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# `sklearn.preprocessing`.StandardScaler

class sklearn.preprocessing.StandardScaler(*, copy=True, with_mean=True, with_std=True)

  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.

  Read more in the User Guide.

  Parameters:
    copy bool, default=True

      If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.

    with_mean bool, default=True

      If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.

    with_std bool, default=True

      If True, scale the data to unit variance (or equivalently, unit standard deviation).

  Attributes:
    scale_ndarray of shape (n_features,) or None

      Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using `np.sqrt(var_)`. If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1. `scale_` is equal to `None` when `with_std=False`.

      New in version 0.17: scale_

    mean_ndarray of shape (n_features,) or None

      The mean value for each feature in the training set. Equal to `None` when `with_mean=False` and `with_std=False`.

    var_ndarray of shape (n_features,) or None

      The variance for each feature in the training set. Used to compute `scale_`. Equal to `None` when `with_mean=False` and `with_std=False`.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_samples_seen_int or ndarray of shape (n_features,)

      The number of samples processed by the estimator for each feature. If there are no missing samples, the `n_samples_seen` will be an integer, otherwise it will be an array of dtype int. If `sample_weights` are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments across `partial_fit` calls.

  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)

    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.

      y None

        Ignored.

      sample_weight array-like of shape (n_samples,), default=None

        Individual weights for each sample.

        New in version 0.24: parameter sample_weight support to StandardScaler.

    Returns:
      self object

        Fitted scaler.

  fit_transform(X, y=None, **fit_params)

    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:
      X array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-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_out ndarray of str objects

        Same as input features.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  inverse_transform(X, copy=None)

    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.

      copy bool, 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)

    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.

      y None

        Ignored.

      sample_weight array-like of shape (n_samples,), default=None

        Individual weights for each sample.

        New in version 0.24: parameter sample_weight support to StandardScaler.

    Returns:
      self object

        Fitted scaler.

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

    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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

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

    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:
      copy str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `copy` parameter in `inverse_transform`.

    Returns:
      self object

        The updated object.

  set_output(*, transform=None)

    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

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

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

    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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `partial_fit`.

    Returns:
      self object

        The updated object.

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

    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:
      copy str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `copy` parameter in `transform`.

    Returns:
      self object

        The updated object.

  transform(X, copy=None)

    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.

      copy bool, 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 1.2

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 1.0

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

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