SciPy

We assume that you called the transform method and imported scipy library.

Spatial distance

from scipy.spatial.distance import pdist, squareform
from gower_metric import Gower

# fit Gower model first
gower = Gower(feature_types=feature_types).fit(df)

# calculate the numerical representation of original data
df_num = gower.transform(df)

# calculate pairwise distance using scipy
array_scipy = pdist(df_num, metric=gower)

# convert to square matrix form
matrix_scipy = squareform(array_scipy)

There are no changes required, as scipy supports custom NaN handling in its distance functions, which, by definition, is one of the features of Gower’s metric.

Cophenet cluster hierarchy

from scipy.cluster.hierarchy import cophenet, single
from scipy.spatial.distance import pdist, squareform
from gower_metric import Gower

gower = Gower(feature_types=feature_types).fit(df)
df_num = gower.transform(df)

Z = single(pdist(df_num, metric=gower))
c, _ = cophenet(Z, pdist(df_num, metric=gower))

# print the results if needed
print(f"{squareform(cophenet(Z))}")