<aside> 👨🏼‍💻 This page introduce the most important features of Python generators.

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Martí Grau is R&D and IA engineer

Description

• Python generators are a simple way of creating iterators.
• A generator in Python avoids to implement iterators with classes __ iter__() and __ next__() methods (which is long and counterintuitive).
• To create a generator we only must define a function with yield statement instead of return. (yield statement pauses the function saving all its states and later continues from there on successive calls.)

Advantages

1) Easy to implement

Generators can be implemented in a clear and concise way as compared to their iterator class counterpart.

# Using Iterator Class
class PowTwo:
    def __init__(self, max=0):
        self.n = 0
        self.max = max

    def __iter__(self):
        return self

    def __next__(self):
        if self.n > self.max:
            raise StopIteration

        result = 2 ** self.n
        self.n += 1
        return result

# Using generator function
def PowTwoGen(max=0):
    n = 0
    while n < max:
        yield 2 ** n
        n += 1

2) Memory Efficient

Generator implementation of such sequences is memory friendly and is preferred since it only produces one item at a time.

3) Represent Infinite Stream

Infinite streams cannot be stored in memory, and since generators produce only one item at a time, they can represent an infinite stream of data.

def all_even():
    n = 0
    while True:
        yield n
        n += 2

4) Pipelining Generators

Multiple generators can be used to pipeline a series of operations.

# 1 generator that produces the numbers in the Fibonacci series
# 2 generator for squaring numbers.
def fibonacci_numbers(nums):
    x, y = 0, 1
    for _ in range(nums):
        x, y = y, x+y
        yield x

def square(nums):
    for num in nums:
        yield num**2

print(sum(square(fibonacci_numbers(10))))