stackoverflow 网站上，有人问了个“How to make a flat list out of list of lists”问题，正是我们在上篇文章中提出的问题。在回答中，有人分析了 7 种方法的时间性能。import functools
import itertools
import numpy
import operator
import perfplot
def forfor(a):
return [item for sublist in a for item in sublist]
def sum_brackets(a):
return sum(a, [])
def functools_reduce(a):
return functools.reduce(operator.concat, a)
def functools_reduce_iconcat(a):
return functools.reduce(operator.iconcat, a, [])
def itertools_chain(a):
return list(itertools.chain.from_iterable(a))
def numpy_flat(a):
return list(numpy.array(a).flat)
def numpy_concatenate(a):
return list(numpy.concatenate(a))
perfplot.show(
setup=lambda n: [list(range(10))] * n,
kernels=[
forfor, sum_brackets, functools_reduce, functools_reduce_iconcat,
itertools_chain, numpy_flat, numpy_concatenate
],
n_range=[2**k for k in range(16)],
logx=True,
logy=True,
xlabel='num lists'
)perfplot （注：这是该测试者本人开发的库）作可视化，结果很直观地展示出，随着数据量的增加，这几种方法的效率变化。from itertools import chain
from functools import reduce
from collections import Iterable # or from collections.abc import Iterable
import operator
from iteration_utilities import deepflatten
def nested_list_comprehension(lsts):
return [item for sublist in lsts for item in sublist]
def itertools_chain_from_iterable(lsts):
return list(chain.from_iterable(lsts))
def pythons_sum(lsts):
return sum(lsts, [])
def reduce_add(lsts):
return reduce(lambda x, y: x + y, lsts)
def pylangs_flatten(lsts):
return list(flatten(lsts))
def flatten(items):
"""Yield items from any nested iterable; see REF."""
for x in items:
if isinstance(x, Iterable) and not isinstance(x, (str, bytes)):
yield from flatten(x)
else:
yield x
def reduce_concat(lsts):
return reduce(operator.concat, lsts)
def iteration_utilities_deepflatten(lsts):
return list(deepflatten(lsts, depth=1))
from simple_benchmark import benchmark
b = benchmark(
[nested_list_comprehension, itertools_chain_from_iterable, pythons_sum, reduce_add,
pylangs_flatten, reduce_concat, iteration_utilities_deepflatten],
arguments={2**i: [[0]*5]*(2**i) for i in range(1, 13)},
argument_name='number of inner lists'
)
b.plot()/* It's tempting to use PyNumber_InPlaceAdd instead of
PyNumber_Add here, to avoid quadratic running time
when doing 'sum(list_of_lists, [])'. However, this
would produce a change in behaviour: a snippet like
empty = []
sum([[x] for x in range(10)], empty)
would change the value of empty. */纯函数 ，为了多次执行时能返回同样的结果。operator.iconcat(a, b)
operator.__iconcat__(a, b)
a = iconcat(a, b) is equivalent to a += b for a and b sequences.