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+%global _empty_manifest_terminate_build 0
+Name:		python-dataenforce
+Version:	0.1.2
+Release:	1
+Summary:	Enforce column names & data types of pandas DataFrames
+License:	Apache Software License
+URL:		https://github.com/CedricFR/dataenforce
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/e0/88/ecaec8b4c615c9368028ee1369e9251cb9278b16d691a941ae1f39bc9af6/dataenforce-0.1.2.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Overview
+
+`dataenforce` is a Python package used to enforce column names & types of pandas DataFrames using Python 3 type hinting.
+
+It is a common issue in Data Analysis to pass dataframes into functions without a clear idea of which columns are included or not, and as columns are added to or removed from input data, code can break in unexpected ways. With `dataenforce`, you can provide a clear interface to your functions and ensure that the input dataframes will have the right format when your code is used.
+
+# How to install
+
+Install with pip:
+```
+pip install dataenforce
+```
+
+You can also pip install it from the sources, or just import the `dataenforce` folder.
+
+# How to use
+
+There are two parts in `dataenforce`: the type-hinting part, and the validation. You can use type-hinting with the provided class to indicate what shape the input dataframes should have, and the validation decorator to additionally ensure the format is respected in every function call.
+
+## Type-hinting: `Dataset`
+
+The `Dataset` type indicates that we expect a `pandas.DataFrame`
+
+### Column name checking
+
+```
+from dataenforce import Dataset
+
+def process_data(data: Dataset["id", "name", "location"])
+  pass
+```
+
+The code above specifies that `data` must be a DataFrame with exactly the 3 mentioned columns. If you want to only specify a subset of columns which is required, you can use an ellipsis:
+```
+def process_data(data: Dataset["id", "name", "location", ...])
+  pass
+```
+
+### dtype checking
+
+```
+def process_data(data: Dataset["id": int, "name": object, "latitude": float, "longitude": float])
+  pass
+```
+
+The code above specifies the column names which must be there, with associated types. A combination of only names & with types is possible: `Dataset["id": int, "name"]`.
+
+### Reusing dataframe formats
+
+As you're likely to use the same column subsets several times in your code, you can define them to reuse & combine them later:
+```
+DName = Dataset["id", "name"]
+DLocation = Dataset["id", "latitude", "longitude"]
+
+# Expects columns id, name
+def process1(data: DName):
+  pass
+
+# Expects columns id, name, latitude, longitude, timestamp
+def process2(data: Dataset[DName, DLocation, "timestamp"])
+  pass
+```
+
+## Enforcing: `@validate`
+
+The `@validate` decorator ensures that input `Dataset`s have the right format when the function is called, otherwise raises `TypeError`.
+
+```
+from dataenforce import Dataset, validate
+import pandas as pd
+
+@validate
+def process_data(data: Dataset["id", "name"]):
+  pass
+
+process_data(pd.DataFrame(dict(id=[1,2], name=["Alice", "Bob"]))) # Works
+process_data(pd.DataFrame(dict(id=[1,2]))) # Raises a TypeError, column name missing
+```
+
+# How to test
+
+`dataenforce` uses `pytest` as a testing library. If you have `pytest` installed, just run `PYTHONPATH="." pytest` in the command line while being in the root folder.
+
+# Notes
+
+* You can use `dataenforce` to type-hint the return value of a function, but it is not currently possible to `validate` it (it is not included in the checks)
+* You can't use `@validate` on a function where you use non-base class type-hints as strings (like `def f() -> "MyClass"`). Issue related to PEP 563
+* This work is at experimental state. It is not production-ready. Please raise issues & send pull requests if you find/solve some bugs
+* `dataenforce` is released under the Apache License 2.0, meaning you can freely use the library and redistribute it, provided Copyright is kept
+* Dependencies: Pandas & Numpy
+* Tested with Python 3.6, 3.7, 3.8
+
+
+
+%package -n python3-dataenforce
+Summary:	Enforce column names & data types of pandas DataFrames
+Provides:	python-dataenforce
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dataenforce
+# Overview
+
+`dataenforce` is a Python package used to enforce column names & types of pandas DataFrames using Python 3 type hinting.
+
+It is a common issue in Data Analysis to pass dataframes into functions without a clear idea of which columns are included or not, and as columns are added to or removed from input data, code can break in unexpected ways. With `dataenforce`, you can provide a clear interface to your functions and ensure that the input dataframes will have the right format when your code is used.
+
+# How to install
+
+Install with pip:
+```
+pip install dataenforce
+```
+
+You can also pip install it from the sources, or just import the `dataenforce` folder.
+
+# How to use
+
+There are two parts in `dataenforce`: the type-hinting part, and the validation. You can use type-hinting with the provided class to indicate what shape the input dataframes should have, and the validation decorator to additionally ensure the format is respected in every function call.
+
+## Type-hinting: `Dataset`
+
+The `Dataset` type indicates that we expect a `pandas.DataFrame`
+
+### Column name checking
+
+```
+from dataenforce import Dataset
+
+def process_data(data: Dataset["id", "name", "location"])
+  pass
+```
+
+The code above specifies that `data` must be a DataFrame with exactly the 3 mentioned columns. If you want to only specify a subset of columns which is required, you can use an ellipsis:
+```
+def process_data(data: Dataset["id", "name", "location", ...])
+  pass
+```
+
+### dtype checking
+
+```
+def process_data(data: Dataset["id": int, "name": object, "latitude": float, "longitude": float])
+  pass
+```
+
+The code above specifies the column names which must be there, with associated types. A combination of only names & with types is possible: `Dataset["id": int, "name"]`.
+
+### Reusing dataframe formats
+
+As you're likely to use the same column subsets several times in your code, you can define them to reuse & combine them later:
+```
+DName = Dataset["id", "name"]
+DLocation = Dataset["id", "latitude", "longitude"]
+
+# Expects columns id, name
+def process1(data: DName):
+  pass
+
+# Expects columns id, name, latitude, longitude, timestamp
+def process2(data: Dataset[DName, DLocation, "timestamp"])
+  pass
+```
+
+## Enforcing: `@validate`
+
+The `@validate` decorator ensures that input `Dataset`s have the right format when the function is called, otherwise raises `TypeError`.
+
+```
+from dataenforce import Dataset, validate
+import pandas as pd
+
+@validate
+def process_data(data: Dataset["id", "name"]):
+  pass
+
+process_data(pd.DataFrame(dict(id=[1,2], name=["Alice", "Bob"]))) # Works
+process_data(pd.DataFrame(dict(id=[1,2]))) # Raises a TypeError, column name missing
+```
+
+# How to test
+
+`dataenforce` uses `pytest` as a testing library. If you have `pytest` installed, just run `PYTHONPATH="." pytest` in the command line while being in the root folder.
+
+# Notes
+
+* You can use `dataenforce` to type-hint the return value of a function, but it is not currently possible to `validate` it (it is not included in the checks)
+* You can't use `@validate` on a function where you use non-base class type-hints as strings (like `def f() -> "MyClass"`). Issue related to PEP 563
+* This work is at experimental state. It is not production-ready. Please raise issues & send pull requests if you find/solve some bugs
+* `dataenforce` is released under the Apache License 2.0, meaning you can freely use the library and redistribute it, provided Copyright is kept
+* Dependencies: Pandas & Numpy
+* Tested with Python 3.6, 3.7, 3.8
+
+
+
+%package help
+Summary:	Development documents and examples for dataenforce
+Provides:	python3-dataenforce-doc
+%description help
+# Overview
+
+`dataenforce` is a Python package used to enforce column names & types of pandas DataFrames using Python 3 type hinting.
+
+It is a common issue in Data Analysis to pass dataframes into functions without a clear idea of which columns are included or not, and as columns are added to or removed from input data, code can break in unexpected ways. With `dataenforce`, you can provide a clear interface to your functions and ensure that the input dataframes will have the right format when your code is used.
+
+# How to install
+
+Install with pip:
+```
+pip install dataenforce
+```
+
+You can also pip install it from the sources, or just import the `dataenforce` folder.
+
+# How to use
+
+There are two parts in `dataenforce`: the type-hinting part, and the validation. You can use type-hinting with the provided class to indicate what shape the input dataframes should have, and the validation decorator to additionally ensure the format is respected in every function call.
+
+## Type-hinting: `Dataset`
+
+The `Dataset` type indicates that we expect a `pandas.DataFrame`
+
+### Column name checking
+
+```
+from dataenforce import Dataset
+
+def process_data(data: Dataset["id", "name", "location"])
+  pass
+```
+
+The code above specifies that `data` must be a DataFrame with exactly the 3 mentioned columns. If you want to only specify a subset of columns which is required, you can use an ellipsis:
+```
+def process_data(data: Dataset["id", "name", "location", ...])
+  pass
+```
+
+### dtype checking
+
+```
+def process_data(data: Dataset["id": int, "name": object, "latitude": float, "longitude": float])
+  pass
+```
+
+The code above specifies the column names which must be there, with associated types. A combination of only names & with types is possible: `Dataset["id": int, "name"]`.
+
+### Reusing dataframe formats
+
+As you're likely to use the same column subsets several times in your code, you can define them to reuse & combine them later:
+```
+DName = Dataset["id", "name"]
+DLocation = Dataset["id", "latitude", "longitude"]
+
+# Expects columns id, name
+def process1(data: DName):
+  pass
+
+# Expects columns id, name, latitude, longitude, timestamp
+def process2(data: Dataset[DName, DLocation, "timestamp"])
+  pass
+```
+
+## Enforcing: `@validate`
+
+The `@validate` decorator ensures that input `Dataset`s have the right format when the function is called, otherwise raises `TypeError`.
+
+```
+from dataenforce import Dataset, validate
+import pandas as pd
+
+@validate
+def process_data(data: Dataset["id", "name"]):
+  pass
+
+process_data(pd.DataFrame(dict(id=[1,2], name=["Alice", "Bob"]))) # Works
+process_data(pd.DataFrame(dict(id=[1,2]))) # Raises a TypeError, column name missing
+```
+
+# How to test
+
+`dataenforce` uses `pytest` as a testing library. If you have `pytest` installed, just run `PYTHONPATH="." pytest` in the command line while being in the root folder.
+
+# Notes
+
+* You can use `dataenforce` to type-hint the return value of a function, but it is not currently possible to `validate` it (it is not included in the checks)
+* You can't use `@validate` on a function where you use non-base class type-hints as strings (like `def f() -> "MyClass"`). Issue related to PEP 563
+* This work is at experimental state. It is not production-ready. Please raise issues & send pull requests if you find/solve some bugs
+* `dataenforce` is released under the Apache License 2.0, meaning you can freely use the library and redistribute it, provided Copyright is kept
+* Dependencies: Pandas & Numpy
+* Tested with Python 3.6, 3.7, 3.8
+
+
+
+%prep
+%autosetup -n dataenforce-0.1.2
+
+%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-dataenforce -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
+- Package Spec generated