itswajdy / categorical_features_euclidean_distance Goto Github PK
View Code? Open in Web Editor NEWA python package to compute pairwise Euclidean distances on datasets with categorical features in little time
License: MIT License
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myfakehubcategorical_features_euclidean_distance's Issues
similarity matrix should be symmetrical with 1s on diagonal ?
similarity matrix should be symmetrical with 1s on diagonal ?
df1 = pd.DataFrame.from_dict({
'col1': ['c1', 'c2', 'c1'],
'col2': [4, 5, 6],
'col3': [7, 8, 9]
})
df2 = pd.DataFrame.from_dict({
'col1': ['c1', 'c3', 'c2'],
'col2': [2, 5, 8],
'col3': [3, 6, 9],
})
distances = euclidean_distances(df1, df2)
distances
array([[4.47213595, 2. , 4.69041576],
[6. , 2.44948974, 3.16227766],
[7.21110255, 3.46410162, 2.44948974]])
but what is secret - why it is faster ?
great code
but what is secret - why it is faster ?
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