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

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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.

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.

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)}

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