Creating batches with Python

batching.py

"""
This is a study on the fastest way to split an iteralbe (list) into batches of a set size. 
Note that this study omits attempts parallelization and does not use generators.
"""

from typing import List
from itertools import islice

def batch(iterable: List, batch_size: int) -> List[List]:
    res = []
    for i in range(0, len(iterable), batch_size):
        if i + batch_size > len(iterable):
            res.append(iterable[i:i+len(iterable)])
        else:
            res.append(iterable[i:i+batch_size])
    return res

def batch1(iterable: List, batch_size: int) -> List[List]:
    iterable = iterable[::-1]
    res = []
    while iterable:
        batch = []
        for i in range(batch_size):
            try:
                batch.append(iterable.pop())
            except IndexError:
                break
        res.append(batch)
    return res

def batch2(iterable: List, batch_size: int) -> List[List]:
    res = []
    for i in range(0, len(iterable), batch_size):
        res.append([x for x in islice(iterable, i, i+batch_size)])
    return res

def batch3(iterable: List, batch_size: int) -> List[List]:
    return [[x for x in islice(iterable, i, i+batch_size)] for i in range(0, len(iterable), batch_size)]

a = [x for x in range(20000)]
  
# Paste these into IPython to find out which is faster!
# %timeit batch(a, 443)
# %timeit batch1(a, 443)
# %timeit batch2(a, 443)
#%timeit batch3(a, 443)

# My results were:
# 78.1 µs ± 1.06 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
# 6.13 ms ± 60.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# 2.59 ms ± 20.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# 2.58 ms ± 20.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# %timeit batch3(a, 443)