Skip to content
Related Articles

Related Articles

Improve Article

ML | Handle Missing Data with Simple Imputer

  • Difficulty Level : Basic
  • Last Updated : 18 May, 2020

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

Attention reader! Don’t stop learning now. Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.

My Personal Notes arrow_drop_up
Recommended Articles
Page :