[*t for t in [(1, 'a'), (2, 'b'), (3, 'c')]]

Another problem with this is that it is very hard to generalize to the case where the item included in a comprehension is a transformation on iterated values. E.g. what does this do?

[math.exp(*t) for t in [(1,2),(3,4)]]

Maybe that somehow magically gets us:

[2.7182, 7.38905, 20.0855, 54.5981]

Or maybe the syntax would be:

[*math.exp(t) for t in [(1,2),(3,4)]]

Neither of those follows conventional Python semantics for function calling or sequence unpacking. So maybe that remains a type error or syntax error. But then we exclude a very common pattern of using comprehensions to create collections of *transformed* data, not simply of filtered data.

In contrast, either of these are unambiguous and obvious:

[math.exp(t) for t in flatten([(1,2),(3,4)])]

Or:

[math.exp(n) for t in [(1,2),(3,4)] for n in t]

Obviously, picking math.exp() is arbitrary and any unary function would be the same issue.

Keeping medicines from the bloodstreams of the sick; food
from the bellies of the hungry; books from the hands of the
uneducated; technology from the underdeveloped; and putting
advocates of freedom in prisons. Intellectual property is
to the 21st century what the slave trade was to the 16th.