Why perform normalization of data before KNN Imputation?
For classification algorithms like KNN, we measure the distances between pairs of samples and these distances are influenced by the measurement units also. 
For example: Let’s say, we are applying KNN on a data set having 3 features. 
1st feature : Range from 1 to 100
2nd feature : Range from 1 to 200
3rd feature : Range from 1 to 10000
This will led to generated clusters based on 3rd feature. Since, the difference between  1st and 2nd are smaller as compared to 3rd one. To avoid this wrong clustering, we need to have normalization in place.
I am working on dataset in which almost every feature has missiong values. I want to impute missing values with KNN method. But as KNN works on distance metrics so it is advised to perform normalization of dataset before its use. Iam using scikit-learn library for this.
But how can I perform normalization with missing values.