%global _empty_manifest_terminate_build 0
Name: python-gower
Version: 0.1.2
Release: 1
Summary: Python implementation of Gowers distance, pairwise between records in two data sets
License: MIT
URL: https://github.com/wwwjk366/gower
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7c/b8/f02ffa72009105e981b21fe957895107d1b3c81dece43167d28d8acfdfb0/gower-0.1.2.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
%description
<!-- badges: start -->
[](https://travis-ci.com/wwwjk366/gower)
[](https://pypi.org/project/gower/)
[](https://pepy.tech/project/gower/month)
<!-- badges: end -->
# Introduction
Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1)
More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
# Examples
## Installation
```
pip install gower
```
## Generate some data
```python
import numpy as np
import pandas as pd
import gower
Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
'gender':['M','M','N','M','F','F','F','F',None],
'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
'has_children':[1,0,1,1,1,0,0,1,None],
'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
Yd = Xd.iloc[1:3,:]
X = np.asarray(Xd)
Y = np.asarray(Yd)
```
## Find the distance matrix
```python
gower.gower_matrix(X)
```
array([[0. , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
0.52622986, 0.59697855, 0.47778758, nan],
[0.3590238 , 0. , 0.6964303 , 0.3138769 , 0.523629 ,
0.16720603, 0.45600235, 0.6539635 , nan],
[0.6707398 , 0.6964303 , 0. , 0.6552807 , 0.6728013 ,
0.6969697 , 0.740428 , 0.8151941 , nan],
[0.31787416, 0.3138769 , 0.6552807 , 0. , 0.4824794 ,
0.48108295, 0.74818605, 0.34332284, nan],
[0.16872811, 0.523629 , 0.6728013 , 0.4824794 , 0. ,
0.35750175, 0.43237334, 0.3121036 , nan],
[0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
0. , 0.2898751 , 0.4878362 , nan],
[0.59697855, 0.45600235, 0.740428 , 0.74818605, 0.43237334,
0.2898751 , 0. , 0.57476616, nan],
[0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
0.4878362 , 0.57476616, 0. , nan],
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan]], dtype=float32)
## Find Top n results
```python
gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
```
{'index': array([4, 3, 1, 7, 5]),
'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
dtype=float32)}
%package -n python3-gower
Summary: Python implementation of Gowers distance, pairwise between records in two data sets
Provides: python-gower
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gower
<!-- badges: start -->
[](https://travis-ci.com/wwwjk366/gower)
[](https://pypi.org/project/gower/)
[](https://pepy.tech/project/gower/month)
<!-- badges: end -->
# Introduction
Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1)
More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
# Examples
## Installation
```
pip install gower
```
## Generate some data
```python
import numpy as np
import pandas as pd
import gower
Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
'gender':['M','M','N','M','F','F','F','F',None],
'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
'has_children':[1,0,1,1,1,0,0,1,None],
'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
Yd = Xd.iloc[1:3,:]
X = np.asarray(Xd)
Y = np.asarray(Yd)
```
## Find the distance matrix
```python
gower.gower_matrix(X)
```
array([[0. , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
0.52622986, 0.59697855, 0.47778758, nan],
[0.3590238 , 0. , 0.6964303 , 0.3138769 , 0.523629 ,
0.16720603, 0.45600235, 0.6539635 , nan],
[0.6707398 , 0.6964303 , 0. , 0.6552807 , 0.6728013 ,
0.6969697 , 0.740428 , 0.8151941 , nan],
[0.31787416, 0.3138769 , 0.6552807 , 0. , 0.4824794 ,
0.48108295, 0.74818605, 0.34332284, nan],
[0.16872811, 0.523629 , 0.6728013 , 0.4824794 , 0. ,
0.35750175, 0.43237334, 0.3121036 , nan],
[0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
0. , 0.2898751 , 0.4878362 , nan],
[0.59697855, 0.45600235, 0.740428 , 0.74818605, 0.43237334,
0.2898751 , 0. , 0.57476616, nan],
[0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
0.4878362 , 0.57476616, 0. , nan],
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan]], dtype=float32)
## Find Top n results
```python
gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
```
{'index': array([4, 3, 1, 7, 5]),
'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
dtype=float32)}
%package help
Summary: Development documents and examples for gower
Provides: python3-gower-doc
%description help
<!-- badges: start -->
[](https://travis-ci.com/wwwjk366/gower)
[](https://pypi.org/project/gower/)
[](https://pepy.tech/project/gower/month)
<!-- badges: end -->
# Introduction
Gower's distance calculation in Python. Gower Distance is a distance measure that can be used to calculate distance between two entity whose attribute has a mixed of categorical and numerical values. [Gower (1971) A general coefficient of similarity and some of its properties. Biometrics 27 857–874.](https://www.jstor.org/stable/2528823?seq=1)
More details and examples can be found on my personal website here:(https://www.thinkdatascience.com/post/2019-12-16-introducing-python-package-gower/)
Core functions are wrote by [Marcelo Beckmann](https://sourceforge.net/projects/gower-distance-4python/files/).
# Examples
## Installation
```
pip install gower
```
## Generate some data
```python
import numpy as np
import pandas as pd
import gower
Xd=pd.DataFrame({'age':[21,21,19, 30,21,21,19,30,None],
'gender':['M','M','N','M','F','F','F','F',None],
'civil_status':['MARRIED','SINGLE','SINGLE','SINGLE','MARRIED','SINGLE','WIDOW','DIVORCED',None],
'salary':[3000.0,1200.0 ,32000.0,1800.0 ,2900.0 ,1100.0 ,10000.0,1500.0,None],
'has_children':[1,0,1,1,1,0,0,1,None],
'available_credit':[2200,100,22000,1100,2000,100,6000,2200,None]})
Yd = Xd.iloc[1:3,:]
X = np.asarray(Xd)
Y = np.asarray(Yd)
```
## Find the distance matrix
```python
gower.gower_matrix(X)
```
array([[0. , 0.3590238 , 0.6707398 , 0.31787416, 0.16872811,
0.52622986, 0.59697855, 0.47778758, nan],
[0.3590238 , 0. , 0.6964303 , 0.3138769 , 0.523629 ,
0.16720603, 0.45600235, 0.6539635 , nan],
[0.6707398 , 0.6964303 , 0. , 0.6552807 , 0.6728013 ,
0.6969697 , 0.740428 , 0.8151941 , nan],
[0.31787416, 0.3138769 , 0.6552807 , 0. , 0.4824794 ,
0.48108295, 0.74818605, 0.34332284, nan],
[0.16872811, 0.523629 , 0.6728013 , 0.4824794 , 0. ,
0.35750175, 0.43237334, 0.3121036 , nan],
[0.52622986, 0.16720603, 0.6969697 , 0.48108295, 0.35750175,
0. , 0.2898751 , 0.4878362 , nan],
[0.59697855, 0.45600235, 0.740428 , 0.74818605, 0.43237334,
0.2898751 , 0. , 0.57476616, nan],
[0.47778758, 0.6539635 , 0.8151941 , 0.34332284, 0.3121036 ,
0.4878362 , 0.57476616, 0. , nan],
[ nan, nan, nan, nan, nan,
nan, nan, nan, nan]], dtype=float32)
## Find Top n results
```python
gower.gower_topn(Xd.iloc[0:2,:], Xd.iloc[:,], n = 5)
```
{'index': array([4, 3, 1, 7, 5]),
'values': array([0.16872811, 0.31787416, 0.3590238 , 0.47778758, 0.52622986],
dtype=float32)}
%prep
%autosetup -n gower-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-gower -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.2-1
- Package Spec generated