-
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 atfit
are discarded upontransform
. - 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 andn_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.
preprocessing.Imputer()
Examples using
2017-01-15 04:25:06
Please login to continue.