{ "cells": [ { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import janitor\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib.ticker import MaxNLocator\n", "import math\n" ] }, { "cell_type": "code", "execution_count": 13, "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": 14, "outputs": [], "source": [ "outdir=\"EU_CH_scope\"\n", "\n", "appln = pd.read_csv(f\"{outdir}/tls_201_scope.csv\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 15, "outputs": [], "source": [ "\n", "appln_title = pd.read_csv(f\"{outdir}/tls_202_scope.csv\")\n", "\n", "pers = pd.read_csv(f\"{outdir}/tls_206_scope.csv\")\n", "pers['psn_sector'] = pers['psn_sector'].fillna(\"UNKNOWN\")\n", "\n", "appln_pers = pd.read_csv(f\"{outdir}/tls_207_scope.csv\")\n", "\n", "appln_cpc = pd.read_csv(f\"{outdir}/tls_224_scope.csv\")\n", "\n", "cpc_def = pd.read_csv(\"CPC_data/cpc_defs.csv\", low_memory=False)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 106, "outputs": [ { "data": { "text/plain": " person_id person_name \n27780 38752029 BOTH HENDRIK \\\n42972 47186505 Zhang, Richard S. \n39368 45225635 Roche Diagnostics Operations, Inc. \n17349 13164592 LI, QINGHUA \n38005 44432440 YANG, CHENGGUAN \n17354 13164808 JAVAHERIAN, KASHI \n48330 48886066 KRAUSE FALK \n25002 24615058 BUTLER, HANS \n70951 54647336 Schmieder, Wolfgang \n8906 11399816 De Boer, Dirk Kornelis Gerhardus \n\n person_name_orig_lg person_address person_ctry_code \n27780 BOTH HENDRIK NaN NL \\\n42972 Zhang, Richard S. Shanghai CN \n39368 Roche Diagnostics Operations, Inc. Indiana,IN US \n17349 LI, QINGHUA NaN US \n38005 YANG, CHENGGUAN NaN CN \n17354 JAVAHERIAN, KASHI NaN US \n48330 KRAUSE FALK NaN DE \n25002 BUTLER, HANS NaN NL \n70951 Schmieder, Wolfgang Baden-Wurtemberg DE \n8906 De Boer, Dirk Kornelis Gerhardus Den Bosch NL \n\n nuts nuts_level doc_std_name_id doc_std_name \n27780 NL 0 695378 BOTH HENDRIK \\\n42972 NaN 9 414822 HARBOURT CYRUS DAVID \n39368 NaN 9 167898 WANG XIAOBO \n17349 NaN 9 77857 LI QINGHUA \n38005 NaN 9 19222547 YANG CHENGGUAN \n17354 NaN 9 245041 JAVAHERIAN KASHI \n48330 DE 0 597644 KRAUSE FALK \n25002 NL 0 103820 BUTLER HANS \n70951 DE 0 19636429 MERUVA JAYAPRAKASH \n8906 NL 0 1980622 DE BOER DIRK KORNELIS GERHARDUS \n\n psn_id psn_name psn_level psn_sector \n27780 3219299 BOTH HENDRIK 0 UNKNOWN \\\n42972 37071034 ZHANG RICHARD S. 1 INDIVIDUAL \n39368 27413631 ROCHE DIAGNOSTICS OPERATIONS 2 COMPANY \n17349 19316195 LI QINGHUA 1 INDIVIDUAL \n38005 36198377 YANG, CHENGGUAN 1 INDIVIDUAL \n17354 14769626 JAVAHERIAN, KASHI 1 INDIVIDUAL \n48330 17940076 KRAUSE FALK 0 UNKNOWN \n25002 3827604 BUTLER, HANS 1 INDIVIDUAL \n70951 28724833 SCHMIEDER, WOLFGANG 0 UNKNOWN \n8906 6348065 DE BOER, DIRK KORNELIS GERHARDUS 1 INDIVIDUAL \n\n han_id han_name han_harmonized \n27780 138752029 BOTH HENDRIK 0 \n42972 147186505 Zhang, Richard S. 0 \n39368 2461595 ROCHE DIAGNOSTICS OPERATIONS INC 2 \n17349 4304276 LI QINGHUA 1 \n38005 144432440 YANG, CHENGGUAN 0 \n17354 113164808 JAVAHERIAN, KASHI 0 \n48330 148886066 KRAUSE FALK 0 \n25002 124615058 BUTLER, HANS 0 \n70951 154647336 Schmieder, Wolfgang 0 \n8906 111399816 De Boer, Dirk Kornelis Gerhardus 0 ", "text/html": "
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appln_idcpc_class_symbol
461523550229597C08G 18/6674
511948569882441C08K 5/06
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83905418994277H04B 7/0452
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" }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_cpc.sample(10)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 16, "outputs": [ { "data": { "text/plain": "325243" }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(appln_pers)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 17, "outputs": [ { "data": { "text/plain": "65136" }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(appln)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 18, "outputs": [], "source": [ "# Number of collaborative applications\n" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 39, "outputs": [], "source": [ "record_col = \"appln_id\"" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 19, "outputs": [ { "data": { "text/plain": "'C:\\\\Users\\\\radvanyi\\\\PycharmProjects\\\\ZSI_analytics\\\\PATSTAT'" }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getcwd()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 20, "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" }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln[\"appln_filing_year\"].describe()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 61, "outputs": [ { "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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" }, "execution_count": 61, "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", " names=[\"appln_id\",\"appln_auth\",\"appln_kind\",\"filing_year\",\"nb_applicants\",'nb_inventors'])\n", "\n", "appln_f[\"filing_year\"] = appln_f[\"filing_year\"].astype(int)\n", "\n", "appln_f = appln_f[appln_f[\"filing_year\"]>2010]\n", "\n", "appln_f[\"appln_auth\"] = appln_f[\"appln_auth\"].str.replace(\"MA\",\"CN\")\n", "\n", "appln_f.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 40, "outputs": [ { "data": { "text/plain": "appln_kind\nA 735889\nU 37157\nF 9\nD 3\nName: count, dtype: int64" }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_f[\"appln_kind\"].value_counts()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Co-patents per year" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 62, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hovertemplate": "Year:%{x:d}
Number of co-publications:%{y:d}", "legendgroup": "", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "text+markers+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 ], "textfont": { "size": 12 }, "textposition": "top center", "x": [ 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "xaxis": "x", "y": [ 52293, 52911, 57065, 59627, 66787, 72521, 79736, 83326, 88838, 94178, 54385, 101 ], "yaxis": "y", "type": "scatter" }, { "hovertemplate": "Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}", "legendgroup": "", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "text+markers+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 ], "textfont": { "size": 12 }, "textposition": "top center", "texttemplate": "%{y:.0%}", "x": [ 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "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 ], "yaxis": "y2", "type": "scatter" } ], "layout": { "template": { "data": { "barpolar": [ { "marker": { "line": { 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = (appln_f.groupby(['filing_year'])[record_col].nunique(dropna=False)\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True)[record_col][0]\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", "data = data.sort_values(by =[\"filing_year\"], ascending=[True])\n", "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n", "\n", "year_output = px.line(data,x=\"filing_year\", y=record_col, markers=True, text=record_col)\n", "year_output.update_traces(textposition='top center',textfont_size=12)\n", "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", "\n", "year_rel_output = px.line(data,x=\"filing_year\", y=record_col+\"_relative_growth\", markers=True, text=record_col+\"_relative_growth\")\n", "year_rel_output.update_traces(textposition='top center',textfont_size=12, texttemplate='%{y:.0%}')\n", "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", "\n", "figsuper = make_subplots(rows=2, cols=1, subplot_titles=[\"Co-patents per year\",\"Relative growth of co-patents\"],\n", " specs=[\n", " [{\"type\": \"xy\"}],\n", " [{\"type\": \"xy\"}]\n", " ])\n", "\n", "for trace in list(year_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "for trace in list(year_rel_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n", "# for i in[\"xaxis\",\"xaxis2\"]:\n", "# figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.5]\n", "#\n", "figsuper['layout'][\"yaxis\"][\"range\"] = [0,150000]\n", "figsuper['layout'][\"yaxis2\"][\"range\"] = [-1,1.5]\n", "#\n", "# figsuper['layout']['yaxis'].update(tickformat=\".0f\")\n", "# figsuper['layout']['yaxis3'].update(tickformat=\".0f\")\n", "\n", "figsuper.show(config= dict(displayModeBar = False, responsive = True))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 63, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "EP not found in ISO2\n" ] }, { "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
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360 rows × 7 columns

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" }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = (appln_f.groupby(['filing_year',\"appln_auth\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", "data[\"appln_auth\"].str.replace(\"MO\",\"CN\")\n", "\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset=\"appln_auth\"),\n", " on=[\"appln_auth\"], suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "data = data.sort_values(by =[\"appln_auth\",\"filing_year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('appln_auth',as_index=False)[record_col].cumsum())\n", "data[\"Country\"] = cc.pandas_convert(series=data[\"appln_auth\"], to='name_short')\n", "data[\"Country\"] = data[\"Country\"].str.replace(\"not found\",\"European Patent Office\")\n", "data" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 68, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hovertemplate": "%{hovertext}
%{x}
Co-publications: %{y}", "hovertext": [ "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria", "Austria" ], "legendgroup": "", "line": { "color": "#636efa", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "markers+lines", "name": "", "orientation": "v", "showlegend": false, "x": [ 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "xaxis": "x25", "y": [ 705.0, 607.0, 673.0, 658.0, 710.0, 635.0, 613.0, 613.0, 621.0, 630.0, 349.0, 0.0 ], "yaxis": "y25", "type": "scatter" }, { "hovertemplate": "%{hovertext}
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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": "
\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 \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 \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 \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_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" }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers_f[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 146, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "alignmentgroup": "True", "hovertemplate": "appln_id_percent=%{x}
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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[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", " else:\n", " data = pers_sub.groupby(c+\"_fullname\",as_index=False)[record_col].nunique()\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()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 126, "outputs": [ { "data": { "text/plain": " appln_auth appln_id appln_id_percent\n0 AT 6016 0.009362\n1 BE 1772 0.002758\n2 BG 229 0.000356\n3 CH 1926 0.002997\n4 CN 304654 0.474115\n5 CZ 1373 0.002137\n6 DE 124035 0.193028\n7 DK 1480 0.002303\n8 EE 46 0.000072\n9 EP 54082 0.084165\n10 ES 8484 0.013203\n11 FI 4719 0.007344\n12 FR 57391 0.089314\n13 GB 51069 0.079476\n14 GR 515 0.000801\n15 HR 135 0.000210\n16 HU 807 0.001256\n17 IE 70 0.000109\n18 IT 5535 0.008614\n19 LT 58 0.000090\n20 LU 1054 0.001640\n21 LV 60 0.000093\n22 MA 363 0.000565\n23 NL 7377 0.011480\n24 PL 1854 0.002885\n25 PT 385 0.000599\n26 RO 235 0.000366\n27 SE 6207 0.009660\n28 SI 430 0.000669\n29 SK 213 0.000331", "text/html": "
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appln_authappln_idappln_id_percent
0AT60160.009362
1BE17720.002758
2BG2290.000356
3CH19260.002997
4CN3046540.474115
5CZ13730.002137
6DE1240350.193028
7DK14800.002303
8EE460.000072
9EP540820.084165
10ES84840.013203
11FI47190.007344
12FR573910.089314
13GB510690.079476
14GR5150.000801
15HR1350.000210
16HU8070.001256
17IE700.000109
18IT55350.008614
19LT580.000090
20LU10540.001640
21LV600.000093
22MA3630.000565
23NL73770.011480
24PL18540.002885
25PT3850.000599
26RO2350.000366
27SE62070.009660
28SI4300.000669
29SK2130.000331
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" }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# percent of publications related to\n", "# -authority country\n", "# -inventor country\n", "# -applicant country" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# top 25\n", "# - inventor\n", "# - applicant\n", "# in china, EU, EUextended" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 111, "outputs": [ { "data": { "text/plain": " person_id psn_sector han_id \n269495 48715290 COMPANY 148715290 \\\n225401 45096499 GOV NON-PROFIT 986555 \n426484 58455492 NaN 158455492 \n50805 15325724 INDIVIDUAL 115325724 \n112503 28064269 NaN 128064269 \n... ... ... ... \n464708 59491689 NaN 159491689 \n169950 42286280 INDIVIDUAL 4099527 \n485679 60214508 NaN 160214508 \n542210 62188978 NaN 162188978 \n752136 81407527 NaN 181407527 \n\n han_name \n269495 TECHFLOW MARINE LIMITED \n225401 FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC... \n426484 DING ZHIJIAO \n50805 FOURNIER, THIERRY \n112503 ADRIAN WALLER \n... ... \n464708 LIU YANZHI \n169950 RIEMANN STEFAN \n485679 WAN BO \n542210 CHU WEI \n752136 GARCIA MONSONIS, Javier \n\n[100 rows x 4 columns]", "text/html": "
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person_idpsn_sectorhan_idhan_name
26949548715290COMPANY148715290TECHFLOW MARINE LIMITED
22540145096499GOV NON-PROFIT986555FUNDACIO INSTITUT CATALA D INVESTIGACIO QUIMIC...
42648458455492NaN158455492DING ZHIJIAO
5080515325724INDIVIDUAL115325724FOURNIER, THIERRY
11250328064269NaN128064269ADRIAN WALLER
...............
46470859491689NaN159491689LIU YANZHI
16995042286280INDIVIDUAL4099527RIEMANN STEFAN
48567960214508NaN160214508WAN BO
54221062188978NaN162188978CHU WEI
75213681407527NaN181407527GARCIA MONSONIS, Javier
\n

100 rows × 4 columns

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" }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers_haninfo = pd.read_csv(f\"{outdir}/tls_206_scope.csv\", usecols=[\"person_id\",\"han_id\",\"han_name\",\"psn_sector\"])\n", "appln_pers_haninfo.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 95, "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]", "text/html": "
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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
....................................
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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100 rows × 11 columns

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" }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f = pd.read_csv(f\"{outdir}/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[\"cpc_id\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\")\n", "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class_symbol\"].str.replace(\" \",\"\").str[0]\n", "appln_ipc_f[\"ipc_classname\"] = appln_ipc_f[\"ipc_class\"].replace({\"A\":\"Human necessities\",\n", " \"B\":\"Performing operations; transporting\",\n", " \"B\":\"Operations; transporting\",\n", " \"C\":\"Chemistry; metallurgy\",\n", " \"D\":\"Textiles; paper\",\n", " \"E\":\"Fixed constructions\",\n", " \"F\":\"Mechanical engineering; lightning; heating; weapons; blasting\",\n", " \"F\":\"Engineering\",\n", " \"G\":\"Physics\",\n", " \"H\":\"Electricity\"})\n", "\n", "appln_ipc_f[\"ipc_class\"] = appln_ipc_f[\"ipc_class\"]+\"-\"+appln_ipc_f[\"ipc_classname\"]\n", "appln_ipc_f.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 96, "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": 97, "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": "
\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 \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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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
\n

96 rows × 6 columns

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" }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = (appln_ipc_f.groupby(['filing_year','ipc_class'])[record_col].nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"filing_year\"], ascending=True).drop_duplicates(subset='ipc_class'),\n", " on='ipc_class', suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", "data = data.sort_values(by =[\"ipc_class\",\"filing_year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('ipc_class',as_index=False)[record_col].cumsum())\n", "data" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 98, "outputs": [], "source": [ "group = ['filing_year','ipc_class']\n", "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True, labels={\n", " record_col: 'Number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Yearly output of co-publications\", template='plotly')\n", "fig.update_traces(hovertemplate='%{y:d}')\n", "fig.update_layout(hovermode='x unified')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", "year_output_by_domain = go.Figure(fig)\n", "\n", "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True, labels={\n", " record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Relative growth in the output of co-publications\", template='plotly')\n", "\n", "fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n", "fig.update_traces(hovertemplate='%{y:.0f}00%')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", "rel_output_by_domain = go.Figure(fig)\n", "\n", "\n", "fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n", " labels={\n", " record_col+\"_cumsum\": 'Cumulative number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Cumulative number of co-publications\", template='plotly')\n", "fig.update_traces(hovertemplate='%{y:d}')\n", "fig.update_layout(hovermode='x unified')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", "cumsum_by_domain = go.Figure(fig)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 104, "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", " 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\"] = [0,210000]\n", "for i in range(2,5):\n", " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", "figsuper['layout'][\"font\"][\"size\"]=12\n", "for a in figsuper['layout'][\"annotations\"]:\n", " a[\"font\"][\"size\"] = 14\n", "figsuper.show(config= dict(displayModeBar = False, responsive = True))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 37, "outputs": [ { "data": { "text/plain": "1964" }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[\"filing_year\"].min()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 34, "outputs": [ { "data": { "text/plain": "615464" }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 35, "outputs": [ { "data": { "text/plain": "591442" }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f.merge(cpc_def, on=\"cpc_id\")[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 29, "outputs": [ { "data": { "text/plain": "array(['A', 'S', 'C'], dtype=object)" }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_ipc_f[\"ipc_class_level\"].unique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 133, "outputs": [], "source": [ "tls_801 = pd.read_csv(r\"./EU_CH_scope/table_tls801.csv\", low_memory=False)\n", "# tls_801.head()\n", "scope_df = tls_801[((tls_801.eu_member==\"Y\")|\n", " (tls_801.ctry_code == 'CN')|\n", " (tls_801.ctry_code == 'NO')|\n", " (tls_801.ctry_code == 'CH')|\n", " (tls_801.ctry_code == 'UK'))]\n", "scope_countries = scope_df[\"ctry_code\"].unique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 134, "outputs": [ { "data": { "text/plain": "array(['AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI',\n 'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL',\n 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK'], dtype=object)" }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scope_countries" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 25, "outputs": [ { "data": { "text/plain": " person_id appln_id applt_seq_nr invt_seq_nr\n0 1 413601768 1 0\n1 21 332015605 1 0\n2 21 333490084 1 0\n3 21 335903805 1 0\n4 76 352908776 1 0\n.. ... ... ... ...\n95 220 417147567 1 0\n96 220 417932952 1 0\n97 220 420712526 1 0\n98 220 420927947 1 0\n99 220 420947631 1 0\n\n[100 rows x 4 columns]", "text/html": "
\n\n\n \n \n \n \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 \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
person_idappln_idapplt_seq_nrinvt_seq_nr
0141360176810
12133201560510
22133349008410
32133590380510
47635290877610
...............
9522041714756710
9622041793295210
9722042071252610
9822042092794710
9922042094763110
\n

100 rows × 4 columns

\n
" }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appln_pers.head(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 26, "outputs": [ { "ename": "KeyError", "evalue": "'person_ctry_code'", "output_type": "error", "traceback": [ "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3649\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3648\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n", "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", "File \u001B[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n", "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'", "\nThe above exception was the direct cause of the following exception:\n", "\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)", "Cell \u001B[1;32mIn[26], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m \u001B[43mappln_pers\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgroupby\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfilter\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mgr\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\n\u001B[0;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mappln_auth\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m==\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mCN\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43many\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\groupby\\generic.py:1741\u001B[0m, in \u001B[0;36mDataFrameGroupBy.filter\u001B[1;34m(self, func, dropna, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1738\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m name, group \u001B[38;5;129;01min\u001B[39;00m gen:\n\u001B[0;32m 1739\u001B[0m \u001B[38;5;28mobject\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;21m__setattr__\u001B[39m(group, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m\"\u001B[39m, name)\n\u001B[1;32m-> 1741\u001B[0m res \u001B[38;5;241m=\u001B[39m func(group, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1743\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1744\u001B[0m res \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39msqueeze()\n", "Cell \u001B[1;32mIn[26], line 1\u001B[0m, in \u001B[0;36m\u001B[1;34m(gr)\u001B[0m\n\u001B[1;32m----> 1\u001B[0m appln_ch \u001B[38;5;241m=\u001B[39m appln_pers\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((\u001B[43mgr\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mperson_ctry_code\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 2\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m==\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mCN\u001B[39m\u001B[38;5;124m\"\u001B[39m))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 3\u001B[0m appln_scope \u001B[38;5;241m=\u001B[39m appln_ch\u001B[38;5;241m.\u001B[39mgroupby(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m)\u001B[38;5;241m.\u001B[39mfilter(\u001B[38;5;28;01mlambda\u001B[39;00m gr: ((gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mperson_ctry_code\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries))\u001B[38;5;241m.\u001B[39many()\u001B[38;5;241m|\u001B[39m\n\u001B[0;32m 4\u001B[0m (gr[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_auth\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39misin(eu_countries)))\u001B[38;5;241m.\u001B[39many())\n\u001B[0;32m 5\u001B[0m appln_scope[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappln_id\u001B[39m\u001B[38;5;124m\"\u001B[39m]\u001B[38;5;241m.\u001B[39mnunique()\n", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\frame.py:3745\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3743\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m 3744\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 3745\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3746\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m 3747\u001B[0m indexer \u001B[38;5;241m=\u001B[39m [indexer]\n", "File \u001B[1;32m~\\.conda\\envs\\MOME_BIGDATA\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3651\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m 3649\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m 3650\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[1;32m-> 3651\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m 3652\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m 3653\u001B[0m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m 3654\u001B[0m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m 3655\u001B[0m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[0;32m 3656\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n", "\u001B[1;31mKeyError\u001B[0m: 'person_ctry_code'" ] } ], "source": [ "appln_ch = appln_pers.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"]==\"CN\").any()|\n", " (gr[\"appln_auth\"]==\"CN\")).any())\n", "appln_scope = appln_ch.groupby(\"appln_id\").filter(lambda gr: ((gr[\"person_ctry_code\"].isin(eu_countries)).any()|\n", " (gr[\"appln_auth\"].isin(eu_countries))).any())\n", "appln_scope[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_scope" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_scope[appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_scope[~appln_scope[\"appln_id\"].isin(appln[\"appln_id\"])]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_cpc[\"cpc_id\"] = appln_cpc[\"cpc_class_symbol\"].str.replace(\" \",\"\")\n", "\n", "appln_cpc_merge = appln_cpc.merge(cpc_def,on='cpc_id')\n", "print (f\"CPC Merge loss: {(1-len(appln_cpc_merge)/len(appln_cpc))*100}%\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "len(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True])" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")[\"appln_nr\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\").merge(appln, on =\"appln_id\")" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_title.merge(appln_cpc_merge[appln_cpc_merge[\"data_scope\"]==True], on=\"appln_id\")[\"appln_id\"].nunique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "cpc_def[cpc_def[\"data_scope\"]==True]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_title.sample(100)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_data = appln.merge(appln_title, on=\"appln_id\")\n", "appln_data.head()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_data[\"appln_auth\"].unique()" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_pers" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "pers_sector_primary = pers.groupby(\"han_id\", as_index=False)[\"psn_sector\"].agg(\n", " lambda x: pd.Series.mode(x)[0]).rename(columns={\"psn_sector\":\"psn_sector_primary\"})\n", "persn = pers.merge(pers_sector_primary, on='han_id')\n", "persn" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "pers_sector_primary" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "appln_merge = appln.merge(appln_title, on=\"appln_id\")#.merge(appln_pers,on=\"appln_id\")\n", "appln_merge.to_excel(\"appln_data.xlsx\", index=False)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "person_merge = appln_pers.merge(pers,on=\"person_id\")\n", "person_merge.to_excel(\"person_data.xlsx\", index=False)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "pers[\"person_ctry_code\"].unique()" ], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }