On 12.03.20 16:02, Eric Wieser wrote
`itertools.combinations` is not the only place to make this optimization - parts of numpy use it too, allowing
a = (b * c) + d
to elide the temporary `b*c`. This elision can't happen with the spelling
bc = b * c a = bc + d This is not surprising since the user deliberately created the temporary variable. It's not the responsibility of Numpy to optimize that away (in fact it can't since `bc` might be reused later on).
My suggestion would be to make `del x` an expression, with semantics "unbind the name `x`, and evaluate to its value". This would allow:
>>> [id(del v) for v in itertools.combinations([1, 2, 3], 1)] [2500926200992, 2500926200992, 2500926200992]
and
bc = b * c a = (del bc) + d # in C++, this would be `std::move(bc) + d`
If I wanted to split the computation over multiple lines and yet have it optimized I would just reuse the same (target) name instead of creating a temporary one and then discarding it in the next step: a = b * c a += d If there's many lines to the computation one can place a comment at the top, indicating that the result (`a`) is built incrementally in order to prevent confusion about names.