emrpy.ml
- emrpy.ml.encode_cats_pandas(train_df, cat_cols, test_df=None)
Encode categorical columns with handling for unknown and missing values.
Applies an OrdinalEncoder to the specified columns in a training DataFrame, then transforms an optional test DataFrame using the same encoder settings.
- Return type:
Tuple[DataFrame,Optional[DataFrame],OrdinalEncoder]
Parameters:
- train_dfpandas.DataFrame
DataFrame containing the training data with categorical columns to encode.
- cat_colslist[str]
Names of the categorical columns to encode.
- test_dfpandas.DataFrame, optional (default=None)
Optional DataFrame containing the same categorical columns to transform.
Returns:
: tuple[pandas.DataFrame, pandas.DataFrame or None, OrdinalEncoder]
Encoded copy of train_df with specified columns replaced by integer codes.
Encoded copy of test_df, or None if no test DataFrame was provided.
The fitted OrdinalEncoder instance for use on new data.
Examples:
>>> import pandas as pd >>> from emrpy.ml.encoders import encode_cats_pandas >>> df_train = pd.DataFrame({"color": ["red", "blue", None]}) >>> df_test = pd.DataFrame({"color": ["blue", "yellow", None]}) >>> train_enc, test_enc, encoder = encode_cats_pandas(df_train, ["color"], df_test) >>> train_enc["color"].tolist() [0, 1, -1] >>> test_enc["color"].tolist() [1, -2, -1]
Modules
Machine Learning Encoding Utilities |