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9930472271503SHE, HUIMINSHE, HUIMINShanghaiCNNaN932084338SHE HUIMIN29505777SHE, HUIMIN0UNKNOWN172271503SHE, HUIMIN0
43896211563Zhou, JianZhou, JianBeijingCNNaN933759ZHOU JIAN37354342ZHOU JIAN1INDIVIDUAL106211563Zhou, Jian0
5937352078604KIRCHNER, NICOLEKIRCHNER, NICOLESACHSENSTRAßE 2845665 RECKLINGHAUSEN DEDEDE02139104KIRCHNER NICOLE17194552KIRCHNER, NICOLE0UNKNOWN152078604KIRCHNER, NICOLE0
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3990545320991Zhang, MichaelZhang, MichaelBeijingCNNaN9725806ZHANG MICHAEL37097378ZHANG, MICHAEL1INDIVIDUAL145320991Zhang, Michael0
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5838351832619WEN JU ZHANGWEN JU ZHANGCNCNNaN923069059ZHANG WEN JU34908417WEN JU ZHANG0UNKNOWN151832619WEN JU ZHANG0
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" }, - "execution_count": 106, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -115,14 +197,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 42, "outputs": [ { "data": { "text/plain": " cpc_id cpc_name \n0 A HUMAN NECESSITIES \\\n1 A01 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n2 A01B SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n3 A01B1/00 Hand tools (edge trimmers for lawns A01G3/06 ... \n4 A01B1/02 Spades; Shovels {(hand-operated dredgers E02F3... \n... ... ... \n260486 Y10T483/1873 Indexing matrix \n260487 Y10T483/1882 Rotary disc \n260488 Y10T483/1891 Chain or belt \n260489 Y10T483/19 Miscellaneous \n260490 NaN NaN \n\n section class subclass group main_group cpc_version \n0 A NaN NaN NaN NaN 2023 \\\n1 A 1.0 NaN NaN NaN 2023 \n2 A 1.0 B NaN NaN 2023 \n3 A 1.0 B 1.0 0.0 2023 \n4 A 1.0 B 1.0 2.0 2023 \n... ... ... ... ... ... ... \n260486 Y 10.0 T 483.0 1873.0 2023 \n260487 Y 10.0 T 483.0 1882.0 2023 \n260488 Y 10.0 T 483.0 1891.0 2023 \n260489 Y 10.0 T 483.0 19.0 2023 \n260490 NaN NaN NaN NaN NaN 2022 \n\n version https://git-lfs.github.com/spec/v1 \n0 NaN \\\n1 NaN \n2 NaN \n3 NaN \n4 NaN \n... ... \n260486 NaN \n260487 NaN \n260488 NaN \n260489 NaN \n260490 oid sha256:f138d6bdf2939ba576b96b633d81366123b... \n\n cpc_taxonomy \n0 [('A', 'HUMAN NECESSITIES')] \\\n1 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n2 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n3 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n4 [('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT... \n... ... \n260486 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260487 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260488 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260489 [('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D... \n260490 [] \n\n cpc_fullname \n0 HUMAN NECESSITIES \\\n1 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n2 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n3 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n4 HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM... \n... ... \n260486 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260487 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260488 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260489 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260490 NaN \n\n tax_level_0 \n0 HUMAN NECESSITIES \\\n1 HUMAN NECESSITIES \n2 HUMAN NECESSITIES \n3 HUMAN NECESSITIES \n4 HUMAN NECESSITIES \n... ... \n260486 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260487 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260488 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260489 GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME... \n260490 NaN \n\n tax_level_1 \n0 NaN \\\n1 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n2 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n3 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n4 AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI... \n... ... \n260486 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260487 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260488 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260489 TECHNICAL SUBJECTS COVERED BY FORMER USPC \n260490 NaN \n\n tax_level_2 \n0 NaN \\\n1 NaN \n2 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n3 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n4 SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS... \n... ... \n260486 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260487 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260488 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260489 TECHNICAL SUBJECTS COVERED BY FORMER US CLASSI... \n260490 NaN \n\n tax_level_3 tax_level_4 \n0 NaN NaN \\\n1 NaN NaN \n2 NaN NaN \n3 Hand tools (edge trimmers for lawns A01G3/06 ... NaN \n4 Spades; Shovels {(hand-operated dredgers E02F3... NaN \n... ... ... \n260486 Tool transfer to or from matrix Indexing matrix \n260487 Tool transfer to or from matrix Rotary disc \n260488 Tool transfer to or from matrix Chain or belt \n260489 Miscellaneous NaN \n260490 NaN NaN \n\n tax_level_5 tax_level_6 tax_level_7 data_scope \n0 NaN NaN NaN False \n1 NaN NaN NaN False \n2 NaN NaN NaN False \n3 NaN NaN NaN False \n4 NaN NaN NaN False \n... ... ... ... ... \n260486 NaN NaN NaN False \n260487 NaN NaN NaN False \n260488 NaN NaN NaN False \n260489 NaN NaN NaN False \n260490 NaN NaN NaN False \n\n[260491 rows x 20 columns]", "text/html": "
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cpc_idcpc_namesectionclasssubclassgroupmain_groupcpc_versionversion https://git-lfs.github.com/spec/v1cpc_taxonomycpc_fullnametax_level_0tax_level_1tax_level_2tax_level_3tax_level_4tax_level_5tax_level_6tax_level_7data_scope
0AHUMAN NECESSITIESANaNNaNNaNNaN2023NaN[('A', 'HUMAN NECESSITIES')]HUMAN NECESSITIESHUMAN NECESSITIESNaNNaNNaNNaNNaNNaNNaNFalse
1A01AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...A1.0NaNNaNNaN2023NaN[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...HUMAN NECESSITIESAGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...NaNNaNNaNNaNNaNNaNFalse
2A01BSOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...A1.0BNaNNaN2023NaN[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...HUMAN NECESSITIESAGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...NaNNaNNaNNaNNaNFalse
3A01B1/00Hand tools (edge trimmers for lawns A01G3/06 ...A1.0B1.00.02023NaN[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...HUMAN NECESSITIESAGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...Hand tools (edge trimmers for lawns A01G3/06 ...NaNNaNNaNNaNFalse
4A01B1/02Spades; Shovels {(hand-operated dredgers E02F3...A1.0B1.02.02023NaN[('A', 'HUMAN NECESSITIES'), ('A01', 'AGRICULT...HUMAN NECESSITIES<>AGRICULTURE; FORESTRY; ANIM...HUMAN NECESSITIESAGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTI...SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS...Spades; Shovels {(hand-operated dredgers E02F3...NaNNaNNaNNaNFalse
...............................................................
260486Y10T483/1873Indexing matrixY10.0T483.01873.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixIndexing matrixNaNNaNNaNFalse
260487Y10T483/1882Rotary discY10.0T483.01882.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixRotary discNaNNaNNaNFalse
260488Y10T483/1891Chain or beltY10.0T483.01891.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...Tool transfer to or from matrixChain or beltNaNNaNNaNFalse
260489Y10T483/19MiscellaneousY10.0T483.019.02023NaN[('Y', 'GENERAL TAGGING OF NEW TECHNOLOGICAL D...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPME...TECHNICAL SUBJECTS COVERED BY FORMER USPCTECHNICAL SUBJECTS COVERED BY FORMER US CLASSI...MiscellaneousNaNNaNNaNNaNFalse
260490NaNNaNNaNNaNNaNNaNNaN2022oid sha256:f138d6bdf2939ba576b96b633d81366123b...[]NaNNaNNaNNaNNaNNaNNaNNaNNaNFalse
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260491 rows × 20 columns

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" }, - "execution_count": 27, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -136,14 +218,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 43, "outputs": [ { "data": { - "text/plain": " appln_id cpc_class_symbol\n461523 550229597 C08G 18/6674\n511948 569882441 C08K 5/06\n67828 414454432 G06F 21/105\n83905 418994277 H04B 7/0452\n393107 530331540 H04N 19/70\n406153 534165470 C07K2317/52\n253473 490601228 D06M2200/50\n256305 491586869 G01G 11/003\n279783 497310121 A61K 39/3955\n432151 541221711 G01N 15/1012", - "text/html": "
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appln_idcpc_class_symbol
461523550229597C08G 18/6674
511948569882441C08K 5/06
67828414454432G06F 21/105
83905418994277H04B 7/0452
393107530331540H04N 19/70
406153534165470C07K2317/52
253473490601228D06M2200/50
256305491586869G01G 11/003
279783497310121A61K 39/3955
432151541221711G01N 15/1012
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" + "text/plain": " appln_id cpc_class_symbol\n490979 560314579 B32B 7/12\n139994 445201240 C08G2261/3241\n505928 567683420 C25D 7/123\n130069 442410454 Y02D 10/00\n467350 551851575 H04B 7/0486\n318101 509884390 H01L 21/0228\n350171 519161420 G06F 21/42\n129829 442384375 H04W 88/04\n277164 496690078 H04L 27/26\n240979 487005701 F16F2230/0064", + "text/html": "
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appln_idcpc_class_symbol
490979560314579B32B 7/12
139994445201240C08G2261/3241
505928567683420C25D 7/123
130069442410454Y02D 10/00
467350551851575H04B 7/0486
318101509884390H01L 21/0228
350171519161420G06F 21/42
129829442384375H04W 88/04
277164496690078H04L 27/26
240979487005701F16F2230/0064
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" }, - "execution_count": 28, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -157,13 +239,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 44, "outputs": [ { "data": { "text/plain": "325243" }, - "execution_count": 16, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -177,13 +259,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 45, "outputs": [ { "data": { "text/plain": "65136" }, - "execution_count": 17, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 46, "outputs": [], "source": [ "# Number of collaborative applications\n" @@ -208,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 47, "outputs": [], "source": [ "record_col = \"appln_id\"" @@ -219,13 +301,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 48, "outputs": [ { "data": { "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'" }, - "execution_count": 19, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -239,19 +321,114 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 49, + "outputs": [], + "source": [ + "fromdir=\"first_round\"" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [], + "source": [ + "appln_pers_f = pd.read_csv(f\"{fromdir}/first-filings-with-persons-raw.csv\", header=None,\n", + " names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n", + "\n", + "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", + "\n", + "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [], + "source": [ + "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", + "# tls_801.head()\n", + "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", + " (tls_801.ctry_code == 'CN')|\n", + " (tls_801.ctry_code == 'NO')|\n", + " (tls_801.ctry_code == 'CH')|\n", + " (tls_801.ctry_code == 'UK'))]\n", + "scope_countries = scope_df[\"ctry_code\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 54, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1116\n" + ] + } + ], + "source": [ + "\n", + "\n", + "# appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n", + "# (gr[\"appln_auth\"]==\"CN\")).any())\n", + "# appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n", + "# (gr[\"appln_auth\"].isin(scope_countries))).any())\n", + "\n", + "appln_ch = appln_pers_f.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"]==\"CN\").any())\n", + "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: (gr[\"person_ctry_code\"].isin(scope_countries)).any())\n", + "\n", + "print(appln_scope[\"appln_id\"].nunique())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 73, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1116\n" + ] + } + ], + "source": [ + "appln_pers_f = appln_scope[appln_scope[\"person_ctry_code\"].isin(list(scope_countries)+[\"CN\"])].copy()\n", + "print(appln_pers_f[\"appln_id\"].nunique())" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 71, "outputs": [ { "data": { - "text/plain": "count 65136.000000\nmean 2016.312546\nstd 3.189059\nmin 2011.000000\n25% 2013.000000\n50% 2017.000000\n75% 2019.000000\nmax 2023.000000\nName: appln_filing_year, dtype: float64" + "text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n76 410448968 DE 7067540 0 1 \\\n87 420386087 DE 7067540 0 1 \n88 420386089 DE 7067540 0 1 \n828 410512383 EP 220 0 1 \n1454 333687134 EP 10329 0 1 \n... ... ... ... ... ... \n147073 549690891 EP 85841201 1 0 \n147074 549690895 EP 85841201 1 0 \n147075 549690901 EP 85841201 1 0 \n147076 549690897 EP 85841201 1 0 \n147077 549788919 EP 85774895 2 0 \n\n person_name person_ctry_code psn_sector han_id \n76 Evonik Degussa GmbH DE COMPANY 840063 \\\n87 Evonik Degussa GmbH DE COMPANY 840063 \n88 Evonik Degussa GmbH DE COMPANY 840063 \n828 SIEMENS AKTIENGESELLSCHAFT DE COMPANY 2709504 \n1454 Alcatel Lucent FR COMPANY 68848 \n... ... ... ... ... \n147073 JONSSON, Tony SE UNKNOWN 185841201 \n147074 JONSSON, Tony SE UNKNOWN 185841201 \n147075 JONSSON, Tony SE UNKNOWN 185841201 \n147076 JONSSON, Tony SE UNKNOWN 185841201 \n147077 TRAN NGUYEN, Johnny SE UNKNOWN 185774895 \n\n han_name \n76 EVONIK DEGUSSA GMBH \n87 EVONIK DEGUSSA GMBH \n88 EVONIK DEGUSSA GMBH \n828 SIEMENS AG \n1454 ALCATEL LUCENT \n... ... \n147073 JONSSON, Tony \n147074 JONSSON, Tony \n147075 JONSSON, Tony \n147076 JONSSON, Tony \n147077 TRAN NGUYEN, Johnny \n\n[4434 rows x 10 columns]", + "text/html": "
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appln_idappln_authperson_idinvt_seq_nrapplt_seq_nrperson_nameperson_ctry_codepsn_sectorhan_idhan_name
76410448968DE706754001Evonik Degussa GmbHDECOMPANY840063EVONIK DEGUSSA GMBH
87420386087DE706754001Evonik Degussa GmbHDECOMPANY840063EVONIK DEGUSSA GMBH
88420386089DE706754001Evonik Degussa GmbHDECOMPANY840063EVONIK DEGUSSA GMBH
828410512383EP22001SIEMENS AKTIENGESELLSCHAFTDECOMPANY2709504SIEMENS AG
1454333687134EP1032901Alcatel LucentFRCOMPANY68848ALCATEL LUCENT
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147073549690891EP8584120110JONSSON, TonySEUNKNOWN185841201JONSSON, Tony
147074549690895EP8584120110JONSSON, TonySEUNKNOWN185841201JONSSON, Tony
147075549690901EP8584120110JONSSON, TonySEUNKNOWN185841201JONSSON, Tony
147076549690897EP8584120110JONSSON, TonySEUNKNOWN185841201JONSSON, Tony
147077549788919EP8577489520TRAN NGUYEN, JohnnySEUNKNOWN185774895TRAN NGUYEN, Johnny
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4434 rows × 10 columns

\n
" }, - "execution_count": 20, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln[\"appln_filing_year\"].describe()" + "appln_pers_f" ], "metadata": { "collapsed": false @@ -259,22 +436,27 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 55, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1116\n" + ] + }, { "data": { - "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n445321 902305835 EP A 2017 0 \\\n480488 515101583 CN A 2018 2 \n307745 458113107 CN A 2016 1 \n352956 449856958 EP A 2016 1 \n452231 510599290 CN A 2018 1 \n... ... ... ... ... ... \n424840 496723291 CN A 2017 1 \n84346 378450888 GB A 2012 1 \n736784 576111604 CN A 2021 1 \n617397 528722596 CN A 2019 1 \n65667 411664922 DE A 2012 1 \n\n nb_inventors \n445321 0 \n480488 5 \n307745 3 \n352956 4 \n452231 3 \n... ... \n424840 1 \n84346 0 \n736784 3 \n617397 1 \n65667 1 \n\n[100 rows x 6 columns]", - "text/html": "
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appln_idappln_authappln_kindfiling_yearnb_applicantsnb_inventors
445321902305835EPA201700
480488515101583CNA201825
307745458113107CNA201613
352956449856958EPA201614
452231510599290CNA201813
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424840496723291CNA201711
84346378450888GBA201210
736784576111604CNA202113
617397528722596CNA201911
65667411664922DEA201211
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100 rows × 6 columns

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" + "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n51083 407234637 FI A 2011 1 \\\n240847 442945435 FR A 2015 1 \n685164 531137359 NL A 2020 1 \n235203 438685261 EP A 2015 1 \n430561 497548590 DE A 2017 2 \n... ... ... ... ... ... \n721473 579480994 DE A 2021 1 \n619328 523950441 EP A 2019 2 \n484715 490602095 EP A 2018 1 \n196502 450101287 DE A 2014 1 \n367660 477378741 GB A 2017 1 \n\n nb_inventors \n51083 2 \n240847 5 \n685164 3 \n235203 4 \n430561 8 \n... ... \n721473 3 \n619328 3 \n484715 1 \n196502 6 \n367660 1 \n\n[100 rows x 6 columns]", + "text/html": "
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appln_idappln_authappln_kindfiling_yearnb_applicantsnb_inventors
51083407234637FIA201112
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721473579480994DEA202113
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100 rows × 6 columns

\n
" }, - "execution_count": 61, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "outdir=\"first_round\"\n", - "\n", - "appln_f = pd.read_csv(f\"{outdir}/first-filings-raw.csv\", header=None,\n", + "appln_f = pd.read_csv(f\"{fromdir}/first-filings-raw.csv\", header=None,\n", " names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors'])\n", "\n", "appln_f[\"filing_year\"] = appln_f[\"filing_year\"].astype(int)\n", @@ -283,6 +465,9 @@ "\n", "appln_f[\"appln_auth\"] = appln_f[\"appln_auth\"].str.replace(\"MA\",\"CN\")\n", "\n", + "appln_f = appln_f[appln_f[\"appln_id\"].isin(appln_scope['appln_id'])]\n", + "print(appln_f[\"appln_id\"].nunique())\n", + "\n", "appln_f.sample(100)" ], "metadata": { @@ -291,13 +476,31 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 57, "outputs": [ { "data": { - "text/plain": "appln_kind\nA 735889\nU 37157\nF 9\nD 3\nName: count, dtype: int64" + "text/plain": "appln_kind\nA 1114\nU 13\nName: count, dtype: int64" }, - "execution_count": 40, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 65, "outputs": [ { "data": { @@ -338,23 +541,22 @@ "marker": { "symbol": "circle" }, - "mode": "text+markers+lines", + "mode": "markers+text+lines", "name": "", "orientation": "v", "showlegend": false, "text": [ - 52293.0, - 52911.0, - 57065.0, - 59627.0, - 66787.0, - 72521.0, - 79736.0, - 83326.0, - 88838.0, - 94178.0, - 54385.0, - 101.0 + 52.0, + 48.0, + 73.0, + 64.0, + 86.0, + 97.0, + 148.0, + 179.0, + 166.0, + 145.0, + 58.0 ], "textfont": { "size": 12 @@ -371,23 +573,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x", "y": [ - 52293, - 52911, - 57065, - 59627, - 66787, - 72521, - 79736, - 83326, - 88838, - 94178, - 54385, - 101 + 52, + 48, + 73, + 64, + 86, + 97, + 148, + 179, + 166, + 145, + 58 ], "yaxis": "y", "type": "scatter" @@ -402,23 +602,22 @@ "marker": { "symbol": "circle" }, - "mode": "text+markers+lines", + "mode": "markers+text+lines", "name": "", "orientation": "v", "showlegend": false, "text": [ 0.0, - 0.011818025357122367, - 0.091255043696097, - 0.14024821677853633, - 0.27716902835943624, - 0.3868204157344195, - 0.5247929933260665, - 0.5934446293002887, - 0.6988507065955291, - 0.8009676247298874, - 0.04000535444514562, - -0.9980685751439007 + -0.07692307692307693, + 0.40384615384615385, + 0.23076923076923078, + 0.6538461538461539, + 0.8653846153846154, + 1.8461538461538463, + 2.4423076923076925, + 2.1923076923076925, + 1.7884615384615385, + 0.11538461538461539 ], "textfont": { "size": 12 @@ -436,23 +635,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ 0.0, - 0.011818025357122367, - 0.091255043696097, - 0.14024821677853633, - 0.27716902835943624, - 0.3868204157344195, - 0.5247929933260665, - 0.5934446293002887, - 0.6988507065955291, - 0.8009676247298874, - 0.04000535444514562, - -0.9980685751439007 + -0.07692307692307693, + 0.40384615384615385, + 0.23076923076923078, + 0.6538461538461539, + 0.8653846153846154, + 1.8461538461538463, + 2.4423076923076925, + 2.1923076923076925, + 1.7884615384615385, + 0.11538461538461539 ], "yaxis": "y2", "type": "scatter" @@ -1303,7 +1500,7 @@ "ticks": "outside", "range": [ 0, - 150000 + 250 ] }, "xaxis2": { @@ -1335,7 +1532,7 @@ "tickformat": ".0%", "range": [ -1, - 1.5 + 3.5 ] }, "annotations": [ @@ -1377,7 +1574,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -1429,8 +1626,8 @@ "# for i in[\"xaxis\",\"xaxis2\"]:\n", "# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", "#\n", - "figsuper['layout'][\"yaxis\"][\"range\"] = [0,150000]\n", - "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,1.5]\n", + "figsuper['layout'][\"yaxis\"][\"range\"] = [0,250]\n", + "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,3.5]\n", "#\n", "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", @@ -1443,7 +1640,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 66, "outputs": [ { "name": "stderr", @@ -1454,10 +1651,10 @@ }, { "data": { - "text/plain": " filing_year appln_auth appln_id filing_year_relative_growth \n0 2011 AT 705.0 2011 \\\n1 2012 AT 607.0 2011 \n2 2013 AT 673.0 2011 \n3 2014 AT 658.0 2011 \n4 2015 AT 710.0 2011 \n.. ... ... ... ... \n355 2018 SK 16.0 2011 \n356 2019 SK 15.0 2011 \n357 2020 SK 22.0 2011 \n358 2021 SK 14.0 2011 \n359 2022 SK 0.0 2011 \n\n appln_id_relative_growth appln_id_cumsum Country \n0 0.000000 705.0 Austria \n1 -0.139007 1312.0 Austria \n2 -0.045390 1985.0 Austria \n3 -0.066667 2643.0 Austria \n4 0.007092 3353.0 Austria \n.. ... ... ... \n355 -0.407407 191.0 Slovakia \n356 -0.444444 206.0 Slovakia \n357 -0.185185 228.0 Slovakia \n358 -0.481481 242.0 Slovakia \n359 -1.000000 242.0 Slovakia \n\n[360 rows x 7 columns]", - "text/html": "
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filing_yearappln_authappln_idfiling_year_relative_growthappln_id_relative_growthappln_id_cumsumCountry
02011AT705.020110.000000705.0Austria
12012AT607.02011-0.1390071312.0Austria
22013AT673.02011-0.0453901985.0Austria
32014AT658.02011-0.0666672643.0Austria
42015AT710.020110.0070923353.0Austria
........................
3552018SK16.02011-0.407407191.0Slovakia
3562019SK15.02011-0.444444206.0Slovakia
3572020SK22.02011-0.185185228.0Slovakia
3582021SK14.02011-0.481481242.0Slovakia
3592022SK0.02011-1.000000242.0Slovakia
\n

360 rows × 7 columns

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" + "text/plain": " filing_year appln_auth appln_id filing_year_relative_growth \n0 2011 AT 0.0 2012 \\\n1 2012 AT 1.0 2012 \n2 2013 AT 4.0 2012 \n3 2014 AT 0.0 2012 \n4 2015 AT 1.0 2012 \n.. ... ... ... ... \n160 2017 SE 3.0 2015 \n161 2018 SE 1.0 2015 \n162 2019 SE 0.0 2015 \n163 2020 SE 1.0 2015 \n164 2021 SE 0.0 2015 \n\n appln_id_relative_growth appln_id_cumsum Country \n0 -1.0 0.0 Austria \n1 0.0 1.0 Austria \n2 3.0 5.0 Austria \n3 -1.0 5.0 Austria \n4 0.0 6.0 Austria \n.. ... ... ... \n160 2.0 5.0 Sweden \n161 0.0 6.0 Sweden \n162 -1.0 6.0 Sweden \n163 0.0 7.0 Sweden \n164 -1.0 7.0 Sweden \n\n[165 rows x 7 columns]", + "text/html": "
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filing_yearappln_authappln_idfiling_year_relative_growthappln_id_relative_growthappln_id_cumsumCountry
02011AT0.02012-1.00.0Austria
12012AT1.020120.01.0Austria
22013AT4.020123.05.0Austria
32014AT0.02012-1.05.0Austria
42015AT1.020120.06.0Austria
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1602017SE3.020152.05.0Sweden
1612018SE1.020150.06.0Sweden
1622019SE0.02015-1.06.0Sweden
1632020SE1.020150.07.0Sweden
1642021SE0.02015-1.07.0Sweden
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165 rows × 7 columns

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0.572 + 0.7133333333333333, + 0.9999999999999999 ], "linecolor": "black", "linewidth": 1, @@ -5168,8 +4225,8 @@ "yaxis16": { "anchor": "x16", "domain": [ - 0.428, - 0.572 + 0.7133333333333333, + 0.9999999999999999 ], "linecolor": "black", "linewidth": 1, @@ -5211,8 +4268,8 @@ "yaxis17": { "anchor": "x17", "domain": [ - 0.428, - 0.572 + 0.7133333333333333, + 0.9999999999999999 ], "linecolor": "black", "linewidth": 1, @@ -5254,8 +4311,8 @@ "yaxis18": { "anchor": "x18", "domain": [ - 0.428, - 0.572 + 0.7133333333333333, + 0.9999999999999999 ], "linecolor": "black", "linewidth": 1, @@ -5272,602 +4329,600 @@ "size": 16 } }, - "xaxis19": { - "anchor": "y19", - "domain": [ - 0.0, - 0.15 + "font": { + "size": 22 + } + }, + "config": { + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n", + " y=record_col,\n", + " x='filing_year'\n", + " ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " markers=True,\n", + " labels={\n", + " record_col: 'Number of co-patents',\n", + " },\n", + " title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n", + "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", + "fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_layout({'template':\"plotly\"})\n", + "fig.update_yaxes(title='')\n", + "fig.update_xaxes(title='')\n", + "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + "# fig.show(config= dict(displayModeBar = False, responsive = True))\n", + "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(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 68, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "hovertemplate": "%{hovertext}
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Co-publications: %{y}", + "hovertext": [ + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria", + "Austria" ], - "linecolor": "black", - "linewidth": 1, - "matches": "x", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" - }, - "tickfont": { - "size": 16 + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" }, - "tickangle": 45 - }, - "yaxis19": { - "anchor": "x19", - "domain": [ - 0.6419999999999999, - 0.7859999999999999 - ], - "linecolor": "black", - "linewidth": 1, - "matches": "y", - "mirror": true, - "showgrid": true, - "showline": true, - "ticks": "outside", - "title": { - "text": "" + "marker": { + "symbol": "circle" }, - "tickfont": { - "size": 16 - } + "mode": "markers+lines", + "name": "", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021 + ], + "xaxis": "x13", + "y": [ + -1.0, + 0.0, + 3.0, + -1.0, + 0.0, + -1.0, + -1.0, + -1.0, + 1.0, + 2.0, + -1.0 + ], + "yaxis": "y13", + "type": "scatter" }, - "xaxis20": { - "anchor": "y20", - "domain": [ - 0.16999999999999998, - 0.31999999999999995 + { + "hovertemplate": "%{hovertext}
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Co-publications: %{y}", + "hovertext": [ + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany" ], - "linecolor": "black", - "linewidth": 1, - "matches": "x", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" }, - "tickfont": { - "size": 16 + "marker": { + "symbol": "circle" }, - "tickangle": 45 - }, - "yaxis28": { - "anchor": "x28", - "domain": [ - 0.856, - 1.0 + "mode": "markers+lines", + "name": "", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021 ], - "linecolor": "black", - "linewidth": 1, - "matches": "y", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" - }, - "tickfont": { - "size": 16 - } + "xaxis": "x9", + "y": [ + 0.0, + 0.0, + 1.1333333333333333, + 1.0666666666666667, + 0.6, + 0.26666666666666666, + 1.0, + 0.8666666666666667, + 0.3333333333333333, + 0.4, + 0.0 + ], + "yaxis": "y9", + "type": "scatter" }, - "xaxis29": { - "anchor": "y29", - "domain": [ - 0.6799999999999999, - 0.83 + { + "hovertemplate": "%{hovertext}
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Co-publications: %{y}", + "hovertext": [ + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg" ], - "linecolor": "black", - "linewidth": 1, - "matches": "x", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" + "legendgroup": "", + "line": { + "color": "#636efa", + "dash": "solid" }, - "tickfont": { - "size": 16 - }, - "tickangle": 45 - }, - "yaxis29": { - "anchor": "x29", - "domain": [ - 0.856, - 1.0 - ], - "linecolor": "black", - "linewidth": 1, - "matches": "y", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" - }, - "tickfont": { - "size": 16 - } - }, - "xaxis30": { - "anchor": "y30", - "domain": [ - 0.85, - 1.0 - ], - "linecolor": "black", - "linewidth": 1, - "matches": "x", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" - }, - "tickfont": { - "size": 16 - }, - "tickangle": 45 - }, - "yaxis30": { - "anchor": "x30", - "domain": [ - 0.856, - 1.0 - ], - "linecolor": "black", - "linewidth": 1, - "matches": "y", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", - "title": { - "text": "" - }, - "tickfont": { - "size": 16 - } - }, - "font": { - "size": 22 - } - }, - "config": { - "plotlyServerURL": "https://plotly.com" - } - }, - "text/html": "
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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\"})\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()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 131, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "EP not found in ISO2\n", + "European Patent Office not found in regex\n" + ] + }, + { + "data": { + "text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n136092 512548123 GB 55086687 0 1 \\\n140959 523940692 EP 69747486 0 1 \n145846 517778054 EP 77709457 3 0 \n111339 352908327 FR 46503468 4 0 \n102245 336208056 EP 5019334 1 0 \n... ... ... ... ... ... \n140952 513966940 EP 69747486 0 1 \n143598 546020617 DE 81337072 1 0 \n141356 512140061 NL 71325506 1 0 \n141273 488981411 EP 69749947 2 0 \n124034 407239903 EP 47903112 4 0 \n\n person_name person_ctry_code \n136092 TCL Communication Limited CN \\\n140959 Ningbo Geely Automobile Research & Development... CN \n145846 FORSBERG, Christian SE \n111339 GILIS, FABRICE CN \n102245 Carcone, Luca IT \n... ... ... \n140952 Ningbo Geely Automobile Research & Development... CN \n143598 Zhou, Hulian CN \n141356 JAN VAN DER HORST NL \n141273 Zuo, Fei CN \n124034 Wichmann, Daniel, Dr. DE \n\n psn_sector han_id han_name \n136092 COMPANY 3635036 TCL COMM LTD \\\n140959 COMPANY 3697072 NINGBO GEELY AUTOMOBILE R&D CO LTD \n145846 NaN 177709457 FORSBERG, Christian \n111339 NaN 146503468 GILIS, FABRICE \n102245 NaN 105019334 Carcone, Luca \n... ... ... ... \n140952 COMPANY 3697072 NINGBO GEELY AUTOMOBILE R&D CO LTD \n143598 NaN 181337072 Zhou, Hulian \n141356 NaN 171325506 JAN VAN DER HORST \n141273 NaN 169749947 Zuo, Fei \n124034 NaN 147903112 Wichmann, Daniel, Dr. \n\n person_ctry_code_fullname appln_auth_fullname person_ctry_ISO3 \n136092 China United Kingdom CHN \\\n140959 China European Patent Office CHN \n145846 Sweden European Patent Office SWE \n111339 China France CHN \n102245 Italy European Patent Office ITA \n... ... ... ... \n140952 China European Patent Office CHN \n143598 China Germany CHN \n141356 Netherlands Netherlands NLD \n141273 China European Patent Office CHN \n124034 Germany European Patent Office DEU \n\n person_ctry_type ISO3 auth_ISO3 auth_ctry_type \n136092 China CHN GBR Non-EU associate \n140959 China CHN not found EPO \n145846 EU SWE not found EPO \n111339 China CHN FRA EU \n102245 EU ITA not found EPO \n... ... ... ... ... \n140952 China CHN not found EPO \n143598 China CHN DEU EU \n141356 EU NLD NLD EU \n141273 China CHN not found EPO \n124034 EU DEU not found EPO \n\n[100 rows x 17 columns]", + "text/html": "
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appln_idappln_authperson_idinvt_seq_nrapplt_seq_nrperson_nameperson_ctry_codepsn_sectorhan_idhan_nameperson_ctry_code_fullnameappln_auth_fullnameperson_ctry_ISO3person_ctry_typeISO3auth_ISO3auth_ctry_type
136092512548123GB5508668701TCL Communication LimitedCNCOMPANY3635036TCL COMM LTDChinaUnited KingdomCHNChinaCHNGBRNon-EU associate
140959523940692EP6974748601Ningbo Geely Automobile Research & Development...CNCOMPANY3697072NINGBO GEELY AUTOMOBILE R&D CO LTDChinaEuropean Patent OfficeCHNChinaCHNnot foundEPO
145846517778054EP7770945730FORSBERG, ChristianSENaN177709457FORSBERG, ChristianSwedenEuropean Patent OfficeSWEEUSWEnot foundEPO
111339352908327FR4650346840GILIS, FABRICECNNaN146503468GILIS, FABRICEChinaFranceCHNChinaCHNFRAEU
102245336208056EP501933410Carcone, LucaITNaN105019334Carcone, LucaItalyEuropean Patent OfficeITAEUITAnot foundEPO
......................................................
140952513966940EP6974748601Ningbo Geely Automobile Research & Development...CNCOMPANY3697072NINGBO GEELY AUTOMOBILE R&D CO LTDChinaEuropean Patent OfficeCHNChinaCHNnot foundEPO
143598546020617DE8133707210Zhou, HulianCNNaN181337072Zhou, HulianChinaGermanyCHNChinaCHNDEUEU
141356512140061NL7132550610JAN VAN DER HORSTNLNaN171325506JAN VAN DER HORSTNetherlandsNetherlandsNLDEUNLDNLDEU
141273488981411EP6974994720Zuo, FeiCNNaN169749947Zuo, FeiChinaEuropean Patent OfficeCHNChinaCHNnot foundEPO
124034407239903EP4790311240Wichmann, Daniel, Dr.DENaN147903112Wichmann, Daniel, Dr.GermanyEuropean Patent OfficeDEUEUDEUnot foundEPO
\n

100 rows × 17 columns

\n
" + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n", + "# names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n", + "\n", + "# appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", + "#\n", + "# appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n", + "#\n", + "# tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", + "# # tls_801.head()\n", + "# scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", + "# (tls_801.ctry_code == 'CN')|\n", + "# (tls_801.ctry_code == 'NO')|\n", + "# (tls_801.ctry_code == 'CH')|\n", + "# (tls_801.ctry_code == 'UK'))]\n", + "# scope_countries = scope_df[\"ctry_code\"].unique()\n", + "#\n", + "# appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n", + "\n", + "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", + " appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n", + "\n", + "appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n", + "appln_pers_f[\"ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"person_ctry_code_fullname\"], to='ISO3')\n", + "appln_pers_f[\"person_ctry_type\"] = appln_pers_f[\"ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\")\n", + "\n", + "appln_pers_f[\"auth_ISO3\"] = cc.pandas_convert(series=appln_pers_f[\"appln_auth_fullname\"], to='ISO3')\n", + "appln_pers_f[\"auth_ctry_type\"] = appln_pers_f[\"auth_ISO3\"].apply(lambda x: \"China\" if x == \"CHN\" else \"Non-EU associate\" if x in [\"GBR\",\"CHE\",\"NOR\"] else \"EU\" if x!='not found' else \"EPO\")\n", + "\n", + "appln_pers_f.sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 93, + "outputs": [ + { + "data": { + "text/plain": "array(['DEU', 'FRA', 'SWE', 'ESP', 'BEL', 'CHN', 'CHE', 'AUT', 'FIN',\n 'NLD', 'GRC', 'ITA', 'CZE', 'HUN', 'DNK', 'IRL', 'POL', 'LUX',\n 'CYP', 'PRT', 'NOR', 'ROU'], dtype=object)" + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "appln_pers_f[\"person_ctry_ISO3\"].unique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 137, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "Country type=China
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"mirror": true, + "title": { + "text": "Name" + }, + "categoryorder": "total ascending", "showgrid": true, "showline": true, - "showticklabels": false, + "linewidth": 1, + "linecolor": "black", + "mirror": true, "ticks": "outside", - "title": { - "text": "" - }, - "zeroline": true, - "zerolinewidth": 2, - "zerolinecolor": "grey", - "tickformat": ".0%", "tickfont": { - "size": 16 + "size": 10 } }, - "xaxis30": { - "anchor": "y30", - "domain": [ - 0.85, - 1.0 - ], - "linecolor": "black", - "linewidth": 1, - "matches": "x", - "mirror": true, - "showgrid": true, - "showline": true, - "showticklabels": false, - "ticks": "outside", + "legend": { "title": { - "text": "" + "text": "Country type" }, - "tickfont": { - "size": 16 - }, - "tickangle": 45 + "tracegroupgap": 0 }, - "yaxis30": { - "anchor": "x30", - "domain": [ - 0.856, - 1.0 - ], - "linecolor": "black", + "margin": { + "t": 60 + }, + "barmode": "relative", + "font": { + "family": "Montserrat", + "size": 12 + }, + "yaxis2": { + 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.line(data.sort_values(ascending=True, by='filing_year'),\n", - " y=record_col+\"_relative_growth\",\n", - " x='filing_year'\n", - " ,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " markers=True,\n", - " labels={\n", - " record_col+\"_relative_growth\": 'Relative growth of co-patents (%)',\n", - " },\n", - " title=\"Yearly output of co-patents by patent authority\",hover_name= \"Country\")\n", - "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", - "fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_layout({'template':\"plotly\"})\n", - "fig.update_yaxes(title='')\n", - "fig.update_xaxes(title='')\n", - "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "# fig.show(config= dict(displayModeBar = False, responsive = True))\n", - "\n", - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 142, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "EP not found in ISO2\n" - ] - }, - { - "data": { - "text/plain": " appln_id appln_auth person_id invt_seq_nr applt_seq_nr \n987983 449313178 FR 53981953 1 0 \\\n896220 438489934 FR 52986688 4 0 \n1703159 484622346 EP 57708518 1 0 \n20051 471352386 DE 5235181 0 1 \n151664 337008716 FR 42812566 1 0 \n... ... ... ... ... ... \n818875 436949973 FR 51872306 2 0 \n195230 523590443 DE 16958864 2 0 \n129138 578626678 DE 6193358 0 1 \n276045 449666385 DE 41815193 2 0 \n24195 530683500 DE 5235181 0 1 \n\n person_name person_ctry_code psn_sector han_id \n987983 BREARD MATTHIEU FR NaN 153981953 \\\n896220 ENGBLOM CHRISTER FR NaN 152986688 \n1703159 VAN INGELGEM, Werner BE NaN 157708518 \n20051 Robert Bosch GmbH DE COMPANY 2456791 \n151664 BRESOUS LUC FR NaN 142812566 \n... ... ... ... ... \n818875 BORDERIEUX, LOIC FR NaN 151872306 \n195230 WEYDANZ, WOLFGANG DE INDIVIDUAL 116958864 \n129138 Clariant International Ltd. CH COMPANY 524840 \n276045 WELTER, MARTIN DE INDIVIDUAL 141815193 \n24195 Robert Bosch GmbH DE COMPANY 2456791 \n\n han_name person_ctry_code_fullname \n987983 BREARD MATTHIEU France \\\n896220 ENGBLOM CHRISTER France \n1703159 VAN INGELGEM, Werner Belgium \n20051 ROBERT BOSCH GMBH Germany \n151664 BRESOUS LUC France \n... ... ... \n818875 BORDERIEUX, LOIC France \n195230 WEYDANZ, WOLFGANG Germany \n129138 CLARIANT INT LTD Switzerland \n276045 WELTER, MARTIN Germany \n24195 ROBERT BOSCH GMBH Germany \n\n appln_auth_fullname \n987983 France \n896220 France \n1703159 European Patent Office \n20051 Germany \n151664 France \n... ... \n818875 France \n195230 Germany \n129138 Germany \n276045 Germany \n24195 Germany \n\n[100 rows x 12 columns]", - "text/html": "
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appln_idappln_authperson_idinvt_seq_nrapplt_seq_nrperson_nameperson_ctry_codepsn_sectorhan_idhan_nameperson_ctry_code_fullnameappln_auth_fullname
987983449313178FR5398195310BREARD MATTHIEUFRNaN153981953BREARD MATTHIEUFranceFrance
896220438489934FR5298668840ENGBLOM CHRISTERFRNaN152986688ENGBLOM CHRISTERFranceFrance
1703159484622346EP5770851810VAN INGELGEM, WernerBENaN157708518VAN INGELGEM, WernerBelgiumEuropean Patent Office
20051471352386DE523518101Robert Bosch GmbHDECOMPANY2456791ROBERT BOSCH GMBHGermanyGermany
151664337008716FR4281256610BRESOUS LUCFRNaN142812566BRESOUS LUCFranceFrance
.......................................
818875436949973FR5187230620BORDERIEUX, LOICFRNaN151872306BORDERIEUX, LOICFranceFrance
195230523590443DE1695886420WEYDANZ, WOLFGANGDEINDIVIDUAL116958864WEYDANZ, WOLFGANGGermanyGermany
129138578626678DE619335801Clariant International Ltd.CHCOMPANY524840CLARIANT INT LTDSwitzerlandGermany
276045449666385DE4181519320WELTER, MARTINDEINDIVIDUAL141815193WELTER, MARTINGermanyGermany
24195530683500DE523518101Robert Bosch GmbHDECOMPANY2456791ROBERT BOSCH GMBHGermanyGermany
\n

100 rows × 12 columns

\n
" - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "appln_pers_f = pd.read_csv(f\"{outdir}/first-filings-with-persons-raw.csv\", header=None,\n", - " names=[\"appln_id\",\"appln_auth\",\"person_id\",\"invt_seq_nr\",\"applt_seq_nr\",'person_name',\"person_ctry_code\"])\n", - "\n", - "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", - "\n", - "appln_pers_f = appln_pers_f.merge(appln_pers_haninfo, on=\"person_id\")\n", - "\n", - "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", - "# tls_801.head()\n", - "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", - " (tls_801.ctry_code == 'CN')|\n", - " (tls_801.ctry_code == 'NO')|\n", - " (tls_801.ctry_code == 'CH')|\n", - " (tls_801.ctry_code == 'UK'))]\n", - "scope_countries = scope_df[\"ctry_code\"].unique()\n", - "\n", - "appln_pers_f = appln_pers_f[appln_pers_f[\"person_ctry_code\"].isin(scope_countries)]\n", - "\n", - "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", - " appln_pers_f[c+\"_fullname\"] = cc.pandas_convert(series=appln_pers_f[c], to='name_short')\n", - "\n", - "appln_pers_f[\"appln_auth_fullname\"] = appln_pers_f[\"appln_auth_fullname\"].str.replace(\"not found\",\"European Patent Office\")\n", - "\n", - "appln_pers_f.sample(100)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 143, - "outputs": [ - { - "data": { - "text/plain": "283091" + "uniformtext": { + "minsize": 10 + } + }, + "config": { + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" }, - "execution_count": 143, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "appln_pers_f[\"appln_id\"].nunique()" + "color_discrete_map= {'China': '#EF553B',\n", + " 'EU': '#636EFA',\n", + " 'Non-EU associate': '#00CC96',\n", + " 'EPO':'#9F2B68'}\n", + "\n", + "labeld_dict = dict({\n", + " record_col: 'Number of co-patents',\n", + " \"person_ctry_type\": \"Country type\",\n", + " \"han_label\": \"Name\",\n", + " \"Country_Type\":\"Country type\",\n", + " \"Eurovoc_Class\":\"Region\"\n", + " },**c_dict)\n", + "\n", + "for ctry in [\"China\",'EU']:\n", + " ctry_subset = appln_pers_f[appln_pers_f[\"person_ctry_type\"].str.contains(ctry)]\n", + " for role in [\"invt\",\"applt\"]:\n", + " pers_sub = ctry_subset[ctry_subset[role+\"_seq_nr\"]!=0]\n", + " data = (pers_sub\n", + " .groupby([\"han_id\",'han_name'],as_index=False)\n", + " .agg({'appln_id': 'nunique', 'person_ctry_ISO3': 'first',\"person_ctry_type\":\"first\"})\n", + " .sort_values(by=\"appln_id\", ascending=False)\n", + " .head(15))\n", + " data[\"han_label\"] = data[\"han_name\"].str.upper().str.strip(\".\")+ \" (\"+data[\"person_ctry_ISO3\"]+\")\"\n", + "\n", + " fig = px.bar(data, x=record_col, y=\"han_label\", color=\"person_ctry_type\",\n", + " color_discrete_map=color_discrete_map,text_auto=True,labels=labeld_dict)\n", + " fig.update_layout(font_family=\"Montserrat\",\n", + " yaxis={'categoryorder':'total ascending'})\n", + "\n", + " figsuper = fig\n", + " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", + " figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "\n", + " figsuper['layout'][\"font\"][\"size\"]=12\n", + " for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + "\n", + " figsuper.update_layout(uniformtext_minsize=10)\n", + "\n", + " figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", + "\n", + " figsuper.show()" ], "metadata": { "collapsed": false @@ -10393,7 +14964,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 140, "outputs": [ { "data": { @@ -10401,62 +14972,46 @@ "data": [ { "alignmentgroup": "True", - "hovertemplate": "appln_id_percent=%{x}
person_ctry_code_fullname=%{y}", - "legendgroup": "", + "hovertemplate": "Country type=EU
Percent of related co-patents=%{x}
Country=%{y}", + "legendgroup": "EU", "marker": { - "color": "#636efa", + "color": "#636EFA", "pattern": { "shape": "" } }, - "name": "", - "offsetgroup": "", + "name": "EU", + "offsetgroup": "EU", "orientation": "h", - "showlegend": false, + "showlegend": true, "textposition": "auto", + "texttemplate": "%{x}", "x": [ - 0.03914342553564487, - 0.02023674321821383, - 7.736457388507359E-4, - 0.0026639328643185438, - 7.35535111321143E-4, - 1.562535728713309E-4, - 0.007404894928999901, - 0.013007157175850057, - 4.1921690282552195E-4, - 0.023906796649313628, - 0.24829836048080367, - 0.5011776183906644, - 0.0025153014169531315, - 0.00601385702416976, - 0.004477998734727166, - 0.012103935303398705, - 2.9726289473082465E-4, - 3.163182084956211E-4, - 0.0025267346052120095, - 1.562535728713309E-4, - 0.04088508121374727, - 0.0037195972468882675, - 0.009802053400611294, - 0.0024619465384117015, - 0.0019360198785033193, - 0.0018597986234441337, - 0.0027630204958954854, - 0.04001996996882551, - 0.026601218015655844, - 0.03892619495872619 + 0.026192703461178673, + 0.027128157156220765, + 0.005612722170252572, + 0.0037418147801683817, + 0.01216089803554724, + 0.21889616463985032, + 0.333021515434986, + 0.0018709073900841909, + 0.002806361085126286, + 9.354536950420954E-4, + 0.0205799812909261, + 9.354536950420954E-4, + 0.023386342376052385, + 0.0037418147801683817, + 9.354536950420954E-4, + 9.354536950420954E-4, + 0.025257249766136577, + 0.25631431244153413 ], "xaxis": "x", "y": [ "Austria", "Belgium", - "Bulgaria", - "China", - "Croatia", - "Cyprus", "Czech Republic", "Denmark", - "Estonia", "Finland", "France", "Germany", @@ -10464,19 +15019,66 @@ "Hungary", "Ireland", "Italy", - "Latvia", - "Lithuania", "Luxembourg", - "Malta", "Netherlands", - "Norway", "Poland", "Portugal", "Romania", - "Slovakia", - "Slovenia", "Spain", - "Sweden", + "Sweden" + ], + "yaxis": "y", + "type": "bar" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Country type=China
Percent of related co-patents=%{x}
Country=%{y}", + "legendgroup": "China", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "China", + "offsetgroup": "China", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "texttemplate": "%{x}", + "x": [ + 0.5116931711880262 + ], + "xaxis": "x", + "y": [ + "China" + ], + "yaxis": "y", + "type": "bar" + }, + { + "alignmentgroup": "True", + "hovertemplate": "Country type=Non-EU associate
Percent of related co-patents=%{x}
Country=%{y}", + "legendgroup": "Non-EU associate", + "marker": { + "color": "#00CC96", + "pattern": { + "shape": "" + } + }, + "name": "Non-EU associate", + "offsetgroup": "Non-EU associate", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "texttemplate": "%{x}", + "x": [ + 9.354536950420954E-4, + 0.01028999064546305 + ], + "xaxis": "x", + "y": [ + "Norway", "Switzerland" ], "yaxis": "y", @@ -10486,69 +15088,83 @@ "layout": { "template": { "data": { - "histogram2dcontour": [ + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ { - "type": "histogram2dcontour", "colorbar": { "outlinewidth": 0, "ticks": "" }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] + "type": "choropleth" } ], - "choropleth": [ + "contourcarpet": [ { - "type": "choropleth", "colorbar": { "outlinewidth": 0, "ticks": "" - } + }, + "type": "contourcarpet" } ], - "histogram2d": [ + "contour": [ { - "type": "histogram2d", "colorbar": { "outlinewidth": 0, "ticks": "" @@ -10594,12 +15210,12 @@ 1.0, "#f0f921" ] - ] + ], + "type": "contour" } ], - "heatmap": [ + "heatmapgl": [ { - "type": "heatmap", "colorbar": { "outlinewidth": 0, "ticks": "" @@ -10645,12 +15261,12 @@ 1.0, "#f0f921" ] - ] + ], + "type": "heatmapgl" } ], - "heatmapgl": [ + "heatmap": [ { - "type": "heatmapgl", "colorbar": { "outlinewidth": 0, "ticks": "" @@ -10696,21 +15312,12 @@ 1.0, "#f0f921" ] - ] - } - ], - "contourcarpet": [ - { - "type": "contourcarpet", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } + ], + "type": "heatmap" } ], - "contour": [ + "histogram2dcontour": [ { - "type": "contour", "colorbar": { "outlinewidth": 0, "ticks": "" @@ -10756,12 +15363,12 @@ 1.0, "#f0f921" ] - ] + ], + "type": "histogram2dcontour" } ], - "surface": [ + "histogram2d": [ { - "type": "surface", "colorbar": { "outlinewidth": 0, "ticks": "" @@ -10807,283 +15414,232 @@ 1.0, "#f0f921" ] - ] + ], + "type": "histogram2d" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" } ], "mesh3d": [ { - "type": "mesh3d", "colorbar": { "outlinewidth": 0, "ticks": "" - } - } - ], - "scatter": [ - { - "marker": { - "line": { - "color": "#283442" - 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "# percent of publications related to\n", + "# -authority country\n", + "# -inventor country\n", + "# -applicant country\n", + "\n", + "color_discrete_map= {'China': '#EF553B',\n", + " 'EU': '#636EFA',\n", + " 'Non-EU associate': '#00CC96',\n", + " 'EPO':'#7F00FF'}#CC8899\n", + "\n", + "c_dict = {\"count\":\"Number of co-publications\",\n", + " \"percent_of_copubs\":\"Percent of co-publications\",\n", + " \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n", + "\n", + "labeld_dict = dict({\n", + " record_col+\"_percent\": 'Percent of related co-patents',\n", + " \"person_ctry_code_fullname\": \"Country\",\n", + " \"person_ctry_type\": \"Country type\",\n", + " \"appln_auth_fullname\": \"Authority\",\n", + " \"auth_ctry_type\": \"Authority type\",\n", + " \"Country_Type\":\"Country type\",\n", + " \"Eurovoc_Class\":\"Region\"\n", + " },**c_dict)\n", + "\n", "for c in [\"person_ctry_code\",\"appln_auth\"]:\n", " pers_sub = appln_pers_f\n", " if c == \"person_ctry_code\":\n", " for role in [\"invt\",\"applt\"]:\n", " pers_sub = appln_pers_f[appln_pers_f[role+\"_seq_nr\"]!=0]\n", "\n", - " data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\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\")\n", - " fig.show()\n", + " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"person_ctry_type\",\n", + " labels = labeld_dict)\n", + " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", + " yaxis={'categoryorder':'total ascending'},)\n", + " figsuper = fig\n", + " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", + " figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "\n", + " figsuper['layout'][\"font\"][\"size\"]=12\n", + " for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + "\n", + " figsuper.update_layout(uniformtext_minsize=10)\n", + " figsuper.show()\n", " else:\n", - " data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\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\")\n", - " fig.show()" + " fig = px.bar(data, x=record_col+\"_percent\", y=c+\"_fullname\",color_discrete_map=color_discrete_map,text_auto=True,color=\"auth_ctry_type\",\n", + " labels = labeld_dict)\n", + " fig.update_layout(xaxis_tickformat='.1%',font_family=\"Montserrat\",\n", + " yaxis={'categoryorder':'total ascending'},)\n", + " figsuper = fig\n", + " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", + " figsuper.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", + "\n", + " figsuper['layout'][\"font\"][\"size\"]=12\n", + " for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + "\n", + " figsuper.update_layout(uniformtext_minsize=10)\n", + " figsuper.show()" ], "metadata": { "collapsed": false @@ -13295,12 +18135,7 @@ "cell_type": "code", "execution_count": null, "outputs": [], - "source": [ - "# percent of publications related to\n", - "# -authority country\n", - "# -inventor country\n", - "# -applicant country" - ], + "source": [], "metadata": { "collapsed": false } @@ -13319,6 +18154,15 @@ "collapsed": false } }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": 111, @@ -13343,24 +18187,26 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 80, "outputs": [ { "data": { - "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n209158 404772731 CN A 2012 1 \\\n924078 507793803 CN A 2018 1 \n399887 425594860 FR A 2014 1 \n521812 443742521 CN A 2015 1 \n1279727 524830291 CN A 2019 1 \n... ... ... ... ... ... \n967174 496304374 GB A 2018 1 \n602289 492364744 SE A 2016 1 \n229323 410162871 FR A 2013 1 \n1051611 507623754 FR A 2018 1 \n1050112 500224940 CN A 2018 1 \n\n nb_inventors ipc_class_symbol ipc_class_level cpc_id \n209158 7 C07C 41/16 A C07C41/16 \\\n924078 12 B29C 35/02 A B29C35/02 \n399887 2 A61P 7/04 A A61P7/04 \n521812 1 H04W 48/16 A H04W48/16 \n1279727 1 H01L 51/56 A H01L51/56 \n... ... ... ... ... \n967174 2 A61F 13/53 A A61F13/53 \n602289 3 C08L 5/08 A C08L5/08 \n229323 5 H05K 13/04 A H05K13/04 \n1051611 4 F02C 7/32 A F02C7/32 \n1050112 1 A61P 31/00 A A61P31/00 \n\n ipc_class ipc_classname \n209158 C-Chemistry; metallurgy Chemistry; metallurgy \n924078 B-Operations; transporting Operations; transporting \n399887 A-Human necessities Human necessities \n521812 H-Electricity Electricity \n1279727 H-Electricity Electricity \n... ... ... \n967174 A-Human necessities Human necessities \n602289 C-Chemistry; metallurgy Chemistry; metallurgy \n229323 H-Electricity Electricity \n1051611 F-Engineering Engineering \n1050112 A-Human necessities Human necessities \n\n[100 rows x 11 columns]", - 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appln_idappln_authappln_kindfiling_yearnb_applicantsnb_inventorsipc_class_symbolipc_class_levelcpc_idipc_classipc_classname
209158404772731CNA201217C07C 41/16AC07C41/16C-Chemistry; metallurgyChemistry; metallurgy
924078507793803CNA2018112B29C 35/02AB29C35/02B-Operations; transportingOperations; transporting
399887425594860FRA201412A61P 7/04AA61P7/04A-Human necessitiesHuman necessities
521812443742521CNA201511H04W 48/16AH04W48/16H-ElectricityElectricity
1279727524830291CNA201911H01L 51/56AH01L51/56H-ElectricityElectricity
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967174496304374GBA201812A61F 13/53AA61F13/53A-Human necessitiesHuman necessities
602289492364744SEA201613C08L 5/08AC08L5/08C-Chemistry; metallurgyChemistry; metallurgy
229323410162871FRA201315H05K 13/04AH05K13/04H-ElectricityElectricity
1051611507623754FRA201814F02C 7/32AF02C7/32F-EngineeringEngineering
1050112500224940CNA201811A61P 31/00AA61P31/00A-Human necessitiesHuman necessities
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" + "text/plain": " appln_id appln_auth appln_kind filing_year nb_applicants \n1322402 523940692 EP A 2019 2 \\\n765767 476494118 EP A 2017 1 \n34130 378211797 DE U 2011 1 \n789796 480385078 GB A 2017 1 \n701348 451567585 NL A 2016 1 \n... ... ... ... ... ... \n1361812 536054394 EP A 2020 1 \n242632 424885398 DE A 2013 1 \n1108772 503145148 EP A 2018 1 \n538788 447597102 EP A 2015 1 \n272481 412013115 EP A 2013 1 \n\n nb_inventors ipc_class_symbol ipc_class_level cpc_id \n1322402 2 B60W 50/14 A B60W50/14 \\\n765767 4 B60H 1/34 A B60H1/34 \n34130 0 F04B 17/04 A F04B17/04 \n789796 2 H04W 76/14 A H04W76/14 \n701348 3 E04F 13/077 A E04F13/077 \n... ... ... ... ... \n1361812 4 G05B 23/02 A G05B23/02 \n242632 1 F02M 51/06 A F02M51/06 \n1108772 2 G08G 1/01 A G08G1/01 \n538788 6 H01F 38/30 A H01F38/30 \n272481 3 C04B 28/04 A C04B28/04 \n\n ipc_class ipc_classname \n1322402 B-Operations; transporting Operations; transporting \n765767 B-Operations; transporting Operations; transporting \n34130 F-Engineering Engineering \n789796 H-Electricity Electricity \n701348 E-Fixed constructions Fixed constructions \n... ... ... \n1361812 G-Physics Physics \n242632 F-Engineering Engineering \n1108772 G-Physics Physics \n538788 H-Electricity Electricity \n272481 C-Chemistry; metallurgy Chemistry; metallurgy \n\n[100 rows x 11 columns]", + "text/html": "
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appln_idappln_authappln_kindfiling_yearnb_applicantsnb_inventorsipc_class_symbolipc_class_levelcpc_idipc_classipc_classname
1322402523940692EPA201922B60W 50/14AB60W50/14B-Operations; transportingOperations; transporting
765767476494118EPA201714B60H 1/34AB60H1/34B-Operations; transportingOperations; transporting
34130378211797DEU201110F04B 17/04AF04B17/04F-EngineeringEngineering
789796480385078GBA201712H04W 76/14AH04W76/14H-ElectricityElectricity
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1361812536054394EPA202014G05B 23/02AG05B23/02G-PhysicsPhysics
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100 rows × 11 columns

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" }, - "execution_count": 95, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "appln_ipc_f = pd.read_csv(f\"{outdir}/first-filings-with-IPC-raw.csv\", header=None,\n", + "appln_ipc_f = pd.read_csv(f\"{fromdir}/first-filings-with-IPC-raw.csv\", header=None,\n", " names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors',\n", " 'ipc_class_symbol','ipc_class_level'])\n", "\n", "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"filing_year\"]>2010]\n", + "appln_ipc_f = appln_ipc_f[appln_ipc_f[\"appln_id\"].isin(appln_f[\"appln_id\"])]\n", + "\n", "appln_ipc_f[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n", "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n", "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n", @@ -13383,7 +18229,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 81, "outputs": [], "source": [ "group = 'ipc_class'\n", @@ -13427,14 +18273,14 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 82, "outputs": [ { "data": { - "text/plain": " filing_year ipc_class appln_id filing_year_relative_growth \n0 2011 A-Human necessities 6063.0 2011 \\\n1 2012 A-Human necessities 5804.0 2011 \n2 2013 A-Human necessities 6301.0 2011 \n3 2014 A-Human necessities 6415.0 2011 \n4 2015 A-Human necessities 7455.0 2011 \n.. ... ... ... ... \n91 2018 H-Electricity 19820.0 2011 \n92 2019 H-Electricity 21883.0 2011 \n93 2020 H-Electricity 23436.0 2011 \n94 2021 H-Electricity 13101.0 2011 \n95 2022 H-Electricity 10.0 2011 \n\n appln_id_relative_growth appln_id_cumsum \n0 0.000000 6063.0 \n1 -0.042718 11867.0 \n2 0.039254 18168.0 \n3 0.058057 24583.0 \n4 0.229589 32038.0 \n.. ... ... \n91 0.607070 121682.0 \n92 0.774345 143565.0 \n93 0.900268 167001.0 \n94 0.062272 180102.0 \n95 -0.999189 180112.0 \n\n[96 rows x 6 columns]", - "text/html": "
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filing_yearipc_classappln_idfiling_year_relative_growthappln_id_relative_growthappln_id_cumsum
02011A-Human necessities6063.020110.0000006063.0
12012A-Human necessities5804.02011-0.04271811867.0
22013A-Human necessities6301.020110.03925418168.0
32014A-Human necessities6415.020110.05805724583.0
42015A-Human necessities7455.020110.22958932038.0
.....................
912018H-Electricity19820.020110.607070121682.0
922019H-Electricity21883.020110.774345143565.0
932020H-Electricity23436.020110.900268167001.0
942021H-Electricity13101.020110.062272180102.0
952022H-Electricity10.02011-0.999189180112.0
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96 rows × 6 columns

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" + "text/plain": " filing_year ipc_class appln_id filing_year_relative_growth \n0 2011 A-Human necessities 7.0 2011 \\\n1 2012 A-Human necessities 5.0 2011 \n2 2013 A-Human necessities 18.0 2011 \n3 2014 A-Human necessities 9.0 2011 \n4 2015 A-Human necessities 10.0 2011 \n.. ... ... ... ... \n83 2017 H-Electricity 71.0 2011 \n84 2018 H-Electricity 65.0 2011 \n85 2019 H-Electricity 27.0 2011 \n86 2020 H-Electricity 27.0 2011 \n87 2021 H-Electricity 11.0 2011 \n\n appln_id_relative_growth appln_id_cumsum \n0 0.000000 7.0 \n1 -0.285714 12.0 \n2 1.571429 30.0 \n3 0.285714 39.0 \n4 0.428571 49.0 \n.. ... ... \n83 3.437500 213.0 \n84 3.062500 278.0 \n85 0.687500 305.0 \n86 0.687500 332.0 \n87 -0.312500 343.0 \n\n[88 rows x 6 columns]", + "text/html": "
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filing_yearipc_classappln_idfiling_year_relative_growthappln_id_relative_growthappln_id_cumsum
02011A-Human necessities7.020110.0000007.0
12012A-Human necessities5.02011-0.28571412.0
22013A-Human necessities18.020111.57142930.0
32014A-Human necessities9.020110.28571439.0
42015A-Human necessities10.020110.42857149.0
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832017H-Electricity71.020113.437500213.0
842018H-Electricity65.020113.062500278.0
852019H-Electricity27.020110.687500305.0
862020H-Electricity27.020110.687500332.0
872021H-Electricity11.02011-0.312500343.0
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" }, - "execution_count": 97, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -13459,7 +18305,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 83, "outputs": [], "source": [ "group = ['filing_year','ipc_class']\n", @@ -13562,7 +18408,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 87, "outputs": [ { "data": { @@ -13590,7 +18436,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 89497 + 108 ], "xaxis": "x", "y": [ @@ -13621,7 +18467,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 135217 + 357 ], "xaxis": "x", "y": [ @@ -13652,7 +18498,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 67549 + 150 ], "xaxis": "x", "y": [ @@ -13683,7 +18529,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 10113 + 15 ], "xaxis": "x", "y": [ @@ -13714,7 +18560,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 22377 + 50 ], "xaxis": "x", "y": [ @@ -13745,7 +18591,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 79587 + 155 ], "xaxis": "x", "y": [ @@ -13776,7 +18622,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 173875 + 248 ], "xaxis": "x", "y": [ @@ -13807,7 +18653,7 @@ "textposition": "outside", "texttemplate": "%{x}", "x": [ - 180112 + 343 ], "xaxis": "x", "y": [ @@ -13844,23 +18690,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 6063.0, - 11867.0, - 18168.0, - 24583.0, - 32038.0, - 40655.0, - 49707.0, - 59381.0, - 70112.0, - 82632.0, - 89489.0, - 89497.0 + 7.0, + 12.0, + 30.0, + 39.0, + 49.0, + 60.0, + 73.0, + 83.0, + 98.0, + 103.0, + 108.0 ], "yaxis": "y2", "type": "scatter" @@ -13893,23 +18737,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 9701.0, - 19336.0, - 29659.0, - 40258.0, - 51884.0, - 65032.0, - 79813.0, - 94384.0, - 110267.0, - 126130.0, - 135208.0, - 135217.0 + 17.0, + 26.0, + 47.0, + 64.0, + 77.0, + 97.0, + 138.0, + 199.0, + 272.0, + 333.0, + 357.0 ], "yaxis": "y2", "type": "scatter" @@ -13942,23 +18784,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 5191.0, - 10324.0, - 15572.0, - 20769.0, - 26352.0, - 32549.0, - 39394.0, - 46367.0, - 54295.0, - 62755.0, - 67534.0, - 67549.0 + 9.0, + 20.0, + 34.0, + 43.0, + 53.0, + 63.0, + 79.0, + 88.0, + 114.0, + 141.0, + 150.0 ], "yaxis": "y2", "type": "scatter" @@ -13991,23 +18831,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 722.0, - 1433.0, - 2135.0, - 2935.0, - 3809.0, - 4883.0, - 6036.0, - 7173.0, - 8295.0, - 9427.0, - 10113.0, - 10113.0 + 1.0, + 1.0, + 1.0, + 2.0, + 5.0, + 6.0, + 9.0, + 11.0, + 13.0, + 15.0, + 15.0 ], "yaxis": "y2", "type": "scatter" @@ -14040,23 +18878,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 1778.0, - 3511.0, - 5299.0, - 7056.0, - 8874.0, - 11120.0, - 13401.0, - 15820.0, - 18371.0, - 20908.0, - 22371.0, - 22377.0 + 2.0, + 4.0, + 4.0, + 5.0, + 6.0, + 10.0, + 14.0, + 24.0, + 31.0, + 45.0, + 50.0 ], "yaxis": "y2", "type": "scatter" @@ -14089,23 +18925,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 6773.0, - 13777.0, - 20601.0, - 27508.0, - 34627.0, - 42407.0, - 50441.0, - 58672.0, - 66665.0, - 74603.0, - 79586.0, - 79587.0 + 8.0, + 11.0, + 28.0, + 34.0, + 43.0, + 51.0, + 67.0, + 94.0, + 122.0, + 148.0, + 155.0 ], "yaxis": "y2", "type": "scatter" @@ -14138,23 +18972,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 8043.0, - 17580.0, - 28796.0, - 40515.0, - 55110.0, - 71043.0, - 90391.0, - 111772.0, - 134884.0, - 160331.0, - 173864.0, - 173875.0 + 6.0, + 18.0, + 30.0, + 46.0, + 71.0, + 89.0, + 112.0, + 151.0, + 198.0, + 232.0, + 248.0 ], "yaxis": "y2", "type": "scatter" @@ -14187,23 +19019,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x2", "y": [ - 12333.0, - 24463.0, - 36955.0, - 50401.0, - 66145.0, - 82925.0, - 101862.0, - 121682.0, - 143565.0, - 167001.0, - 180102.0, - 180112.0 + 16.0, + 33.0, + 44.0, + 63.0, + 99.0, + 142.0, + 213.0, + 278.0, + 305.0, + 332.0, + 343.0 ], "yaxis": "y2", "type": "scatter" @@ -14233,23 +19063,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x3", "y": [ - 6063.0, - 5804.0, - 6301.0, - 6415.0, - 7455.0, - 8617.0, - 9052.0, - 9674.0, - 10731.0, - 12520.0, - 6857.0, - 8.0 + 7.0, + 5.0, + 18.0, + 9.0, + 10.0, + 11.0, + 13.0, + 10.0, + 15.0, + 5.0, + 5.0 ], "yaxis": "y3", "type": "scatter" @@ -14279,23 +19107,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x3", "y": [ - 9701.0, - 9635.0, - 10323.0, - 10599.0, - 11626.0, - 13148.0, - 14781.0, - 14571.0, - 15883.0, - 15863.0, - 9078.0, - 9.0 + 17.0, + 9.0, + 21.0, + 17.0, + 13.0, + 20.0, + 41.0, + 61.0, + 73.0, + 61.0, + 24.0 ], "yaxis": "y3", "type": "scatter" @@ -14325,23 +19151,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x3", "y": [ - 5191.0, - 5133.0, - 5248.0, - 5197.0, - 5583.0, - 6197.0, - 6845.0, - 6973.0, - 7928.0, - 8460.0, - 4779.0, - 15.0 + 9.0, + 11.0, + 14.0, + 9.0, + 10.0, + 10.0, + 16.0, + 9.0, + 26.0, + 27.0, + 9.0 ], "yaxis": "y3", "type": "scatter" @@ -14371,22 +19195,20 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x3", "y": [ - 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21381.0, - 23112.0, - 25447.0, - 13533.0, - 11.0 + 6.0, + 12.0, + 12.0, + 16.0, + 25.0, + 18.0, + 23.0, + 39.0, + 47.0, + 34.0, + 16.0 ], "yaxis": "y3", "type": "scatter" @@ -14555,23 +19371,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x3", "y": [ - 12333.0, - 12130.0, - 12492.0, - 13446.0, - 15744.0, - 16780.0, - 18937.0, - 19820.0, - 21883.0, - 23436.0, - 13101.0, - 10.0 + 16.0, + 17.0, + 11.0, + 19.0, + 36.0, + 43.0, + 71.0, + 65.0, + 27.0, + 27.0, + 11.0 ], "yaxis": "y3", "type": "scatter" @@ -14601,23 +19415,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.04271812634009566, - 0.0392544944746825, - 0.05805706745835395, - 0.22958931222167245, - 0.42124360877453404, - 0.4929902688438067, - 0.5955797460003299, - 0.7699158832261257, - 1.0649843311891802, - 0.13095827148276432, - -0.9986805211941283 + -0.2857142857142857, + 1.5714285714285714, + 0.2857142857142857, + 0.42857142857142855, + 0.5714285714285714, + 0.8571428571428571, + 0.42857142857142855, + 1.1428571428571428, + -0.2857142857142857, + -0.2857142857142857 ], "yaxis": "y4", "type": "scatter" @@ -14647,23 +19459,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.006803422327595093, - 0.06411710132975981, - 0.09256777651788475, - 0.19843315122152355, - 0.35532419338212556, - 0.5236573549118647, - 0.5020101020513349, - 0.6372538913514071, - 0.6351922482218328, - -0.06422018348623854, - -0.9990722605916916 + -0.47058823529411764, + 0.23529411764705882, + 0.0, + -0.23529411764705882, + 0.17647058823529413, + 1.411764705882353, + 2.588235294117647, + 3.2941176470588234, + 2.588235294117647, + 0.4117647058823529 ], "yaxis": "y4", "type": "scatter" @@ -14693,23 +19503,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.0111731843575419, - 0.010980543247929108, - 0.0011558466576767482, - 0.07551531496821422, - 0.1937969562704681, - 0.31862839529955694, - 0.34328645732999424, - 0.52725871701021, - 0.629743787324215, - -0.07936813716047005, - -0.9971103833558081 + 0.2222222222222222, + 0.5555555555555556, + 0.0, + 0.1111111111111111, + 0.1111111111111111, + 0.7777777777777778, + 0.0, + 1.8888888888888888, + 2.0, + 0.0 ], "yaxis": "y4", "type": "scatter" @@ -14739,22 +19547,20 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.015235457063711912, - -0.027700831024930747, - 0.10803324099722991, - 0.21052631578947367, - 0.48753462603878117, - 0.5969529085872576, - 0.574792243767313, - 0.554016620498615, - 0.5678670360110804, - -0.04986149584487535, + -1.0, + -1.0, + 0.0, + 2.0, + 0.0, + 2.0, + 1.0, + 1.0, + 1.0, -1.0 ], "yaxis": "y4", @@ -14785,23 +19591,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.02530933633295838, - 0.00562429696287964, - -0.011811023622047244, - 0.02249718785151856, - 0.26321709786276715, - 0.2829021372328459, - 0.36051743532058494, - 0.4347581552305962, - 0.4268841394825647, - -0.17716535433070865, - -0.9966254218222722 + 0.0, + -1.0, + -0.5, + -0.5, + 1.0, + 1.0, + 4.0, + 2.5, + 6.0, + 1.5 ], "yaxis": "y4", "type": "scatter" @@ -14831,23 +19635,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - 0.034106009153993797, - 0.0075298981249077215, - 0.019784438210541856, - 0.051085191200354346, - 0.14867857670160933, - 0.18618042226487524, - 0.21526649933559722, - 0.1801269747526945, - 0.172006496382696, - -0.26428465967813375, - -0.9998523549387273 + -0.625, + 1.125, + -0.25, + 0.125, + 0.0, + 1.0, + 2.375, + 2.5, + 2.25, + -0.125 ], "yaxis": "y4", "type": "scatter" @@ -14877,23 +19679,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - 0.18575158522939203, - 0.3945045381076713, - 0.45704339176924036, - 0.814621409921671, - 0.9809772472957852, - 1.4055700609225414, - 1.658336441626259, - 1.8735546437896307, - 2.163869203033694, - 0.6825811264453562, - -0.9986323511127688 + 1.0, + 1.0, + 1.6666666666666667, + 3.1666666666666665, + 2.0, + 2.8333333333333335, + 5.5, + 6.833333333333333, + 4.666666666666667, + 1.6666666666666667 ], "yaxis": "y4", "type": "scatter" @@ -14923,23 +19723,21 @@ 2018, 2019, 2020, - 2021, - 2022 + 2021 ], "xaxis": "x4", "y": [ 0.0, - -0.016459904321738425, - 0.012892240330819751, - 0.09024568231573826, - 0.27657504256871807, - 0.36057731290034867, - 0.5354739317278845, - 0.6070704613638206, - 0.7743452525743939, - 0.9002675747993188, - 0.06227195329603503, - -0.9991891672747912 + 0.0625, + -0.3125, + 0.1875, + 1.25, + 1.6875, + 3.4375, + 3.0625, + 0.6875, + 0.6875, + -0.3125 ], "yaxis": "y4", "type": "scatter" @@ -15777,7 +20575,7 @@ "tickformat": ".0f", "range": [ 0, - 210000 + 400 ] }, "yaxis": { @@ -15955,7 +20753,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -16011,7 +20809,7 @@ "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\"] = [0,210000]\n", + "figsuper['layout'][f'xaxis'][\"range\"] = [0,400]\n", "for i in range(2,5):\n", " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", "figsuper['layout'][\"font\"][\"size\"]=12\n", @@ -16193,8 +20991,8 @@ "source": [ "appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n", " (gr[\"appln_auth\"]==\"CN\")).any())\n", - "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(eu_countries)).any()|\n", - " (gr[\"appln_auth\"].isin(eu_countries))).any())\n", + "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(scope_countries)).any()|\n", + " (gr[\"appln_auth\"].isin(scope_countries))).any())\n", "appln_scope[\"appln_id\"].nunique()" ], "metadata": { diff --git a/PATSTAT/patstat_data_filter_process.ipynb b/PATSTAT/patstat_data_filter_process.ipynb index afd761b..be243e9 100644 --- a/PATSTAT/patstat_data_filter_process.ipynb +++ b/PATSTAT/patstat_data_filter_process.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -10,7 +10,7 @@ "output_type": "stream", "text": [ "CPU times: total: 125 ms\n", - "Wall time: 247 ms\n" + "Wall time: 143 ms\n" ] } ], @@ -21,14 +21,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -39,14 +39,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -57,14 +57,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": "{'temporary-directory': 'D:\\\\PATSTAT\\\\dask_temp',\n 'visualization': {'engine': None},\n 'tokenize': {'ensure-deterministic': False},\n 'dataframe': {'backend': 'pandas',\n 'shuffle': {'method': None, 'compression': None},\n 'parquet': {'metadata-task-size-local': 512, 'metadata-task-size-remote': 1},\n 'dtype_backend': 'pandas',\n 'convert_string': False},\n 'array': {'backend': 'numpy',\n 'rechunk': {'method': 'tasks'},\n 'svg': {'size': 120},\n 'slicing': {'split-large-chunks': None}},\n 'optimization': {'annotations': {'fuse': True},\n 'fuse': {'active': None,\n 'ave-width': 1,\n 'max-width': None,\n 'max-height': inf,\n 'max-depth-new-edges': None,\n 'subgraphs': None,\n 'rename-keys': True}}}" }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -107,22 +107,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: total: 39min 33s\n", - "Wall time: 20min 19s\n" + "CPU times: total: 41min 20s\n", + "Wall time: 18min 32s\n" ] } ], @@ -136,9 +136,21 @@ "tls_206.to_parquet(\"tls_206.parquet\")\n", "\n", "tls_207 = dd.read_csv(\"table_tls207.csv\", low_memory=False)\n", - "tls_207.to_parquet(\"tls_207.parquet\")" + "tls_207.to_parquet(\"tls_207.parquet\")\n", + "\n", + "tls_204 = dd.read_csv(\"table_tls204.csv\", low_memory=False)\n", + "tls_207.to_parquet(\"tls_204.parquet\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", "execution_count": 11,