Class KBinsDiscretizer (2.30.0) 

KBinsDiscretizer(
    n_bins: int = 5, strategy: typing.Literal["uniform", "quantile"] = "quantile"
)

Bin continuous data into intervals.

Methods

KBinsDiscretizer

KBinsDiscretizer(
    n_bins: int = 5, strategy: typing.Literal["uniform", "quantile"] = "quantile"
)

Bin continuous data into intervals.

__init__

__init__(n_bins=5, strategy="quantile")

API documentation for __init__ method.

fit

fit(X, y=None)

Fit the estimator.

Returns
Type Description
KBinsDiscretizer Fitted scaler.

fit_transform

fit_transform(X, y=None)

Fit to data, then transform it.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). Input samples.
y bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). Default None. Target values (None for unsupervised transformations).
Returns
Type Description
bigframes.dataframe.DataFrame DataFrame of shape (n_samples, n_features_new). Transformed DataFrame.

get_params

get_params(deep=True)

Get parameters for this estimator.

Parameter
Name Description
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
Type Description
Dictionary A dictionary of parameter names mapped to their values.

to_gbq

to_gbq(model_name, replace=False)

Save the transformer as a BigQuery model.

Parameters
Name Description
model_name str

The name of the model.
replace bool, default False

Determine whether to replace if the model already exists. Default to False.

transform

transform(X)

Discretize the data.

Returns
Type Description
bigframes.dataframe.DataFrame Transformed result.