%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 [![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower) [![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/) [![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month) # 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 [![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower) [![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/) [![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month) # 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 [![Build Status](https://travis-ci.com/wwwjk366/gower.svg?branch=master)](https://travis-ci.com/wwwjk366/gower) [![PyPI version](https://badge.fury.io/py/gower.svg)](https://pypi.org/project/gower/) [![Downloads](https://pepy.tech/badge/gower/month)](https://pepy.tech/project/gower/month) # 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 - 0.1.2-1 - Package Spec generated