The error `TypeError: unsupported operand type(s) for +: ‘QuerySet’ and ‘QuerySet’` in Django occurs because you are trying to combine two QuerySet objects using the `+` operator, which is not supported by Django ORM (Object-Relational Mapping). To combine results from two QuerySets into a single list, you can use Python's list concatenation, as follows: ```python queryset1 = Model1.objects.filter(...) queryset2 = Model2.objects.filter(...) combined_list = list(queryset1) + list(queryset2) ``` However, if both QuerySets are of the same model, or if they are different models but you still want to work with them as QuerySets (for example, to perform further filtering or ordering), you can use the `|` operator to create a combined QuerySet using Django's `Q` object: ```python from django.db.models import Q queryset1 = Model.objects.filter(...) queryset2 = Model.objects.filter(...) combined_queryset = queryset1 | queryset2 ``` If you need to combine QuerySets of different models and still want to use it as a QuerySet (perhaps for further annotations, aggregations, or if they share a common interface), you can consider using `union()`, which is a set operation that returns a new QuerySet containing objects from both QuerySets. Note that `union()` requires that the fields of the QuerySets are identical. ```python combined_queryset = queryset1.union(queryset2) ``` Please keep in mind that `union()` does not allow for ordering across the combined sets and removes duplicates by default (you can pass `all=True` to `union()` if you want to keep duplicates). Finally, if these solutions are not suitable for your needs, perhaps because you need to maintain a complex ordering, or require additional processing that cannot be done within the ORM, you might consider iterating over both QuerySets and handling the combination manually in your Python code. Remember that using list concatenation or combining QuerySets with `|` or `union()` could have performance implications, as they may execute multiple queries depending on the use case, and might not be suitable for very large QuerySets. Always evaluate the efficiency of your approach in the context of your specific application and dataset sizes.