Basic examples

Here we provide some basic code examples to demonstrate how to use the package with different types of data.

import numpy as np

from gower_metric import Gower

data = np.array([[1, 'a', 3.5], [2, 'b', 4.0], [3, 'a', 2.5], [4, 'c', 5.0]], dtype=object)

feature_types = {
   0: "ratio_scale_interval",
   1: "categorical_nominal",
   2: "ratio_scale_interval"
}

gower = Gower(feature_types=feature_types)
gower.fit(data)

Note

When passing numpy arrays with mixed data types, ensure that the array’s dtype is set to object to accommodate different types of data.

We can also pass pandas DataFrames directly:

import pandas as pd

from gower_metric import Gower

df = pd.DataFrame({
    "age": [23, 45, 23, 31],
    "gender": ["Female", "Male", "Female", "Male"],
    "income": [35000, 81000, 40000, 30000],
    "married": [0, 1, 1, 0],
    "infected": [1, 1, 0, 0],
})

feature_types = {
    "age": "ratio_scale_interval",
    "gender": "categorical_nominal",
    "income": "numeric",
    "married": "binary_symmetric",
    "infected": "binary_asymmetric",
}

gower = Gower(feature_types, scale="iqr").fit(df)

More advanced examples, as well as categorical ordinal example, can be found in next section.