`sklearn.preprocessing`.StandardScaler#

class sklearn.preprocessing.StandardScaler(*, copy=True, with_mean=True, with_std=True)[source]#

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).

For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

StandardScaler is sensitive to outliers, and the features may scale differently from each other in the presence of outliers. For an example visualization, refer to Compare StandardScaler with other scalers.

This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False to avoid breaking the sparsity structure of the data.

See also

scale: Equivalent function without the estimator API.
PCA: Further removes the linear correlation across features with ‘whiten=True’.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

We use a biased estimator for the standard deviation, equivalent to numpy.std(x, ddof=0). Note that the choice of ddof is unlikely to affect model performance.

Examples

>>> from sklearn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler()
>>> print(scaler.mean_)
[0.5 0.5]
>>> print(scaler.transform(data))
[[-1. -1.]
 [-1. -1.]
 [ 1.  1.]
 [ 1.  1.]]
>>> print(scaler.transform([[2, 2]]))
[[3. 3.]]

Methods

`fit`(X[, y, sample_weight])	Compute the mean and std to be used for later scaling.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X[, copy])	Scale back the data to the original representation.
`partial_fit`(X[, y, sample_weight])	Online computation of mean and std on X for later scaling.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_inverse_transform_request`(*[, copy])	Request metadata passed to the `inverse_transform` method.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`set_partial_fit_request`(*[, sample_weight])	Request metadata passed to the `partial_fit` method.
`set_transform_request`(*[, copy])	Request metadata passed to the `transform` method.
`transform`(X[, copy])	Perform standardization by centering and scaling.

fit(X, y=None, sample_weight=None)[source]#

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

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data used to compute the mean and standard deviation used for later scaling along the features axis.
yNone: Ignored.
sample_weightarray-like of shape (n_samples,), default=None: Individual weights for each sample.

New in version 0.24: parameter sample_weight support to StandardScaler.

Returns:

selfobject: Fitted scaler.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].
If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects: Same as input features.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

inverse_transform(X, copy=None)[source]#

Scale back the data to the original representation.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data used to scale along the features axis.
copybool, default=None: Copy the input X or not.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features): Transformed array.

partial_fit(X, y=None, sample_weight=None)[source]#

Online computation of mean and std on X for later scaling.

All of X is processed as a single batch. This is intended for cases when fit is not feasible due to very large number of n_samples or because X is read from a continuous stream.

The algorithm for incremental mean and std is given in Equation 1.5a,b in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. “Algorithms for computing the sample variance: Analysis and recommendations.” The American Statistician 37.3 (1983): 242-247:

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data used to compute the mean and standard deviation used for later scaling along the features axis.
yNone: Ignored.
sample_weightarray-like of shape (n_samples,), default=None: Individual weights for each sample.

New in version 0.24: parameter sample_weight support to StandardScaler.

Returns:

selfobject: Fitted scaler.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_inverse_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the inverse_transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for copy parameter in inverse_transform.

Returns:

selfobject: The updated object.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

New in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

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

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to partial_fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

set_transform_request(*, copy: bool | None | str = '$UNCHANGED$') → StandardScaler[source]#

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for copy parameter in transform.

Returns:

selfobject: The updated object.

transform(X, copy=None)[source]#

Perform standardization by centering and scaling.

Parameters:

X{array-like, sparse matrix of shape (n_samples, n_features): The data used to scale along the features axis.
copybool, default=None: Copy the input X or not.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features): Transformed array.

Examples using `sklearn.preprocessing.StandardScaler`#

Release Highlights for scikit-learn 1.4

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.22

Classifier comparison

A demo of K-Means clustering on the handwritten digits data

Comparing different clustering algorithms on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Demo of DBSCAN clustering algorithm

Demo of HDBSCAN clustering algorithm

Principal Component Regression vs Partial Least Squares Regression

Factor Analysis (with rotation) to visualize patterns

Combine predictors using stacking

Faces recognition example using eigenfaces and SVMs

Prediction Latency

Model-based and sequential feature selection

Comparing Linear Bayesian Regressors

L1 Penalty and Sparsity in Logistic Regression

Lasso model selection via information criteria

Lasso model selection: AIC-BIC / cross-validation

MNIST classification using multinomial logistic + L1

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Common pitfalls in the interpretation of coefficients of linear models

Advanced Plotting With Partial Dependence

Displaying Pipelines

Displaying estimators and complex pipelines

Evaluation of outlier detection estimators

Introducing the set_output API

Visualizations with Display Objects

Detection error tradeoff (DET) curve

Precision-Recall

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Nearest Neighbors Classification

Varying regularization in Multi-layer Perceptron

Column Transformer with Mixed Types

Pipelining: chaining a PCA and a logistic regression

Compare the effect of different scalers on data with outliers

Feature discretization

Importance of Feature Scaling

RBF SVM parameters

SVM-Anova: SVM with univariate feature selection

sklearn.preprocessing.StandardScaler#

Examples using sklearn.preprocessing.StandardScaler#

`sklearn.preprocessing`.StandardScaler#

Examples using `sklearn.preprocessing.StandardScaler`#