What Is Generator Iterator

Python Tutorials - Herong's Tutorial Examples

∟What Is Generator Iterator

This section provides a quick introduction of generator iterator, which is created by a generator function or a generator expression.

What Is Generator Iterator - A generator iterator is an iterator object created by a generator function or a generator expression.

A generator iterator is also iterable. iter(generator) returns the generator itself.

What Is Generator Function - A generator function is a special function that contains one or more "yield" statements.

What Is "yield" Statement - A "yield" statement is a simple statement with this syntax:

yield EXPRESSION

The logic of using a generator function can be described as:

When the generator function is called, the function body is not executed at all. Python system will create a generator iterator, which wraps the generator function body inside the iterator. This iterator is then returned to the caller.
When the next(iterator) function (or the iterator.__next__() method) is called for the first time, the body of the generator function gets executed until the first "yield" statement. The yield EXPRESSION is evaluated and returned as the next item.
When the next(iterator) function is called again, the execution of the generator function gets resumed after the previous "yield" statement until the next "yield" statement. The yield EXPRESSION is evaluated and returned as the next item.
When the next(iterator) function is called again, and the execution of the generator function reaches the end of the function, StopIteration exception is raised.

In order to understand the behavior of "yield" statements in a generator function, I wrote the following tutorial example, yield_test.py:

#  yield_test.py
#- Copyright 2022 (c) HerongYang.com. All Rights Reserved.
#

def get_squares(n):
   i = 0
   while i < n:
      print('get_squares():', i)
      yield i*i
      print('get_squares(): after yield')
      i += 1

g = get_squares(4)
print("object g: id and type =", id(g), type(g))

i = iter(g)
print("object i: id and type =", id(i), type(i))

print(next(i))
print(next(i))
print(next(i))
print(next(i))
print(next(i))
print(next(i))

If you run this sample code, you should get:

herong$ python yield_test.py

('object g: id and type =', 4528495104, <type 'generator'>)
('object i: id and type =', 4528495104, <type 'generator'>)

('get_squares():', 0)
0

get_squares(): after yield
('get_squares():', 1)
1

get_squares(): after yield
('get_squares():', 2)
4

get_squares(): after yield
('get_squares():', 3)
9

get_squares(): after yield
Traceback (most recent call last):
  File "yield_test.py", line 23, in <module>
    print(next(i))
StopIteration

The output confirms that Python system converts the get_squares() function into a "get_squares" class, and implements both __iter()__() and __next__() methods. So the function call get_squares(5) becomes a constructor call to create a "generator" object, which is an "iterable" and "iterator" at the same time.