more-itertools VS python-ds

I'd bet that reading through more-itertools would be a good exercise.

check out the code examples in itertools and the source code of more-itertools if you want to see cases where next and iter get used by themselves.

from itertools import islice # Copied from the more-itertools library (MIT license) # https://github.com/more-itertools/more-itertools def batched(iterable, n): "Batch data into lists of length n. The last batch may be shorter." # batched('ABCDEFG', 3) --> ABC DEF G if n < 1: raise ValueError('n must be at least one') it = iter(iterable) while (batch := list(islice(it, n))): yield batch

more_itertools.chunked

I've had a tab open to more-itertools on github for weeks; maybe I should go read it...

The popular more-itertools library implements (among many others) a chunked method which yields lists of size n from an iterator. There is also the ichunked method which yields iterators of size n instead of lists.

There's a function called consecutive_groups in the more-itertools library that will do this for you efficiently.

Here are some that might answer your question: - algorithms is a library which contains many of the most useful algorithms for sorting, searching, working with trees, math algorithms like factorials, prime finders and many more - data classes to save you the trouble of writing everytime special methods in a class like init, repr, set, get - box allows the use of dot on dictionaries to access the keys - more-itertools for more routines to operate on iterables than those itertools provide.

python-ds – No non-sense and no BS repo for how data structure code should be in Python - simple and elegant