The thread on operators as first-class citizens keeps getting vague ideas about assignment overloading that wouldn't actually work, or don't even make sense. I think it's worth writing down the simplest design that would actually work, so people can see why it's not a good idea (or explain why they think it would be anyway).
in pseudocode, just as x += y means this:
xval = globals()['x']
result = xval.__iadd__(y)
result = xval.__add__(y)
globals()['x'] = result
… x = y would mean this:
xval = globals()['x']
result = xval.__iassign__(y)
except (LookupErrorr, AttributeError):
result = y
globals()['x'] = result
If you don't understand why this would work or why it wouldn't be a great idea (or want to nitpick details), read on; otherwise, you can skip the rest of this message.
First, why is there even a problem? Because Python doesn't even have "variables" in the same sense that languages like C++ that allow assignment overloading do.
In C++, a variable is an "lvalue", a location with identity and type, and an object is just a value that lives in a location. So assignment is an operation on variables: x = 2 is the same as XClass::operator=(&x, y).
In Python, an object is a value that lives wherever it wants, with identity and type, and a variable is just a name that can be bound to a value in a namespace. So assignment is an operation on namespaces, not on variables: x = 2 is the same as dict.__settem__(globals(), 'x', 2).
The same thing is true for more complicated assignments. For example, a.x = 2 is just an operation on a's namespace instead of the global namespace: type(a).__setattr__(a, 'x', 2). Likewise, a.b['x'] = 2 is type(a.b).__setitem__(a.b, 'x', 2), And so on,
But Python allows overloading augmented assignment. How does that work? There's a perfectly normal namespace lookup at the start and namespace store at the end—but in between, the existing value of the target gets to specify the value being assigned.
Immutable types like int don't define __iadd__, and __add__ creates and returns a new object. So, x += y ends up the same as x = x + y.
But mutable types like list define an __iadd__ that mutates self in-place and then returns self, so x gets harmlessly rebound to the same object it was already bound to. So x += y ends up the same as x.extend(y); x = x.
The exact same technique would work for overloading normal assignment. The only difference is that x += y is illegal if x is unbound, while x = y obviously has to be legal (and mean there is no value to intercept the assignment). So, the fallback happens when xval doesn't define __iassign__, but also when x isn't bound at all.
So, for immutable types like eint, and almost all mutable types like list—and when x is unbound—x = y does the same thing it always did.
But special types that want to act like transparent mutable handles define an __iassign__ that mutates self in place and returns self, so x gets harmlessly rebound to the same object. So x = y ends up the same as, say, x.set_target(y); x = x.
This all works the same if the variables are local rather than global, or for more complicated targets like attribution or subscription, and even for target lists; the intercept still happens the same way, between the (more complicated) lookup and storage steps.
Now, why is this a bad idea?
First, the benefit of __iassign__ is a lot smaller than __iadd__. A sizable fraction of "x += y" statements are for mutable "x" values, but only a rare handful of "x = y" statements would be for special handle "x" values. Even the same cost for a much smaller benefit would be a much harder sell.
But the runtime performance cost difference is huge. If augmented assignment weren't overloadable, it would still have to lookup the value, lookup and call a special method on it, and store the value. The only cost overloading adds is trying two special methods instead of one, which is tiny. But regular assignment doesn't have to do a value lookup or a special method call at all, only a store; adding those steps would roughly double the cost of every new variable assignment, and even more for every reassignment. And assignments are very common in Python, even within inner loops, so we're talking about a huge slowdown to almost every program out there.
Also, the fact that assignment always means assignment makes Python code easier both for humans to skim, and for automated programs to process. Consider, for example, a static type checker like mypy. Today, x = 2 means that x must now be an int, always. But if x could be a Signal object with an overloaded __iassign__, then, x = 2 might mean that x must now be an int, or it might mean that x must now be whatever type(x).__iassign__ returns.
Finally, the complexity of __iassign__ is at least a little higher than __iadd__. Notice that in my pseudocode above, I cheated—obviously the xval = and result = lines are not supposed to recursively call the same pseudocode, but to directly store a value in new temporary local variable. In the real implementation, there wouldn't even be such a temporary variable (in CPython, the values would just be pushed on the stack), but for documenting the behavior, teaching it to students, etc., that doesn't matter. Being precise here wouldn't be hugely difficult, but it is a little more difficult than with __iadd__, where there's no similar potential confusion even possible.