{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 2, "outputs": [], "source": [ "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", "# tls_801.head()\n", "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", " (tls_801.ctry_code == 'NO')|\n", " (tls_801.ctry_code == 'CH')|\n", " (tls_801.ctry_code == 'GB'))]\n", "scope_countries = scope_df[\"ctry_code\"].unique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 44, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ctry_code iso_alpha3 st3_name organisation_flag continent eu_member \n", "137 MA MAR Morocco Africa \\\n", "\n", " epo_member oecd_member discontinued \n", "137 \n" ] } ], "source": [ "print(tls_801[tls_801[\"ctry_code\"]==\"MA\"])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "outdir=\"raw_files_csv\"" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "data": { "text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n8641708 531710630 CN 201811380088 A 2018-11-20 \\\n5246513 489770910 CN 201711004155 A 2017-10-24 \n6383260 503205376 CN 201810447617 A 2018-05-11 \n1437288 417129813 GR 20120100519 A 2012-10-12 \n4304665 479561975 CN 201510807721 A 2015-11-19 \n8099995 524172492 CN 201911055629 A 2019-10-31 \n2496404 443580404 CN 201510312381 A 2015-06-08 \n2862771 448112136 CN 201510736913 A 2015-11-03 \n8746800 533540106 CN 202010109483 A 2020-02-22 \n12407625 580940603 CN 202210840515 A 2022-07-18 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n8641708 2018 201811380088 PI NaN \\\n5246513 2017 201711004155 PI NaN \n6383260 2018 201810447617 PI NaN \n1437288 2012 20120100519 PI NaN \n4304665 2015 201510807721 PI NaN \n8099995 2019 201911055629 PI NaN \n2496404 2015 2015103123811 PI NaN \n2862771 2015 2015107369134 PI NaN \n8746800 2020 202010109483 PI NaN \n12407625 2022 202210840515 PI NaN \n\n internat_appln_id ... earliest_pat_publn_id granted \n8641708 0 ... 531710631 N \\\n5246513 0 ... 489770911 N \n6383260 0 ... 503205377 N \n1437288 0 ... 417129814 Y \n4304665 0 ... 479561976 N \n8099995 0 ... 524172493 N \n2496404 0 ... 443580405 N \n2862771 0 ... 448112137 N \n8746800 0 ... 533540107 Y \n12407625 0 ... 580940604 N \n\n docdb_family_id inpadoc_family_id docdb_family_size \n8641708 70744210 531710630 1 \\\n5246513 61141626 489770910 1 \n6383260 64213024 503205376 1 \n1437288 50483672 417129813 1 \n4304665 58885008 479561975 1 \n8099995 68978631 524172492 1 \n2496404 53812114 443580404 1 \n2862771 54991770 448112136 1 \n8746800 71149074 533540106 1 \n12407625 83518531 580940603 1 \n\n nb_citing_docdb_fam nb_applicants nb_inventors is_prior is_pct \n8641708 0 1 1 1 0 \n5246513 0 1 5 1 0 \n6383260 0 1 3 1 0 \n1437288 0 1 1 1 0 \n4304665 0 1 1 1 0 \n8099995 0 1 3 1 0 \n2496404 3 2 3 1 0 \n2862771 2 1 5 1 0 \n8746800 0 1 5 1 0 \n12407625 0 1 6 1 0 \n\n[10 rows x 28 columns]", "text/html": "
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appln_idappln_authappln_nrappln_kindappln_filing_dateappln_filing_yearappln_nr_originalipr_typereceiving_officeinternat_appln_id...earliest_pat_publn_idgranteddocdb_family_idinpadoc_family_iddocdb_family_sizenb_citing_docdb_famnb_applicantsnb_inventorsis_prioris_pct
8641708531710630CN201811380088A2018-11-202018201811380088PINaN0...531710631N70744210531710630101110
5246513489770910CN201711004155A2017-10-242017201711004155PINaN0...489770911N61141626489770910101510
6383260503205376CN201810447617A2018-05-112018201810447617PINaN0...503205377N64213024503205376101310
1437288417129813GR20120100519A2012-10-12201220120100519PINaN0...417129814Y50483672417129813101110
4304665479561975CN201510807721A2015-11-192015201510807721PINaN0...479561976N58885008479561975101110
8099995524172492CN201911055629A2019-10-312019201911055629PINaN0...524172493N68978631524172492101310
2496404443580404CN201510312381A2015-06-0820152015103123811PINaN0...443580405N53812114443580404132310
2862771448112136CN201510736913A2015-11-0320152015107369134PINaN0...448112137N54991770448112136121510
8746800533540106CN202010109483A2020-02-222020202010109483PINaN0...533540107Y71149074533540106101510
12407625580940603CN202210840515A2022-07-182022202210840515PINaN0...580940604N83518531580940603101610
\n

10 rows × 28 columns

\n
" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln = pd.read_csv(f\"{outdir}/01_patent_subset.csv\", low_memory=False)\n", "appln.sample(10)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 5, "outputs": [ { "data": { "text/plain": "(54368949, 13184309)" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers = pd.read_csv(f\"{outdir}/02_persons_2011_2022.csv\",low_memory=False)\n", "len(appln_pers[\"appln_id\"]), len(appln[\"appln_id\"])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 6, "outputs": [ { "data": { "text/plain": "(54368949, 12646904)" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers[\"appln_id\"].isin(appln[\"appln_id\"]).sum(), appln[\"appln_id\"].isin(appln_pers[\"appln_id\"]).sum()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 7, "outputs": [ { "data": { "text/plain": "12415" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ch_ids = appln_pers[appln_pers[\"person_ctry_code\"] == \"CN\"][\"appln_id\"].unique()\n", "appln_eu_ids = appln_pers[appln_pers[\"person_ctry_code\"].isin(scope_countries)][\"appln_id\"].unique()\n", "\n", "appln_scope_ids = np.intersect1d(appln_ch_ids, appln_eu_ids, assume_unique=False)\n", "appln_scope = appln_pers[((appln_pers[\"appln_id\"].isin(appln_scope_ids)) &\n", " (appln_pers[\"person_ctry_code\"].isin(list(scope_countries) + [\"CN\"])))]\n", "\n", "appln_scope[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 8, "outputs": [ { "data": { "text/plain": "(6584667, 18789)" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers_haninfo = pd.read_csv(f\"first_round/tls_206_scope_v2.csv\",\n", " usecols=[\"person_id\", \"han_id\", \"han_name\", \"psn_sector\"])\n", "\n", "appln_pers_haninfo[\"han_id\"].nunique(), appln_scope[\"han_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "pers_sector_primary = appln_pers_haninfo.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n", " lambda x: \"UNKNOWN\" if len(pd.Series.mode(x))==0 else pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 80, "outputs": [ { "data": { "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n 'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n 'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n 'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type',\n 'psn_sector_primary_x', 'psn_sector_primary_y'],\n dtype='object')" }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers_f.columns" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 81, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12415\n" ] } ], "source": [ "appln_pers_f = appln_scope.merge(pers_sector_primary, on='han_id', how=\"left\").rename(columns={\"psn_sector_primary_y\":\"psn_sector_primary\"})\n", "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_primary\"].fillna(\"UNKNOWN\")\n", "print(appln_pers_f[\"appln_id\"].nunique())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 283, "outputs": [], "source": [ "appln_pers_f[\"han_name\"] = appln_pers_f[\"han_name\"].str.replace(\"Zhejiang Geely Holding Group Co., Ltd.\",\n", " \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\")\n", "\n", "appln_pers_f.loc[appln_pers_f[\"han_name\"] == \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\", \"psn_sector_primary\"] = \"COMPANY\"\n", "appln_pers_f.loc[appln_pers_f[\"han_name\"] == \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\", \"han_id\"] = 3345915" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 282, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 3345915 185816744]\n" ] } ], "source": [ "print(appln_pers_f.loc[appln_pers_f[\"han_name\"] == \"ZHEJIANG GEELY HOLDINGGROUP CO LTD\", \"han_id\"].unique())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 29, "outputs": [], "source": [ "g = appln_pers.groupby('appln_id')[\"person_ctry_code\"]\n", "missing_ctry_data = g.count().rsub(g.size(), axis=0).ge(1).astype(int).reset_index()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 186, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n", "21408 556911606 MA 42012433 2 0 \\\n", "\n", " person_name person_ctry_code han_id han_name \n", "21408 TURTINEN, SAMULI FI 142012433 TURTINEN, SAMULI \\\n", "\n", " psn_sector_x psn_sector_y person_ctry_code_fullname appln_auth_fullname \n", "21408 INDIVIDUAL INDIVIDUAL Finland Morocco \\\n", "\n", " person_ctry_ISO3 person_ctry_type auth_ISO3 auth_ctry_type \n", "21408 FIN EU MAR EU \\\n", "\n", " psn_sector_primary_x psn_sector_primary \n", "21408 INDIVIDUAL INDIVIDUAL \n" ] } ], "source": [ "print(appln_pers_f.sample())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 190, "outputs": [ { "data": { "text/plain": "(10810, 10084)" }, "execution_count": 190, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appl_eu_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n", " (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n", "\n", "appl_ch_applt = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0)&\n", " (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n", "\n", "len(appl_eu_applt),len(appl_ch_applt)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 201, "outputs": [ { "data": { "text/plain": "(7638, 8788)" }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appl_eu_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n", " (appln_pers_f[\"person_ctry_type\"]!=\"China\"))][\"appln_id\"].unique()\n", "\n", "appl_ch_invt = appln_pers_f[((appln_pers_f[\"invt_seq_nr\"]>0)&\n", " (appln_pers_f[\"person_ctry_type\"]==\"China\"))][\"appln_id\"].unique()\n", "\n", "len(appl_eu_invt),len(appl_ch_invt)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 194, "outputs": [ { "data": { "text/plain": "12197" }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]>0))][\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 41, "outputs": [], "source": [ "# missing_ctry_data[missing_ctry_data[\"person_ctry_code\"]>0]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 31, "outputs": [], "source": [ "appln_ana = appln.merge(missing_ctry_data,on=\"appln_id\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 32, "outputs": [ { "data": { "text/plain": "(12646904, 13184309)" }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ana[\"appln_id\"].nunique(),appln[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 45, "outputs": [ { "data": { "text/plain": "" }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ctry_ana = appln_ana.groupby(\"appln_auth\")[\"person_ctry_code\"]\n", "(ctry_ana.sum()/ctry_ana.size()).sort_values().plot(kind=\"bar\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 307, "outputs": [], "source": [ "ctry_ana_data = appln_ana.groupby(\"appln_auth\", as_index=False).agg({\"person_ctry_code\":[\"sum\",\"size\"]})\n", "ctry_ana_data.columns = [\"appln_auth\",\"sum\",\"size\"]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 351, "outputs": [], "source": [ "ctry_ana_data[\"perc\"] = (ctry_ana_data[\"sum\"] / ctry_ana_data[\"size\"])+0.00000001" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 340, "outputs": [], "source": [ "ctry_ana_data[\"appln_auth_fullname\"] = ctry_ana_data[\"appln_auth\"].map(iso2_to_namedict)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 311, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_auth sum size perc\n", "0 AT 30 12228 0.002453\n", "1 BE 22 7389 0.002977\n", "2 BG 801 1432 0.559358\n", "3 CH 2 10377 0.000193\n", "4 CN 11402816 11402826 0.999999\n", "5 CZ 6 6804 0.000882\n", "6 DE 44 403046 0.000109\n", "7 DK 5 3284 0.001523\n", "8 EE 0 185 0.000000\n", "9 EP 388 145452 0.002668\n", "10 ES 1871 18528 0.100982\n", "11 FI 2 8532 0.000234\n", "12 FR 119 141082 0.000843\n", "13 GB 116211 178661 0.650455\n", "14 GR 436 4078 0.106915\n", "15 HR 0 1144 0.000000\n", "16 HU 5 3191 0.001567\n", "17 IE 295 754 0.391247\n", "18 IT 61336 62577 0.980168\n", "19 LT 5 1052 0.004753\n", "20 LU 7 3943 0.001775\n", "21 LV 0 1262 0.000000\n", "22 MA 2002 2481 0.806933\n", "23 NL 4796 17799 0.269453\n", "24 PL 4 40093 0.000100\n", "25 PT 3 2141 0.001401\n", "26 RO 4 8561 0.000467\n", "27 SE 5779 11039 0.523508\n", "28 SI 8 2518 0.003177\n", "29 SK 9 1627 0.005532\n", "30 WO 718 142818 0.005027\n" ] } ], "source": [ "print(ctry_ana_data)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 292, "outputs": [ { "data": { "text/plain": "" }, "execution_count": 292, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ch_ana = appln_ana.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\")[\"person_ctry_code\"]\n", "((1-(ch_ana.sum()/ch_ana.size()))*100).plot(kind=\"line\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 302, "outputs": [], "source": [ "ana_data = appln_ana.query(\"appln_auth=='CN'\").groupby(\"appln_filing_year\", as_index=False).agg({\"person_ctry_code\":[\"sum\",\"size\"]})" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 317, "outputs": [], "source": [ "ana_data.columns = [\"appln_filing_year\",\"sum\",\"size\"]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 314, "outputs": [], "source": [ "ana_data[\"perc\"] = ana_data[\"sum\"] / ana_data[\"size\"]\n", "ana_data[\"c_perc\"] = 1-(ana_data[\"sum\"] / ana_data[\"size\"])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 350, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_auth sum size perc appln_auth_fullname\n", "0 AT 30 12228 0.002453 Austria\n", "1 BE 22 7389 0.002977 Belgium\n", "2 BG 801 1432 0.559358 Bulgaria\n", "3 CH 2 10377 0.000193 Switzerland\n", "4 CN 11402816 11402826 0.999999 China\n", "5 CZ 6 6804 0.000882 Czechia\n", "6 DE 44 403046 0.000109 Germany\n", "7 DK 5 3284 0.001523 Denmark\n", "8 EE 0 185 0.000000 Estonia\n", "9 EP 388 145452 0.002668 EPO\n", "10 ES 1871 18528 0.100982 Spain\n", "11 FI 2 8532 0.000234 Finland\n", "12 FR 119 141082 0.000843 France\n", "13 GB 116211 178661 0.650455 United Kingdom\n", "14 GR 436 4078 0.106915 Greece\n", "15 HR 0 1144 0.000000 Croatia\n", "16 HU 5 3191 0.001567 Hungary\n", "17 IE 295 754 0.391247 Ireland\n", "18 IT 61336 62577 0.980168 Italy\n", "19 LT 5 1052 0.004753 Lithuania\n", "20 LU 7 3943 0.001775 Luxembourg\n", "21 LV 0 1262 0.000000 Latvia\n", "22 MA 2002 2481 0.806933 Morocco\n", "23 NL 4796 17799 0.269453 Netherlands\n", "24 PL 4 40093 0.000100 Poland\n", "25 PT 3 2141 0.001401 Portugal\n", "26 RO 4 8561 0.000467 Romania\n", "27 SE 5779 11039 0.523508 Sweden\n", "28 SI 8 2518 0.003177 Slovenia\n", "29 SK 9 1627 0.005532 Slovakia\n", "30 WO 718 142818 0.005027 WIPO\n" ] } ], "source": [ "print(ctry_ana_data)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 370, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "perc=%{x}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ctry_nan = px.bar(ctry_ana_data, x=\"perc\", y=\"appln_auth_fullname\",text_auto=True,\n", " color_discrete_map={\"China\": 'rgba(255,0,0,0.4)'})\n", "\n", "# Specify the index of the bar you want to color differently\n", "index_to_color = 4 # Change this to the index of the bar you want to color differently\n", "\n", "# Define colors for all bars\n", "default_color = '#636EFA'\n", "highlighted_color = '#EF553B'\n", "\n", "# Update the trace to set different colors for the specific bar\n", "ctry_nan.update_traces(\n", " marker_color=[highlighted_color if i == index_to_color else default_color for i in range(len(ctry_ana_data[\"appln_auth_fullname\"]))]\n", ")\n", "\n", "year_nan = px.line(ana_data, x=\"appln_filing_year\", y=\"c_perc\", markers=True)\n", "year_nan.update_traces(textposition='top center', textfont_size=20, texttemplate='%{y:.0%}')\n", "year_nan.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", "year_nan.update_traces(marker=dict(color='#EF553B'), line=dict(color='#EF553B'))\n", "\n", "figsuper = make_subplots(rows=1, cols=2, subplot_titles=[\"Percentage of patents with
missing nationality information
(by appl. authority)\",\n", " \"Percentage of patents with
\"\n", " \"valid nationality information\"\n", " \"
(Chinese appl. authority)\"],\n", " specs=[[{\"type\": \"xy\"},{\"type\": \"xy\"}],])\n", "\n", "for trace in list(ctry_nan.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "for trace in list(year_nan.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\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=\"0.4%\")\n", "figsuper['layout']['xaxis'].update(tickformat=\"0.2%\")\n", "figsuper.update_layout(uniformtext_minsize=15)\n", "# for i in[\"xaxis\",\"xaxis2\"]:\n", "# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", "#\n", "# figsuper['layout'][\"yaxis\"][\"range\"] = [0, 2500]\n", "# figsuper['layout'][\"yaxis2\"][\"range\"] = [-1, 3.5]\n", "#\n", "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", "\n", "figsuper.show(config=dict(displayModeBar=False, responsive=True), height=800, width=1600, scale=4)\n", "\n", "figsuper.write_image(f\"{fromdir}/plots/nan_trends.png\", height=700, width=1000, scale=4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 315, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_filing_year sum size perc c_perc\n", "0 2011 330910 330911 0.999997 0.000003\n", "1 2012 460444 460447 0.999993 0.000007\n", "2 2013 597342 597345 0.999995 0.000005\n", "3 2014 686551 686554 0.999996 0.000004\n", "4 2015 837510 837510 1.000000 0.000000\n", "5 2016 1040006 1040006 1.000000 0.000000\n", "6 2017 1246112 1246112 1.000000 0.000000\n", "7 2018 1375656 1375656 1.000000 0.000000\n", "8 2019 1223376 1223376 1.000000 0.000000\n", "9 2020 1412334 1412334 1.000000 0.000000\n", "10 2021 1350843 1350843 1.000000 0.000000\n", "11 2022 841732 841732 1.000000 0.000000\n" ] } ], "source": [ "print(ana_data)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 287, "outputs": [ { "data": { "text/plain": "" }, "execution_count": 287, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nl_ana = appln_ana.query(\"appln_auth=='NL'\").groupby(\"appln_filing_year\")[\"person_ctry_code\"]\n", "((1-(nl_ana.sum()/nl_ana.size()))*100).plot(kind=\"line\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 11, "outputs": [], "source": [ "# That one Chinese auth co-patent" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 16, "outputs": [ { "data": { "text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n124 419052362 CN 5361204 0 1 \\\n137 419052362 CN 51988464 1 0 \n\n person_name person_ctry_code han_id \n124 Schneider Electric Industries SAS FR 808003 \\\n137 JUZHONG SONG CN 151988464 \n\n han_name psn_sector_x psn_sector_y \n124 SCHNEIDER ELECT IND SAS COMPANY COMPANY \n137 JUZHONG SONG UNKNOWN UNKNOWN ", "text/html": "
\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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
appln_idappln_authperson_idinvt_seq_nrapplt_seq_nrperson_nameperson_ctry_codehan_idhan_namepsn_sector_xpsn_sector_y
124419052362CN536120401Schneider Electric Industries SASFR808003SCHNEIDER ELECT IND SASCOMPANYCOMPANY
137419052362CN5198846410JUZHONG SONGCN151988464JUZHONG SONGUNKNOWNUNKNOWN
\n
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" }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln.query('appln_id==419052362')" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 14, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12415\n" ] }, { "data": { "text/plain": " appln_id appln_auth appln_nr appln_kind appln_filing_date \n863506 407017979 WO 2011083302 W 2011-12-01 \\\n7100916 510736006 WO 2019058105 W 2019-03-29 \n1603837 419656375 WO 2012087307 W 2012-12-24 \n7183441 511620156 WO 2017107161 W 2017-10-20 \n5588243 494153147 EP 18171845 A 2018-05-11 \n... ... ... ... ... ... \n2295082 440740099 WO 2013088240 W 2013-11-29 \n12569735 582929150 WO 2021092424 W 2021-05-08 \n8050187 523796882 WO 2018091667 W 2018-06-15 \n5582748 494089044 WO 2016104438 W 2016-11-03 \n716562 405080010 WO 2013056669 W 2013-03-28 \n\n appln_filing_year appln_nr_original ipr_type receiving_office \n863506 2011 CN2011/083302 PI CN \\\n7100916 2019 EP2019/058105 PI EP \n1603837 2012 CN2012/087307 PI CN \n7183441 2017 CN2017/107161 PI CN \n5588243 2018 18171845 PI NaN \n... ... ... ... ... \n2295082 2013 CN2013/088240 PI CN \n12569735 2021 CN2021/092424 PI CN \n8050187 2018 CN2018/091667 PI CN \n5582748 2016 CN2016/104438 PI CN \n716562 2013 EP2013/056669 PI EP \n\n internat_appln_id ... earliest_pat_publn_id granted \n863506 0 ... 407072959 N \\\n7100916 0 ... 538911303 N \n1603837 0 ... 419656376 N \n7183441 0 ... 511620157 N \n5588243 0 ... 520464429 N \n... ... ... ... ... \n2295082 0 ... 440740100 N \n12569735 0 ... 582929151 N \n8050187 0 ... 523796883 N \n5582748 0 ... 494089045 N \n716562 0 ... 422430381 N \n\n docdb_family_id inpadoc_family_id docdb_family_size \n863506 48534637 407017979 2 \\\n7100916 66001221 510736006 4 \n1603837 51019632 419626782 4 \n7183441 66173145 511620156 4 \n5588243 62152456 494153147 3 \n... ... ... ... \n2295082 53198253 440740099 4 \n12569735 84027865 582929150 1 \n8050187 68841789 523796882 4 \n5582748 62076012 494089044 3 \n716562 48045501 405080010 5 \n\n nb_citing_docdb_fam nb_applicants nb_inventors is_prior is_pct \n863506 1 6 5 1 1 \n7100916 0 2 2 1 1 \n1603837 12 2 5 1 1 \n7183441 1 2 3 1 1 \n5588243 3 1 2 1 0 \n... ... ... ... ... ... \n2295082 2 5 4 1 1 \n12569735 0 2 1 1 1 \n8050187 2 2 3 1 1 \n5582748 1 2 4 1 1 \n716562 9 3 2 1 1 \n\n[100 rows x 28 columns]", "text/html": "
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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_f = appln[appln[\"appln_id\"].isin(appln_scope['appln_id'])].copy()\n", "print(appln_f[\"appln_id\"].nunique())\n", "\n", "appln_f.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 202, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:1: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:2: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:3: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\1187413588.py:4: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] } ], "source": [ "appln_f[\"ch_applt\"] = appln_f[\"appln_id\"].isin(appl_ch_applt)\n", "appln_f[\"eu_applt\"] = appln_f[\"appln_id\"].isin(appl_eu_applt)\n", "appln_f[\"ch_invt\"] = appln_f[\"appln_id\"].isin(appl_ch_invt)\n", "appln_f[\"eu_invt\"] = appln_f[\"appln_id\"].isin(appl_eu_invt)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 204, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eu_applt False True \n", "ch_applt \n", "False 218 2113\n", "True 1387 8697\n", "eu_invt False True \n", "ch_invt \n", "False 74 3553\n", "True 4703 4085\n" ] } ], "source": [ "applt_co_occur = pd.crosstab(appln_f[\"ch_applt\"], appln_f[\"eu_applt\"])\n", "print(applt_co_occur)\n", "invt_co_occur = pd.crosstab(appln_f[\"ch_invt\"], appln_f[\"eu_invt\"])\n", "print(invt_co_occur)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 205, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:11: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n", "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_11448\\3989028682.py:12: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", "\n" ] } ], "source": [ "def bool_calc(eu,ch):\n", " if eu==True and ch==False:\n", " return \"European \"\n", " if ch==True and eu==False:\n", " return \"Chinese \"\n", " if ch==False and eu==False:\n", " return \"Third-party \"\n", " if ch==True and eu==True:\n", " return \"Co-\"\n", "\n", "appln_f[\"ownership\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_applt\"],x[\"ch_applt\"])+\"ownership\" ,axis=1)\n", "appln_f[\"invention\"] = appln_f.apply(lambda x: bool_calc(x[\"eu_invt\"],x[\"ch_invt\"])+\"invention\" ,axis=1)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 207, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "invention Chinese invention Co-invention European invention \n", "ownership \n", "Chinese ownership 0 465 922 \\\n", "Co-ownership 3994 1998 2631 \n", "European ownership 709 1404 0 \n", "Third-party ownership 0 218 0 \n", "\n", "invention Third-party invention \n", "ownership \n", "Chinese ownership 0 \n", "Co-ownership 74 \n", "European ownership 0 \n", "Third-party ownership 0 \n" ] } ], "source": [ "ship_co_occur = pd.crosstab(appln_f[\"ownership\"], appln_f[\"invention\"])\n", "print(ship_co_occur)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 52, "outputs": [], "source": [ "record_col=\"appln_id\"\n", "fromdir =outdir" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 209, "outputs": [], "source": [ "ship_co_occur.to_excel(f\"{fromdir}/plots/_ships.xlsx\",index=True)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 55, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hovertemplate": "Year:%{x:d}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Co-patents per year\n", "data = (appln_f.groupby(['appln_filing_year'])[record_col].nunique(dropna=False)\n", " .reset_index()\n", " .rename(columns={0: record_col}))\n", "data[record_col + \"_relative_growth\"] = data[data[record_col] > 0].sort_values(by=[\"appln_filing_year\"], ascending=True)[record_col][0]\n", "data[record_col + \"_relative_growth\"] = (data[record_col] - data[record_col + \"_relative_growth\"]) / data[\n", " record_col + \"_relative_growth\"]\n", "\n", "data = data.sort_values(by=[\"appln_filing_year\"], ascending=[True])\n", "data[record_col + \"_cumsum\"] = (data[record_col].cumsum())\n", "\n", "year_output = px.line(data, x=\"appln_filing_year\", y=record_col, markers=True, text=record_col)\n", "year_output.update_traces(textposition='top center', textfont_size=12)\n", "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", "\n", "year_rel_output = px.line(data, x=\"appln_filing_year\", y=record_col + \"_relative_growth\", markers=True,\n", " text=record_col + \"_relative_growth\")\n", "year_rel_output.update_traces(textposition='top center', textfont_size=12, texttemplate='%{y:.0%}')\n", "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", "\n", "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\", \"Relative growth of co-patents\"],\n", " specs=[\n", " [{\"type\": \"xy\"}],\n", " [{\"type\": \"xy\"}]\n", " ])\n", "\n", "for trace in list(year_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "for trace in list(year_rel_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", "figsuper.update_layout(yaxis={'categoryorder': 'total ascending'}, barmode='relative')\n", "figsuper.update_yaxes(\n", " showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True, showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template': \"plotly\", \"font_family\": \"Montserrat\"})\n", "figsuper['layout']['yaxis2'].update(zerolinecolor='grey', tickformat=\".0%\")\n", "# for i in[\"xaxis\",\"xaxis2\"]:\n", "# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", "#\n", "figsuper['layout'][\"yaxis\"][\"range\"] = [0, 2500]\n", "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1, 3.5]\n", "#\n", "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", "\n", "figsuper.show(config=dict(displayModeBar=False, responsive=True), height=800, width=1600, scale=4)\n", "\n", "figsuper.write_image(f\"{fromdir}/plots/overall_distr&trends.png\", height=900, width=900, scale=4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 61, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hovertemplate": "%{hovertext}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = (appln_f.groupby(['appln_filing_year',\"appln_auth\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", "# data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n", "\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"appln_filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n", " on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "data = data.sort_values(by =[\"appln_auth\",\"appln_filing_year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n", "\n", "data = data.merge(tls_801,left_on='appln_auth', right_on =\"ctry_code\")\n", "data[\"Country\"] = data[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")\n", "\n", "\n", "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n", " y=record_col,\n", " x='appln_filing_year'\n", " ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", " markers=True,\n", " labels={\n", " record_col: 'Number of co-patents',\n", " },\n", " title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n", "fig.update_traces(hovertemplate='%{hovertext}
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Co-publications: %{y}')\n", "fig.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "fig.update_layout({'template':\"plotly\"},font_family=\"Montserrat\")\n", "fig.update_yaxes(title='')\n", "fig.update_xaxes(title='')\n", "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", "# fig.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper_ppt = go.Figure(fig)\n", "\n", "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", "# figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n", "for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 22\n", "\n", "s=16\n", "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", "figsuper_ppt.show(height=800,width=1600,scale = 4)\n", "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_outputs.png\",height=800,width=1600,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 64, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hovertemplate": "%{hovertext}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n", " y=record_col+\"_relative_growth\",\n", " x='appln_filing_year'\n", " ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", " markers=True,\n", " labels={\n", " record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n", " },\n", " title=\"Yearly rel. output of co-patents by patent authority\",hover_name= \"Country\")\n", "fig.update_traces(hovertemplate='%{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\"},font_family=\"Montserrat\")\n", "fig.update_yaxes(title='')\n", "fig.update_xaxes(title='')\n", "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", "# fig.show(config= dict(displayModeBar = False, responsive = True))\n", "\n", "figsuper_ppt = go.Figure(fig)\n", "\n", "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", "for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 22\n", "\n", "s=16\n", "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", "figsuper_ppt.show(height=800,width=1600,scale = 4)\n", "figsuper_ppt.write_image(f\"{fromdir}/plots/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 74, "outputs": [], "source": [ "appln_pers_f = appln_scope\n", "appln_pers_f[\"psn_sector_primary\"] = appln_pers_f[\"psn_sector_x\"]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 68, "outputs": [ { "data": { "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n 'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n 'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n 'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type'],\n dtype='object')" }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_scope.columns" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 93, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ctry_code iso_alpha3 st3_name organisation_flag \n", "0 AD AND Andorra \\\n", "1 AE ARE United Arab Emirates \n", "2 AF AFG Afghanistan \n", "3 AG ATG Antigua and Barbuda \n", "4 AI AIA Anguilla \n", ".. ... ... ... ... \n", "235 YE YEM Yemen \n", "236 YU YUG Yugoslavia/Serbia and Montenegro \n", "237 ZA ZAF South Africa \n", "238 ZM ZMB Zambia \n", "239 ZW ZWE Zimbabwe \n", "\n", " continent eu_member epo_member oecd_member discontinued \n", "0 Europe \n", "1 Asia \n", "2 Asia \n", "3 North America \n", "4 North America \n", ".. ... ... ... ... ... \n", "235 Asia \n", "236 Europe Y \n", "237 Africa \n", "238 Africa \n", "239 Africa \n", "\n", "[240 rows x 9 columns]\n" ] } ], "source": [ "print(tls_801)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 102, "outputs": [], "source": [ "iso2_to_namedict = dict(zip(tls_801[\"ctry_code\"],tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")))\n", "name_to_iso3 = dict(zip(tls_801[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\"),tls_801[\"iso_alpha3\"]))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 286, "outputs": [ { "data": { "text/plain": "{'AD': 'Andorra',\n 'AE': 'United Arab Emirates',\n 'AF': 'Afghanistan',\n 'AG': 'Antigua and Barbuda',\n 'AI': 'Anguilla',\n 'AL': 'Albania',\n 'AM': 'Armenia',\n 'AN': 'Netherlands Antilles ',\n 'AO': 'Angola',\n 'AP': 'ARIPO',\n 'AR': 'Argentina',\n 'AT': 'Austria',\n 'AU': 'Australia',\n 'AW': 'Aruba',\n 'AZ': 'Azerbaijan',\n 'BA': 'Bosnia and Herzegovina',\n 'BB': 'Barbados',\n 'BD': 'Bangladesh',\n 'BE': 'Belgium',\n 'BF': 'Burkina Faso',\n 'BG': 'Bulgaria',\n 'BH': 'Bahrain',\n 'BI': 'Burundi',\n 'BJ': 'Benin',\n 'BM': 'Bermuda',\n 'BN': 'Brunei Darussalam',\n 'BO': 'Plurinational State of',\n 'BQ': 'Bonaire, Sint Eustatius and Saba',\n 'BR': 'Brazil',\n 'BS': 'Bahamas',\n 'BT': 'Bhutan',\n 'BU': 'Burma',\n 'BV': 'Bouvet Island',\n 'BW': 'Botswana',\n 'BX': 'BOIP',\n 'BY': 'Belarus',\n 'BZ': 'Belize',\n 'CA': 'Canada',\n 'CD': 'Democratic Republic of the Congo',\n 'CF': 'Central African Republic',\n 'CG': 'Congo',\n 'CH': 'Switzerland',\n 'CI': \"Côte d'Ivoire\",\n 'CK': 'Cook Islands',\n 'CL': 'Chile',\n 'CM': 'Cameroon',\n 'CN': 'China',\n 'CO': 'Colombia',\n 'CR': 'Costa Rica',\n 'CS': 'Czechoslovakia',\n 'CU': 'Cuba',\n 'CV': 'Cabo Verde',\n 'CW': 'Curaçao',\n 'CY': 'Cyprus',\n 'CZ': 'Czechia',\n 'DD': 'German Democratic Republic',\n 'DE': 'Germany',\n 'DJ': 'Djibouti',\n 'DK': 'Denmark',\n 'DL': 'German Democratic Republic',\n 'DM': 'Dominica',\n 'DO': 'Dominican Republic',\n 'DZ': 'Algeria',\n 'EA': 'EAPO',\n 'EC': 'Ecuador',\n 'EE': 'Estonia',\n 'EG': 'Egypt',\n 'EH': 'Western Sahara',\n 'EM': 'EUIPO',\n 'EP': 'EPO',\n 'ER': 'Eritrea',\n 'ES': 'Spain',\n 'ET': 'Ethiopia',\n 'EU': 'European Union',\n 'FI': 'Finland',\n 'FJ': 'Fiji',\n 'FK': 'Malvinas',\n 'FO': 'Faroe Islands',\n 'FR': 'France',\n 'GA': 'Gabon',\n 'GB': 'United Kingdom',\n 'GC': 'GCC Patent Office',\n 'GD': 'Grenada',\n 'GE': 'Georgia',\n 'GG': 'Guernsey',\n 'GH': 'Ghana',\n 'GI': 'Gibraltar',\n 'GL': 'Greenland',\n 'GM': 'Gambia',\n 'GN': 'Guinea',\n 'GQ': 'Equatorial Guinea',\n 'GR': 'Greece',\n 'GS': 'South Georgia and the South Sandwich Islands',\n 'GT': 'Guatemala',\n 'GW': 'Guinea-Bissau',\n 'GY': 'Guyana',\n 'HK': 'Hong Kong, China',\n 'HN': 'Honduras',\n 'HR': 'Croatia',\n 'HT': 'Haiti',\n 'HU': 'Hungary',\n 'IB': 'WIPO',\n 'ID': 'Indonesia',\n 'IE': 'Ireland',\n 'IL': 'Israel',\n 'IM': 'Isle of Man',\n 'IN': 'India',\n 'IQ': 'Iraq',\n 'IR': 'Islamic Republic of',\n 'IS': 'Iceland',\n 'IT': 'Italy',\n 'JE': 'Jersey',\n 'JM': 'Jamaica',\n 'JO': 'Jordan',\n 'JP': 'Japan',\n 'KE': 'Kenya',\n 'KG': 'Kyrgyzstan',\n 'KH': 'Cambodia',\n 'KI': 'Kiribati',\n 'KM': 'Comoros',\n 'KN': 'Saint Kitts and Nevis',\n 'KP': \"Democratic People's Republic of Korea\",\n 'KR': 'Republic of Korea',\n 'KW': 'Kuwait',\n 'KY': 'Cayman Islands',\n 'KZ': 'Kazakhstan',\n 'LA': \"Lao People's Democratic Republic\",\n 'LB': 'Lebanon',\n 'LC': 'Saint Lucia',\n 'LI': 'Liechtenstein',\n 'LK': 'Sri Lanka',\n 'LR': 'Liberia',\n 'LS': 'Lesotho',\n 'LT': 'Lithuania',\n 'LU': 'Luxembourg',\n 'LV': 'Latvia',\n 'LY': 'Libya',\n 'MA': 'Morocco',\n 'MC': 'Monaco',\n 'MD': 'Republic of Moldova',\n 'ME': 'Montenegro',\n 'MG': 'Madagascar',\n 'MK': 'North Macedonia',\n 'ML': 'Mali',\n 'MM': 'Myanmar',\n 'MN': 'Mongolia',\n 'MO': 'Macao, China',\n 'MP': 'Northern Mariana Islands',\n 'MR': 'Mauritania',\n 'MS': 'Montserrat',\n 'MT': 'Malta',\n 'MU': 'Mauritius',\n 'MV': 'Maldives',\n 'MW': 'Malawi',\n 'MX': 'Mexico',\n 'MY': 'Malaysia',\n 'MZ': 'Mozambique',\n nan: 'Namibia',\n 'NE': 'Niger',\n 'NG': 'Nigeria',\n 'NI': 'Nicaragua',\n 'NL': 'Netherlands',\n 'NO': 'Norway',\n 'NP': 'Nepal',\n 'NR': 'Nauru',\n 'NZ': 'New Zealand',\n 'OA': 'OAPI',\n 'OM': 'Oman',\n 'PA': 'Panama',\n 'PE': 'Peru',\n 'PG': 'Papua New Guinea',\n 'PH': 'Philippines',\n 'PK': 'Pakistan',\n 'PL': 'Poland',\n 'PT': 'Portugal',\n 'PW': 'Palau',\n 'PY': 'Paraguay',\n 'QA': 'Qatar',\n 'QZ': 'CPVO',\n 'RO': 'Romania',\n 'RS': 'Serbia',\n 'RU': 'Russian Federation',\n 'RW': 'Rwanda',\n 'SA': 'Saudi Arabia',\n 'SB': 'Solomon Islands',\n 'SC': 'Seychelles',\n 'SD': 'Sudan',\n 'SE': 'Sweden',\n 'SG': 'Singapore',\n 'SH': 'Saint Helena, Ascension and Tristan da Cunha',\n 'SI': 'Slovenia',\n 'SK': 'Slovakia',\n 'SL': 'Sierra Leone',\n 'SM': 'San Marino',\n 'SN': 'Senegal',\n 'SO': 'Somalia',\n 'SR': 'Suriname',\n 'SS': 'South Sudan',\n 'ST': 'Sao Tome and Principe',\n 'SU': 'Soviet Union',\n 'SV': 'El Salvador',\n 'SX': 'Dutch part',\n 'SY': 'Syrian Arab Republic',\n 'SZ': 'Eswatini',\n 'TC': 'Turks and Caicos Islands',\n 'TD': 'Chad',\n 'TG': 'Togo',\n 'TH': 'Thailand',\n 'TJ': 'Tajikistan',\n 'TL': 'Timor-Leste',\n 'TM': 'Turkmenistan',\n 'TN': 'Tunisia',\n 'TO': 'Tonga',\n 'TR': 'Türkiye',\n 'TT': 'Trinidad and Tobago',\n 'TV': 'Tuvalu',\n 'TW': 'Taiwan province of China',\n 'TZ': 'United Republic of Tanzania',\n 'UA': 'Ukraine',\n 'UG': 'Uganda',\n 'US': 'United States of America',\n 'UY': 'Uruguay',\n 'UZ': 'Uzbekistan',\n 'VA': 'Holy See',\n 'VC': 'Saint Vincent and the Grenadines',\n 'VE': 'Bolivarian Republic of',\n 'VG': 'British Virgin Islands',\n 'VN': 'Viet Nam',\n 'VU': 'Vanuatu',\n 'WO': 'WIPO',\n 'WS': 'Samoa',\n 'XN': 'NPI',\n 'XU': 'UPOV',\n 'XV': 'VPI',\n 'YD': 'Democratic Yemen',\n 'YE': 'Yemen',\n 'YU': 'Yugoslavia/Serbia and Montenegro',\n 'ZA': 'South Africa',\n 'ZM': 'Zambia',\n 'ZW': 'Zimbabwe'}" }, "execution_count": 286, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iso2_to_namedict" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 125, "outputs": [ { "data": { "text/plain": "{'Andorra': 'AND',\n 'United Arab Emirates': 'ARE',\n 'Afghanistan': 'AFG',\n 'Antigua and Barbuda': 'ATG',\n 'Anguilla': 'AIA',\n 'Albania': 'ALB',\n 'Armenia': 'ARM',\n 'Netherlands Antilles ': 'ANT',\n 'Angola': 'AGO',\n 'ARIPO': ' ',\n 'Argentina': 'ARG',\n 'Austria': 'AUT',\n 'Australia': 'AUS',\n 'Aruba': 'ABW',\n 'Azerbaijan': 'AZE',\n 'Bosnia and Herzegovina': 'BIH',\n 'Barbados': 'BRB',\n 'Bangladesh': 'BGD',\n 'Belgium': 'BEL',\n 'Burkina Faso': 'BFA',\n 'Bulgaria': 'BGR',\n 'Bahrain': 'BHR',\n 'Burundi': 'BDI',\n 'Benin': 'BEN',\n 'Bermuda': 'BMU',\n 'Brunei Darussalam': 'BRN',\n 'Plurinational State of': 'BOL',\n 'Bonaire, Sint Eustatius and Saba': 'BES',\n 'Brazil': 'BRA',\n 'Bahamas': 'BHS',\n 'Bhutan': 'BTN',\n 'Burma': 'BUR',\n 'Bouvet Island': 'BVT',\n 'Botswana': 'BWA',\n 'BOIP': ' ',\n 'Belarus': 'BLR',\n 'Belize': 'BLZ',\n 'Canada': 'CAN',\n 'Democratic Republic of the Congo': 'COD',\n 'Central African Republic': 'CAF',\n 'Congo': 'COG',\n 'Switzerland': 'CHE',\n \"Côte d'Ivoire\": 'CIV',\n 'Cook Islands': 'COK',\n 'Chile': 'CHL',\n 'Cameroon': 'CMR',\n 'China': 'CHN',\n 'Colombia': 'COL',\n 'Costa Rica': 'CRI',\n 'Czechoslovakia': 'CSK',\n 'Cuba': 'CUB',\n 'Cabo Verde': 'CPV',\n 'Curaçao': 'CUW',\n 'Cyprus': 'CYP',\n 'Czechia': 'CZE',\n 'German Democratic Republic': 'DDR',\n 'Germany': 'DEU',\n 'Djibouti': 'DJI',\n 'Denmark': 'DNK',\n 'Dominica': 'DMA',\n 'Dominican Republic': 'DOM',\n 'Algeria': 'DZA',\n 'EAPO': ' ',\n 'Ecuador': 'ECU',\n 'Estonia': 'EST',\n 'Egypt': 'EGY',\n 'Western Sahara': 'ESH',\n 'EUIPO': ' ',\n 'EPO': ' ',\n 'Eritrea': 'ERI',\n 'Spain': 'ESP',\n 'Ethiopia': 'ETH',\n 'European Union': ' ',\n 'Finland': 'FIN',\n 'Fiji': 'FJI',\n 'Malvinas': 'FLK',\n 'Faroe Islands': 'FRO',\n 'France': 'FRA',\n 'Gabon': 'GAB',\n 'United Kingdom': 'GBR',\n 'GCC Patent Office': ' ',\n 'Grenada': 'GRD',\n 'Georgia': 'GEO',\n 'Guernsey': 'GGY',\n 'Ghana': 'GHA',\n 'Gibraltar': 'GIB',\n 'Greenland': 'GRL',\n 'Gambia': 'GMB',\n 'Guinea': 'GIN',\n 'Equatorial Guinea': 'GNQ',\n 'Greece': 'GRC',\n 'South Georgia and the South Sandwich Islands': 'SGS',\n 'Guatemala': 'GTM',\n 'Guinea-Bissau': 'GNB',\n 'Guyana': 'GUY',\n 'Hong Kong, China': 'HKG',\n 'Honduras': 'HND',\n 'Croatia': 'HRV',\n 'Haiti': 'HTI',\n 'Hungary': 'HUN',\n 'WIPO': ' ',\n 'Indonesia': 'IDN',\n 'Ireland': 'IRL',\n 'Israel': 'ISR',\n 'Isle of Man': 'IMN',\n 'India': 'IND',\n 'Iraq': 'IRQ',\n 'Islamic Republic of': 'IRN',\n 'Iceland': 'ISL',\n 'Italy': 'ITA',\n 'Jersey': 'JEY',\n 'Jamaica': 'JAM',\n 'Jordan': 'JOR',\n 'Japan': 'JPN',\n 'Kenya': 'KEN',\n 'Kyrgyzstan': 'KGZ',\n 'Cambodia': 'KHM',\n 'Kiribati': 'KIR',\n 'Comoros': 'COM',\n 'Saint Kitts and Nevis': 'KNA',\n \"Democratic People's Republic of Korea\": 'PRK',\n 'Republic of Korea': 'KOR',\n 'Kuwait': 'KWT',\n 'Cayman Islands': 'CYM',\n 'Kazakhstan': 'KAZ',\n \"Lao People's Democratic Republic\": 'LAO',\n 'Lebanon': 'LBN',\n 'Saint Lucia': 'LCA',\n 'Liechtenstein': 'LIE',\n 'Sri Lanka': 'LKA',\n 'Liberia': 'LBR',\n 'Lesotho': 'LSO',\n 'Lithuania': 'LTU',\n 'Luxembourg': 'LUX',\n 'Latvia': 'LVA',\n 'Libya': 'LBY',\n 'Morocco': 'MAR',\n 'Monaco': 'MCO',\n 'Republic of Moldova': 'MDA',\n 'Montenegro': 'MNE',\n 'Madagascar': 'MDG',\n 'North Macedonia': 'MKD',\n 'Mali': 'MLI',\n 'Myanmar': 'MMR',\n 'Mongolia': 'MNG',\n 'Macao, China': 'MAC',\n 'Northern Mariana Islands': 'MNP',\n 'Mauritania': 'MRT',\n 'Montserrat': 'MSR',\n 'Malta': 'MLT',\n 'Mauritius': 'MUS',\n 'Maldives': 'MDV',\n 'Malawi': 'MWI',\n 'Mexico': 'MEX',\n 'Malaysia': 'MYS',\n 'Mozambique': 'MOZ',\n 'Namibia': 'NAM',\n 'Niger': 'NER',\n 'Nigeria': 'NGA',\n 'Nicaragua': 'NIC',\n 'Netherlands': 'NLD',\n 'Norway': 'NOR',\n 'Nepal': 'NPL',\n 'Nauru': 'NRU',\n 'New Zealand': 'NZL',\n 'OAPI': ' ',\n 'Oman': 'OMN',\n 'Panama': 'PAN',\n 'Peru': 'PER',\n 'Papua New Guinea': 'PNG',\n 'Philippines': 'PHL',\n 'Pakistan': 'PAK',\n 'Poland': 'POL',\n 'Portugal': 'PRT',\n 'Palau': 'PLW',\n 'Paraguay': 'PRY',\n 'Qatar': 'QAT',\n 'CPVO': ' ',\n 'Romania': 'ROU',\n 'Serbia': 'SRB',\n 'Russian Federation': 'RUS',\n 'Rwanda': 'RWA',\n 'Saudi Arabia': 'SAU',\n 'Solomon Islands': 'SLB',\n 'Seychelles': 'SYC',\n 'Sudan': 'SDN',\n 'Sweden': 'SWE',\n 'Singapore': 'SGP',\n 'Saint Helena, Ascension and Tristan da Cunha': 'SHN',\n 'Slovenia': 'SVN',\n 'Slovakia': 'SVK',\n 'Sierra Leone': 'SLE',\n 'San Marino': 'SMR',\n 'Senegal': 'SEN',\n 'Somalia': 'SOM',\n 'Suriname': 'SUR',\n 'South Sudan': 'SSD',\n 'Sao Tome and Principe': 'STP',\n 'Soviet Union': 'SUN',\n 'El Salvador': 'SLV',\n 'Dutch part': 'SXM',\n 'Syrian Arab Republic': 'SYR',\n 'Eswatini': 'SWZ',\n 'Turks and Caicos Islands': 'TCA',\n 'Chad': 'TCD',\n 'Togo': 'TGO',\n 'Thailand': 'THA',\n 'Tajikistan': 'TJK',\n 'Timor-Leste': 'TLS',\n 'Turkmenistan': 'TKM',\n 'Tunisia': 'TUN',\n 'Tonga': 'TON',\n 'Türkiye': 'TUR',\n 'Trinidad and Tobago': 'TTO',\n 'Tuvalu': 'TUV',\n 'Taiwan province of China': 'TWN',\n 'United Republic of Tanzania': 'TZA',\n 'Ukraine': 'UKR',\n 'Uganda': 'UGA',\n 'United States of America': 'USA',\n 'Uruguay': 'URY',\n 'Uzbekistan': 'UZB',\n 'Holy See': 'VAT',\n 'Saint Vincent and the Grenadines': 'VCT',\n 'Bolivarian Republic of': 'VEN',\n 'British Virgin Islands': 'VGB',\n 'Viet Nam': 'VNM',\n 'Vanuatu': 'VUT',\n 'Samoa': 'WSM',\n 'NPI': ' ',\n 'UPOV': ' ',\n 'VPI': ' ',\n 'Democratic Yemen': 'YMD',\n 'Yemen': 'YEM',\n 'Yugoslavia/Serbia and Montenegro': 'YUG',\n 'South Africa': 'ZAF',\n 'Zambia': 'ZMB',\n 'Zimbabwe': 'ZWE'}" }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name_to_iso3" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 267, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "Country type=China
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", " appln_pers_f[c+\"_fullname\"] = appln_pers_f[c].map(iso2_to_namedict)\n", "\n", "# appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n", "appln_pers_f[\"person_ctry_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n", "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"person_ctry_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n", "\n", "appln_pers_f[\"auth_ISO3\"] = appln_pers_f[\"appln_auth_fullname\"].map(name_to_iso3)\n", "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"Patent Office\" if x==\" \" else \"EU\")\n", "\n", "# top 25\n", "# - inventor\n", "# - applicant\n", "# in china, EU, EUextended\n", "color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96',\n", " 'EPO':'#9F2B68'}\n", "\n", "labeld_dict = dict({\n", " record_col: 'Number of co-patents',\n", " \"person_ctry_type\": \"Country type\",\n", " \"psn_sector_primary\":\"Sector\",\n", " \"han_label\": \"Name\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " })\n", "for trait in [\"person_ctry_type\",\"psn_sector_primary\"]:\n", " for ctry in [\"China\",'EU']:\n", " ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n", " # for role in [\"invt\",\"applt\"]:\n", " if True:\n", " role = \"applt\"\n", " pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n", " data = (pers_sub\n", " .groupby([\"han_id\",'han_name'],as_index=False)\n", " .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n", " .sort_values(by=\"appln_id\", ascending=False)\n", " .head(15))\n", " data[\"han_label\"] = (data[\"han_name\"]\n", " .str.upper()\n", " .str.strip(\".\")\n", " .str.replace(\"&\",\"&
\")\n", " + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n", "\n", " fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n", " color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n", " fig.update_layout(font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'})\n", "\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=12\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", "\n", " figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n", " figsuper.write_image(f\"{fromdir}/plots/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 127, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n", "\n", "divide by zero encountered in log10\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "coloraxis": "coloraxis", "name": "0", "texttemplate": "%{text}", "x": [ "Germany", "Sweden", "Finland", "United Kingdom", "France", "Denmark", "Switzerland", "Italy", "Poland", "Netherlands", "Belgium", "Spain", "Austria", "Ireland", 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "applt_sub = appln_pers_f[((appln_pers_f[\"applt_seq_nr\"]!=0)&\n", " (appln_pers_f[\"psn_sector_primary\"]!=\"INDIVIDUAL\"))]\n", "invt_sub = appln_pers_f[appln_pers_f[\"invt_seq_nr\"]!=0]\n", "\n", "eu_applt_subset = applt_sub[~applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n", "ch_applt_subset = applt_sub[applt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n", "\n", "eu_invt_subset = invt_sub[~invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n", "ch_invt_subset = invt_sub[invt_sub[\"person_ctry_type\"].str.contains(\"China\")]\n", "\n", "\n", "patstat_applt_dipol = eu_applt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n", "patstat_applt_inv_dipol = eu_invt_subset.merge(ch_applt_subset, on=record_col, suffixes=('_eu', '_ch'))\n", "\n", "co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n", " values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "\n", "co_occur = co_occur.T\n", "co_occur[\"sum\"] = co_occur.sum(axis=1)\n", "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n", "\n", "co_occur = (co_occur\n", " .sort_values(by=\"sum\", ascending=False)\n", " .head(20)\n", " .drop(columns=\"sum\")\n", " .replace(0,np.nan)\n", " .dropna(axis=1,how=\"all\")\n", " .replace(np.nan,0)\n", " .astype(int))\n", "\n", "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n", " labels=dict(x=\"Inventor (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n", " x=list(co_occur.columns),\n", " text_auto=True,\n", " y=list(co_occur.index))\n", "\n", "\n", "fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " font_family=\"Montserrat\",\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", "\n", "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n", "fig.update(layout_coloraxis_showscale=False)\n", "fig.update_xaxes(tickangle= -45)\n", "fig.update_yaxes(\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", "figsuper_ppt = fig\n", "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n", "for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 16\n", "\n", "s=16\n", "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", "\n", "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n", "\n", "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_applt_ctry.png\",height=900,width=1200,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 128, "outputs": [], "source": [ "patstat_applt_dipol_2 = (patstat_applt_dipol\n", " .groupby([\"han_name_ch\",\"han_name_eu\"], as_index=False)\n", " .agg({\"psn_sector_primary_ch\":\"first\",\n", " \"psn_sector_primary_eu\":\"first\",\n", " \"person_ctry_ISO3_eu\":\"first\",\n", " record_col:\"nunique\"})\n", " .sort_values(by=record_col, ascending=False)\n", " .head(-1))\n", "patstat_applt_dipol_2[\"applt_eu_label\"] = (patstat_applt_dipol_2[\"han_name_eu\"]\n", " +\" (\"\n", " +patstat_applt_dipol_2[\"person_ctry_ISO3_eu\"]\n", " +\"; \"\n", " +patstat_applt_dipol_2[\"psn_sector_primary_eu\"]\n", " +\")\")\n", "\n", "patstat_applt_dipol_2[\"applt_ch_label\"] = (patstat_applt_dipol_2[\"han_name_ch\"]\n", " +\" (\"\n", " +patstat_applt_dipol_2[\"psn_sector_primary_ch\"]\n", " +\")\")\n", "\n", "patstat_applt_dipol_2[[\"applt_ch_label\",\"applt_eu_label\",\"appln_id\"]].to_excel(rf\"{fromdir}/plots/applt_table.xlsx\", index=False)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 185, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\radvanyi\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:329: RuntimeWarning:\n", "\n", "divide by zero encountered in log10\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "coloraxis": "coloraxis", "name": "0", "texttemplate": "%{text}", "x": [ "ALCATEL LUCENT", "BAYER MATERIALSCIENCE AG", "BEIERSDORF AG", "CG RAIL CHINESISCH DEUTSCHES FORSCHUNGS & ENTWICKLUNGSZENTRUM FUR BAHN ", "CNRS", "COVESTRO DEUTSCHLAND AG", "CTF SOLAR GMBH", "FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV", "FRESENIUS MEDICAL CARE AG&CO KGAA", "FRESENIUS MEDICAL CARE DEUTSCHLAND GMBH", "FRIEDRICH ALEXANDER UNIVERSITAT ERLANGEN NURNBERG", "HENKEL AG&CO KGAA", "HENKEL IP & HOLDING GMBH", "LEICA BIOSYSTEMS NUSSLOCH GMBH", "NOKIA CORP", "NOKIA TECH LTD", "RHODIA OPERATIONS", "ROBERT BOSCH GMBH", "SIEMENS AG", "VOLKSWAGEN AG" ], "y": [ "ABLESTIK SHANGHAI LTD", "ALCATEL LUCENT SHANGHAI BELL CO LTD", "BAYER MATERIALSCIENCE CHINA CO LTD", "BEIERSDORF DAILY CHEMICAL WUHAN CO LTD", "CHINA TRIUMPH INT ENGINEERING CO LTD", "CRRC QINGDAO SIFANG CO LTD", "DALIAN INSTITUTE OF CHEMICAL PHYSICS CHINESE ACADEMY OF SCIENCES", "FRESENIUS MEDICAL CARE R&D SHANGHAI CO LTD", "HENKEL CHINA CO LTD", "HENKEL CHINA INVESTMENT CO LTD", "HUAWEI TECH CO LTD", "LEICA MICROSYSTEMS LTD", "NAVTEQ SHANGHAI TRADING CO LTD", "NOKIA CHINA INVESTMENT CO LTD", "NOKIA SHANGHAI BELL CO LTD", "NOKIA TECH BEIJING CO LTD", "RHODIA CHINA CO LTD", "SIEMENS LTD CHINA", "SOLVAY CHINA CO LTD", "TSINGHUA UNIV" ], "z": [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.414973347970818, 1.6989700043360187, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 2.591064607026499, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3010299956639813, 1.6989700043360187, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 1.9030899869919435, 0.0, 0.0, 0.0, 1.4771212547196624, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 2.57978359661681, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.414973347970818, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 2.230448921378274, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.6989700043360187, 2.5185139398778875, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.8512583487190755, 1.845098040014257, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.255272505103306, 1.3010299956639813, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3979400086720375, 0.0, 0.0, 1.9030899869919435, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.278753600952829, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.7160033436347994, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.110589710299249, 2.7634279935629373, 0.0, 0.0, 0.0, 0.0 ], [ 1.6020599913279625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.902546779313991, 3.8129133566428557, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.4771212547196624, 3.1398790864012365, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 1.4771212547196624, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3010299956639813, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.374748346010104, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.079181246047625, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.230448921378274, 0.0, 0.0 ] ], "type": "heatmap", "xaxis": "x", "yaxis": "y", "hovertemplate": "Applicant (EU): %{x}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "co_occur = pd.crosstab(patstat_applt_dipol['han_name_eu'], patstat_applt_dipol['han_name_ch'],\n", " values=patstat_applt_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "# co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n", "# values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "\n", "co_occur = co_occur.T\n", "co_occur[\"SUM\"] = co_occur.sum(axis=1)\n", "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n", "\n", "co_occur = co_occur.drop(columns = co_occur.columns[~co_occur.columns.str.isupper()])\n", "co_occur = co_occur.drop(index = co_occur.index[~co_occur.index.str.isupper()])\n", "\n", "co_occur.columns = co_occur.columns.str.split(\"&\").str[0:2].str.join(\"&\")\n", "\n", "top=20\n", "co_occur = (co_occur\n", " .sort_values(by=\"SUM\", ascending=False)\n", " .head(20)\n", " .drop(columns=\"SUM\")\n", " .replace(0,np.nan)\n", " .dropna(axis=1,how=\"all\")\n", " .replace(np.nan,0)\n", " .astype(int)\n", " .iloc[0:top,0:top]).copy()\n", "\n", "co_occur = co_occur.sort_index(axis=1).sort_index(axis=0)\n", "\n", "\n", "\n", "\n", "fig = px.imshow(np.nan_to_num(np.log10(co_occur)+1,neginf=0),#co_occur,\n", " labels=dict(x=\"Applicant (EU)\", y=\"Applicant (CN)\", z=co_occur.values.astype(str)),# color=\"Co-patent\"),\n", " x=list(co_occur.columns),\n", " text_auto=True,\n", " y=list(co_occur.index))\n", "\n", "\n", "fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " font_family=\"Montserrat\",\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", "\n", "fig.update_traces(text=co_occur, texttemplate=\"%{text}\")\n", "fig.update(layout_coloraxis_showscale=False)\n", "fig.update_xaxes(tickangle= -45)\n", "fig.update_yaxes(\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", "figsuper_ppt = fig\n", "figsuper_ppt['layout'][\"font\"][\"size\"]=16\n", "for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 16\n", "\n", "s=12\n", "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=30)\n", "\n", "figsuper_ppt.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper_ppt.write_image(rf\"{fromdir}/plots/CN_EU_coapplt.png\",height=900,width=1200,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 167, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([], dtype='object', name='han_name_eu')\n" ] } ], "source": [ "print(co_occur.columns[~co_occur.columns.str.isupper()])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 162, "outputs": [ { "data": { "text/plain": "346" }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len((co_occur.columns.str.isupper()))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 277, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "Country type=EU
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"ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0.0, 1.0 ], "title": { "text": "Percent of related co-patents" }, "tickformat": ".1%", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside" }, "yaxis": { "anchor": "x", "domain": [ 0.0, 1.0 ], "title": { "text": "Application authority" }, "categoryorder": "total ascending", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside", "tickfont": { "size": 14 } }, "legend": { "title": { "text": "Authority type" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "barmode": "relative", "font": { "family": "Montserrat", "size": 14 }, "yaxis2": { "categoryorder": "total ascending", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside" }, "yaxis3": { "categoryorder": "total ascending", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside" }, "uniformtext": { "minsize": 10 } }, "config": { "plotlyServerURL": "https://plotly.com" } }, "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# percent of publications related to\n", "# -authority country\n", "# -inventor country\n", "# -applicant country\n", "\n", "color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96',\n", " 'Patent Office':'#7F00FF'}#CC8899\n", "\n", "\n", "labeld_dict = dict({\n", " record_col+\"_percent\": 'Percent of related co-patents',\n", " \"person_ctry_code_fullname\": \"Country\",\n", " \"person_ctry_type\": \"Country type\",\n", " \"appln_auth_fullname\": \"Application authority\",\n", " \"auth_ctry_type\": \"Authority type\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " })\n", "\n", "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", " pers_sub = appln_pers_f\n", " if c == \"person_ctry_code\":\n", " for role in [\"invt\",\"applt\"]:\n", " pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n", " if role == 'applt':\n", " pers_sub = pers_sub[~pers_sub[\"han_name\"].str.contains(\",\", na=False)]\n", " role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n", " labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n", "\n", "\n", " data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n", " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n", " labels = labeld_dict)\n", " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},)\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=14\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", " figsuper.write_image(rf\"{fromdir}/plots/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n", " figsuper.show()\n", " else:\n", " data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n", " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n", " labels = labeld_dict)\n", " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},)\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=14\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", " figsuper.write_image(rf\"{fromdir}/plots/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n", " figsuper.show()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 216, "outputs": [], "source": [ "appln_ipc_f = pd.read_csv(f\"{fromdir}/tls_209_IPC_scope.csv\", header=None, low_memory=False,\n", " names=[\"appln_id\",\"ipc_class_symbol\",'ipc_class_level',\n", " \"ipc_version\",\"ipc_value\",\"ipc_position\",\"ipc_gener_auth\"])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 217, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_id ipc_class_symbol ipc_class_level ipc_version ipc_value \n", "16217052 503169456 C05G 3/00 A 2006-01-01 I \\\n", "24369959 538809014 A63F 13/822 A 2014-01-01 I \n", "19214707 515601904 H04N 21/234 A 2011-01-01 I \n", "30677055 561927715 B29C 43/36 A 2006-01-01 I \n", "18318129 511207450 A61P 35/00 A 2006-01-01 I \n", "20060243 519634252 B32B 27/06 A 2006-01-01 I \n", "19322567 516137263 G06Q 10/06 A 2012-01-01 I \n", "21584214 526531915 E04G 21/02 A 2006-01-01 I \n", "9670129 475595704 C25D 17/00 A 2006-01-01 I \n", "19597992 517583469 H04B 7/0426 A 2017-01-01 I \n", "\n", " ipc_position ipc_gener_auth \n", "16217052 L CN \n", "24369959 F CN \n", "19214707 F CN \n", "30677055 L CN \n", "18318129 L CN \n", "20060243 L CN \n", "19322567 F EP \n", "21584214 F CN \n", "9670129 F CN \n", "19597992 L CN \n" ] } ], "source": [ "print(appln_ipc_f.sample(10))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 235, "outputs": [], "source": [ "appln_ipc_f[\"appln_id\"]=pd.to_numeric(appln_ipc_f[\"appln_id\"],errors='coerce')\n", "appln_ipc_f = appln_ipc_f.dropna(axis=0,how='any')\n", "appln_ipc_f[\"appln_id\"] = appln_ipc_f[\"appln_id\"].astype('int64')" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 236, "outputs": [ { "data": { "text/plain": "dtype('int64')" }, "execution_count": 236, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[\"appln_id\"].dtype" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 237, "outputs": [ { "data": { "text/plain": "12291" }, "execution_count": 237, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])][\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 241, "outputs": [], "source": [ "\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\"])].copy()\n", "\n", "appln_ipc_f = appln_ipc_f.merge(appln_f[[\"appln_id\",\"appln_filing_year\"]].drop_duplicates(),\n", " on=\"appln_id\")\n", "\n", "appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n", "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n", "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n", " \"B\":\"Performing operations; transporting\",\n", " \"B\":\"Operations; transporting\",\n", " \"C\":\"Chemistry; metallurgy\",\n", " \"D\":\"Textiles; paper\",\n", " \"E\":\"Fixed constructions\",\n", " \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n", " \"F\":\"Engineering\",\n", " \"G\":\"Physics\",\n", " \"H\":\"Electricity\"})\n", "\n", "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 374, "outputs": [], "source": [ "co_occur = pd.crosstab(appln_ipc_f['ipc_class'], appln_ipc_f['ipc_class'],\n", " values=appln_ipc_f[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "# co_occur = pd.crosstab(patstat_applt_inv_dipol['person_ctry_code_fullname_eu'], patstat_applt_inv_dipol['han_name_ch'],\n", "# values=patstat_applt_inv_dipol[record_col], aggfunc='nunique').fillna(0).astype(int)\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 378, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14071\n" ] } ], "source": [ "print(co_occur.sum().sum())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 371, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipc_class SUM H-Electricity G-Physics \n", "ipc_class \n", "A-Human necessities 1057 0 0 \\\n", "B-Operations; transporting 1482 0 0 \n", "C-Chemistry; metallurgy 1200 0 0 \n", "D-Textiles; paper 77 0 0 \n", "E-Fixed constructions 170 0 0 \n", "F-Engineering 753 0 0 \n", "G-Physics 2594 0 2594 \n", "H-Electricity 6738 6738 0 \n", "\n", "ipc_class B-Operations; transporting \n", "ipc_class \n", "A-Human necessities 0 \\\n", "B-Operations; transporting 1482 \n", "C-Chemistry; metallurgy 0 \n", "D-Textiles; paper 0 \n", "E-Fixed constructions 0 \n", "F-Engineering 0 \n", "G-Physics 0 \n", "H-Electricity 0 \n", "\n", "ipc_class C-Chemistry; metallurgy A-Human necessities \n", "ipc_class \n", "A-Human necessities 0 1057 \\\n", "B-Operations; transporting 0 0 \n", "C-Chemistry; metallurgy 1200 0 \n", "D-Textiles; paper 0 0 \n", "E-Fixed constructions 0 0 \n", "F-Engineering 0 0 \n", "G-Physics 0 0 \n", "H-Electricity 0 0 \n", "\n", "ipc_class F-Engineering E-Fixed constructions \n", "ipc_class \n", "A-Human necessities 0 0 \\\n", "B-Operations; transporting 0 0 \n", "C-Chemistry; metallurgy 0 0 \n", "D-Textiles; paper 0 0 \n", "E-Fixed constructions 0 170 \n", "F-Engineering 753 0 \n", "G-Physics 0 0 \n", "H-Electricity 0 0 \n", "\n", "ipc_class D-Textiles; paper \n", "ipc_class \n", "A-Human necessities 0 \n", "B-Operations; transporting 0 \n", "C-Chemistry; metallurgy 0 \n", "D-Textiles; paper 77 \n", "E-Fixed constructions 0 \n", "F-Engineering 0 \n", "G-Physics 0 \n", "H-Electricity 0 \n" ] } ], "source": [ "\n", "co_occur = co_occur.T\n", "co_occur[\"SUM\"] = co_occur.sum(axis=1)\n", "co_occur = co_occur[co_occur.sum(0).sort_values(ascending=False).index]\n", "\n", "print(co_occur)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 373, "outputs": [ { "data": { "text/plain": "12291" }, "execution_count": 373, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 372, "outputs": [ { "data": { "text/plain": "14071" }, "execution_count": 372, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co_occur[\"SUM\"].sum()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 242, "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": 243, "outputs": [], "source": [ "data = (appln_ipc_f.groupby(['appln_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=[\"appln_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\",\"appln_filing_year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 245, "outputs": [], "source": [ "group = ['appln_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": 252, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "cliponaxis": true, "hovertemplate": "%{x:d}", "legendgroup": "A-Human necessities", "marker": { "color": "#636efa", "pattern": { "shape": "" } }, 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "\n", "# dom_distr\n", "# year_output_by_domain\n", "# rel_output_by_domain\n", "# cumsum_by_domain\n", "\n", "figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of IPC classes\",\"Cumulative sum of related co-patents\",\n", " \"Related co-patents per year\",\"Relative growth of co-patents\"])\n", "\n", "\n", "for trace in list(dom_distr.select_traces()):\n", " trace.showlegend=False\n", " # trace.barmode\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "for trace in list(cumsum_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "for trace in list(year_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", "for trace in list(rel_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", "\n", "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "\n", "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n", "figsuper['layout']['yaxis2'].update(tickformat=\".0f\")\n", "figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", "figsuper['layout'][f'xaxis'][\"range\"] = [0,8000]\n", "for i in range(2,5):\n", " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", 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"legend": { "title": { "text": "Authority type" }, "tracegroupgap": 0 }, "margin": { "t": 60 }, "barmode": "relative", "font": { "family": "Montserrat", "size": 14 }, "yaxis2": { "categoryorder": "total ascending", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside" }, "yaxis3": { "categoryorder": "total ascending", "showgrid": true, "showline": true, "linewidth": 1, "linecolor": "black", "mirror": true, "ticks": "outside" }, "uniformtext": { "minsize": 10 } }, "config": { "plotlyServerURL": "https://plotly.com" } }, "text/html": "
" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for c in [\"ownership\",'invention']:\n", " os.makedirs(rf\"{fromdir}/plots/{c}\", exist_ok=True)\n", " for v in appln_f[c].unique():\n", " os.makedirs(rf\"{fromdir}/plots/{c}/{v}\", exist_ok=True)\n", " appln_sub = appln_f[appln_f[c]==v]\n", " pers_sub = appln_pers_f[appln_pers_f[\"appln_id\"].isin(appln_sub[\"appln_id\"].unique())]\n", "\n", "\n", " data = (appln_sub.groupby(['appln_filing_year',\"appln_auth\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", "\n", " data = data.merge(data[data[record_col]>0].sort_values(by=[\"appln_filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n", " on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n", " data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", " data = data.sort_values(by =[\"appln_auth\",\"appln_filing_year\"], ascending=[True,True])\n", " data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n", "\n", " data = data.merge(tls_801,left_on='appln_auth', right_on =\"ctry_code\")\n", " data[\"Country\"] = data[\"st3_name\"].str.split(\"(\").str[-1].str.strip(\")\")\n", "\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n", " y=record_col,\n", " x='appln_filing_year'\n", " ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", " markers=True,\n", " labels={\n", " record_col: 'Number of co-patents',\n", " },\n", " title=None,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\"},font_family=\"Montserrat\")\n", " fig.update_yaxes(title='')\n", " fig.update_xaxes(title='')\n", " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", " # fig.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper_ppt = go.Figure(fig)\n", "\n", " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", " # figsuper['layout'][\"xaxis\"][\"range\"] = [20,2022.5]\n", " for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 22\n", "\n", " s=16\n", " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", " figsuper_ppt.show(height=800,width=1600,scale = 4)\n", " figsuper_ppt.write_image(f\"{fromdir}/plots/{c}/{v}/yearly_outputs.png\",height=800,width=1600,scale = 4)\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='appln_filing_year'),\n", " y=record_col+\"_relative_growth\",\n", " x='appln_filing_year'\n", " ,facet_col=\"Country\",facet_col_wrap=5,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", " markers=True,\n", " labels={\n", " record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n", " },\n", " title=None,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\"},font_family=\"Montserrat\")\n", " fig.update_yaxes(title='')\n", " fig.update_xaxes(title='')\n", " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", " # fig.show(config= dict(displayModeBar = False, responsive = True))\n", "\n", " figsuper_ppt = go.Figure(fig)\n", "\n", " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", " for a in figsuper_ppt['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 22\n", "\n", " s=16\n", " figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", " figsuper_ppt.show(height=800,width=1600,scale = 4)\n", " figsuper_ppt.write_image(f\"{fromdir}/plots/{c}/{v}/yearly_rel_outputs.png\",height=800,width=1600,scale = 4)\n", "\n", " # appln auth\n", "\n", " color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96',\n", " 'Patent Office':'#7F00FF'}#CC8899\n", "\n", "\n", " labeld_dict = dict({\n", " record_col+\"_percent\": 'Percent of related co-patents',\n", " \"person_ctry_code_fullname\": \"Country\",\n", " \"person_ctry_type\": \"Country type\",\n", " \"appln_auth_fullname\": \"Application authority\",\n", " \"auth_ctry_type\": \"Authority type\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " })\n", " data = pers_sub.groupby(\"appln_auth_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=\"appln_auth_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n", " labels = labeld_dict)\n", " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},)\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=14\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", " figsuper.write_image(rf\"{fromdir}/plots/{c}/{v}/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n", " figsuper.show()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 271, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " appln_id ipc_class_symbol ipc_class_level ipc_version ipc_value \n", "23874 550835553 G01N 1/06 A 2006-01-01 I \\\n", "3831 418654151 H04W 80/06 A 2009-01-01 N \n", "8289 457610141 G06F 21/62 A 2013-01-01 I \n", "6806 445832184 G05B 19/402 A 2006-01-01 I \n", "4167 420532142 H04B 1/525 A 2015-01-01 I \n", "5028 425096990 A61K 8/33 A 2006-01-01 I \n", "26776 575542874 A61K 8/34 A 2006-01-01 I \n", "123 335293658 G07C 9/00 A 2006-01-01 I \n", "25066 557127958 C07C 229/34 A 2006-01-01 I \n", "1187 379446285 H04R 1/28 A 2006-01-01 I \n", "\n", " ipc_position ipc_gener_auth cpc_id ipc_class \n", "23874 F CN G01N1/06 G-Physics \\\n", "3831 L CN H04W80/06 H-Electricity \n", "8289 L EP G06F21/62 G-Physics \n", "6806 F CN G05B19/402 G-Physics \n", "4167 L EP H04B1/525 H-Electricity \n", "5028 F CN A61K8/33 A-Human necessities \n", "26776 L CN A61K8/34 A-Human necessities \n", "123 L GB G07C9/00 G-Physics \n", "25066 L CN C07C229/34 C-Chemistry; metallurgy \n", "1187 L CN H04R1/28 H-Electricity \n", "\n", " ipc_classname appln_filing_year \n", "23874 Physics 2019 \n", "3831 Electricity 2012 \n", "8289 Physics 2016 \n", "6806 Physics 2014 \n", "4167 Electricity 2014 \n", "5028 Human necessities 2013 \n", "26776 Human necessities 2020 \n", "123 Physics 2011 \n", "25066 Chemistry; metallurgy 2020 \n", "1187 Electricity 2011 \n" ] } ], "source": [ "print(appln_ipc_f.sample(10))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 285, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "Country type=EU
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for col in ['ipc_class',]:\n", " os.makedirs(rf\"{fromdir}/plots/{col}\", exist_ok=True)\n", " for val in appln_ipc_f[col].unique():\n", " os.makedirs(rf\"{fromdir}/plots/{col}/{val}\", exist_ok=True)\n", " id_sub = appln_ipc_f[appln_ipc_f[col]==val][\"appln_id\"].unique()\n", " appln_sub = appln_f[appln_f[\"appln_id\"].isin(id_sub)]\n", " appln_pers_f_sub = appln_pers_f[appln_pers_f[\"appln_id\"].isin(appln_sub[\"appln_id\"].unique())]\n", "\n", " # percent of publications related to\n", " # -authority country\n", " # -inventor country\n", " # -applicant country\n", " color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96',\n", " 'Patent Office':'#7F00FF'}#CC8899\n", "\n", "\n", " labeld_dict = dict({\n", " record_col+\"_percent\": 'Percent of related co-patents',\n", " \"person_ctry_code_fullname\": \"Country\",\n", " \"person_ctry_type\": \"Country type\",\n", " \"appln_auth_fullname\": \"Application authority\",\n", " \"auth_ctry_type\": \"Authority type\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " })\n", "\n", " for c in [\"person_ctry_code\",\"appln_auth\"]:\n", " pers_sub = appln_pers_f_sub\n", " if c == \"person_ctry_code\":\n", " for role in [\"invt\",\"applt\"]:\n", " pers_sub = appln_pers_f_sub[appln_pers_f_sub[role+\"_seq_nr\"]!=0]\n", " role_name = {\"invt\":\"Inventor's\", \"applt\":\"Applicant's\"}.get(role)\n", " labeld_dict[\"person_ctry_code_fullname\"] = role_name+\" country\"\n", "\n", "\n", " data = (pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"}))\n", " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n", " labels = labeld_dict)\n", " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},)\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=14\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", " figsuper.write_image(rf\"{fromdir}/plots/{col}/{val}/{role}_loc_ctry.png\",height=900,width=1200,scale = 4)\n", " figsuper.show()\n", " else:\n", " data = pers_sub.groupby(c+\"_fullname\",as_index=False).agg({'appln_id': 'nunique', 'auth_ISO3': 'first',\"auth_ctry_type\":\"first\"})\n", " data[record_col+\"_percent\"] = data[record_col]/pers_sub[record_col].nunique()\n", " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n", " labels = labeld_dict)\n", " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},)\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=14\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 14\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", " figsuper.write_image(rf\"{fromdir}/plots/{col}/{val}/auth_loc_ctry.png\",height=900,width=1200,scale = 4)\n", "\n", "\n", " # top 25\n", " # - inventor\n", " # - applicant\n", " # in china, EU, EUextended\n", " color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96',\n", " 'EPO':'#9F2B68'}\n", "\n", " labeld_dict = dict({\n", " record_col: 'Number of co-patents',\n", " \"person_ctry_type\": \"Country type\",\n", " \"psn_sector_primary\":\"Sector\",\n", " \"han_label\": \"Name\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " })\n", " for trait in [\"person_ctry_type\",\"psn_sector_primary\"]:\n", " for ctry in [\"China\",'EU']:\n", " ctry_subset = appln_pers_f_sub[appln_pers_f_sub[\"person_ctry_type\"].str.contains(ctry)]\n", " # for role in [\"invt\",\"applt\"]:\n", " if True:\n", " role = \"applt\"\n", " pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n", " pers_sub = pers_sub[~(pers_sub[\"han_name\"].str.contains(\",\",na=False))]\n", " pers_sub = pers_sub[pers_sub[\"psn_sector_primary\"]!=\"INDIVIDUAL\"]\n", " data = (pers_sub\n", " .groupby([\"han_id\",'han_name'],as_index=False)\n", " .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',trait:\"first\"})\n", " .sort_values(by=\"appln_id\", ascending=False)\n", " .head(15))\n", " data[\"han_label\"] = (data[\"han_name\"]\n", " .str.upper()\n", " .str.strip(\".\")\n", " .str.replace(\"&\",\"&
\")\n", " + \" (\"+data[\"person_ctry_ISO3\"]+\")\")\n", "\n", " fig = px.bar(data, x=record_col, y=\"han_label\", color=trait,\n", " color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n", " fig.update_layout(font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'})\n", "\n", " figsuper = fig\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "\n", " figsuper['layout'][\"font\"][\"size\"]=12\n", " for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 12\n", "\n", " figsuper.update_layout(uniformtext_minsize=10)\n", "\n", " figsuper.show(config= dict(displayModeBar = False, responsive = True),height=800,width=1600,scale = 4)\n", " figsuper.write_image(rf\"{fromdir}/plots/{col}/{val}/top15_applt_{trait}_{ctry}.png\",height=900,width=1200,scale = 4)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 275, "outputs": [ { "data": { "text/plain": "Index(['appln_id', 'appln_auth', 'person_id', 'invt_seq_nr', 'applt_seq_nr',\n 'person_name', 'person_ctry_code', 'han_id', 'han_name', 'psn_sector_x',\n 'psn_sector_y', 'person_ctry_code_fullname', 'appln_auth_fullname',\n 'person_ctry_ISO3', 'person_ctry_type', 'auth_ISO3', 'auth_ctry_type',\n 'psn_sector_primary_x', 'psn_sector_primary'],\n dtype='object')" }, "execution_count": 275, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ctry_subset.columns" ], "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", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }