Label encoding across multiple columns in scikit-learn

Question

I\'m trying to use scikit-learn\'s LabelEncoder to encode a pandas DataFrame of string labels. As the dataframe has many (50+) columns, I want to a

Answer 1

Using Neuraxle

TLDR; You here can use the FlattenForEach wrapper class to simply transform your df like: FlattenForEach(LabelEncoder(), then_unflatten=True).fit_transform(df).

With this method, your label encoder will be able to fit and transform within a regular scikit-learn Pipeline. Let's simply import:

from sklearn.preprocessing import LabelEncoder
from neuraxle.steps.column_transformer import ColumnTransformer
from neuraxle.steps.loop import FlattenForEach

Same shared encoder for columns:

Here is how one shared LabelEncoder will be applied on all the data to encode it:

    p = FlattenForEach(LabelEncoder(), then_unflatten=True)

Result:

    p, predicted_output = p.fit_transform(df.values)
    expected_output = np.array([
        [6, 7, 6, 8, 7, 7],
        [1, 3, 0, 1, 5, 3],
        [4, 2, 2, 4, 4, 2]
    ]).transpose()
    assert np.array_equal(predicted_output, expected_output)

Different encoders per column:

And here is how a first standalone LabelEncoder will be applied on the pets, and a second will be shared for the columns owner and location. So to be precise, we here have a mix of different and shared label encoders:

    p = ColumnTransformer([
        # A different encoder will be used for column 0 with name "pets":
        (0, FlattenForEach(LabelEncoder(), then_unflatten=True)),
        # A shared encoder will be used for column 1 and 2, "owner" and "location":
        ([1, 2], FlattenForEach(LabelEncoder(), then_unflatten=True)),
    ], n_dimension=2)

Result:

    p, predicted_output = p.fit_transform(df.values)
    expected_output = np.array([
        [0, 1, 0, 2, 1, 1],
        [1, 3, 0, 1, 5, 3],
        [4, 2, 2, 4, 4, 2]
    ]).transpose()
    assert np.array_equal(predicted_output, expected_output)