From 212f38088708bd372a6687fae925dd2269182faa Mon Sep 17 00:00:00 2001 From: Carlo Bottai Date: Mon, 8 Feb 2021 10:10:17 +0100 Subject: [PATCH] Use 2019 shapefile, add USPC class, and fix bugs --- CONTRIBUTORS.md | 1 + Makefile | 24 ++++-- README.md | 50 +++++++----- docs/README_base.md | 8 +- makefile.png | Bin 243274 -> 295252 bytes src/make-msa-label-database.py | 13 ++- src/make-patent-database.py | 87 ++------------------ src/make-patent-info-database.py | 118 ++++++++++++++++++++++++++- src/make-patent-inventor-database.py | 13 ++- src/make-patent-msa-database.py | 41 +++++----- src/make-readme-tables.py | 10 +++ 11 files changed, 221 insertions(+), 144 deletions(-) create mode 100644 CONTRIBUTORS.md diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md new file mode 100644 index 0000000..469376d --- /dev/null +++ b/CONTRIBUTORS.md @@ -0,0 +1 @@ +Carlo Bottai (Eindhoven University of Technology) diff --git a/Makefile b/Makefile index 2eca432..6bfa1ec 100644 --- a/Makefile +++ b/Makefile @@ -23,6 +23,7 @@ DATA_DIR_INTM = $(DATA_DIR)/interim DATA_DIR_PROC = $(DATA_DIR)/processed DATA_DIR_USPTO = $(DATA_DIR_RAW)/patentsview +DATA_DIR_PATEX = $(DATA_DIR_RAW)/patex DATA_DIR_SHP = $(DATA_DIR_RAW)/cartography SCRIPT_DIR = src @@ -35,19 +36,26 @@ USPTO_URL = https://s3.amazonaws.com/data.patentsview.org/20200929/download USPTO_FILES = patent.tsv.zip application.tsv.zip patent_inventor.tsv.zip location.tsv.zip uspatentcitation.tsv.zip USPTO_TARGETS := $(foreach F,$(USPTO_FILES),$(DATA_DIR_USPTO)/$F) -SHP_URL = https://www2.census.gov/geo/tiger/GENZ2018/shp -SHP_FILES = cb_2018_us_cbsa_20m.zip -SHP_TARGETS := $(foreach F,$(SHP_FILES),$(DATA_DIR_SHP)/$F) - $(USPTO_TARGETS): $(DATA_DIR_USPTO)/%: $(SCRIPT_DIR)/download.py python $< -i $(USPTO_URL)/$* -o $@ +PATEX_URL = https://bulkdata.uspto.gov/data/patent/pair/economics/2019 +PATEX_FILES = application_data.csv.zip +PATEX_TARGETS := $(foreach F,$(PATEX_FILES),$(DATA_DIR_PATEX)/$F) + +$(PATEX_TARGETS): $(DATA_DIR_PATEX)/%: $(SCRIPT_DIR)/download.py + python $< -i $(PATEX_URL)/$* -o $@ + +SHP_URL = https://www2.census.gov/geo/tiger/GENZ2019/shp +SHP_FILES = cb_2019_us_cbsa_20m.zip +SHP_TARGETS := $(foreach F,$(SHP_FILES),$(DATA_DIR_SHP)/$F) + $(SHP_TARGETS): $(DATA_DIR_SHP)/%: $(SCRIPT_DIR)/download.py python $< -i $(SHP_URL)/$* -o $@ ################################################# -$(DATA_DIR_INTM)/msa_patent.tsv.zip: $(SCRIPT_DIR)/make-patent-database.py $(DATA_DIR_USPTO)/patent.tsv.zip $(DATA_DIR_USPTO)/application.tsv.zip $(DATA_DIR_USPTO)/patent_inventor.tsv.zip $(DATA_DIR_USPTO)/location.tsv.zip $(DATA_DIR_SHP)/cb_2018_us_cbsa_20m.zip +$(DATA_DIR_INTM)/msa_patent.tsv.zip: $(SCRIPT_DIR)/make-patent-database.py $(DATA_DIR_USPTO)/patent.tsv.zip $(DATA_DIR_USPTO)/patent_inventor.tsv.zip $(DATA_DIR_USPTO)/location.tsv.zip $(DATA_DIR_SHP)/cb_2019_us_cbsa_20m.zip python $< -I $(filter-out $<,$^) -o $@ $(DATA_DIR_PROC)/msa_patent.tsv.zip: $(SCRIPT_DIR)/make-patent-msa-database.py $(DATA_DIR_INTM)/msa_patent.tsv.zip @@ -56,15 +64,15 @@ $(DATA_DIR_PROC)/msa_patent.tsv.zip: $(SCRIPT_DIR)/make-patent-msa-database.py $ $(DATA_DIR_PROC)/msa_patent_inventor.tsv.zip: $(SCRIPT_DIR)/make-patent-inventor-database.py $(DATA_DIR_INTM)/msa_patent.tsv.zip python $< -i $(filter-out $<,$^) -o $@ -$(DATA_DIR_PROC)/msa_patent_info.tsv.zip: $(SCRIPT_DIR)/make-patent-info-database.py $(DATA_DIR_INTM)/msa_patent.tsv.zip - python $< -i $(filter-out $<,$^) -o $@ - $(DATA_DIR_PROC)/msa_label.tsv.zip: $(SCRIPT_DIR)/make-msa-label-database.py $(DATA_DIR_INTM)/msa_patent.tsv.zip python $< -i $(filter-out $<,$^) -o $@ $(DATA_DIR_PROC)/msa_citation.tsv.zip: $(SCRIPT_DIR)/make-citation-database.py $(DATA_DIR_PROC)/msa_patent.tsv.zip $(DATA_DIR_USPTO)/uspatentcitation.tsv.zip python $< -I $(filter-out $<,$^) -o $@ +$(DATA_DIR_PROC)/msa_patent_info.tsv.zip: $(SCRIPT_DIR)/make-patent-info-database.py $(DATA_DIR_PROC)/msa_patent.tsv.zip $(DATA_DIR_PROC)/msa_citation.tsv.zip $(DATA_DIR_USPTO)/patent.tsv.zip $(DATA_DIR_USPTO)/application.tsv.zip $(DATA_DIR_PATEX)/application_data.csv.zip + python $< -I $(filter-out $<,$^) -o $@ + $(DOCS_DIR)/README_tables.md: $(SCRIPT_DIR)/make-readme-tables.py $(DATA_DIR_PROC)/msa_patent.tsv.zip $(DATA_DIR_PROC)/msa_patent_inventor.tsv.zip $(DATA_DIR_PROC)/msa_patent_info.tsv.zip $(DATA_DIR_PROC)/msa_label.tsv.zip $(DATA_DIR_PROC)/msa_citation.tsv.zip python $< -I $(filter-out $<,$^) -o $@ README.md: $(DOCS_DIR)/README_base.md $(DOCS_DIR)/README_tables.md diff --git a/README.md b/README.md index c3db681..9679fc8 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,12 @@ # Patenting in the US Metropolitan Areas This repository builds a database that collects information about the US patent applications developed by inventors located in Metropolitan Statistical Areas (MSA). +The data are aggregated at the Core Based Statistical Area (CBSA) level, based on the localization (latitude and longitude) of each inventor, as provided by PatentsView. The boundaries of each CBSA are constant over time and based on the data provided by the US Census (version 2019). To each inventor, within a patent, is assigned a fraction of the patent proportional to the size of the "inventing team". As well, a fractional count of the inventors of each patent, located in a given metropolitan area, is provided. + +Moreover, for each patent (partly) invented in a metropolitan area, the forward citations received by the patent are provided. + +Lastly, of each of these patents (and citing patents), the application and publication dates, the number of claims, and the main USPC patent class are reported. + ## Reproducibility To reproduce the database tables, please follow these steps: 1. Install [Git](https://git-scm.com/) @@ -35,7 +41,7 @@ Otherwise, please fork the repository, modify the code as you think is the best, ### Database The database is released under a [*CC-BY 4.0 License*](https://creativecommons.org/licenses/by/4.0/). -The raw data, elaborted by the scripts contained in this repository, are from [PatentsView](https://www.patentsview.org/) and [US Census](https://www.census.gov/). You can find further references to the raw files used in the Makefile file. +The raw data, elaborted by the scripts contained in this repository, are from [PatentsView](https://www.patentsview.org/), the [US Census](https://www.census.gov/), and the USPTO's [Patent Examination Research Dataset (PatEx)](https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-examination-research-dataset-public-pair). You can find further references to the raw files used in the Makefile file. ## Folders structure ``` @@ -74,31 +80,35 @@ The following tables describe the database files, showing the first five rows of ### msa_patent_inventor | patent_id | inventor_id | inventor_share | |-------------|---------------|------------------| -| 10000000 | 5073021-1 | 1 | -| 10000007 | 9473749-3 | 0.2 | -| 10000007 | 9862137-3 | 0.2 | -| 10000007 | 9862137-4 | 0.2 | -| 10000007 | 9862137-5 | 0.2 | +| 10000000 | 5073021-1 | 1 | +| 10003756 | 10003756-2 | 0.5 | +| 10003780 | 9495415-4 | 0.2 | +| 10006993 | 5763054-3 | 1 | +| 10007786 | 6067410-1 | 0.333333 | ### msa_patent_info -| patent_id | grant_date | appln_date | num_claims | -|-------------|--------------|--------------|--------------| -| 10000000 | 2018-06-19 | 2015-03-10 | 20 | -| 10000007 | 2018-06-19 | 2016-06-10 | 24 | -| 10000008 | 2018-06-19 | 2014-12-01 | 11 | -| 10000009 | 2018-06-19 | 2015-02-05 | 21 | -| 10000010 | 2018-06-19 | 2016-06-29 | 20 | +| patent_id | grant_date | appln_date | num_claims | uspc_class | +|-------------|--------------|--------------|--------------|--------------| +| 10000000 | 2018-06-19 | 2015-03-10 | 20 | 356 | +| 10000002 | 2018-06-19 | 2014-12-30 | 9 | 428 | +| 10000003 | 2018-06-19 | 2013-03-12 | 18 | 156 | +| 10000004 | 2018-06-19 | 2015-12-17 | 6 | 428 | +| 10000005 | 2018-06-19 | 2012-08-03 | 4 | 156 | + +Notes: +* Rename *patent_id* as *forward_citation_id* to merge this table with the *msa_citation* table. +* 5.4% of the *patent_id*s have no *uspc_class* (most of which, very old or very recent patents). ### msa_label -| cbsa_id | csa_id | cbsa_label | -|-----------|----------|---------------------------------------| -| 31080 | 348 | Los Angeles-Long Beach-Anaheim, CA | -| 33340 | 376 | Milwaukee-Waukesha-West Allis, WI | -| 35620 | 408 | New York-Newark-Jersey City, NY-NJ-PA | -| 41860 | 488 | San Francisco-Oakland-Hayward, CA | -| 40380 | 464 | Rochester, NY | +| csa_id | cbsa_id | cbsa_label | +|----------|-----------|---------------------------------------| +| 348 | 31080 | Los Angeles-Long Beach-Anaheim, CA | +| 376 | 33340 | Milwaukee-Waukesha, WI | +| 408 | 35620 | New York-Newark-Jersey City, NY-NJ-PA | +| 488 | 41860 | San Francisco-Oakland-Berkeley, CA | +| 464 | 40380 | Rochester, NY | ### msa_citation diff --git a/docs/README_base.md b/docs/README_base.md index 1678a99..3fa5417 100644 --- a/docs/README_base.md +++ b/docs/README_base.md @@ -1,6 +1,12 @@ # Patenting in the US Metropolitan Areas This repository builds a database that collects information about the US patent applications developed by inventors located in Metropolitan Statistical Areas (MSA). +The data are aggregated at the Core Based Statistical Area (CBSA) level, based on the localization (latitude and longitude) of each inventor, as provided by PatentsView. The boundaries of each CBSA are constant over time and based on the data provided by the US Census (version 2019). To each inventor, within a patent, is assigned a fraction of the patent proportional to the size of the "inventing team". As well, a fractional count of the inventors of each patent, located in a given metropolitan area, is provided. + +Moreover, for each patent (partly) invented in a metropolitan area, the forward citations received by the patent are provided. + +Lastly, of each of these patents (and citing patents), the application and publication dates, the number of claims, and the main USPC patent class are reported. + ## Reproducibility To reproduce the database tables, please follow these steps: 1. Install [Git](https://git-scm.com/) @@ -35,7 +41,7 @@ Otherwise, please fork the repository, modify the code as you think is the best, ### Database The database is released under a [*CC-BY 4.0 License*](https://creativecommons.org/licenses/by/4.0/). -The raw data, elaborted by the scripts contained in this repository, are from [PatentsView](https://www.patentsview.org/) and [US Census](https://www.census.gov/). You can find further references to the raw files used in the Makefile file. +The raw data, elaborted by the scripts contained in this repository, are from [PatentsView](https://www.patentsview.org/), the [US Census](https://www.census.gov/), and the USPTO's [Patent Examination Research Dataset (PatEx)](https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-examination-research-dataset-public-pair). You can find further references to the raw files used in the Makefile file. ## Folders structure ``` diff --git a/makefile.png b/makefile.png index bd49056fc0f1b6921e06bfd88787e11cd751c49a..f9ae669a5fad48f2510e5c0a6dc8a3e53c4399a6 100644 GIT binary patch literal 295252 zcmZ^L1wd6>*DV$(A}y#i5~4^dEeIkZsi>%wij;JRgosFkfJlc@DoCe?kdQ;SgmgED z1Jd>8ChmXld;jD6@REnU)}C|BF~=NhdnqeQpTWO?kAs7AMpj1h9u5xSE)LGI$dkD6 zmBuAU8u$_7BcM`L#N(tmFTeP5kVa6NvM z!4-$JT#-JN^8N3TGqq-BMfDE?M=a7D(3a`y>80nb+Rs~)kJl34eSVYRGUF-XQY!Zd12${YrE;>3ktvaTN4kY%+a?08sS^j2Q-rU?w9KVKYCRvBrA+7)ISC|amaBW@rU%#V3T!ZDf`R`XFlM~y{X{n-jlk~(P zW}%)nO$<4LD;33GNqjYDcJ)X1i{PTzfRxc-I*~@^P)Sm^5#Ac6kai*=n#0#mi1+@_ zWhBJ+65pDo&C6+)5T1j7u6)mFk%Es}D12|n=C>*G_gS^v<}9{qCc#Y2stQdd1WI`l zpMUXQQKsK{b9g9m%o^Qw1ZF(O8?jBNdFfrs02;Q_Ku%0)i!55EHIxR=Tjp&#dZ z;pLa)7~Bi`pV?SOd*m$_>^!>yUNgPtVkC~dO|d?T7HMMexPPyPDfmTD4NIgXX$>>w znwoUKKELLg+DY4Mf}_3{Qg0}$4vKE*C*QE6o#UZqD5rOW3m4nAY8Hp9ZMhBC-Ar;k z?Bc7?p-1Lc` 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b/src/make-msa-label-database.py index d5b6dc3..bf69a5a 100644 --- a/src/make-msa-label-database.py +++ b/src/make-msa-label-database.py @@ -25,18 +25,17 @@ def main(): df_patent = pd.read_table( args.input, - dtype=str) + usecols=[ + 'cbsa_id', + 'csa_id', + 'cbsa_label']) \ + .drop_duplicates() dir, file = os.path.split(args.output) if not os.path.exists(dir): os.makedirs(dir) - df_patent[[ - 'cbsa_id', - 'csa_id', - 'cbsa_label']] \ - .drop_duplicates() \ - .to_csv( + df_patent.to_csv( args.output, sep='\t', index=False, diff --git a/src/make-patent-database.py b/src/make-patent-database.py index fbb24a5..94c0e6e 100644 --- a/src/make-patent-database.py +++ b/src/make-patent-database.py @@ -14,94 +14,23 @@ import pandas as pd import geopandas as gpd import os -import requests from parse_args import parse_io -def fix_dates(dataframe:pd.DataFrame, dates_column:str): - """Fix wrong dates in the PatentsView database - Some (grant and application) dates on PatentsView are wrongly reported - and cannot be converted into proper dates. However, if you look on the - PatentsView website, most (all?) are correct. Therefore, this module - uses the PatentsView APIs to retrieve the correct dates - (or, as a second-best, it tries to fix them with a simple heuristic) - """ - # Use the PatentsView API to fix those application dates - # that cannot be coerced into proper dates - dataframe['date_'] = pd.to_datetime( - dataframe[dates_column], errors='coerce') - dataframe.sort_values(by=['patent_id','date_'], inplace=True) - dataframe.set_index('patent_id', inplace=True) - patents_to_fix = ','.join([f'{{"patent_number":"{patent_id}"}}' \ - for patent_id in dataframe[dataframe.date_.isna()].index]) - patents_to_fix_n = sum(dataframe.date_.isna()) - if patents_to_fix_n>0: - query = ''.join([ - 'https://api.patentsview.org/patents/query?q={"_or":[', - patents_to_fix, - ']}&f=["patent_number","patent_date"]&o={"per_page":', - str(patents_to_fix_n), '}']) - response = requests.get(query) - df_fix = pd.DataFrame(response.json()['patents'], dtype=str) - df_fix.rename(columns={ - 'patent_number':'patent_id', - 'patent_date':dates_column}, inplace=True) - df_fix.sort_values(by=['patent_id',dates_column], inplace=True) - df_fix.set_index('patent_id', inplace=True) - dataframe.update(df_fix) - dataframe.drop(columns='date_', inplace=True) - dataframe.reset_index(inplace=True) - dataframe.sort_values(by=['patent_id',dates_column], inplace=True) - - # At this point, all the mistakes should have been fixed - # Anyhow, the script will fix dates that are possibly still wrong - # applying some heuristic with the best guesses we can do, - # given the information provided - # Fix any date that has "00" as day, putting "01" inplace - subset = dataframe[dates_column].str.endswith('00') - if len(subset)>0: - dataframe.loc[ - subset,dates_column] = dataframe.loc[ - subset,dates_column].str[:-2] + '01' - # Fix any date that who's year doesn't start with "19" or "20", - # putting "19" inplace - subset = dataframe[dates_column].str[:2].isin(['19','20']) - dataframe.loc[ - ~subset,dates_column] = '19' + dataframe.loc[ - ~subset,dates_column].str[2:] - - return dataframe - - def main(): args = parse_io() df_patent = pd.read_table( args.input_list[0], # patent.tsv.zip usecols=[ - 'id', - 'date', - 'num_claims'], + 'id'], dtype=str) \ .rename(columns={ - 'id':'patent_id', - 'date':'grant_date'}) + 'id':'patent_id'}) df_patent = df_patent[df_patent.patent_id.str.isnumeric()] - df_application = pd.read_table( - args.input_list[1], # application.tsv.zip - usecols=[ - 'patent_id', - 'date'], - dtype=str) \ - .rename(columns={ - 'date':'appln_date'}) - - df_patent = pd.merge(df_patent, df_application) - del df_application - df_patent_inventor = pd.read_table( - args.input_list[2], # patent_inventor.tsv.zip + args.input_list[1], # patent_inventor.tsv.zip dtype=str) \ .dropna() @@ -120,7 +49,7 @@ def main(): how='left') df_location = pd.read_table( - args.input_list[3], # location.tsv.zip + args.input_list[2], # location.tsv.zip usecols=[ 'id', 'latitude', @@ -148,8 +77,8 @@ def main(): inplace=True) # M1 = Metropolitan areas - df_cbsa = gpd.read_file( # cb_2018_us_cbsa_20m.zip - f'zip://{args.input_list[4]}') \ + df_cbsa = gpd.read_file( # cb_2019_us_cbsa_20m.zip + f'zip://{args.input_list[3]}') \ .query('LSAD=="M1"') \ .drop(columns=['LSAD','ALAND','AWATER']) \ .rename(columns={ @@ -162,9 +91,7 @@ def main(): op='within') \ .drop(columns='index_right') df_patent = pd.DataFrame(df_patent) - - for date_column in ['grant_date', 'appln_date']: - df_patent = fix_dates(df_patent, date_column) + del df_cbsa dir, file = os.path.split(args.output) if not os.path.exists(dir): diff --git a/src/make-patent-info-database.py b/src/make-patent-info-database.py index bc5e576..9a4afff 100644 --- a/src/make-patent-info-database.py +++ b/src/make-patent-info-database.py @@ -7,6 +7,7 @@ * grant_date <- grant date -- YYYY-MM-DD * appln_date <- application date -- YYYY-MM-DD * num_claims <- number of claims +* uspc_class <- USPC main class Author: Carlo Bottai Copyright (c) 2021 - Carlo Bottai @@ -18,14 +19,126 @@ import pandas as pd import os +import requests from parse_args import parse_io +def fix_dates(dataframe:pd.DataFrame, dates_column:str): + """Fix wrong dates in the PatentsView database + Some (grant and application) dates on PatentsView are wrongly reported + and cannot be converted into proper dates. However, if you look on the + PatentsView website, most (all?) are correct. Therefore, this module + uses the PatentsView APIs to retrieve the correct dates + (or, as a second-best, it tries to fix them with a simple heuristic) + """ + # Use the PatentsView API to fix those application dates + # that cannot be coerced into proper dates + dataframe['date_'] = pd.to_datetime( + dataframe[dates_column], errors='coerce') + dataframe.sort_values(by=['patent_id','date_'], inplace=True) + dataframe.set_index('patent_id', inplace=True) + patents_to_fix = ','.join([f'{{"patent_number":"{patent_id}"}}' \ + for patent_id in dataframe[dataframe.date_.isna()].index]) + patents_to_fix_n = sum(dataframe.date_.isna()) + if patents_to_fix_n>0: + query = ''.join([ + 'https://api.patentsview.org/patents/query?q={"_or":[', + patents_to_fix, + ']}&f=["patent_number","patent_date"]&o={"per_page":', + str(patents_to_fix_n), '}']) + response = requests.get(query) + df_fix = pd.DataFrame(response.json()['patents'], dtype=str) + df_fix.rename(columns={ + 'patent_number':'patent_id', + 'patent_date':dates_column}, inplace=True) + df_fix.sort_values(by=['patent_id',dates_column], inplace=True) + df_fix.set_index('patent_id', inplace=True) + dataframe.update(df_fix) + dataframe.drop(columns='date_', inplace=True) + dataframe.reset_index(inplace=True) + dataframe.sort_values(by=['patent_id',dates_column], inplace=True) + + # At this point, all the mistakes should have been fixed + # Anyhow, the script will fix dates that are possibly still wrong + # applying some heuristic with the best guesses we can do, + # given the information provided + # Fix any date that has "00" as day, putting "01" inplace + subset = dataframe[dates_column].str.endswith('00') + if len(subset)>0: + dataframe.loc[ + subset,dates_column] = dataframe.loc[ + subset,dates_column].str[:-2] + '01' + # Fix any date that who's year doesn't start with "19" or "20", + # putting "19" inplace + subset = dataframe[dates_column].str[:2].isin(['19','20']) + dataframe.loc[ + ~subset,dates_column] = '19' + dataframe.loc[ + ~subset,dates_column].str[2:] + + return dataframe + + def main(): args = parse_io() - df_patent = pd.read_table(args.input) + df_msa_patent = pd.read_table( + args.input_list[0], # msa_patent.tsv.zip + usecols=['patent_id'], + dtype=str) + df_msa_citation = pd.read_table( + args.input_list[1], # msa_citation.tsv.zip + usecols=['forward_citation_id'], + dtype=str) + + patent_ids = set(df_msa_patent.patent_id) \ + .union(df_msa_citation.forward_citation_id) + + del df_msa_patent, df_msa_citation + + df_patent = pd.read_table( + args.input_list[2], # patent.tsv.zip + usecols=[ + 'id', + 'date', + 'num_claims'], + dtype=str) \ + .rename(columns={ + 'id':'patent_id', + 'date':'grant_date'}) + + df_patent = df_patent[df_patent.patent_id.isin(patent_ids)] + + df_application = pd.read_table( + args.input_list[3], # application.tsv.zip + usecols=[ + 'patent_id', + 'date'], + dtype=str) \ + .rename(columns={ + 'date':'appln_date'}) + + df_patent = pd.merge(df_patent, df_application) + del df_application + + for date_column in ['grant_date', 'appln_date']: + df_patent = fix_dates(df_patent, date_column) + + df_patex = pd.read_csv( + args.input_list[4], # application_data.csv.zip + usecols=[ + 'uspc_class', + 'patent_number'], + dtype=str) \ + .rename(columns={ + 'patent_number': 'patent_id'}) \ + .dropna() + + df_patent = pd.merge( + df_patent, df_patex, + how='left') + del df_patex + dir, file = os.path.split(args.output) if not os.path.exists(dir): os.makedirs(dir) @@ -34,7 +147,8 @@ def main(): 'patent_id', 'grant_date', 'appln_date', - 'num_claims']] \ + 'num_claims', + 'uspc_class']] \ .drop_duplicates() \ .to_csv( args.output, diff --git a/src/make-patent-inventor-database.py b/src/make-patent-inventor-database.py index e0927c9..3cf3f79 100644 --- a/src/make-patent-inventor-database.py +++ b/src/make-patent-inventor-database.py @@ -25,18 +25,17 @@ def main(): df_patent = pd.read_table( args.input, - dtype=str) + usecols=[ + 'patent_id', + 'inventor_id', + 'inventor_share']) \ + .drop_duplicates() dir, file = os.path.split(args.output) if not os.path.exists(dir): os.makedirs(dir) - df_patent[[ - 'patent_id', - 'inventor_id', - 'inventor_share']] \ - .drop_duplicates() \ - .to_csv( + df_patent.to_csv( args.output, sep='\t', index=False, diff --git a/src/make-patent-msa-database.py b/src/make-patent-msa-database.py index 4f87ee2..63f183e 100644 --- a/src/make-patent-msa-database.py +++ b/src/make-patent-msa-database.py @@ -15,6 +15,7 @@ """ +from matplotlib import use import pandas as pd import os from parse_args import parse_io @@ -23,30 +24,32 @@ def main(): args = parse_io() - df_patent = pd.read_table(args.input) + df_patent = pd.read_table( + args.input, + usecols=[ + 'patent_id', + 'inventor_id', + 'inventor_share', + 'cbsa_id']) \ + .drop_duplicates() dir, file = os.path.split(args.output) if not os.path.exists(dir): os.makedirs(dir) - df_patent[[ - 'patent_id', - 'inventor_id', - 'inventor_share', - 'cbsa_id']] \ - .drop_duplicates() \ - .drop(columns='inventor_id') \ - .groupby(['patent_id','cbsa_id'], as_index=False) \ - .agg({ - 'inventor_share':'sum'}) \ - .rename(columns={'inventor_share':'cbsa_share'}) \ - .to_csv( - args.output, - sep='\t', - index=False, - compression={ - 'method':'zip', - 'archive_name':file.replace('.zip','')}) + df_patent \ + .drop(columns='inventor_id') \ + .groupby(['patent_id','cbsa_id'], as_index=False) \ + .agg({ + 'inventor_share':'sum'}) \ + .rename(columns={'inventor_share':'cbsa_share'}) \ + .to_csv( + args.output, + sep='\t', + index=False, + compression={ + 'method':'zip', + 'archive_name':file.replace('.zip','')}) if __name__ == '__main__': diff --git a/src/make-readme-tables.py b/src/make-readme-tables.py index 59b862c..850bda2 100644 --- a/src/make-readme-tables.py +++ b/src/make-readme-tables.py @@ -35,6 +35,16 @@ def main(): tablefmt='github') f_out.write(f'### {file.split(".")[0]}\n') f_out.write(readme_table) + if file=='msa_patent_info.tsv.zip': + frac_no_uspc = df[df.uspc_class.isna()] \ + .patent_id.nunique()/df.patent_id.nunique() + f_out.write(( + '\n\nNotes:\n' + '* Rename *patent_id* as *forward_citation_id* ' + 'to merge this table with the *msa_citation* table.\n' + f'* {frac_no_uspc:.1%} ' + 'of the *patent_id*s have no *uspc_class* ' + '(most of which, very old or very recent patents).')) f_out.write('\n\n\n')
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