Either type of imputation makes sense and will likely work when values are missing completely at random (MCAR), but could be problematic if values are missing not at random (MNAR). For example in a dataset about credit card payment history, a user may be missing a credit score if they don’t have any previous credit history (eg. a student). In this example you can’t just easily use the approaches talked about here.