Imputation for categorical values (Specifically via KNN imputation)

I have a toy dataset that has a number of features. Some features have continuous values, others are one-hot encoded. I've set around 1% of each feature to be NaN to test different imputation methods with, such as sklearn's KNNImputer

The one-hot encoded features are treated as continuous values, which means instead of being assigned to 1 or 0, they're getting the average of their neighbors (e.g. if the three closest neighbors' values are \[0,1,1\], the imputed value will be …

datascience imputation knn values