To know if a generator is empty from the start with Python, we can call next to see if the StopIteration exception is raised. def peek(iterable): try: first = next(iterable) except StopIteration: return None return first, itertools. chain([first], iterable) res = peek(my_sequence) if res is None: # ... else: # ...
Create Generators in Python It is fairly simple to create a generator in Python. It is as easy as defining a normal function, but with a yield statement instead of a return statement. If a function contains at least one yield statement (it may contain other yield or return statements), it becomes a generator function.
Generator-Function : A generator-function is defined like a normal function, but whenever it needs to generate a value, it does so with the yield keyword rather than return. If the body of a def contains yield, the function automatically becomes a generator function.
Generators are memory efficient since they only require memory for the one value they yield. Generators are lazy: they only yield values when explicitly asked.
You can use return
once in a generator; it stops iteration without yielding anything, and thus provides an explicit alternative to letting the function run out of scope. So use yield
to turn the function into a generator, but precede it with return
to terminate the generator before yielding anything.
>>> def f():
... return
... yield
...
>>> list(f())
[]
I'm not sure it's that much better than what you have -- it just replaces a no-op if
statement with a no-op yield
statement. But it is more idiomatic. Note that just using yield
doesn't work.
>>> def f():
... yield
...
>>> list(f())
[None]
iter(())
?This question asks specifically about an empty generator function. For that reason, I take it to be a question about the internal consistency of Python's syntax, rather than a question about the best way to create an empty iterator in general.
If question is actually about the best way to create an empty iterator, then you might agree with Zectbumo about using iter(())
instead. However, it's important to observe that iter(())
doesn't return a function! It directly returns an empty iterable. Suppose you're working with an API that expects a callable that returns an iterable each time it's called, just like an ordinary generator function. You'll have to do something like this:
def empty():
return iter(())
(Credit should go to Unutbu for giving the first correct version of this answer.)
Now, you may find the above clearer, but I can imagine situations in which it would be less clear. Consider this example of a long list of (contrived) generator function definitions:
def zeros():
while True:
yield 0
def ones():
while True:
yield 1
...
At the end of that long list, I'd rather see something with a yield
in it, like this:
def empty():
return
yield
or, in Python 3.3 and above (as suggested by DSM), this:
def empty():
yield from ()
The presence of the yield
keyword makes it clear at the briefest glance that this is just another generator function, exactly like all the others. It takes a bit more time to see that the iter(())
version is doing the same thing.
It's a subtle difference, but I honestly think the yield
-based functions are more readable and maintainable.
See also this great answer from user3840170 that uses dis
to show another reason why this approach is preferable: it emits the fewest instructions when compiled.
iter(())
You don't require a generator. C'mon guys!
Python 3.3 (because I'm on a yield from
kick, and because @senderle stole my first thought):
>>> def f():
... yield from ()
...
>>> list(f())
[]
But I have to admit, I'm having a hard time coming up with a use case for this for which iter([])
or (x)range(0)
wouldn't work equally well.
Another option is:
(_ for _ in ())
Like @senderle said, use this:
def empty():
return
yield
I’m writing this answer mostly to share another justification for it.
One reason for choosing this solution above the others is that it is optimal as far as the interpreter is concerned.
>>> import dis
>>> def empty_yield_from():
... yield from ()
...
>>> def empty_iter():
... return iter(())
...
>>> def empty_return():
... return
... yield
...
>>> def noop():
... pass
...
>>> dis.dis(empty_yield_from)
2 0 LOAD_CONST 1 (())
2 GET_YIELD_FROM_ITER
4 LOAD_CONST 0 (None)
6 YIELD_FROM
8 POP_TOP
10 LOAD_CONST 0 (None)
12 RETURN_VALUE
>>> dis.dis(empty_iter)
2 0 LOAD_GLOBAL 0 (iter)
2 LOAD_CONST 1 (())
4 CALL_FUNCTION 1
6 RETURN_VALUE
>>> dis.dis(empty_return)
2 0 LOAD_CONST 0 (None)
2 RETURN_VALUE
>>> dis.dis(noop)
2 0 LOAD_CONST 0 (None)
2 RETURN_VALUE
As we can see, the empty_return
has exactly the same bytecode as a regular empty function; the rest perform a number of other operations that don’t change the behaviour anyway. The only difference between empty_return
and noop
is that the former has the generator flag set:
>>> dis.show_code(noop)
Name: noop
Filename: <stdin>
Argument count: 0
Positional-only arguments: 0
Kw-only arguments: 0
Number of locals: 0
Stack size: 1
Flags: OPTIMIZED, NEWLOCALS, NOFREE
Constants:
0: None
>>> dis.show_code(empty_return)
Name: empty_return
Filename: <stdin>
Argument count: 0
Positional-only arguments: 0
Kw-only arguments: 0
Number of locals: 0
Stack size: 1
Flags: OPTIMIZED, NEWLOCALS, GENERATOR, NOFREE
Constants:
0: None
Of course, the strength of this argument is very dependent on the particular implementation of Python in use; a sufficiently smart alternative interpreter may notice that the other operations amount to nothing useful and optimise them out. However, even if such optimisations are present, they require the interpreter to spend time performing them and to safeguard against optimisation assumptions being broken, like the iter
identifier at global scope being rebound to something else (even though that would most likely indicate a bug if it actually happened). In the case of empty_return
there is simply nothing to optimise, so even the relatively naïve CPython will not waste time on any spurious operations.
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