I noticed that the set.issubset cpython implementation casts its iterable argument to a set. In some cases, casting the whole iterable to a set is unnecessary (see https://bugs.python.org/issue18032). Although the latter suggestion is to perform early termination, my suggestion is to use the intersection instead.

    # PyAnySet_Check coming from the cpython source code.
    def issubset(self, other):
        # Intersection suggestion:
        if not PyAnySet_Check(other):
            return len(self.intersection(other)) == len(self)
        # Usual implementation for sets.
        else:
            return ...

The main advantage that this implementation has is its memory performance, using only O(min(len(self), len(other))) memory, since it never stores elements it does not need.

I'm assuming that set construction costs O(n) set.__contains__ calls. This implementation uses len(other) calls to self.__contains__ and tmp.__contains__, where tmp = set(other). The current implementation uses len(self) + len(other) calls to tmp.__contains__.

Thus, I suspect the current implementation only has a chance at running noticeably faster when len(self) << len(other), where it performs fewer calls to set.__contains__. This is, however, also where the proposed implementation has significantly superior memory performance.

We care more about the running speed than the memory usage. Since sets only store pointers to data, they are typically smaller than the data they refer to.

I've attached some instrumentation code for running experiments to verify the workload under various scenarios.

I've run a few more experiments and this looks like a net win more often than not. Go ahead and submit a PR so we can evaluate it further.

Would not testing len(self.difference(other)) == 0 be more efficient? Making a copy of a set and removing elements one by one may be faster than add elements one by one, because we only need to allocate a single chunk of memory for a set.

It depends on relative values of len(self), len(other) and len(set(other)). For example, the following code may be optimal in some cases:

    tmp = set()
    it = iter(other)
    for item in it:
        # if item in self: ?
        tmp.add(item)
        if len(tmp) >= len(self):
            self = self.difference(tmp, it)
            if not self:
                return True
            self.difference_update(other)
            return not self
    else:
        return False  # len(self) > len(set(other))

The disadvantage of such optimizations is that they make the code much more bigger. The current code is small and simple, and good enough in most cases.

As you say, which implementation performs better likely depends on the nature of the sets. I would suspect that using set.difference won't be substantially faster than using set.intersection in the best case, but it would be much slower if len(self) is much larger than len(self.intersection(other)) e.g. set(range(1_000_000)).issubset(range(10)). Overall I think that using set.intersection will have more well-rounded performance.

Would not testing len(self.difference(other)) == 0
be more efficient?

Yes, I think so.

See also bpo-18032.

It will be even more efficient after applying a set.intersection() optimization in bpo-43464.