How to Label Encode Multiple Columns Efficiently in Scikit-Learn?

Label Encoding Across Multiple Columns in Scikit-Learn

Label encoding is a common technique to transform categorical data into numerical features. While it's possible to create a separate LabelEncoder instance for each column, in cases where multiple columns require label encoding, it's more efficient to use a single encoder.

Consider a DataFrame with numerous columns of string labels. To label encode the entire DataFrame, a straightforward approach might be to pass the entire DataFrame to the LabelEncoder, as shown below:

import pandas as pd
from sklearn.preprocessing import LabelEncoder 

df = pd.DataFrame({
    'pets': ['cat', 'dog', 'cat', 'monkey', 'dog', 'dog'], 
    'owner': ['Champ', 'Ron', 'Brick', 'Champ', 'Veronica', 'Ron'], 
    'location': ['San_Diego', 'New_York', 'New_York', 'San_Diego', 'San_Diego', 
                 'New_York']
})

le = LabelEncoder()

le.fit(df)

However, this approach results in the following error:

ValueError: bad input shape (6, 3)

To resolve this issue, a solution is to apply the LabelEncoder to each column in the DataFrame using the apply function. This method transforms each column independently, allowing for efficient one-step encoding of multiple columns:

df.apply(LabelEncoder().fit_transform)

Alternatively, in scikit-learn 0.20 and later, the recommended approach is to use the OneHotEncoder:

OneHotEncoder().fit_transform(df)

The OneHotEncoder now supports encoding string input directly.

For more flexibility, the ColumnTransformer can be used to apply label encoding to specific columns or only to certain data types within the columns.

For inverse transformations and transforming future data, a defaultdict can be used to retain the label encoders for each column. By accessing the encoders from the dictionary, it's possible to decode or encode data with the original LabelEncoder instances.

Additionally, libraries like Neuraxle provide the FlattenForEach step, which facilitates the application of the same LabelEncoder to flattened data.

For cases where different LabelEncoders are needed for different columns or when only a subset of columns should be encoded, the ColumnTransformer offers granular control over the selection and encoding process.

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