Generators

Overview

• Generators are a simple and powerful tool for creating iterators.
• They are written like regular functions but use the yield statement whenever they want to return data.
• Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed).
• When it terminates, it raises StopIteration exception.

>>> def reverse(data):
...    for index in range(len(data)-1, -1, -1):
...        yield data[index]
>>>
>>> for char in reverse('golf'):
...    print(char)
f
l
o
g

• Anything that can be done with generators can also be done with class-based iterators as described in the previous section.
• What makes generators so compact is that the __iter__() and __next__() methods are created automatically.

• List comprehension:

>>> [x**2 for x in range(10)] # list comprehension
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> (x**2 for x in range(10)) # list comprehension
<generator object <genexpr> at 0x10c90e650>

• To convert a generator to a list, use the list() function.
• all() and any() functions can be used to check if all or any of the elements in a generator are true.

>>> list((x**2 for x in range(10)))
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> all((x**2>0 for x in range(10)))
True

• all((...generator)) and any((...generator)) can also be used with all([...list]) and any([...list]).

• But, generators will be more efficient than lists when the condition is more complex and the list is very long.

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Code

def fib(n):
    a, b = 0, 1
    while n > 0:
        yield a
        a, b = b, a + b
        n -= 1

fib_gen = fib(10)
print(f"{fib_gen=}")

for i in fib_gen:
    print(i)