I am writing a very basic program to predict missing values in a dataset using scikit-learn's Imputer class.
I have made a NumPy array, created an Imputer object with strategy='mean' and performed fit_transform() on the NumPy array.
When I print the array after performing fit_transform(), the 'Nan's remain, and I dont get any prediction.
What am I doing wrong here? How do I go about predicting the missing values?
import numpy as np
from sklearn.preprocessing import Imputer
X = np.array([[23.56],[53.45],['NaN'],[44.44],[77.78],['NaN'],[234.44],[11.33],[79.87]])
print X
imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit_transform(X)
print X
Per the documentation, sklearn.preprocessing.Imputer.fit_transform
returns a new array, it doesn't alter the argument array. The minimal fix is therefore:
X = imp.fit_transform(X)