sklearn.preprocessing.Imputer

class sklearn.preprocessing.Imputer(missing_values=’NaN’, strategy=’mean’, axis=0, verbose=0, copy=True) [source]

Imputation transformer for completing missing values.

Read more in the User Guide.

Parameters:

missing_values : integer or “NaN”, optional (default=”NaN”) The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”. strategy : string, optional (default=”mean”) The imputation strategy. If “mean”, then replace missing values using the mean along the axis. If “median”, then replace missing values using the median along the axis. If “most_frequent”, then replace missing using the most frequent value along the axis. axis : integer, optional (default=0) The axis along which to impute. If axis=0, then impute along columns. If axis=1, then impute along rows. verbose : integer, optional (default=0) Controls the verbosity of the imputer. copy : boolean, optional (default=True) If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False: If X is not an array of floating values; If X is sparse and missing_values=0; If axis=0 and X is encoded as a CSR matrix; If axis=1 and X is encoded as a CSC matrix.

Attributes:

statistics_ : array of shape (n_features,) The imputation fill value for each feature if axis == 0.

Notes

• When axis=0, columns which only contained missing values at fit are discarded upon transform.
• When axis=1, an exception is raised if there are rows for which it is not possible to fill in the missing values (e.g., because they only contain missing values).

Methods

fit(X[, y])	Fit the imputer on X.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Impute all missing values in X.

__init__(missing_values=’NaN’, strategy=’mean’, axis=0, verbose=0, copy=True) [source]

fit(X, y=None) [source]

Fit the imputer on X.

Parameters:	X : {array-like, sparse matrix}, shape (n_samples, n_features) Input data, where n_samples is the number of samples and n_features is the number of features.
Returns:	self : Imputer Returns self.

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:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

set_params(**params) [source]

Set the parameters of this estimator.

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

Returns:	self :

transform(X) [source]

Impute all missing values in X.

Parameters:	X : {array-like, sparse matrix}, shape = [n_samples, n_features] The input data to complete.

Examples using sklearn.preprocessing.Imputer