automatic import of python-pandas-ml

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+%global _empty_manifest_terminate_build 0
+Name:		python-pandas-ml
+Version:	0.6.1
+Release:	1
+Summary:	pandas, scikit-learn and xgboost integration
+License:	BSD
+URL:		http://pandas-ml.readthedocs.org/en/stable
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ac/69/f63b234546e39558e8121980daaf7389e52554a608da50005f52dc14f53f/pandas_ml-0.6.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pandas
+Requires:	python3-enum34
+
+%description
+Overview
+~~~~~~~~
+`pandas <http://pandas.pydata.org/>`_, `scikit-learn <http://scikit-learn.org/>`_
+and `xgboost <http://xgboost.readthedocs.org/en/latest/index.html>`_ integration.
+Installation
+~~~~~~~~~~~~
+    $ pip install pandas_ml
+Documentation
+~~~~~~~~~~~~~
+http://pandas-ml.readthedocs.org/en/stable/
+Example
+~~~~~~~
+    >>> import pandas_ml as pdml
+    >>> import sklearn.datasets as datasets
+    # create ModelFrame instance from sklearn.datasets
+    >>> df = pdml.ModelFrame(datasets.load_digits())
+    >>> type(df)
+    <class 'pandas_ml.core.frame.ModelFrame'>
+    # binarize data (features), not touching target
+    >>> df.data = df.data.preprocessing.binarize()
+    >>> df.head()
+       .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
+    0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
+    1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
+    3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
+    4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    [5 rows x 65 columns]
+    # split to training and test data
+    >>> train_df, test_df = df.model_selection.train_test_split()
+    # create estimator (accessor is mapped to sklearn namespace)
+    >>> estimator = df.svm.LinearSVC()
+    # fit to training data
+    >>> train_df.fit(estimator)
+    # predict test data
+    >>> test_df.predict(estimator)
+    0     4
+    1     2
+    2     7
+    448    5
+    449    8
+    Length: 450, dtype: int64
+    # Evaluate the result
+    >>> test_df.metrics.confusion_matrix()
+    Predicted   0   1   2   3   4   5   6   7   8   9
+    Target
+    0          52   0   0   0   0   0   0   0   0   0
+    1           0  37   1   0   0   1   0   0   3   3
+    2           0   2  48   1   0   0   0   1   1   0
+    3           1   1   0  44   0   1   0   0   3   1
+    4           1   0   0   0  43   0   1   0   0   0
+    5           0   1   0   0   0  39   0   0   0   0
+    6           0   1   0   0   1   0  35   0   0   0
+    7           0   0   0   0   2   0   0  42   1   0
+    8           0   2   1   0   1   0   0   0  33   1
+    9           0   2   1   2   0   0   0   0   1  38
+Supported Packages
+~~~~~~~~~~~~~~~~~~
+- ``scikit-learn``
+- ``patsy``
+- ``xgboost``
+
+%package -n python3-pandas-ml
+Summary:	pandas, scikit-learn and xgboost integration
+Provides:	python-pandas-ml
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pandas-ml
+Overview
+~~~~~~~~
+`pandas <http://pandas.pydata.org/>`_, `scikit-learn <http://scikit-learn.org/>`_
+and `xgboost <http://xgboost.readthedocs.org/en/latest/index.html>`_ integration.
+Installation
+~~~~~~~~~~~~
+    $ pip install pandas_ml
+Documentation
+~~~~~~~~~~~~~
+http://pandas-ml.readthedocs.org/en/stable/
+Example
+~~~~~~~
+    >>> import pandas_ml as pdml
+    >>> import sklearn.datasets as datasets
+    # create ModelFrame instance from sklearn.datasets
+    >>> df = pdml.ModelFrame(datasets.load_digits())
+    >>> type(df)
+    <class 'pandas_ml.core.frame.ModelFrame'>
+    # binarize data (features), not touching target
+    >>> df.data = df.data.preprocessing.binarize()
+    >>> df.head()
+       .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
+    0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
+    1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
+    3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
+    4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    [5 rows x 65 columns]
+    # split to training and test data
+    >>> train_df, test_df = df.model_selection.train_test_split()
+    # create estimator (accessor is mapped to sklearn namespace)
+    >>> estimator = df.svm.LinearSVC()
+    # fit to training data
+    >>> train_df.fit(estimator)
+    # predict test data
+    >>> test_df.predict(estimator)
+    0     4
+    1     2
+    2     7
+    448    5
+    449    8
+    Length: 450, dtype: int64
+    # Evaluate the result
+    >>> test_df.metrics.confusion_matrix()
+    Predicted   0   1   2   3   4   5   6   7   8   9
+    Target
+    0          52   0   0   0   0   0   0   0   0   0
+    1           0  37   1   0   0   1   0   0   3   3
+    2           0   2  48   1   0   0   0   1   1   0
+    3           1   1   0  44   0   1   0   0   3   1
+    4           1   0   0   0  43   0   1   0   0   0
+    5           0   1   0   0   0  39   0   0   0   0
+    6           0   1   0   0   1   0  35   0   0   0
+    7           0   0   0   0   2   0   0  42   1   0
+    8           0   2   1   0   1   0   0   0  33   1
+    9           0   2   1   2   0   0   0   0   1  38
+Supported Packages
+~~~~~~~~~~~~~~~~~~
+- ``scikit-learn``
+- ``patsy``
+- ``xgboost``
+
+%package help
+Summary:	Development documents and examples for pandas-ml
+Provides:	python3-pandas-ml-doc
+%description help
+Overview
+~~~~~~~~
+`pandas <http://pandas.pydata.org/>`_, `scikit-learn <http://scikit-learn.org/>`_
+and `xgboost <http://xgboost.readthedocs.org/en/latest/index.html>`_ integration.
+Installation
+~~~~~~~~~~~~
+    $ pip install pandas_ml
+Documentation
+~~~~~~~~~~~~~
+http://pandas-ml.readthedocs.org/en/stable/
+Example
+~~~~~~~
+    >>> import pandas_ml as pdml
+    >>> import sklearn.datasets as datasets
+    # create ModelFrame instance from sklearn.datasets
+    >>> df = pdml.ModelFrame(datasets.load_digits())
+    >>> type(df)
+    <class 'pandas_ml.core.frame.ModelFrame'>
+    # binarize data (features), not touching target
+    >>> df.data = df.data.preprocessing.binarize()
+    >>> df.head()
+       .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
+    0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
+    1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
+    3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
+    4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
+    [5 rows x 65 columns]
+    # split to training and test data
+    >>> train_df, test_df = df.model_selection.train_test_split()
+    # create estimator (accessor is mapped to sklearn namespace)
+    >>> estimator = df.svm.LinearSVC()
+    # fit to training data
+    >>> train_df.fit(estimator)
+    # predict test data
+    >>> test_df.predict(estimator)
+    0     4
+    1     2
+    2     7
+    448    5
+    449    8
+    Length: 450, dtype: int64
+    # Evaluate the result
+    >>> test_df.metrics.confusion_matrix()
+    Predicted   0   1   2   3   4   5   6   7   8   9
+    Target
+    0          52   0   0   0   0   0   0   0   0   0
+    1           0  37   1   0   0   1   0   0   3   3
+    2           0   2  48   1   0   0   0   1   1   0
+    3           1   1   0  44   0   1   0   0   3   1
+    4           1   0   0   0  43   0   1   0   0   0
+    5           0   1   0   0   0  39   0   0   0   0
+    6           0   1   0   0   1   0  35   0   0   0
+    7           0   0   0   0   2   0   0  42   1   0
+    8           0   2   1   0   1   0   0   0  33   1
+    9           0   2   1   2   0   0   0   0   1  38
+Supported Packages
+~~~~~~~~~~~~~~~~~~
+- ``scikit-learn``
+- ``patsy``
+- ``xgboost``
+
+%prep
+%autosetup -n pandas-ml-0.6.1
+
+%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-ml -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.1-1
+- Package Spec generated