class*, n_bins: Union[int, List[int]] = 5, encode: str = 'onehot', strategy: str = 'quantile', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False)

Bases: BaseTransformer

Bin continuous data into intervals.

  • n_bins – int or array-like of shape (n_features,), default=5 The number of bins to produce. Raises ValueError if n_bins < 2.

  • encode

    {‘onehot’, ‘onehot-dense’, ‘ordinal’}, default=’onehot’ Method used to encode the transformed result.

    • ’onehot’: Encode the transformed result with one-hot encoding and return a sparse representation.

    • ’onehot-dense’: Encode the transformed result with one-hot encoding and return separate column for

      each encoded value.

    • ’ordinal’: Return the bin identifier encoded as an integer value.

  • strategy

    {‘uniform’, ‘quantile’}, default=’quantile’ Strategy used to define the widths of the bins.

    • ’uniform’: All bins in each feature have identical widths.

    • ’quantile’: All bins in each feature have the same number of points.

  • input_cols – str or Iterable [column_name], default=None The name(s) of one or more columns in the input DataFrame containing feature(s) to be discretized. Input columns must be specified before fit with this argument or after initialization with the set_input_cols method. This argument is optional for API consistency.

  • output_cols – str or Iterable [column_name], default=None The name(s) to assign output columns in the output DataFrame. The number of columns specified must equal the number of input columns. Output columns must be specified before transform with this argument or after initialization with the set_output_cols method. This argument is optional for API consistency.

  • passthrough_cols – A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference.

  • drop_input_cols – boolean, default=False Remove input columns from output if set True.


ndarray of ndarray of shape (n_features,) The edges of each bin. Contain arrays of varying shapes (n_bins_, )


ndarray of shape (n_features,), dtype=np.int_ Number of bins per feature.

Base class for all transformers.


fit(dataset: Union[DataFrame, DataFrame]) BaseEstimator

Runs universal logics for all fit implementations.

get_input_cols() List[str]

Input columns getter.


Input columns.

get_label_cols() List[str]

Label column getter.


Label column(s).

get_output_cols() List[str]

Get output column names. Expand output column names for ‘onehot-dense’ encoding.


Output column names.

get_params(deep: bool = True) Dict[str, Any]

Get parameters for this transformer.


deep – If True, will return the parameters for this transformer and contained subobjects that are transformers.


Parameter names mapped to their values.

get_passthrough_cols() List[str]

Passthrough columns getter.


Passthrough column(s).

get_sample_weight_col() Optional[str]

Sample weight column getter.


Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) Dict[str, Any]

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

  • default_sklearn_obj – Sklearn object used to get default parameter values. Necessary when sklearn_added_keyword_to_version_dict is provided.

  • sklearn_initial_keywords – Initial keywords in sklearn.

  • sklearn_unused_keywords – Sklearn keywords that are unused in snowml.

  • snowml_only_keywords – snowml only keywords not present in sklearn.

  • sklearn_added_keyword_to_version_dict – Added keywords mapped to the sklearn versions in which they were added.

  • sklearn_added_kwarg_value_to_version_dict – Added keyword argument values mapped to the sklearn versions in which they were added.

  • sklearn_deprecated_keyword_to_version_dict – Deprecated keywords mapped to the sklearn versions in which they were deprecated.

  • sklearn_removed_keyword_to_version_dict – Removed keywords mapped to the sklearn versions in which they were removed.


Sklearn parameter names mapped to their values.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) None
set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) Base

Input columns setter.


input_cols – A single input column or multiple input columns.



set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) Base

Label column setter.


label_cols – A single label column or multiple label columns if multi task learning.



set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) Base

Output columns setter.


output_cols – A single output column or multiple output columns.



set_params(**params: Dict[str, Any]) None

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


**params – Transformer parameter names mapped to their values.


SnowflakeMLException – Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) Base

Passthrough columns setter.


passthrough_cols – Column(s) that should not be used or modified by the estimator/transformer. Estimator/Transformer just passthrough these columns without any modifications.



set_sample_weight_col(sample_weight_col: Optional[str]) Base

Sample weight column setter.


sample_weight_col – A single column that represents sample weight.



to_lightgbm() Any
to_sklearn() Any
to_xgboost() Any
transform(dataset: Union[DataFrame, DataFrame]) Union[DataFrame, DataFrame, csr_matrix]

Discretize the data.


dataset – Input dataset.


Discretized output data based on input type. - If input is snowpark DataFrame, returns snowpark DataFrame - If input is a pd.DataFrame and ‘self.encdoe=onehot’, returns ‘csr_matrix’ - If input is a pd.DataFrame and ‘self.encode in [‘ordinal’, ‘onehot-dense’]’, returns ‘pd.DataFrame’