Class StandardScaler (0.4.0)

Version 0.4.0

• 1.29.0 (latest)
• 1.28.0
• 1.27.0
• 1.26.0
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

StandardScaler()

Standardize features by removing the mean and scaling to unit variance.

The standard score of a sample x is calculated as:z = (x - u) / s where u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using transform.

Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).

.. rubric:: Examples

from bigframes.ml.preprocessing import StandardScaler
import <xref uid="bigframes.pandas">bigframes.pandas</xref> as bpd

scaler = StandardScaler()
data = bpd.DataFrame({"a": [0, 0, 1, 1], "b":[0, 0, 1, 1]})
scaler.fit(data)
print(scaler.transform(data))
print(scaler.transform(bpd.DataFrame({"a": [2], "b":[2]})))

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.StandardScaler

Compute the mean and std to be used for later scaling.

Parameter
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series The Dataframe or Series with training data.

Returns
Type	Description
StandardScaler	Fitted scaler.

fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> bigframes.dataframe.DataFrame

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: bool = True) -> typing.Dict[str, typing.Any]

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.

transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Perform standardization by centering and scaling.

Parameter
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series The DataFrame or Series to be transformed.

Returns
Type	Description
bigframes.dataframe.DataFrame	Transformed result.