sklearn.impute.SimpleImputer

class sklearn.impute.SimpleImputer(missing_values=nan, strategy=’mean’, fill_value=None, verbose=0, copy=True)[source]

Imputation transformer for completing missing values.

Read more in the User Guide.

Parameters:
missing_values : number, string, np.nan (default) or None

The placeholder for the missing values. All occurrences of missing_values will be imputed.

strategy : string, optional (default=”mean”)

The imputation strategy.

  • If “mean”, then replace missing values using the mean along each column. Can only be used with numeric data.
  • If “median”, then replace missing values using the median along each column. Can only be used with numeric data.
  • If “most_frequent”, then replace missing using the most frequent value along each column. Can be used with strings or numeric data.
  • If “constant”, then replace missing values with fill_value. Can be used with strings or numeric data.

New in version 0.20: strategy=”constant” for fixed value imputation.

fill_value : string or numerical value, optional (default=None)

When strategy == “constant”, fill_value is used to replace all occurrences of missing_values. If left to the default, fill_value will be 0 when imputing numerical data and “missing_value” for strings or object data types.

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 encoded as a CSR matrix.
Attributes:
statistics_ : array of shape (n_features,)

The imputation fill value for each feature.

See also

IterativeImputer
Multivariate imputation of missing values.

Notes

Columns which only contained missing values at fit are discarded upon transform if strategy is not “constant”.

Examples

>>> import numpy as np
>>> from sklearn.impute import SimpleImputer
>>> imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
>>> imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
... 
SimpleImputer(copy=True, fill_value=None, missing_values=nan,
       strategy='mean', verbose=0)
>>> X = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
>>> print(imp_mean.transform(X))
... 
[[ 7.   2.   3. ]
 [ 4.   3.5  6. ]
 [10.   3.5  9. ]]

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’, fill_value=None, 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 : SimpleImputer
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.