Generators make non-sequences into sequences

Iterators are a central part of Python, to the point of the basic for loop being exclusively a for-each. Generator functions are also a key idiom. Any function that yields values is a generator. It’s common for a generator to loop over an input iterator and yield new values based on the inputs (a map/filter scenario), but it’s also perfectly reasonable for the input not to be sequential at all.

Here are several useful scenarios for generators that take non-iterable arguments and make them into something iterable.

Ancestry (bottom up)

Hierarchies can be implemented in a number of ways. Let’s say we have objects with a parent reference, which can refer to another object or None. This may be found in ORM objects because it’s the typical way to represent hierarchy in a relational database table.

def ancestry(node):
    while node:
        yield node
        node = node.parent

This makes walking upward from a child node to the root as simple as for item in ancestry(leaf): ...

Hierarchy (top down)

Walking a hierarchy from the top down is implemented in standard library functions like os.walk and ast.walk. As an alternative to an object having a parent reference, let’s say each object has a children list, which is empty at leaf nodes.

from collections import deque

def walk_depth(node):
    yield node
    for child in node.children:
        yield from walk_depth(child)

def walk_breadth(node):
    visit = deque()
    visit.append(node)
    while visit:
        node = visit.popleft()
        yield node
        visit.extend(node.children)

Now we for item in walk_depth(parent): ... or for item in walk_breadth(parent): ... for a search of the tree.

Corners of a rectangle or grid adjacency

There is no rule that a generator function has to have a loop in it. You can also yield a fixed number of things. This is useful when calculating the same relationships over and over again, such as points of a shape or around a coordinate. Granted, in high-performance, low-level code these operations would likely be unrolled and inlined, but for readability it’s much nicer to enable iteration.

def corners(rect):
    yield (rect.x, rect.y)
    yield (rect.x + rect.width, rect.y)
    yield (rect.x, rect.y + rect.height)
    yield (rect.x + rect.width, rect.y + rect.height)

def adjacent(x, y):
    yield (x + 1, y)
    yield (x, y + 1)
    yield (x - 1, y)
    yield (x, y - 1)

Now instead of repeatedly coding multiple statements every time you want to search adjacent grids, you can just type for x2, y2 in adjacent(x, y): ...

Running at intervals

There are various use cases for running a while True: loop inside of a generator. A very simple one is to abstract the idea of doing something forever but waiting in between.

import time

def interval(seconds):
    while True:
        yield
        time.sleep(seconds)

This can be used as for _ in interval(5): ...

Polling for changes

Another case for looping forever is to poll something. Let’s say we have an endpoint at https://example.com/temperature that gives us the current temperature in Exampleland. Assuming we want to check it repeatedly and take action only when it changes, we could do this:

import requests

def poll_changes(url, seconds=1):
    text = None
    for _ in interval(seconds):
        response = requests.get(url)
        if response.text != text:
            yield response.text
            text = response.text

Now you can for text in poll_changes('https://example.com/temperature'): ...