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+%global _empty_manifest_terminate_build 0
+Name:		python-anonypy
+Version:	0.1.7
+Release:	1
+Summary:	Anonymization library for python
+License:	MIT License
+URL:		https://github.com/glassonion1/anonypy
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/04/5d/adc4824c45316d48c1448082a8144ba07378f5b22dfb08a5b3bd112e7e49/anonypy-0.1.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+# AnonyPy
+Anonymization library for python.
+AnonyPy provides following privacy preserving techniques for the anonymization.
+- K Anonymity
+- L Diversity
+- T Closeness
+
+## The Anonymization method
+- Anonymization method aims at making the individual record be indistinguishable among a group record by using techniques of generalization and suppression.
+- Turning a dataset into a k-anonymous (and possibly l-diverse or t-close) dataset is a complex problem, and finding the optimal partition into k-anonymous groups is an NP-hard problem.
+- AnonyPy uses "Mondrian" algorithm to partition the original data into smaller and smaller groups
+- The algorithm assumes that we have converted all attributes into numerical or categorical values and that we are able to measure the “span” of a given attribute Xi.
+
+## Install
+```
+$ pip install anonypy
+```
+
+## Usage
+```python
+import anonypy
+import pandas as pd
+
+data = [
+    [6, "1", "test1", "x", 20],
+    [6, "1", "test1", "x", 30],
+    [8, "2", "test2", "x", 50],
+    [8, "2", "test3", "w", 45],
+    [8, "1", "test2", "y", 35],
+    [4, "2", "test3", "y", 20],
+    [4, "1", "test3", "y", 20],
+    [2, "1", "test3", "z", 22],
+    [2, "2", "test3", "y", 32],
+]
+
+columns = ["col1", "col2", "col3", "col4", "col5"]
+categorical = set(("col2", "col3", "col4"))
+
+def main():
+    df = pd.DataFrame(data=data, columns=columns)
+
+    for name in categorical:
+        df[name] = df[name].astype("category")
+
+    feature_columns = ["col1", "col2", "col3"]
+    sensitive_column = "col4"
+
+    p = anonypy.Preserver(df, feature_columns, sensitive_column)
+    rows = p.anonymize_k_anonymity(k=2)
+
+    dfn = pd.DataFrame(rows)
+    print(dfn)
+```
+
+Original data
+```bash
+   col1 col2   col3 col4  col5
+0     6    1  test1    x    20
+1     6    1  test1    x    30
+2     8    2  test2    x    50
+3     8    2  test3    w    45
+4     8    1  test2    y    35
+5     4    2  test3    y    20
+6     4    1  test3    y    20
+7     2    1  test3    z    22
+8     2    2  test3    y    32
+```
+
+The created anonymized data is below(Guarantee 2-anonymity).
+```bash
+  col1 col2         col3 col4  count
+0  2-4    2        test3    y      2
+1  2-4    1        test3    y      1
+2  2-4    1        test3    z      1
+3  6-8    1  test1,test2    x      2
+4  6-8    1  test1,test2    y      1
+5    8    2  test3,test2    w      1
+6    8    2  test3,test2    x      1
+```
+
+
+
+
+%package -n python3-anonypy
+Summary:	Anonymization library for python
+Provides:	python-anonypy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-anonypy
+# AnonyPy
+Anonymization library for python.
+AnonyPy provides following privacy preserving techniques for the anonymization.
+- K Anonymity
+- L Diversity
+- T Closeness
+
+## The Anonymization method
+- Anonymization method aims at making the individual record be indistinguishable among a group record by using techniques of generalization and suppression.
+- Turning a dataset into a k-anonymous (and possibly l-diverse or t-close) dataset is a complex problem, and finding the optimal partition into k-anonymous groups is an NP-hard problem.
+- AnonyPy uses "Mondrian" algorithm to partition the original data into smaller and smaller groups
+- The algorithm assumes that we have converted all attributes into numerical or categorical values and that we are able to measure the “span” of a given attribute Xi.
+
+## Install
+```
+$ pip install anonypy
+```
+
+## Usage
+```python
+import anonypy
+import pandas as pd
+
+data = [
+    [6, "1", "test1", "x", 20],
+    [6, "1", "test1", "x", 30],
+    [8, "2", "test2", "x", 50],
+    [8, "2", "test3", "w", 45],
+    [8, "1", "test2", "y", 35],
+    [4, "2", "test3", "y", 20],
+    [4, "1", "test3", "y", 20],
+    [2, "1", "test3", "z", 22],
+    [2, "2", "test3", "y", 32],
+]
+
+columns = ["col1", "col2", "col3", "col4", "col5"]
+categorical = set(("col2", "col3", "col4"))
+
+def main():
+    df = pd.DataFrame(data=data, columns=columns)
+
+    for name in categorical:
+        df[name] = df[name].astype("category")
+
+    feature_columns = ["col1", "col2", "col3"]
+    sensitive_column = "col4"
+
+    p = anonypy.Preserver(df, feature_columns, sensitive_column)
+    rows = p.anonymize_k_anonymity(k=2)
+
+    dfn = pd.DataFrame(rows)
+    print(dfn)
+```
+
+Original data
+```bash
+   col1 col2   col3 col4  col5
+0     6    1  test1    x    20
+1     6    1  test1    x    30
+2     8    2  test2    x    50
+3     8    2  test3    w    45
+4     8    1  test2    y    35
+5     4    2  test3    y    20
+6     4    1  test3    y    20
+7     2    1  test3    z    22
+8     2    2  test3    y    32
+```
+
+The created anonymized data is below(Guarantee 2-anonymity).
+```bash
+  col1 col2         col3 col4  count
+0  2-4    2        test3    y      2
+1  2-4    1        test3    y      1
+2  2-4    1        test3    z      1
+3  6-8    1  test1,test2    x      2
+4  6-8    1  test1,test2    y      1
+5    8    2  test3,test2    w      1
+6    8    2  test3,test2    x      1
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for anonypy
+Provides:	python3-anonypy-doc
+%description help
+# AnonyPy
+Anonymization library for python.
+AnonyPy provides following privacy preserving techniques for the anonymization.
+- K Anonymity
+- L Diversity
+- T Closeness
+
+## The Anonymization method
+- Anonymization method aims at making the individual record be indistinguishable among a group record by using techniques of generalization and suppression.
+- Turning a dataset into a k-anonymous (and possibly l-diverse or t-close) dataset is a complex problem, and finding the optimal partition into k-anonymous groups is an NP-hard problem.
+- AnonyPy uses "Mondrian" algorithm to partition the original data into smaller and smaller groups
+- The algorithm assumes that we have converted all attributes into numerical or categorical values and that we are able to measure the “span” of a given attribute Xi.
+
+## Install
+```
+$ pip install anonypy
+```
+
+## Usage
+```python
+import anonypy
+import pandas as pd
+
+data = [
+    [6, "1", "test1", "x", 20],
+    [6, "1", "test1", "x", 30],
+    [8, "2", "test2", "x", 50],
+    [8, "2", "test3", "w", 45],
+    [8, "1", "test2", "y", 35],
+    [4, "2", "test3", "y", 20],
+    [4, "1", "test3", "y", 20],
+    [2, "1", "test3", "z", 22],
+    [2, "2", "test3", "y", 32],
+]
+
+columns = ["col1", "col2", "col3", "col4", "col5"]
+categorical = set(("col2", "col3", "col4"))
+
+def main():
+    df = pd.DataFrame(data=data, columns=columns)
+
+    for name in categorical:
+        df[name] = df[name].astype("category")
+
+    feature_columns = ["col1", "col2", "col3"]
+    sensitive_column = "col4"
+
+    p = anonypy.Preserver(df, feature_columns, sensitive_column)
+    rows = p.anonymize_k_anonymity(k=2)
+
+    dfn = pd.DataFrame(rows)
+    print(dfn)
+```
+
+Original data
+```bash
+   col1 col2   col3 col4  col5
+0     6    1  test1    x    20
+1     6    1  test1    x    30
+2     8    2  test2    x    50
+3     8    2  test3    w    45
+4     8    1  test2    y    35
+5     4    2  test3    y    20
+6     4    1  test3    y    20
+7     2    1  test3    z    22
+8     2    2  test3    y    32
+```
+
+The created anonymized data is below(Guarantee 2-anonymity).
+```bash
+  col1 col2         col3 col4  count
+0  2-4    2        test3    y      2
+1  2-4    1        test3    y      1
+2  2-4    1        test3    z      1
+3  6-8    1  test1,test2    x      2
+4  6-8    1  test1,test2    y      1
+5    8    2  test3,test2    w      1
+6    8    2  test3,test2    x      1
+```
+
+
+
+
+%prep
+%autosetup -n anonypy-0.1.7
+
+%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-anonypy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.7-1
+- Package Spec generated