Published Sep 14, 2022
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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). An example shows that generators can be trivially easy to create:
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.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and
much more clear than an approach using instance variables like self.index and
self.data.
In addition to automatic method creation and saving program state, when
generators terminate, they automatically raise StopIteration. In combination,
these features make it easy to create iterators with no more effort than
writing a regular function.