One scaler for all features or one scaler per feature? I have a time series with more than 30 features. For preprocessing with scikit learn do you usually use one scaler per feature or one scaler for all features that should be standardized/normalized?
Sklearn scaler works on feature/column (and thats why you want)

Imagine if it did not. Than you would shift your mean and std in weird-determined-by distribution-of-the-whole-set-kind of way.