将清单大致等份的最佳方法是什么?例如，如果列表有7个元素，并将其分为2部分，我们希望其中一部分有3个元素，而另一部分应该有4个元素。
我正在寻找类似even_split(L, n)的东西，它将L分解为n部分。
def chunks(L, n):
""" Yield successive n-sized chunks from L.
"""
for i in range(0, len(L), n):
yield L[i:i+n]
上面的代码给出了3个块，而不是3个块。我可以简单地转置(遍历这个，取每列的第一个元素，称之为第一部分，然后取第二个元素，把它放在第二部分，等等)，但这破坏了项目的顺序。
当前回答
只要你不想要像连续块这样愚蠢的东西:
>>> def chunkify(lst,n):
... return [lst[i::n] for i in xrange(n)]
...
>>> chunkify(range(13), 3)
[[0, 3, 6, 9, 12], [1, 4, 7, 10], [2, 5, 8, 11]]
其他回答
另一个简单可读的chunker的尝试。
def chunk(iterable, count): # returns a *generator* that divides `iterable` into `count` of contiguous chunks of similar size
assert count >= 1
return (iterable[int(_*len(iterable)/count+0.5):int((_+1)*len(iterable)/count+0.5)] for _ in range(count))
print("Chunk count: ", len(list( chunk(range(105),10))))
print("Chunks: ", list( chunk(range(105),10)))
print("Chunks: ", list(map(list,chunk(range(105),10))))
print("Chunk lengths:", list(map(len, chunk(range(105),10))))
print("Testing...")
for iterable_length in range(100):
for chunk_count in range(1,100):
chunks = list(chunk(range(iterable_length),chunk_count))
assert chunk_count == len(chunks)
assert iterable_length == sum(map(len,chunks))
assert all(map(lambda _:abs(len(_)-iterable_length/chunk_count)<=1,chunks))
print("Okay")
输出:
Chunk count: 10
Chunks: [range(0, 11), range(11, 21), range(21, 32), range(32, 42), range(42, 53), range(53, 63), range(63, 74), range(74, 84), range(84, 95), range(95, 105)]
Chunks: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [11, 12, 13, 14, 15, 16, 17, 18, 19, 20], [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], [32, 33, 34, 35, 36, 37, 38, 39, 40, 41], [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52], [53, 54, 55, 56, 57, 58, 59, 60, 61, 62], [63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73], [74, 75, 76, 77, 78, 79, 80, 81, 82, 83], [84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], [95, 96, 97, 98, 99, 100, 101, 102, 103, 104]]
Chunk lengths: [11, 10, 11, 10, 11, 10, 11, 10, 11, 10]
Testing...
Okay
看看numpy.split:
>>> a = numpy.array([1,2,3,4])
>>> numpy.split(a, 2)
[array([1, 2]), array([3, 4])]
这是另一种变体，它将“剩余”元素均匀地分布在所有块中，一次一个，直到一个都不剩。在这个实现中，较大的块出现在流程的开头。
def chunks(l, k):
""" Yield k successive chunks from l."""
if k < 1:
yield []
raise StopIteration
n = len(l)
avg = n/k
remainders = n % k
start, end = 0, avg
while start < n:
if remainders > 0:
end = end + 1
remainders = remainders - 1
yield l[start:end]
start, end = end, end+avg
例如，从14个元素的列表中生成4个块:
>>> list(chunks(range(14), 4))
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10], [11, 12, 13]]
>>> map(len, list(chunks(range(14), 4)))
[4, 4, 3, 3]
1>
import numpy as np
data # your array
total_length = len(data)
separate = 10
sub_array_size = total_length // separate
safe_separate = sub_array_size * separate
splited_lists = np.split(np.array(data[:safe_separate]), separate)
splited_lists[separate - 1] = np.concatenate(splited_lists[separate - 1],
np.array(data[safe_separate:total_length]))
splited_lists # your output
2>
splited_lists = np.array_split(np.array(data), separate)
看到more_itertools.divide:
n = 2
[list(x) for x in mit.divide(n, range(5, 11))]
# [[5, 6, 7], [8, 9, 10]]
[list(x) for x in mit.divide(n, range(5, 12))]
# [[5, 6, 7, 8], [9, 10, 11]]
通过> pip Install more_itertools安装。
