Encode categorical features as a one-hot numeric array. Examples · LabelEncoder can be used to normalize labels. >>> from sklearn.preprocessing import LabelEncoder >>> le = LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6]) It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme.

Encode categorical features as an integer array.

The target is first binarized using the “one-vs-all” scheme via LabelBinarizer, then the average target value for each class and each category is used for encoding, resulting in n_features * n_classes encoded output features.

A library of sklearn compatible categorical variable encoders - scikit-learn-contrib/category_encoders

OrdinalEncoder provides a parameter encoded_missing_value to encode the missing values without the need to create a pipeline and using SimpleImputer.

Can explicitly configure which columns in the data are encoded by name or index, or infer non-numeric columns regardless of input type · Can drop any columns with very low variance based on training set optionally · Portability: train a transformer on data, pickle it, reuse it later and get the same thing out. Full compatibility with sklearn pipelines, input an array-like dataset like any other transformer (*)

One-Hot Encoding is suitable when the categorical features do not have an inherent order. It transforms each category into a new binary feature (0 or 1). Scikit-learn's OneHotEncoder is the primary tool for this task. Let's examine a simple dataset with a categorical feature: import pandas as pd from sklearn.preprocessing import OneHotEncoder # Sample data data = pd.DataFrame({'color': ['Red', 'Green', 'Blue', 'Green'], 'size': ['M', 'L', 'S', 'M']}) print("Original Data:") print(data) # Select the categorical column(s) to encode categorical_features = ['color'] # Let's encode 'color' first

October 9, 2025 - from sklearn.preprocessing import LabelEncoder # Example categorical labels y = ["cat", "dog", "cat", "bird"] # Create and fit the encoder le = LabelEncoder() y_encoded = le.fit_transform(y) print("Encoded labels:", y_encoded) Output: Encoded labels: [1 2 1 0] After fitting, LabelEncoder stores the unique categories it has seen in the .classes_ attribute, sorted alphabetically.

Transform labels to normalized encoding.

values per feature and transform the data to a binary one-hot encoding. · >>> from sklearn.preprocessing import OneHotEncoder · · One can discard categories not seen during `fit`: ·

Encode categorical features as a one-hot numeric array. Examples · LabelEncoder can be used to normalize labels. >>> from sklearn.preprocessing import LabelEncoder >>> le = LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6]) It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

The target is first binarized using the “one-vs-all” scheme via LabelBinarizer, then the average target value for each class and each category is used for encoding, resulting in n_features * n_classes encoded output features.

We can encode a single feature (e.g. "education") to illustrate how the encoding works. from sklearn.preprocessing import OneHotEncoder encoder = OneHotEncoder(sparse_output=False).set_output(transform="pandas") education_encoded = encoder.fit_transform(education_column) education_encoded

Encode target labels with value between 0 and n_classes-1.

September 17, 2025 - Here we will use Label encoding converts each category into a unique integer, making it suitable for ordinal data or when models need numeric input. ... from sklearn.preprocessing import LabelEncoder le = LabelEncoder() df['class_encoded'] = ...

First, we list out the encoders we will be using to preprocess the categorical features: from sklearn.compose import ColumnTransformer from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, TargetEncoder categorical_preprocessors = [ ("drop", "drop"), ("ordinal", OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)), ( "one_hot", OneHotEncoder(handle_unknown="ignore", max_categories=20, sparse_output=False), ), ("target", TargetEncoder(target_type="continuous")), ]

Transform labels to normalized encoding.

values per feature and transform the data to a binary one-hot encoding. · >>> from sklearn.preprocessing import OneHotEncoder · · One can discard categories not seen during `fit`: ·

Target encoding for categorical features.