In programming language theory, lazy evaluation, or call-by-need is an evaluation strategy which delays the evaluation of an expression until its value is needed (non-strict evaluation). https://en.wikipedia.org/wiki/Lazy_evaluation
lazysort is a sorting algorithm that procrastinates doing work as much as possible.
How lazysort works?
lazysort uses a modified version of dropsort, a "lossy" sorting algorithm, which keeps the "dropped" items in a separate list (called surplus).
lazysort starts by dropsorting the main list, which also yields a surplus list, which is also a list. The surplus
list is, then, treated as another main list and sorted again using the modified dropsort algorithm, which yields
another surplus list and the process is repeated until the last surplus list is empty. Afterwards, starting from the
last surplus list every surplus list is merged with its main list, the result might be another surplus list
which is to be merged with its main list. For merging,
"merges multiple sorted inputs into a single sorted output" by returning an iterator, is used.
A simplified (non-lazy) version of lazysort is shown below (the real version uses generators for truly lazy-evaluation):
import typing def lazysort(l: list) -> list: # Stage 1 stack =  current_list = l while current_list: main, surplus = dropsort(current_list) stack.append(main) current_list = surplus # Stage 2 while len(stack) >= 2: stack.append(merge(stack.pop(), stack.pop())) return list(stack.pop()) def dropsort(l: list) -> typing.Tuple[list, list]: result = [l] surplus =  for i in range(1, len(l)): if l[i] >= result[-1]: result.append(l[i]) else: surplus.append(l[i]) return result, surplus
You can always have a look at the source code for the real implementation.
The benchmark is done under the following conditions:
- Python 3.5.2
- Matplotlib 1.5.3
- Windows 10.0.14393 Home Single Language x64 (Build 14393)
- Intel Core i5-4210U on Lenovo Z50-70
As can be seen from the results, lazysort does not scale.
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