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
strategy : 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.
Python3
import numpy as np# Importing the SimpleImputer classfrom sklearn.impute import SimpleImputer# Imputer object using the mean strategy and# missing_values type for imputationimputer = 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 objectimputer = imputer.fit(data)# Imputing the data data = imputer.transform(data)print("Imputed Data : \n", data) |
Output
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
