gower 0.0.5

Python implementation of Gowers distance, pairwise between records in two data sets

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.

Examples

Installation

pip install gower

Generate some data

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

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

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)}