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# `sklearn.preprocessing`.QuantileTransformer

class sklearn.preprocessing.QuantileTransformer(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=10000, random_state=None, copy=True)

  Transform features using quantiles information.

  This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme.

  The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable.

  For example visualizations, refer to Compare QuantileTransformer with other scalers.

  Read more in the User Guide.

  New in version 0.19.

  Parameters:
    n_quantiles int, default=1000 or n_samples

      Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator.

    output_distribution{‘uniform’, ‘normal’}, default=’uniform’

      Marginal distribution for the transformed data. The choices are ‘uniform’ (default) or ‘normal’.

    ignore_implicit_zeros bool, default=False

      Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros.

    subsample int, default=10_000

      Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices.

    random_state int, RandomState instance or None, default=None

      Determines random number generation for subsampling and smoothing noise. Please see `subsample` for more details. Pass an int for reproducible results across multiple function calls. See Glossary.

    copy bool, default=True

      Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array).

  Attributes:
    n_quantiles_int

      The actual number of quantiles used to discretize the cumulative distribution function.

    quantiles_ndarray of shape (n_quantiles, n_features)

      The values corresponding the quantiles of reference.

    references_ndarray of shape (n_quantiles, )

      Quantiles of references.

    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.

  See also

  `quantile_transform`

    Equivalent function without the estimator API.

  `PowerTransformer`

    Perform mapping to a normal distribution using a power transform.

  `StandardScaler`

    Perform standardization that is faster, but less robust to outliers.

  `RobustScaler`

    Perform robust standardization that removes the influence of outliers but does not put outliers and inliers on the same scale.

  Notes

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

  Examples

      >>> import numpy as np
      >>> from sklearn.preprocessing import QuantileTransformer
      >>> rng = np.random.RandomState(0)
      >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0)
      >>> qt = QuantileTransformer(n_quantiles=10, random_state=0)
      >>> qt.fit_transform(X)
      array([...])

  Methods

  `fit`(X[, y]) | Compute the quantiles used for transforming.
  `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) | Back-projection to the original space.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of this estimator.
  `transform`(X) | Feature-wise transformation of the data.

  fit(X, y=None)

    Compute the quantiles used for transforming.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse `csc_matrix`. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False.

      y None

        Ignored.

    Returns:
      self object

        Fitted transformer.

  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)

    Back-projection to the original space.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse `csc_matrix`. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False.

    Returns:
      Xt{ndarray, sparse matrix} of (n_samples, n_features)

        The projected data.

  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.

  transform(X)

    Feature-wise transformation of the data.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse `csc_matrix`. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False.

    Returns:
      Xt{ndarray, sparse matrix} of shape (n_samples, n_features)

        The projected data.

## Examples using `sklearn.preprocessing.QuantileTransformer`

Partial Dependence and Individual Conditional Expectation Plots

Partial Dependence and Individual Conditional Expectation Plots

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers

Map data to a normal distribution

Map data to a normal distribution