====================
Basic examples
====================

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

.. code-block:: python

   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:

.. code-block:: python

    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.

.. automodule:: 
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    :undoc-members:
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