Understanding Python's yield Keyword and Generators

The article introduces the yield keyword in Python, explaining that it turns a function into a generator that produces values lazily, one at a time, without storing the entire sequence in memory.

It first shows a method def node._get_child_candidates(self, distance, min_dist, max_dist): if self._leftchild and distance - max_dist < self._median: yield self._leftchild if self._rightchild and distance + max_dist >= self._median: yield self._rightchild and how it is used in a search loop:

result, candidates = list(), [self] while candidates: node = candidates.pop() distance = node._get_dist(obj) if distance <= max_dist and distance >= min_dist: result.extend(node._values) candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) return result

To illustrate basic iteration, the article shows a normal list iteration:

mylist = [1, 2, 3] for i in mylist: print(i)

and a list comprehension that creates an iterable:

mylist = [x*x for x in range(3)] for i in mylist: print(i)

It then explains that a generator expression produces an iterator that can be consumed only once:

mygenerator = (x*x for x in range(3)) for i in mygenerator: print(i)

The createGenerator example demonstrates a simple generator function:

def createGenerator(): mylist = range(3) for i in mylist: yield i*i mygenerator = createGenerator() print(mygenerator) # for i in mygenerator: print(i)

The article also mentions the itertools module for advanced iteration, showing how to generate all permutations of four horses:

import itertools horses = [1, 2, 3, 4] races = itertools.permutations(horses) print(list(races))

Further, a detailed walkthrough of a generator with a while True loop illustrates how next() and send() interact with yield :

def foo(): print("starting...") while True: res = yield 4 print("res:", res) g = foo() print(next(g)) # starts execution, yields 4 print("*"*20) print(next(g)) # resumes, res is None, yields 4 again

Using send() to provide a value to the generator:

g = foo() print(next(g)) # yields 4 print(g.send(7)) # sends 7 to res, then yields next 4

Finally, the article compares using a generator versus a list or range for large sequences, showing a memory‑efficient generator that yields numbers up to 10:

def foo(num): print("starting...") while num < 10: num = num + 1 yield num for n in foo(0): print(n)

It notes that in Python 3, range already behaves like the old xrange , providing a lazy sequence without creating a full list.