return coefficients from Pipeline object in sklearn

I think this should work:

sgd_randomized_pipe.named_steps['clf'].coef_

I've found one way to do this is by chained indexing with the steps attribute...

sgd_randomized_pipe.best_estimator_.steps[1][1].coef_

Is this best practice, or is there another way?

You can always use the names you assigned to them while making the pipeline by using the named_steps dict.

scaler = sgd_randomized_pipe.best_estimator_.named_steps['scl']
classifier = sgd_randomized_pipe.best_estimator_.named_steps['clf']

and then access all the attributes like coef_, intercept_ etc. which are available to corresponding fitted estimator.

This is the formal attribute exposed by the Pipeline as specified in the documentation:

named_steps : dict

Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.