls_801.head()\n",
"scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
" (tls_801.ctry_code == 'CN')|\n",
" (tls_801.ctry_code == 'NO')|\n",
" (tls_801.ctry_code == 'CH')|\n",
" (tls_801.ctry_code == 'UK'))]\n",
"scope_countries = scope_df[\"ctry_code\"].unique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 54,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1116\n"
]
}
],
"source": [
"\n",
"\n",
"# appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
"# (gr[\"appln_auth\"]==\"CN\")).any())\n",
"# appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n",
"# (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
"\n",
"appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n",
"appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n",
"\n",
"print(appln_scope[\"appln_id\"].nunique())"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 73,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1116\n"
]
}
],
"source": [
"appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n",
"print(appln_pers_f[\"appln_id\"].nunique())"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 71,
"outputs": [
{
"data": {
"text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n76 410448968 DE 7067540 0 1 \\\n87 420386087 DE 7067540 0 1 \n88 420386089 DE 7067540 0 1 \n828 410512383 EP 220 0 1 \n1454 333687134 EP 10329 0 1 \n... ... ... ... ... ... \n147073 549690891 EP 85841201 1 0 \n147074 549690895 EP 85841201 1 0 \n147075 549690901 EP 85841201 1 0 \n147076 549690897 EP 85841201 1 0 \n147077 549788919 EP 85774895 2 0 \n\n person_name person_ctry_code psn_sector han_id \n76 Evonik Degussa GmbH DE COMPANY 840063 \\\n87 Evonik Degussa GmbH DE COMPANY 840063 \n88 Evonik Degussa GmbH DE COMPANY 840063 \n828 SIEMENS AKTIENGESELLSCHAFT DE COMPANY 2709504 \n1454 Alcatel Lucent FR COMPANY 68848 \n... ... ... ... ... \n147073 JONSSON, Tony SE UNKNOWN 185841201 \n147074 JONSSON, Tony SE UNKNOWN 185841201 \n147075 JONSSON, Tony SE UNKNOWN 185841201 \n147076 JONSSON, Tony SE UNKNOWN 185841201 \n147077 TRAN NGUYEN, Johnny SE UNKNOWN 185774895 \n\n han_name \n76 EVONIK DEGUSSA GMBH \n87 EVONIK DEGUSSA GMBH \n88 EVONIK DEGUSSA GMBH \n828 SIEMENS AG \n1454 ALCATEL LUCENT \n... ... \n147073 JONSSON, Tony \n147074 JONSSON, Tony \n147075 JONSSON, Tony \n147076 JONSSON, Tony \n147077 TRAN NGUYEN, Johnny \n\n[4434 rows x 10 columns]",
"text/html": "\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
\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
\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
\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
\n \n 828 | \n 410512383 | \n EP | \n 220 | \n 0 | \n 1 | \n SIEMENS AKTIENGESELLSCHAFT | \n DE | \n COMPANY | \n 2709504 | \n SIEMENS AG | \n
\n \n 1454 | \n 333687134 | \n EP | \n 10329 | \n 0 | \n 1 | \n Alcatel Lucent | \n FR | \n COMPANY | \n 68848 | \n ALCATEL LUCENT | \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
\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
\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
\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
\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
\n \n
\n
4434 rows × 10 columns
\n
"
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_pers_f"
],
"metadata": {
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"1116\n"
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"text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n51083 407234637 FI A 2011 1 \\\n240847 442945435 FR A 2015 1 \n685164 531137359 NL A 2020 1 \n235203 438685261 EP A 2015 1 \n430561 497548590 DE A 2017 2 \n... ... ... ... ... ... \n721473 579480994 DE A 2021 1 \n619328 523950441 EP A 2019 2 \n484715 490602095 EP A 2018 1 \n196502 450101287 DE A 2014 1 \n367660 477378741 GB A 2017 1 \n\n nb_inventors \n51083 2 \n240847 5 \n685164 3 \n235203 4 \n430561 8 \n... ... \n721473 3 \n619328 3 \n484715 1 \n196502 6 \n367660 1 \n\n[100 rows x 6 columns]",
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\n \n \n | \n appln_id | \n appln_auth | \n appln_kind | \n filing_year | \n nb_applicants | \n nb_inventors | \n
\n \n \n \n 51083 | \n 407234637 | \n FI | \n A | \n 2011 | \n 1 | \n 2 | \n
\n \n 240847 | \n 442945435 | \n FR | \n A | \n 2015 | \n 1 | \n 5 | \n
\n \n 685164 | \n 531137359 | \n NL | \n A | \n 2020 | \n 1 | \n 3 | \n
\n \n 235203 | \n 438685261 | \n EP | \n A | \n 2015 | \n 1 | \n 4 | \n
\n \n 430561 | \n 497548590 | \n DE | \n A | \n 2017 | \n 2 | \n 8 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 721473 | \n 579480994 | \n DE | \n A | \n 2021 | \n 1 | \n 3 | \n
\n \n 619328 | \n 523950441 | \n EP | \n A | \n 2019 | \n 2 | \n 3 | \n
\n \n 484715 | \n 490602095 | \n EP | \n A | \n 2018 | \n 1 | \n 1 | \n
\n \n 196502 | \n 450101287 | \n DE | \n A | \n 2014 | \n 1 | \n 6 | \n
\n \n 367660 | \n 477378741 | \n GB | \n A | \n 2017 | \n 1 | \n 1 | \n
\n \n
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100 rows × 6 columns
\n
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},
"execution_count": 55,
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],
"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)"
],
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},
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"outputs": [
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"data": {
"text/plain": "appln_kind\nA 1114\nU 13\nName: count, dtype: int64"
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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],
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},
{
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"outputs": [],
"source": [
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],
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},
{
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"displayModeBar": false,
"responsive": true,
"plotlyServerURL": "https://plotly.com"
}
},
"text/html": ""
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
"data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"\n",
"data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n",
"data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n",
"\n",
"year_output = px.line(data,x=\"filing_year\", y=record_col, markers=True, text=record_col)\n",
"year_output.update_traces(textposition='top center',textfont_size=12)\n",
"year_output.update_traces(hovertemplate='Year:%{x:d}
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}
Rel.growth in co-publications:%{y:.0%}')\n",
"\n",
"figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\",\"Relative growth of co-patents\"],\n",
" specs=[\n",
" [{\"type\": \"xy\"}],\n",
" [{\"type\": \"xy\"}]\n",
" ])\n",
"\n",
"for trace in list(year_output.select_traces()):\n",
" figsuper.add_trace(trace,\n",
" row=1, col=1\n",
" )\n",
"\n",
"for trace in list(year_rel_output.select_traces()):\n",
" figsuper.add_trace(trace,\n",
" row=2, col=1\n",
" )\n",
"\n",
"figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n",
"figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n",
"# for i in[\"xaxis\",\"xaxis2\"]:\n",
"# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n",
"#\n",
"figsuper['layout'][\"yaxis\"][\"range\"] = [0,250]\n",
"figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,3.5]\n",
"#\n",
"# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n",
"# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n",
"\n",
"figsuper.show(config= dict(displayModeBar = False, responsive = True))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 66,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"EP not found in ISO2\n"
]
},
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"text/plain": " filing_year appln_auth appln_id filing_year_relative_growth \n0 2011 AT 0.0 2012 \\\n1 2012 AT 1.0 2012 \n2 2013 AT 4.0 2012 \n3 2014 AT 0.0 2012 \n4 2015 AT 1.0 2012 \n.. ... ... ... ... \n160 2017 SE 3.0 2015 \n161 2018 SE 1.0 2015 \n162 2019 SE 0.0 2015 \n163 2020 SE 1.0 2015 \n164 2021 SE 0.0 2015 \n\n appln_id_relative_growth appln_id_cumsum Country \n0 -1.0 0.0 Austria \n1 0.0 1.0 Austria \n2 3.0 5.0 Austria \n3 -1.0 5.0 Austria \n4 0.0 6.0 Austria \n.. ... ... ... \n160 2.0 5.0 Sweden \n161 0.0 6.0 Sweden \n162 -1.0 6.0 Sweden \n163 0.0 7.0 Sweden \n164 -1.0 7.0 Sweden \n\n[165 rows x 7 columns]",
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\n \n \n | \n filing_year | \n appln_auth | \n appln_id | \n filing_year_relative_growth | \n appln_id_relative_growth | \n appln_id_cumsum | \n Country | \n
\n \n \n \n 0 | \n 2011 | \n AT | \n 0.0 | \n 2012 | \n -1.0 | \n 0.0 | \n Austria | \n
\n \n 1 | \n 2012 | \n AT | \n 1.0 | \n 2012 | \n 0.0 | \n 1.0 | \n Austria | \n
\n \n 2 | \n 2013 | \n AT | \n 4.0 | \n 2012 | \n 3.0 | \n 5.0 | \n Austria | \n
\n \n 3 | \n 2014 | \n AT | \n 0.0 | \n 2012 | \n -1.0 | \n 5.0 | \n Austria | \n
\n \n 4 | \n 2015 | \n AT | \n 1.0 | \n 2012 | \n 0.0 | \n 6.0 | \n Austria | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 160 | \n 2017 | \n SE | \n 3.0 | \n 2015 | \n 2.0 | \n 5.0 | \n Sweden | \n
\n \n 161 | \n 2018 | \n SE | \n 1.0 | \n 2015 | \n 0.0 | \n 6.0 | \n Sweden | \n
\n \n 162 | \n 2019 | \n SE | \n 0.0 | \n 2015 | \n -1.0 | \n 6.0 | \n Sweden | \n
\n \n 163 | \n 2020 | \n SE | \n 1.0 | \n 2015 | \n 0.0 | \n 7.0 | \n Sweden | \n
\n \n 164 | \n 2021 | \n SE | \n 0.0 | \n 2015 | \n -1.0 | \n 7.0 | \n Sweden | \n
\n \n
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165 rows × 7 columns
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},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = (appln_f.groupby(['filing_year',\"appln_auth\"])[record_col]\n",
" .nunique(dropna=False).unstack()\n",
" .fillna(0)\n",
" .stack()\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
"\n",
"data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n",
"\n",
"data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n",
" on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"data = data.sort_values(by =[\"appln_auth\",\"filing_year\"], ascending=[True,True])\n",
"data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n",
"data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n",
"data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n",
"data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 67,
"outputs": [
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"fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
" y=record_col,\n",
" x='filing_year'\n",
" ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
" markers=True,\n",
" labels={\n",
" record_col: 'Number of co-patents',\n",
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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",
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" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"fig.update_layout({'template':\"plotly\"})\n",
"fig.update_yaxes(title='')\n",
"fig.update_xaxes(title='')\n",
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"\n",
"figsuper_ppt['layout'][\"font\"][\"size\"]=22\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",
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"source": [
"fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n",
" y=record_col+\"_relative_growth\",\n",
" x='filing_year'\n",
" ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n",
" markers=True,\n",
" labels={\n",
" record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n",
" },\n",
" title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n",
"fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n",
"fig.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"fig.update_layout({'template':\"plotly\"})\n",
"fig.update_yaxes(title='')\n",
"fig.update_xaxes(title='')\n",
"fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n",
"# fig.show(config= dict(displayModeBar = False, responsive = True))\n",
"\n",
"figsuper_ppt = go.Figure(fig)\n",
"\n",
"figsuper_ppt['layout'][\"font\"][\"size\"]=22\n",
"for a in figsuper_ppt['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 22\n",
"\n",
"s=16\n",
"figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n",
"figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n",
"figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n",
"figsuper_ppt.show()"
],
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}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
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},
{
"cell_type": "code",
"execution_count": 131,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"EP not found in ISO2\n",
"European Patent Office not found in regex\n"
]
},
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\n \n 141356 | \n 512140061 | \n NL | \n 71325506 | \n 1 | \n 0 | \n JAN VAN DER HORST | \n NL | \n NaN | \n 171325506 | \n JAN VAN DER HORST | \n Netherlands | \n Netherlands | \n NLD | \n EU | \n NLD | \n NLD | \n EU | \n
\n \n 141273 | \n 488981411 | \n EP | \n 69749947 | \n 2 | \n 0 | \n Zuo, Fei | \n CN | \n NaN | \n 169749947 | \n Zuo, Fei | \n China | \n European Patent Office | \n CHN | \n China | \n CHN | \n not found | \n EPO | \n
\n \n 124034 | \n 407239903 | \n EP | \n 47903112 | \n 4 | \n 0 | \n Wichmann, Daniel, Dr. | \n DE | \n NaN | \n 147903112 | \n Wichmann, Daniel, Dr. | \n Germany | \n European Patent Office | \n DEU | \n EU | \n DEU | \n not found | \n EPO | \n
\n \n
\n
100 rows × 17 columns
\n
"
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n",
"# names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n",
"\n",
"# appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
"#\n",
"# appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n",
"#\n",
"# tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
"# # tls_801.head()\n",
"# scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
"# (tls_801.ctry_code == 'CN')|\n",
"# (tls_801.ctry_code == 'NO')|\n",
"# (tls_801.ctry_code == 'CH')|\n",
"# (tls_801.ctry_code == 'UK'))]\n",
"# scope_countries = scope_df[\"ctry_code\"].unique()\n",
"#\n",
"# appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n",
"\n",
"for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
" appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n",
"\n",
"appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n",
"appln_pers_f[\"ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n",
"appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n",
"\n",
"appln_pers_f[\"auth_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"appln_auth_fullname\"], to='ISO3')\n",
"appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\" if x!='not found' else \"EPO\")\n",
"\n",
"appln_pers_f.sample(100)"
],
"metadata": {
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},
{
"cell_type": "code",
"execution_count": 93,
"outputs": [
{
"data": {
"text/plain": "array(['DEU', 'FRA', 'SWE', 'ESP', 'BEL', 'CHN', 'CHE', 'AUT', 'FIN',\n 'NLD', 'GRC', 'ITA', 'CZE', 'HUN', 'DNK', 'IRL', 'POL', 'LUX',\n 'CYP', 'PRT', 'NOR', 'ROU'], dtype=object)"
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},
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},
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"tickfont": {
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},
"legend": {
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},
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},
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},
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},
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"source": [
"color_discrete_map= {'China': '#EF553B',\n",
" 'EU': '#636EFA',\n",
" 'Non-EU associate': '#00CC96',\n",
" 'EPO':'#9F2B68'}\n",
"\n",
"labeld_dict = dict({\n",
" record_col: 'Number of co-patents',\n",
" \"person_ctry_type\": \"Country type\",\n",
" \"han_label\": \"Name\",\n",
" \"Country_Type\":\"Country type\",\n",
" \"Eurovoc_Class\":\"Region\"\n",
" },**c_dict)\n",
"\n",
"for ctry in [\"China\",'EU']:\n",
" ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n",
" for role in [\"invt\",\"applt\"]:\n",
" pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n",
" data = (pers_sub\n",
" .groupby([\"han_id\",'han_name'],as_index=False)\n",
" .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"})\n",
" .sort_values(by=\"appln_id\", ascending=False)\n",
" .head(15))\n",
" data[\"han_label\"] = data[\"han_name\"].str.upper().str.strip(\".\")+ \" (\"+data[\"person_ctry_ISO3\"]+\")\"\n",
"\n",
" fig = px.bar(data, x=record_col, y=\"han_label\", color=\"person_ctry_type\",\n",
" color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n",
" fig.update_layout(font_family=\"Montserrat\",\n",
" yaxis={'categoryorder':'total ascending'})\n",
"\n",
" figsuper = fig\n",
" figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
" figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"\n",
" figsuper['layout'][\"font\"][\"size\"]=12\n",
" for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
" figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
"\n",
" figsuper.update_layout(uniformtext_minsize=10)\n",
"\n",
" figsuper.show(config= dict(displayModeBar = False, responsive = True))\n",
"\n",
" figsuper.show()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 140,
"outputs": [
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"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",
"c_dict = {\"count\":\"Number of co-publications\",\n",
" \"percent_of_copubs\":\"Percent of co-publications\",\n",
" \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n",
"\n",
"labeld_dict = dict({\n",
" record_col+\"_percent\": 'Percent of related co-patents',\n",
" \"person_ctry_code_fullname\": \"Country\",\n",
" \"person_ctry_type\": \"Country type\",\n",
" \"appln_auth_fullname\": \"Authority\",\n",
" \"auth_ctry_type\": \"Authority type\",\n",
" \"Country_Type\":\"Country type\",\n",
" \"Eurovoc_Class\":\"Region\"\n",
" },**c_dict)\n",
"\n",
"for c in [\"person_ctry_code\",\"appln_auth\"]:\n",
" pers_sub = appln_pers_f\n",
" if c == \"person_ctry_code\":\n",
" for role in [\"invt\",\"applt\"]:\n",
" pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n",
"\n",
" data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n",
" data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
" fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n",
" labels = labeld_dict)\n",
" fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
" yaxis={'categoryorder':'total ascending'},)\n",
" figsuper = fig\n",
" figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
" figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"\n",
" figsuper['layout'][\"font\"][\"size\"]=12\n",
" for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
" figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
"\n",
" figsuper.update_layout(uniformtext_minsize=10)\n",
" figsuper.show()\n",
" else:\n",
" data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n",
" data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n",
" fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n",
" labels = labeld_dict)\n",
" fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n",
" yaxis={'categoryorder':'total ascending'},)\n",
" figsuper = fig\n",
" figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n",
" figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
" figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"\n",
" figsuper['layout'][\"font\"][\"size\"]=12\n",
" for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
" figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n",
"\n",
" figsuper.update_layout(uniformtext_minsize=10)\n",
" figsuper.show()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 126,
"outputs": [
{
"data": {
"text/plain": " appln_auth appln_id appln_id_percent\n0 AT 6016 0.009362\n1 BE 1772 0.002758\n2 BG 229 0.000356\n3 CH 1926 0.002997\n4 CN 304654 0.474115\n5 CZ 1373 0.002137\n6 DE 124035 0.193028\n7 DK 1480 0.002303\n8 EE 46 0.000072\n9 EP 54082 0.084165\n10 ES 8484 0.013203\n11 FI 4719 0.007344\n12 FR 57391 0.089314\n13 GB 51069 0.079476\n14 GR 515 0.000801\n15 HR 135 0.000210\n16 HU 807 0.001256\n17 IE 70 0.000109\n18 IT 5535 0.008614\n19 LT 58 0.000090\n20 LU 1054 0.001640\n21 LV 60 0.000093\n22 MA 363 0.000565\n23 NL 7377 0.011480\n24 PL 1854 0.002885\n25 PT 385 0.000599\n26 RO 235 0.000366\n27 SE 6207 0.009660\n28 SI 430 0.000669\n29 SK 213 0.000331",
"text/html": "\n\n
\n \n \n | \n appln_auth | \n appln_id | \n appln_id_percent | \n
\n \n \n \n 0 | \n AT | \n 6016 | \n 0.009362 | \n
\n \n 1 | \n BE | \n 1772 | \n 0.002758 | \n
\n \n 2 | \n BG | \n 229 | \n 0.000356 | \n
\n \n 3 | \n CH | \n 1926 | \n 0.002997 | \n
\n \n 4 | \n CN | \n 304654 | \n 0.474115 | \n
\n \n 5 | \n CZ | \n 1373 | \n 0.002137 | \n
\n \n 6 | \n DE | \n 124035 | \n 0.193028 | \n
\n \n 7 | \n DK | \n 1480 | \n 0.002303 | \n
\n \n 8 | \n EE | \n 46 | \n 0.000072 | \n
\n \n 9 | \n EP | \n 54082 | \n 0.084165 | \n
\n \n 10 | \n ES | \n 8484 | \n 0.013203 | \n
\n \n 11 | \n FI | \n 4719 | \n 0.007344 | \n
\n \n 12 | \n FR | \n 57391 | \n 0.089314 | \n
\n \n 13 | \n GB | \n 51069 | \n 0.079476 | \n
\n \n 14 | \n GR | \n 515 | \n 0.000801 | \n
\n \n 15 | \n HR | \n 135 | \n 0.000210 | \n
\n \n 16 | \n HU | \n 807 | \n 0.001256 | \n
\n \n 17 | \n IE | \n 70 | \n 0.000109 | \n
\n \n 18 | \n IT | \n 5535 | \n 0.008614 | \n
\n \n 19 | \n LT | \n 58 | \n 0.000090 | \n
\n \n 20 | \n LU | \n 1054 | \n 0.001640 | \n
\n \n 21 | \n LV | \n 60 | \n 0.000093 | \n
\n \n 22 | \n MA | \n 363 | \n 0.000565 | \n
\n \n 23 | \n NL | \n 7377 | \n 0.011480 | \n
\n \n 24 | \n PL | \n 1854 | \n 0.002885 | \n
\n \n 25 | \n PT | \n 385 | \n 0.000599 | \n
\n \n 26 | \n RO | \n 235 | \n 0.000366 | \n
\n \n 27 | \n SE | \n 6207 | \n 0.009660 | \n
\n \n 28 | \n SI | \n 430 | \n 0.000669 | \n
\n \n 29 | \n SK | \n 213 | \n 0.000331 | \n
\n \n
\n
"
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# top 25\n",
"# - inventor\n",
"# - applicant\n",
"# in china, EU, EUextended"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 111,
"outputs": [
{
"data": {
"text/plain": " person_id psn_sector han_id \n269495 48715290 COMPANY 148715290 \\\n225401 45096499 GOV NON-PROFIT 986555 \n426484 58455492 NaN 158455492 \n50805 15325724 INDIVIDUAL 115325724 \n112503 28064269 NaN 128064269 \n... ... ... ... \n464708 59491689 NaN 159491689 \n169950 42286280 INDIVIDUAL 4099527 \n485679 60214508 NaN 160214508 \n542210 62188978 NaN 162188978 \n752136 81407527 NaN 181407527 \n\n han_name \n269495 TECHFLOW MARINE LIMITED \n225401 FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC... \n426484 DING ZHIJIAO \n50805 FOURNIER, THIERRY \n112503 ADRIAN WALLER \n... ... \n464708 LIU YANZHI \n169950 RIEMANN STEFAN \n485679 WAN BO \n542210 CHU WEI \n752136 GARCIA MONSONIS, Javier \n\n[100 rows x 4 columns]",
"text/html": "\n\n
\n \n \n | \n person_id | \n psn_sector | \n han_id | \n han_name | \n
\n \n \n \n 269495 | \n 48715290 | \n COMPANY | \n 148715290 | \n TECHFLOW MARINE LIMITED | \n
\n \n 225401 | \n 45096499 | \n GOV NON-PROFIT | \n 986555 | \n FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC... | \n
\n \n 426484 | \n 58455492 | \n NaN | \n 158455492 | \n DING ZHIJIAO | \n
\n \n 50805 | \n 15325724 | \n INDIVIDUAL | \n 115325724 | \n FOURNIER, THIERRY | \n
\n \n 112503 | \n 28064269 | \n NaN | \n 128064269 | \n ADRIAN WALLER | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 464708 | \n 59491689 | \n NaN | \n 159491689 | \n LIU YANZHI | \n
\n \n 169950 | \n 42286280 | \n INDIVIDUAL | \n 4099527 | \n RIEMANN STEFAN | \n
\n \n 485679 | \n 60214508 | \n NaN | \n 160214508 | \n WAN BO | \n
\n \n 542210 | \n 62188978 | \n NaN | \n 162188978 | \n CHU WEI | \n
\n \n 752136 | \n 81407527 | \n NaN | \n 181407527 | \n GARCIA MONSONIS, Javier | \n
\n \n
\n
100 rows × 4 columns
\n
"
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n",
"appln_pers_haninfo.sample(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 80,
"outputs": [
{
"data": {
"text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n1322402 523940692 EP A 2019 2 \\\n765767 476494118 EP A 2017 1 \n34130 378211797 DE U 2011 1 \n789796 480385078 GB A 2017 1 \n701348 451567585 NL A 2016 1 \n... ... ... ... ... ... \n1361812 536054394 EP A 2020 1 \n242632 424885398 DE A 2013 1 \n1108772 503145148 EP A 2018 1 \n538788 447597102 EP A 2015 1 \n272481 412013115 EP A 2013 1 \n\n nb_inventors ipc_class_symbol ipc_class_level cpc_id \n1322402 2 B60W 50/14 A B60W50/14 \\\n765767 4 B60H 1/34 A B60H1/34 \n34130 0 F04B 17/04 A F04B17/04 \n789796 2 H04W 76/14 A H04W76/14 \n701348 3 E04F 13/077 A E04F13/077 \n... ... ... ... ... \n1361812 4 G05B 23/02 A G05B23/02 \n242632 1 F02M 51/06 A F02M51/06 \n1108772 2 G08G 1/01 A G08G1/01 \n538788 6 H01F 38/30 A H01F38/30 \n272481 3 C04B 28/04 A C04B28/04 \n\n ipc_class ipc_classname \n1322402 B-Operations; transporting Operations; transporting \n765767 B-Operations; transporting Operations; transporting \n34130 F-Engineering Engineering \n789796 H-Electricity Electricity \n701348 E-Fixed constructions Fixed constructions \n... ... ... \n1361812 G-Physics Physics \n242632 F-Engineering Engineering \n1108772 G-Physics Physics \n538788 H-Electricity Electricity \n272481 C-Chemistry; metallurgy Chemistry; metallurgy \n\n[100 rows x 11 columns]",
"text/html": "\n\n
\n \n \n | \n appln_id | \n appln_auth | \n appln_kind | \n filing_year | \n nb_applicants | \n nb_inventors | \n ipc_class_symbol | \n ipc_class_level | \n cpc_id | \n ipc_class | \n ipc_classname | \n
\n \n \n \n 1322402 | \n 523940692 | \n EP | \n A | \n 2019 | \n 2 | \n 2 | \n B60W 50/14 | \n A | \n B60W50/14 | \n B-Operations; transporting | \n Operations; transporting | \n
\n \n 765767 | \n 476494118 | \n EP | \n A | \n 2017 | \n 1 | \n 4 | \n B60H 1/34 | \n A | \n B60H1/34 | \n B-Operations; transporting | \n Operations; transporting | \n
\n \n 34130 | \n 378211797 | \n DE | \n U | \n 2011 | \n 1 | \n 0 | \n F04B 17/04 | \n A | \n F04B17/04 | \n F-Engineering | \n Engineering | \n
\n \n 789796 | \n 480385078 | \n GB | \n A | \n 2017 | \n 1 | \n 2 | \n H04W 76/14 | \n A | \n H04W76/14 | \n H-Electricity | \n Electricity | \n
\n \n 701348 | \n 451567585 | \n NL | \n A | \n 2016 | \n 1 | \n 3 | \n E04F 13/077 | \n A | \n E04F13/077 | \n E-Fixed constructions | \n Fixed constructions | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 1361812 | \n 536054394 | \n EP | \n A | \n 2020 | \n 1 | \n 4 | \n G05B 23/02 | \n A | \n G05B23/02 | \n G-Physics | \n Physics | \n
\n \n 242632 | \n 424885398 | \n DE | \n A | \n 2013 | \n 1 | \n 1 | \n F02M 51/06 | \n A | \n F02M51/06 | \n F-Engineering | \n Engineering | \n
\n \n 1108772 | \n 503145148 | \n EP | \n A | \n 2018 | \n 1 | \n 2 | \n G08G 1/01 | \n A | \n G08G1/01 | \n G-Physics | \n Physics | \n
\n \n 538788 | \n 447597102 | \n EP | \n A | \n 2015 | \n 1 | \n 6 | \n H01F 38/30 | \n A | \n H01F38/30 | \n H-Electricity | \n Electricity | \n
\n \n 272481 | \n 412013115 | \n EP | \n A | \n 2013 | \n 1 | \n 3 | \n C04B 28/04 | \n A | \n C04B28/04 | \n C-Chemistry; metallurgy | \n Chemistry; metallurgy | \n
\n \n
\n
100 rows × 11 columns
\n
"
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_ipc_f = pd.read_csv(f\"{fromdir}/first-filings-with-IPC-raw.csv\", header=None,\n",
" names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n",
" 'ipc_class_symbol','ipc_class_level'])\n",
"\n",
"appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n",
"appln_ipc_f = appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])]\n",
"\n",
"appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n",
"appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n",
"appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n",
" \"B\":\"Performing operations; transporting\",\n",
" \"B\":\"Operations; transporting\",\n",
" \"C\":\"Chemistry; metallurgy\",\n",
" \"D\":\"Textiles; paper\",\n",
" \"E\":\"Fixed constructions\",\n",
" \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n",
" \"F\":\"Engineering\",\n",
" \"G\":\"Physics\",\n",
" \"H\":\"Electricity\"})\n",
"\n",
"appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]\n",
"appln_ipc_f.sample(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 81,
"outputs": [],
"source": [
"group = 'ipc_class'\n",
"data = appln_ipc_f.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n",
"\n",
"fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',text_auto=True,\n",
" labels={\n",
" record_col: 'Number of co-publications',\n",
" group: \"\",\n",
" },\n",
" title=\"Distribution of Domains\", template='plotly')\n",
"fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n",
"fig.update_traces(hovertemplate='%{x:d}')\n",
"fig.update_traces(textfont_size=12, textangle=0, textposition=\"outside\", cliponaxis=True)\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_layout(yaxis={'categoryorder':'total ascending'})\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"dom_distr = go.Figure(fig)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 82,
"outputs": [
{
"data": {
"text/plain": " filing_year ipc_class appln_id filing_year_relative_growth \n0 2011 A-Human necessities 7.0 2011 \\\n1 2012 A-Human necessities 5.0 2011 \n2 2013 A-Human necessities 18.0 2011 \n3 2014 A-Human necessities 9.0 2011 \n4 2015 A-Human necessities 10.0 2011 \n.. ... ... ... ... \n83 2017 H-Electricity 71.0 2011 \n84 2018 H-Electricity 65.0 2011 \n85 2019 H-Electricity 27.0 2011 \n86 2020 H-Electricity 27.0 2011 \n87 2021 H-Electricity 11.0 2011 \n\n appln_id_relative_growth appln_id_cumsum \n0 0.000000 7.0 \n1 -0.285714 12.0 \n2 1.571429 30.0 \n3 0.285714 39.0 \n4 0.428571 49.0 \n.. ... ... \n83 3.437500 213.0 \n84 3.062500 278.0 \n85 0.687500 305.0 \n86 0.687500 332.0 \n87 -0.312500 343.0 \n\n[88 rows x 6 columns]",
"text/html": "\n\n
\n \n \n | \n filing_year | \n ipc_class | \n appln_id | \n filing_year_relative_growth | \n appln_id_relative_growth | \n appln_id_cumsum | \n
\n \n \n \n 0 | \n 2011 | \n A-Human necessities | \n 7.0 | \n 2011 | \n 0.000000 | \n 7.0 | \n
\n \n 1 | \n 2012 | \n A-Human necessities | \n 5.0 | \n 2011 | \n -0.285714 | \n 12.0 | \n
\n \n 2 | \n 2013 | \n A-Human necessities | \n 18.0 | \n 2011 | \n 1.571429 | \n 30.0 | \n
\n \n 3 | \n 2014 | \n A-Human necessities | \n 9.0 | \n 2011 | \n 0.285714 | \n 39.0 | \n
\n \n 4 | \n 2015 | \n A-Human necessities | \n 10.0 | \n 2011 | \n 0.428571 | \n 49.0 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 83 | \n 2017 | \n H-Electricity | \n 71.0 | \n 2011 | \n 3.437500 | \n 213.0 | \n
\n \n 84 | \n 2018 | \n H-Electricity | \n 65.0 | \n 2011 | \n 3.062500 | \n 278.0 | \n
\n \n 85 | \n 2019 | \n H-Electricity | \n 27.0 | \n 2011 | \n 0.687500 | \n 305.0 | \n
\n \n 86 | \n 2020 | \n H-Electricity | \n 27.0 | \n 2011 | \n 0.687500 | \n 332.0 | \n
\n \n 87 | \n 2021 | \n H-Electricity | \n 11.0 | \n 2011 | \n -0.312500 | \n 343.0 | \n
\n \n
\n
88 rows × 6 columns
\n
"
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n",
" .fillna(0)\n",
" .stack()\n",
" .reset_index()\n",
" .rename(columns={0:record_col}))\n",
"data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n",
" on='ipc_class', suffixes=[None,\"_relative_growth\"])\n",
"data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n",
"\n",
"data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n",
"data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n",
"data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 83,
"outputs": [],
"source": [
"group = ['filing_year','ipc_class']\n",
"fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True, labels={\n",
" record_col: 'Number of co-publications',\n",
" group[-1]: \"Domain\",\n",
" },\n",
" title=\"Yearly output of co-publications\", template='plotly')\n",
"fig.update_traces(hovertemplate='%{y:d}')\n",
"fig.update_layout(hovermode='x unified')\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"\n",
"year_output_by_domain = go.Figure(fig)\n",
"\n",
"fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True, labels={\n",
" record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n",
" group[-1]: \"Domain\",\n",
" },\n",
" title=\"Relative growth in the output of co-publications\", template='plotly')\n",
"\n",
"fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n",
"fig.update_traces(hovertemplate='%{y:.0f}00%')\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"\n",
"rel_output_by_domain = go.Figure(fig)\n",
"\n",
"\n",
"fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n",
" labels={\n",
" record_col+\"_cumsum\": 'Cumulative number of co-publications',\n",
" group[-1]: \"Domain\",\n",
" },\n",
" title=\"Cumulative number of co-publications\", template='plotly')\n",
"fig.update_traces(hovertemplate='%{y:d}')\n",
"fig.update_layout(hovermode='x unified')\n",
"fig.add_shape(\n",
" # Rectangle with reference to the plot\n",
" type=\"rect\",\n",
" xref=\"paper\",\n",
" yref=\"paper\",\n",
" x0=0,\n",
" y0=0,\n",
" x1=1.0,\n",
" y1=1.0,\n",
" line=dict(\n",
" color=\"black\",\n",
" width=0.5,\n",
" )\n",
" )\n",
"fig.update_yaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"fig.update_xaxes(\n",
" showgrid=True,\n",
" ticks=\"outside\")\n",
"\n",
"cumsum_by_domain = go.Figure(fig)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 87,
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"alignmentgroup": "True",
"cliponaxis": true,
"hovertemplate": "%{x:d}",
"legendgroup": "A-Human necessities",
"marker": {
"color": "#636efa",
"pattern": {
"shape": ""
}
},
"name": "A-Human necessities",
"offsetgroup": "A-Human necessities",
"orientation": "h",
"showlegend": false,
"textangle": 0,
"textfont": {
"size": 12
},
"textposition": "outside",
"texttemplate": "%{x}",
"x": [
108
],
"xaxis": "x",
"y": [
"A-Human necessities"
],
"yaxis": "y",
"type": "bar"
},
{
"alignmentgroup": "True",
"cliponaxis": true,
"hovertemplate": "%{x:d}",
"legendgroup": "B-Operations; transporting",
"marker": {
"color": "#EF553B",
"pattern": {
"shape": ""
}
},
"name": "B-Operations; transporting",
"offsetgroup": "B-Operations; transporting",
"orientation": "h",
"showlegend": false,
"textangle": 0,
"textfont": {
"size": 12
},
"textposition": "outside",
"texttemplate": "%{x}",
"x": [
357
],
"xaxis": "x",
"y": [
"B-Operations; transporting"
],
"yaxis": "y",
"type": "bar"
},
{
"alignmentgroup": "True",
"cliponaxis": true,
"hovertemplate": "%{x:d}",
"legendgroup": "C-Chemistry; metallurgy",
"marker": {
"color": "#00cc96",
"pattern": {
"shape": ""
}
},
"name": "C-Chemistry; metallurgy",
"offsetgroup": "C-Chemistry; metallurgy",
"orientation": "h",
"showlegend": false,
"textangle": 0,
"textfont": {
"size": 12
},
"textposition": "outside",
"texttemplate": "%{x}",
"x": [
150
],
"xaxis": "x",
"y": [
"C-Chemistry; metallurgy"
],
"yaxis": "y",
"type": "bar"
},
{
"alignmentgroup": "True",
"cliponaxis": true,
"hovertemplate": "%{x:d}",
"legendgroup": "D-Textiles; paper",
"marker": {
"color": "#ab63fa",
"pattern": {
"shape": ""
}
},
"name": "D-Textiles; paper",
"offsetgroup": "D-Textiles; paper",
"orientation": "h",
"showlegend": false,
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"y": 1.0,
"yanchor": "bottom",
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"x": 0.225,
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"y": 0.375,
"yanchor": "bottom",
"yref": "paper"
},
{
"font": {
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"showarrow": false,
"text": "Relative growth of co-patents",
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}
},
"text/html": ""
},
"metadata": {},
"output_type": "display_data"
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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",
" figsuper.add_trace(trace,\n",
" row=1, col=2\n",
" )\n",
"\n",
"for trace in list(year_output_by_domain.select_traces()):\n",
" trace.showlegend=False\n",
" figsuper.add_trace(trace,\n",
" row=2, col=1\n",
" )\n",
"\n",
"for trace in list(rel_output_by_domain.select_traces()):\n",
" trace.showlegend=False\n",
" figsuper.add_trace(trace,\n",
" row=2, col=2\n",
" )\n",
"\n",
"\n",
"figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n",
"figsuper.update_yaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"figsuper.update_xaxes(\n",
" showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n",
" ticks=\"outside\")\n",
"\n",
"figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n",
"figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n",
"figsuper['layout']['yaxis2'].update(tickformat=\".0f\")\n",
"figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n",
"figsuper['layout'][f'xaxis'][\"range\"] = [0,400]\n",
"for i in range(2,5):\n",
" figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n",
"figsuper['layout'][\"font\"][\"size\"]=12\n",
"for a in figsuper['layout'][\"annotations\"]:\n",
" a[\"font\"][\"size\"] = 14\n",
"figsuper.show(config= dict(displayModeBar = False, responsive = True))"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 37,
"outputs": [
{
"data": {
"text/plain": "1964"
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_ipc_f[\"filing_year\"].min()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 34,
"outputs": [
{
"data": {
"text/plain": "615464"
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_ipc_f[\"appln_id\"].nunique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 35,
"outputs": [
{
"data": {
"text/plain": "591442"
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_ipc_f.merge(cpc_def, on=\"cpc_id\")[\"appln_id\"].nunique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 29,
"outputs": [
{
"data": {
"text/plain": "array(['A', 'S', 'C'], dtype=object)"
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_ipc_f[\"ipc_class_level\"].unique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 133,
"outputs": [],
"source": [
"tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n",
"# tls_801.head()\n",
"scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n",
" (tls_801.ctry_code == 'CN')|\n",
" (tls_801.ctry_code == 'NO')|\n",
" (tls_801.ctry_code == 'CH')|\n",
" (tls_801.ctry_code == 'UK'))]\n",
"scope_countries = scope_df[\"ctry_code\"].unique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 134,
"outputs": [
{
"data": {
"text/plain": "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI',\n 'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL',\n 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'], dtype=object)"
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scope_countries"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 25,
"outputs": [
{
"data": {
"text/plain": " person_id appln_id applt_seq_nr invt_seq_nr\n0 1 413601768 1 0\n1 21 332015605 1 0\n2 21 333490084 1 0\n3 21 335903805 1 0\n4 76 352908776 1 0\n.. ... ... ... ...\n95 220 417147567 1 0\n96 220 417932952 1 0\n97 220 420712526 1 0\n98 220 420927947 1 0\n99 220 420947631 1 0\n\n[100 rows x 4 columns]",
"text/html": "\n\n
\n \n \n | \n person_id | \n appln_id | \n applt_seq_nr | \n invt_seq_nr | \n
\n \n \n \n 0 | \n 1 | \n 413601768 | \n 1 | \n 0 | \n
\n \n 1 | \n 21 | \n 332015605 | \n 1 | \n 0 | \n
\n \n 2 | \n 21 | \n 333490084 | \n 1 | \n 0 | \n
\n \n 3 | \n 21 | \n 335903805 | \n 1 | \n 0 | \n
\n \n 4 | \n 76 | \n 352908776 | \n 1 | \n 0 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 95 | \n 220 | \n 417147567 | \n 1 | \n 0 | \n
\n \n 96 | \n 220 | \n 417932952 | \n 1 | \n 0 | \n
\n \n 97 | \n 220 | \n 420712526 | \n 1 | \n 0 | \n
\n \n 98 | \n 220 | \n 420927947 | \n 1 | \n 0 | \n
\n \n 99 | \n 220 | \n 420947631 | \n 1 | \n 0 | \n
\n \n
\n
100 rows × 4 columns
\n
"
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"appln_pers.head(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 26,
"outputs": [
{
"ename": "KeyError",
"evalue": "'person_ctry_code'",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
"File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
"File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
"\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgr\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\n\u001B[0;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_auth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\groupby\\generic.py:1741\u001B[0m, in \u001B[0;36mDataFrameGroupBy.filter\u001B[1;34m(self, func, dropna, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m name, group \u001B[38;5;129;01min\u001B[39;00m gen:\n\u001B[0;32m 1739\u001B[0m \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__setattr__\u001B[39m(group, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m\"\u001B[39m, name)\n\u001B[1;32m-> 1741\u001B[0m res \u001B[38;5;241m=\u001B[39m func(group, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1743\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1744\u001B[0m res \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39msqueeze()\n",
"Cell \u001B[1;32mIn[26], line 1\u001B[0m, in \u001B[0;36m\u001B[1;34m(gr)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m appln_pers\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 2\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3747\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
"File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3653\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3654\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3655\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3656\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
"\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'"
]
}
],
"source": [
"appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n",
" (gr[\"appln_auth\"]==\"CN\")).any())\n",
"appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n",
" (gr[\"appln_auth\"].isin(scope_countries))).any())\n",
"appln_scope[\"appln_id\"].nunique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_scope"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_scope[appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_scope[~appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_cpc[\"cpc_id\"] = appln_cpc[\"cpc_class_symbol\"].str.replace(\" \",\"\")\n",
"\n",
"appln_cpc_merge = appln_cpc.merge(cpc_def,on='cpc_id')\n",
"print (f\"CPC Merge loss: {(1-len(appln_cpc_merge)/len(appln_cpc))*100}%\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"len(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")[\"appln_nr\"].nunique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\")[\"appln_id\"].nunique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"cpc_def[cpc_def[\"data_scope\"]==True]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln.sample(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_title.sample(100)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_data = appln.merge(appln_title, on=\"appln_id\")\n",
"appln_data.head()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_data[\"appln_auth\"].unique()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_pers"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n",
" lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n",
"persn = pers.merge(pers_sector_primary, on='han_id')\n",
"persn"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"pers_sector_primary"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n",
"appln_merge.to_excel(\"appln_data.xlsx\", index=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"person_merge = appln_pers.merge(pers,on=\"person_id\")\n",
"person_merge.to_excel(\"person_data.xlsx\", index=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"pers[\"person_ctry_code\"].unique()"
],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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