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 : object

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