Generator pipelines allow us to use multiple generators to perform a series of operations all within one expression. We can break down complex operations into smaller, more manageable parts where they can then be pipelined together to achieve the desired output.

To pipeline generators, the output of one generator function can be the input of another generator function. That resulting generator can then be used as input for another generator function, and so on.

Pipeline generators are also often referred to as nested generators. We can use a pipelined generator like in the following example:

def number_generator():
  i = 0
  while True:
    yield i
    i += 1

def even_number_generator(numbers):
  for n in numbers:
    if n % 2 == 0:
      yield n

even_numbers = even_number_generator(number_generator())

for e in even_numbers:
  print(e)
  if e == 100:
    break

The above example contains:

• The infinite generator number_generator() that yields numbers incrementing by 1
• The infinite generator even_number_generator() which takes a generator as a parameter, iterates through that generator and only yields even numbers.
• The even_numbers variable which holds an even_number_generator() object with number_generator() as its argument.

When we iterate over even_numbers only even numbers are output. The even_number_generator() iterates over all numbers using number_generator(). When an even number occurs, that number is returned by even_number_generator().

Let’s practice more with generator pipelines!