def pick_features(d, subset=None):
	"""
	Drop columns from input Pandas data frame that should not be used as features, as return as Numpy array.

	subset: list of specific features to keep, optional
	"""

	if subset is not None:
		return d.drop(d.columns.difference(subset), axis=1)

	# If no subset, drop columns that are known to not be features
	cols_to_drop = ["admission_id", "person_id", "r", "time_at_risk", "daily_rate_not_zero"] + \
		[f"daily_rate_geq_{x}" for x in (1, 2, 3, 5)]
	return d.drop(cols_to_drop, axis=1)