%global _empty_manifest_terminate_build 0 Name: python-pandas-usaddress Version: 0.21 Release: 1 Summary: The usaddress library made easy with Pandas. License: MIT URL: https://github.com/Lyonk71/pandas-usaddress Source0: https://mirrors.nju.edu.cn/pypi/web/packages/63/16/63e09bc175aee1a03dc6ba455e1f21902b3854d199b530a4cac8cf1ea9f4/pandas_usaddress-0.21.tar.gz BuildArch: noarch Requires: python3-pandas Requires: python3-usaddress %description # pandas-usaddress The usaddress library made easy with Pandas. Also supports standardizing addresses to meet USPS standards. # Installation pip install pandas-usaddress # Usage ### Basic Parsing import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field']) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #Output and fields will be identical to usaddress ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field'], granularity='medium', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #The standard output for usaddress has a lot of fields. The granularity parameter #allows you to condense the results you get back for different types of analysis. #see parameter documentation below for all granularity options. #Addresses are often unstandardized. The same address can come as 123 1st ST, or #123 First Street, etc. This can cause issues with analysis such as aggregation, #or record matching. The standardize parameter attempts to standardize the address #to US Postal Service (USPS) standards. ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['street1', 'street2', 'city', 'state'], granularity='single', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #You can also use pandas-usaddress to concatenate and parse multiple address lines. #This can be helpful when you are working with two datasets that have different #field names and you want the field names to be standardized using a specific level of #granularity. It's pretty common for instance that in one dataset will concatenate #address line 1 and 2, and another will not. #You will help the parser do it's job if you try to concatenate fields in approximately #same order that you would write them on an envelope. #In this instance, we are taking multiple address fields and converting them into a #single address line. That's fine to do! %package -n python3-pandas-usaddress Summary: The usaddress library made easy with Pandas. Provides: python-pandas-usaddress BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pandas-usaddress # pandas-usaddress The usaddress library made easy with Pandas. Also supports standardizing addresses to meet USPS standards. # Installation pip install pandas-usaddress # Usage ### Basic Parsing import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field']) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #Output and fields will be identical to usaddress ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field'], granularity='medium', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #The standard output for usaddress has a lot of fields. The granularity parameter #allows you to condense the results you get back for different types of analysis. #see parameter documentation below for all granularity options. #Addresses are often unstandardized. The same address can come as 123 1st ST, or #123 First Street, etc. This can cause issues with analysis such as aggregation, #or record matching. The standardize parameter attempts to standardize the address #to US Postal Service (USPS) standards. ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['street1', 'street2', 'city', 'state'], granularity='single', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #You can also use pandas-usaddress to concatenate and parse multiple address lines. #This can be helpful when you are working with two datasets that have different #field names and you want the field names to be standardized using a specific level of #granularity. It's pretty common for instance that in one dataset will concatenate #address line 1 and 2, and another will not. #You will help the parser do it's job if you try to concatenate fields in approximately #same order that you would write them on an envelope. #In this instance, we are taking multiple address fields and converting them into a #single address line. That's fine to do! %package help Summary: Development documents and examples for pandas-usaddress Provides: python3-pandas-usaddress-doc %description help # pandas-usaddress The usaddress library made easy with Pandas. Also supports standardizing addresses to meet USPS standards. # Installation pip install pandas-usaddress # Usage ### Basic Parsing import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field']) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #Output and fields will be identical to usaddress ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['address_field'], granularity='medium', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #The standard output for usaddress has a lot of fields. The granularity parameter #allows you to condense the results you get back for different types of analysis. #see parameter documentation below for all granularity options. #Addresses are often unstandardized. The same address can come as 123 1st ST, or #123 First Street, etc. This can cause issues with analysis such as aggregation, #or record matching. The standardize parameter attempts to standardize the address #to US Postal Service (USPS) standards. ### Parsing with Address Standardization import pandas as pd import pandas_usaddress #load dataframe df = pd.read_csv('test_file.csv') #initiate usaddress df = pandas_usaddress.tag(df, ['street1', 'street2', 'city', 'state'], granularity='single', standardize=True) #send output to csv df.to_csv('parsed_output.csv') #------------------------------additional details------------------------------ #You can also use pandas-usaddress to concatenate and parse multiple address lines. #This can be helpful when you are working with two datasets that have different #field names and you want the field names to be standardized using a specific level of #granularity. It's pretty common for instance that in one dataset will concatenate #address line 1 and 2, and another will not. #You will help the parser do it's job if you try to concatenate fields in approximately #same order that you would write them on an envelope. #In this instance, we are taking multiple address fields and converting them into a #single address line. That's fine to do! %prep %autosetup -n pandas-usaddress-0.21 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pandas-usaddress -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 0.21-1 - Package Spec generated