sklearn.impute.SimpleImputer  
class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False) [source]
 
Imputation transformer for completing missing values. Read more in the User Guide.  New in version 0.20: SimpleImputer replaces the previous sklearn.preprocessing.Imputer estimator which is now removed.   Parameters 
 
missing_valuesint, float, str, np.nan or None, default=np.nan 

The placeholder for the missing values. All occurrences of missing_values will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, missing_values should be set to np.nan, since pd.NA will be converted to np.nan.  
strategystring, 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 there is more than one such value, only the smallest is returned. 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_valuestring or numerical value, 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.  
verboseinteger, default=0 

Controls the verbosity of the imputer.  
copyboolean, 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; If add_indicator=True.   
add_indicatorboolean, default=False 

If True, a MissingIndicator transform will stack onto output of the imputer’s transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won’t appear on the missing indicator even if there are missing values at transform/test time.    Attributes 
 
statistics_array of shape (n_features,) 

The imputation fill value for each feature. Computing statistics can result in np.nan values. During transform, features corresponding to np.nan statistics will be discarded.  
indicator_MissingIndicator 

Indicator used to add binary indicators for missing values. None if add_indicator is False.      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()
>>> 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.  
inverse_transform(X) Convert the data back to the original representation.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Impute all missing values in X.    
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 
 
selfSimpleImputer 
   
  
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]
 
Convert the data back to the original representation. Inverts the transform operation performed on an array. This operation can only be performed after SimpleImputer is instantiated with add_indicator=True. Note that inverse_transform can only invert the transform in features that have binary indicators for missing values. If a feature has no missing values at fit time, the feature won’t have a binary indicator, and the imputation done at transform time won’t be inverted.  New in version 0.24.   Parameters 
 
Xarray-like of shape (n_samples, n_features + n_features_missing_indicator) 

The imputed data to be reverted to original data. It has to be an augmented array of imputed data and the missing indicator mask.    Returns 
 
X_originalndarray of shape (n_samples, n_features) 

The original X with missing values as it was prior to imputation.     
  
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]
 
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.impute.SimpleImputer
 
  Release Highlights for scikit-learn 0.23  

  Combine predictors using stacking  

  Permutation Importance vs Random Forest Feature Importance (MDI)  

  Imputing missing values with variants of IterativeImputer  

  Imputing missing values before building an estimator  

  Column Transformer with Mixed Types