How to calculate mean and standard deviation of all features in a class identified by k-nearest neighbors? I have classified my data into several neighborhoods using k nearest neighbors. I need to efficiently calculate the mean and standard deviation for all features of data points belonging to a particular neighborhood. 
I am using sklearn.kneighbors.
If you append the predicted neighbourhood onto your data df (let's call this neighbourhood), then using groupby and transform within a loop should do the trick.

As an example:

features = [var_1,var_2,...] # a list of the features to run over
for col in features:
  df[col+'_mean'] = df.groupby('neighbourhood')[col].transform('mean')