Generators

• Creates iterators.
• 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).
  • The local variables and execution state are automatically saved between calls.
  • Easier to write, and much more clear than an approach using instance variables like self.index and self.data when we wanna iterate over attributes in a class.

[!NOTE]

Generators when terminating, automatically raise StopIteration.

next

• Retrieve the next item from the iterator by calling its __next__ method.

from typing import Iterator


def reverse(data: str) -> Iterator[str]:
    for index in range(len(data)-1, -1, -1):
        yield data[index]

for char in reverse('golf'):
    print(char)

Generator Expressions

• Simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions.
• Useful when the generator is used right away by an enclosing function.
  • Especially useful with functions like sum(), min(), and max() that reduce an iterable input to a single value.
• More compact but less versatile than full generator definitions.
• Tend to be more memory friendly than equivalent list comprehensions.

print(sum(i*i for i in range(10)))

YouTube/Aparat

• https://youtu.be/_LOyg2cXfkQ.
• https://aparat.com/v/wvye470.