automatic import of python-pandas-usaddressopeneuler20.03

3 files changed, 340 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..8cd7bad 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/pandas_usaddress-0.21.tar.gz
diff --git a/python-pandas-usaddress.spec b/python-pandas-usaddress.spec
new file mode 100644
index 0000000..58d9403
--- /dev/null
+++ b/python-pandas-usaddress.spec
@@ -0,0 +1,338 @@
+%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 <Python_Bot@openeuler.org> - 0.21-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..116e6f6
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f586f768a9ca259d950d752e7525f875  pandas_usaddress-0.21.tar.gz