sklearn.preprocessing.QuantileTransformer  
class sklearn.preprocessing.QuantileTransformer(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True) [source]
 
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. Read more in the User Guide.  New in version 0.19.   Parameters 
 
n_quantilesint, 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_zerosbool, 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.  
subsampleint, default=1e5 

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_stateint, 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  
copybool, 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.      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. For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py. 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_params([deep]) Get parameters for this estimator.  
inverse_transform(X) Back-projection to the original space.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Feature-wise transformation of the data.    
fit(X, y=None) [source]
 
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.  
yNone 

Ignored.    Returns 
 
selfobject 

Fitted transformer.     
  
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_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) [source]
 
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_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.     
  
transform(X) [source]
 
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  

  Effect of transforming the targets in regression model  

  Map data to a normal distribution  

  Compare the effect of different scalers on data with outliers