SimpleImputer — it is a training class that helps to handle missing data in the predictive model dataset. It replaces NaN values ​​with the specified placeholder. 
This is done with the SimpleImputer () method, which takes the following arguments:

missing_data : The missing_data placeholder which has to be imputed. By default is
stategy : The data which will replace the NaN values ​​from the dataset. The strategy argument can take the values ​​- `mean` (default),` median `,` most_frequent `and` constant `.
fill_value : The constant value to be given to the NaN data using the constant strategy.

Code: Python code illustrating the use of the SimpleImputer class.

import numpy as np   # Importing the SimpleImputer class from sklearn.impute import SimpleImputer   # Imputable object using middle strategy and # missing datatype for imputation imputer = SimpleImputer (missing_data = np.nan,  strategy = `mean` )   data = [[ 12 , np.nan, 34 ], [ 10 , 32 , np.nan],  [np.nan, 11 , 20 ]]   < code class = "functions"> print ( "Original Data:" , data) # Fitting data to the imputer object imputer = imputer.fit (data)   # Data imputation data = imputer.transform (data)   print ( "Imputed Data:" , data)

Exit

 Original Data: 
 [[12, nan, 34] [10, 32, nan] [nan, 11, 20]] 
 Imputed Data: 
 [[12, 21.5 , 34] [10, 32, 27] [11, 11, 20]] 

Remember: average her or median is taken along the matrix column