iterchain 0.1.3

Simple and ergonomic iterator chaining for Python

Iterchain: Iterator chaining for Python

Iterchain is a library intended to make manipulating iterators in Python easier and more ergonomic. The design is heavily inspired by the Rust iterator design, and a lot of the functionality comes from the standard Python itertools library.

Why would I need this?

Say we want to know the sum of all the squares of even numbers up to 100.
How can we do this?

Let's try some straightforward, procedural Python:

>>> total = 0
>>> for i in range(100):
...     if i % 2 is 0:
...         total += i ** 2
...
>>> total
161700

This works, but if you read this for the first time it can take a bit of effort to figure out what's happening, especially in slightly less trivial cases. So, how about we use iterators instead?

Well, let's see:

>>> sum(i**2 for i in range(100) if i % 2 is 0)
161700

That's pretty nice! Much shorter, and much easier to understand.
But there's a problem, this pattern only works for relatively simple manipulations. In those cases you could try using the python map and filter builtins (and the slightly more hidden functools.reduce). They let you construct more complex processing chains.

Let's rewrite our iterator to use those functions instead:

>>> sum(map(lambda x: x**2, filter(lambda x: x % 2 is 0, range(100))))
161700

Okay, now that is a mess...
I don't know about you, but it would take me quite a while to unravel what's happening here. The problem is that the whole expression is inside out. The filter gets applied first, but it's hidden in the middle of the expression, and the sum gets applied last but it is all the way in the front. Makes no sense...

So, how can we improve on this? iterchain of course!
(you probably saw this coming already)

So, let's see how it looks using iterchain:

>>> import iterchain
>>> (iterchain.count(stop=100)
...     .filter(lambda x: x % 2 is 0)
...     .map(lambda x: x**2)
...     .sum())
161700

Isn't this much better? The operations are listed in the order that they're executed, are clearly separated, and you can have as few or as many operations as you want. This is why you should use iterchain!

Iterator manipulation

The heart of this library <3.

Generators

iterchain also provides handy methods that let you build new Iterator instances from scratch. These are contained in the iterchain.generators sub-module, but they're also accessible directly from the iterchain module, which is the preferred way of using them.

For example:

  >>> import iterchain
  >>> iterchain.count().take(4).map(lambda x: x**2).to_list()
  [0, 1, 4, 9]