automatic import of python-pandas-schema

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diff --git a/.gitignore b/.gitignore
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+/pandas_schema-0.3.6.tar.gz
diff --git a/python-pandas-schema.spec b/python-pandas-schema.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pandas-schema
+Version:	0.3.6
+Release:	1
+Summary:	A validation library for Pandas data frames using user-friendly schemas
+License:	MIT
+URL:		https://github.com/TMiguelT/PandasSchema
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/17/2a/d302fd983cdf3fb9a3d24d981e4a3c7340994030c0c46b397b4d9db83bea/pandas_schema-0.3.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-packaging
+
+%description
+PandasSchema is a module for validating tabulated data, such as CSVs
+(Comma Separated Value files), and TSVs (Tab Separated Value files).
+It uses the incredibly powerful data analysis tool Pandas to do so
+quickly and efficiently.
+For example, say your code expects a CSV that looks a bit like this:
+   Given Name,Family Name,Age,Sex,Customer ID
+   Gerald,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,27,Male,7951WVLW
+   Edyta,Majewska,50,Female,7758NSID
+Now you want to be able to ensure that the data in your CSV is in the
+correct format:
+   import pandas as pd
+   from io import StringIO
+   from pandas_schema import Column, Schema
+   from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, CanConvertValidation, MatchesPatternValidation, InRangeValidation, InListValidation
+   schema = Schema([
+       Column('Given Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Family Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Age', [InRangeValidation(0, 120)]),
+       Column('Sex', [InListValidation(['Male', 'Female', 'Other'])]),
+       Column('Customer ID', [MatchesPatternValidation(r'\d{4}[A-Z]{4}')])
+   ])
+   test_data = pd.read_csv(StringIO('''Given Name,Family Name,Age,Sex,Customer ID
+   Gerald ,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,270,male,7951WVLW
+   Edyta,Majewska ,50,Female,775ANSID
+   '''))
+   errors = schema.validate(test_data)
+   for error in errors:
+       print(error)
+PandasSchema would then output
+   {row: 0, column: "Given Name"}: "Gerald " contains trailing whitespace
+   {row: 1, column: "Age"}: "270" was not in the range [0, 120)
+   {row: 1, column: "Sex"}: "male" is not in the list of legal options (Male, Female, Other)
+   {row: 2, column: "Family Name"}: "Majewska " contains trailing whitespace
+   {row: 2, column: "Customer ID"}: "775ANSID" does not match the pattern "\d{4}[A-Z]{4}"
+
+%package -n python3-pandas-schema
+Summary:	A validation library for Pandas data frames using user-friendly schemas
+Provides:	python-pandas-schema
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pandas-schema
+PandasSchema is a module for validating tabulated data, such as CSVs
+(Comma Separated Value files), and TSVs (Tab Separated Value files).
+It uses the incredibly powerful data analysis tool Pandas to do so
+quickly and efficiently.
+For example, say your code expects a CSV that looks a bit like this:
+   Given Name,Family Name,Age,Sex,Customer ID
+   Gerald,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,27,Male,7951WVLW
+   Edyta,Majewska,50,Female,7758NSID
+Now you want to be able to ensure that the data in your CSV is in the
+correct format:
+   import pandas as pd
+   from io import StringIO
+   from pandas_schema import Column, Schema
+   from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, CanConvertValidation, MatchesPatternValidation, InRangeValidation, InListValidation
+   schema = Schema([
+       Column('Given Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Family Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Age', [InRangeValidation(0, 120)]),
+       Column('Sex', [InListValidation(['Male', 'Female', 'Other'])]),
+       Column('Customer ID', [MatchesPatternValidation(r'\d{4}[A-Z]{4}')])
+   ])
+   test_data = pd.read_csv(StringIO('''Given Name,Family Name,Age,Sex,Customer ID
+   Gerald ,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,270,male,7951WVLW
+   Edyta,Majewska ,50,Female,775ANSID
+   '''))
+   errors = schema.validate(test_data)
+   for error in errors:
+       print(error)
+PandasSchema would then output
+   {row: 0, column: "Given Name"}: "Gerald " contains trailing whitespace
+   {row: 1, column: "Age"}: "270" was not in the range [0, 120)
+   {row: 1, column: "Sex"}: "male" is not in the list of legal options (Male, Female, Other)
+   {row: 2, column: "Family Name"}: "Majewska " contains trailing whitespace
+   {row: 2, column: "Customer ID"}: "775ANSID" does not match the pattern "\d{4}[A-Z]{4}"
+
+%package help
+Summary:	Development documents and examples for pandas-schema
+Provides:	python3-pandas-schema-doc
+%description help
+PandasSchema is a module for validating tabulated data, such as CSVs
+(Comma Separated Value files), and TSVs (Tab Separated Value files).
+It uses the incredibly powerful data analysis tool Pandas to do so
+quickly and efficiently.
+For example, say your code expects a CSV that looks a bit like this:
+   Given Name,Family Name,Age,Sex,Customer ID
+   Gerald,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,27,Male,7951WVLW
+   Edyta,Majewska,50,Female,7758NSID
+Now you want to be able to ensure that the data in your CSV is in the
+correct format:
+   import pandas as pd
+   from io import StringIO
+   from pandas_schema import Column, Schema
+   from pandas_schema.validation import LeadingWhitespaceValidation, TrailingWhitespaceValidation, CanConvertValidation, MatchesPatternValidation, InRangeValidation, InListValidation
+   schema = Schema([
+       Column('Given Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Family Name', [LeadingWhitespaceValidation(), TrailingWhitespaceValidation()]),
+       Column('Age', [InRangeValidation(0, 120)]),
+       Column('Sex', [InListValidation(['Male', 'Female', 'Other'])]),
+       Column('Customer ID', [MatchesPatternValidation(r'\d{4}[A-Z]{4}')])
+   ])
+   test_data = pd.read_csv(StringIO('''Given Name,Family Name,Age,Sex,Customer ID
+   Gerald ,Hampton,82,Male,2582GABK
+   Yuuwa,Miyake,270,male,7951WVLW
+   Edyta,Majewska ,50,Female,775ANSID
+   '''))
+   errors = schema.validate(test_data)
+   for error in errors:
+       print(error)
+PandasSchema would then output
+   {row: 0, column: "Given Name"}: "Gerald " contains trailing whitespace
+   {row: 1, column: "Age"}: "270" was not in the range [0, 120)
+   {row: 1, column: "Sex"}: "male" is not in the list of legal options (Male, Female, Other)
+   {row: 2, column: "Family Name"}: "Majewska " contains trailing whitespace
+   {row: 2, column: "Customer ID"}: "775ANSID" does not match the pattern "\d{4}[A-Z]{4}"
+
+%prep
+%autosetup -n pandas-schema-0.3.6
+
+%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-schema -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..80b5f01
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c0296ef4881133432ceaf855851f973d  pandas_schema-0.3.6.tar.gz