Vector similarity with multiple dtypes (string, int, floats etc.)?

Question

I have the following 2 rows in my dataframe:

[1, 1.1, -19, "kuku", "lulu"]
[2.8, 1.1, -20, "kuku", "lilu"]

I want to calculate their similarity by comparing each dimension (equal? 1, otherwise 0) and get the following vector: [0, 1, 0, 1, 0], is there any function that takes a vector and performs such "similarity" against all rows and calculates mean? In our case it would be 2/5 = 0.4.

Answer 1

I would just use a simple = on NumPy arrays, to be casted as int for the vector and numpy.mean() for the mean of the vector:

import numpy as np


a = [1, 1.1, -19, "kuku", "lulu"] 
b = [2.8, 1.1, -20, "kuku", "lilu"]


res = (np.array(a) == np.array(b)).astype(int)
print(res)                                                                                                                                             
# [0 1 0 1 0]
v = res.mean()
print(v)
# 0.4

---

If you do not mind computing everything twice and you can afford the potentially large intermediate temporary objects:

import numpy as np


arr = np.array([
    [1, 1.1, -19, "kuku", "lulu"],
    [2.8, 1.1, -20, "kuku", "lilu"],
    [2.8, 1.1, -20, "kuku", "lulu"]])


corr = arr[None, :, :] == arr[:, None, :]
score = corr.mean(-1)
print(score)
# [[1.  0.4 0.6]
#  [0.4 1.  0.8]
#  [0.6 0.8 1. ]]