ML | Handle Missing Data with Simple Imputer

SimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset. It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments :

missing_values : The missing_values placeholder which has to be imputed. By default is NaN
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 SimpleImputer class.





import numpy as np
# Importing the SimpleImputer class
from sklearn.impute import SimpleImputer
# Imputer object using the mean strategy and 
# missing_values type for imputation
imputer = SimpleImputer(missing_values = np.nan, 
                        strategy ='mean')
data = [[12, np.nan, 34], [10, 32, np.nan], 
        [np.nan, 11, 20]]
print("Original Data : \n", data)
# Fitting the data to the imputer object
imputer =
# Imputing the data     
data = imputer.transform(data)
print("Imputed Data : \n", data)



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

Remember: The mean or median is taken along the column of the matrix

My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.

Improved By : shrutiss48

Article Tags :
Practice Tags :

Be the First to upvote.

Please write to us at to report any issue with the above content.