拥有iterator
对象,是否有比列表理解更快、更好或更正确的方法来获取迭代器返回的对象列表?
user_list = [user for user in user_iterator]
list(your_iterator)
since python 3.5 you can use *
iterable unpacking operator:
user_list = [*your_iterator]
but the pythonic way to do it is:
user_list = list(your_iterator)
@Robino was suggesting to add some tests which make sense, so here is a simple benchmark between 3 possible ways (maybe the most used ones) to convert an iterator to a list:
list(my_iterator)
[*my_iterator]
[e for e in my_iterator]
I have been using simple_bechmark library
from simple_benchmark import BenchmarkBuilder
from heapq import nsmallest
b = BenchmarkBuilder()
@b.add_function()
def convert_by_type_constructor(size):
list(iter(range(size)))
@b.add_function()
def convert_by_list_comprehension(size):
[e for e in iter(range(size))]
@b.add_function()
def convert_by_unpacking(size):
[*iter(range(size))]
@b.add_arguments('Convert an iterator to a list')
def argument_provider():
for exp in range(2, 22):
size = 2**exp
yield size, size
r = b.run()
r.plot()
As you can see there is very hard to make a difference between conversion by the constructor and conversion by unpacking, conversion by list comprehension is the “slowest” approach.
I have been testing also across different Python versions (3.6, 3.7, 3.8, 3.9) by using the following simple script:
import argparse
import timeit
parser = argparse.ArgumentParser(
description='Test convert iterator to list')
parser.add_argument(
'--size', help='The number of elements from iterator')
args = parser.parse_args()
size = int(args.size)
repeat_number = 10000
# do not wait too much if the size is too big
if size > 10000:
repeat_number = 100
def test_convert_by_type_constructor():
list(iter(range(size)))
def test_convert_by_list_comprehension():
[e for e in iter(range(size))]
def test_convert_by_unpacking():
[*iter(range(size))]
def get_avg_time_in_ms(func):
avg_time = timeit.timeit(func, number=repeat_number) * 1000 / repeat_number
return round(avg_time, 6)
funcs = [test_convert_by_type_constructor,
test_convert_by_unpacking, test_convert_by_list_comprehension]
print(*map(get_avg_time_in_ms, funcs))
The script will be executed via a subprocess from a Jupyter Notebook (or a script), the size parameter will be passed through command-line arguments and the script results will be taken from standard output.
from subprocess import PIPE, run
import pandas
simple_data = {'constructor': [], 'unpacking': [], 'comprehension': [],
'size': [], 'python version': []}
size_test = 100, 1000, 10_000, 100_000, 1_000_000
for version in ['3.6', '3.7', '3.8', '3.9']:
print('test for python', version)
for size in size_test:
command = [f'python{version}', 'perf_test_convert_iterator.py', f'--size={size}']
result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)
constructor, unpacking, comprehension = result.stdout.split()
simple_data['constructor'].append(float(constructor))
simple_data['unpacking'].append(float(unpacking))
simple_data['comprehension'].append(float(comprehension))
simple_data['python version'].append(version)
simple_data['size'].append(size)
df_ = pandas.DataFrame(simple_data)
df_
You can get my full notebook from here .
In most of the cases, in my tests, unpacking shows to be faster, but the difference is so small that the results may change from a run to the other. Again, the comprehension approach is the slowest, in fact, the other 2 methods are up to ~ 60% faster.
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