我测试了这里给出的两种替代方案的速度,因为@zenpoy 关心性能。
测试脚本:
import random
from collections import namedtuple
from timeit import timeit
from operator import attrgetter
runs = 10000
size = 10000
random.seed = 42
Person = namedtuple('Person', 'name,age')
seq = [Person(str(random.randint(0, 10 ** 10)), random.randint(0, 100)) for _ in range(size)]
def attrgetter_test_name():
return sorted(seq.copy(), key=attrgetter('name'))
def attrgetter_test_age():
return sorted(seq.copy(), key=attrgetter('age'))
def lambda_test_name():
return sorted(seq.copy(), key=lambda x: x.name)
def lambda_test_age():
return sorted(seq.copy(), key=lambda x: x.age)
print('attrgetter_test_name', timeit(stmt=attrgetter_test_name, number=runs))
print('attrgetter_test_age', timeit(stmt=attrgetter_test_age, number=runs))
print('lambda_test_name', timeit(stmt=lambda_test_name, number=runs))
print('lambda_test_age', timeit(stmt=lambda_test_age, number=runs))
结果:
attrgetter_test_name 44.26793992166096
attrgetter_test_age 31.98247099677627
lambda_test_name 47.97959511074551
lambda_test_age 35.69356267603864
使用 lambda 确实比较慢。最多减慢 10%。
编辑:
进一步的测试显示了使用多个属性进行排序时的结果。添加了以下两个具有相同设置的测试用例:
def attrgetter_test_both():
return sorted(seq.copy(), key=attrgetter('age', 'name'))
def lambda_test_both():
return sorted(seq.copy(), key=lambda x: (x.age, x.name))
print('attrgetter_test_both', timeit(stmt=attrgetter_test_both, number=runs))
print('lambda_test_both', timeit(stmt=lambda_test_both, number=runs))
结果:
attrgetter_test_both 92.80101586919373
lambda_test_both 96.85089983147456
Lambda 仍然表现不佳,但不那么好。现在慢了大约 5%。
测试在 Python 3.6.0 上完成。