编辑2：有人建议这是一个类似问题的副本.我不同意,因为我的问题集中在速度,而另一个问题是什么更“可读”或“更好”(没有更好地定义).虽然问题相似,但所给出的讨论/答案存在很大差异.
EDIT:
I realise from the questions that I could have been clearer.
Sorry for code typos, yes it should be using the proper python operator for addition.Regarding the input data, I just chose a list of random numbers since that’s a common sample. In my case I’m using a dict where I expect a lot of keyerrors, probably 95% of the keys will not exist, and the few that exist will contain clusters of data.
I’m interested in a general discussion though, regardless of the input data set, but of course samples with running times are interesting.
我的标准方法就像许多其他帖子一样
list = (100 random numbers)
d = {}
for x in list:
if x in d:
d[x]+=1
else:
d[x]=1
但我只是想到这个更快,因为我们不必检查字典是否包含密钥.我们只是假设它确实如此,如果没有,我们会处理它.
有什么区别或者Python比我聪明吗？
list = (100 random numbers)
d = {}
for x in list:
try:
d[x]+=1
except KeyError:
d[x] = 1
与数组中的索引,越界,负索引等相同的方法.
解决方法:
您的声明完全错误取决于输入.
如果你有一组不同的键,并经常点击except块,性能就不好了.如果try块占优势,则try / except成语可以在较小的列表上执行.
这是一个基准,显示了几种做同样事情的方法：
from __future__ import print_function
import timeit
import random
import collections
def f1():
d={}
for x in tgt:
if x in d:
d[x]+=1
else:
d[x]=1
return d
def f2():
d = {}
for x in tgt:
try:
d[x]+=1
except KeyError:
d[x] = 1
return d
def f3():
d={}.fromkeys(tgt, 0)
for x in tgt:
d[x]+=1
return d
def f4():
d=collections.defaultdict(int)
for x in tgt:
d[x]+=1
return d
def f5():
return collections.Counter(tgt)
def f6():
d={}
for x in tgt:
d[x]=d.setdefault(x, 0)+1
return d
def f7():
d={}
for x in tgt:
d[x]=d.get(x,0)+1
return d
def cmpthese(funcs, c=10000, rate=True, micro=False):
"""Generate a Perl style function benchmark"""
def pprint_table(table):
"""Perl style table output"""
def format_field(field, fmt='{:,.0f}'):
if type(field) is str: return field
if type(field) is tuple: return field[1].format(field[0])
return fmt.format(field)
def get_max_col_w(table, index):
return max([len(format_field(row[index])) for row in table])
col_paddings=[get_max_col_w(table, i) for i in range(len(table[0]))]
for i,row in enumerate(table):
# left col
row_tab=[row[0].ljust(col_paddings[0])]
# rest of the cols
row_tab+=[format_field(row[j]).rjust(col_paddings[j]) for j in range(1,len(row))]
print(' '.join(row_tab))
results={k.__name__:timeit.Timer(k).timeit(c) for k in funcs}
fastest=sorted(results,key=results.get, reverse=True)
table=[['']]
if rate: table[0].append('rate/sec')
if micro: table[0].append('usec/pass')
table[0].extend(fastest)
for e in fastest:
tmp=[e]
if rate:
tmp.append('{:,}'.format(int(round(float(c)/results[e]))))
if micro:
tmp.append('{:.3f}'.format(1000000*results[e]/float(c)))
for x in fastest:
if x==e: tmp.append('--')
else: tmp.append('{:.1%}'.format((results[x]-results[e])/results[e]))
table.append(tmp)
pprint_table(table)
if __name__=='__main__':
import sys
print(sys.version)
for j in [100,1000]:
for t in [(0,5), (0,50), (0,500)]:
tgt=[random.randint(*t) for i in range(j)]
print('{} rand ints between {}:'.format(j,t))
print('=====')
cmpthese([f1,f2,f3,f4,f5,f6,f7])
print()
我已经包含了一个基于timeit的小型基准测试功能,它将函数从Slowest打印到Fastest,它们之间的百分比差异.
以下是Python 3的结果：
3.4.1 (default, May 19 2014, 13:10:29)
[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)]
100 rand ints between (0, 5):
=====
rate/sec f6 f7 f1 f2 f3 f4 f5
f6 52,756 -- -1.6% -26.2% -27.9% -30.7% -36.7% -46.8%
f7 53,624 1.6% -- -25.0% -26.7% -29.6% -35.7% -46.0%
f1 71,491 35.5% 33.3% -- -2.3% -6.1% -14.2% -28.0%
f2 73,164 38.7% 36.4% 2.3% -- -3.9% -12.2% -26.3%
f3 76,148 44.3% 42.0% 6.5% 4.1% -- -8.7% -23.3%
f4 83,368 58.0% 55.5% 16.6% 13.9% 9.5% -- -16.0%
f5 99,247 88.1% 85.1% 38.8% 35.6% 30.3% 19.0% --
100 rand ints between (0, 50):
=====
rate/sec f2 f6 f7 f4 f3 f1 f5
f2 39,405 -- -17.9% -18.7% -19.1% -41.8% -47.8% -56.3%
f6 47,980 21.8% -- -1.1% -1.6% -29.1% -36.5% -46.8%
f7 48,491 23.1% 1.1% -- -0.5% -28.4% -35.8% -46.2%
f4 48,737 23.7% 1.6% 0.5% -- -28.0% -35.5% -46.0%
f3 67,678 71.7% 41.1% 39.6% 38.9% -- -10.4% -24.9%
f1 75,511 91.6% 57.4% 55.7% 54.9% 11.6% -- -16.3%
f5 90,175 128.8% 87.9% 86.0% 85.0% 33.2% 19.4% --
100 rand ints between (0, 500):
=====
rate/sec f2 f4 f6 f7 f3 f1 f5
f2 25,748 -- -22.0% -41.4% -42.6% -57.5% -66.2% -67.8%
f4 32,996 28.1% -- -24.9% -26.4% -45.6% -56.7% -58.8%
f6 43,930 70.6% 33.1% -- -2.0% -27.5% -42.4% -45.1%
f7 44,823 74.1% 35.8% 2.0% -- -26.1% -41.2% -44.0%
f3 60,624 135.5% 83.7% 38.0% 35.3% -- -20.5% -24.2%
f1 76,244 196.1% 131.1% 73.6% 70.1% 25.8% -- -4.7%
f5 80,026 210.8% 142.5% 82.2% 78.5% 32.0% 5.0% --
1000 rand ints between (0, 5):
=====
rate/sec f7 f6 f1 f3 f2 f4 f5
f7 4,993 -- -6.7% -34.6% -39.4% -44.4% -50.1% -71.1%
f6 5,353 7.2% -- -29.9% -35.0% -40.4% -46.5% -69.0%
f1 7,640 53.0% 42.7% -- -7.3% -14.9% -23.6% -55.8%
f3 8,242 65.1% 54.0% 7.9% -- -8.2% -17.6% -52.3%
f2 8,982 79.9% 67.8% 17.6% 9.0% -- -10.2% -48.1%
f4 10,004 100.4% 86.9% 30.9% 21.4% 11.4% -- -42.1%
f5 17,293 246.4% 223.0% 126.3% 109.8% 92.5% 72.9% --
1000 rand ints between (0, 50):
=====
rate/sec f7 f6 f1 f2 f3 f4 f5
f7 5,051 -- -7.1% -26.5% -29.0% -34.1% -45.7% -71.2%
f6 5,435 7.6% -- -20.9% -23.6% -29.1% -41.5% -69.0%
f1 6,873 36.1% 26.5% -- -3.4% -10.3% -26.1% -60.8%
f2 7,118 40.9% 31.0% 3.6% -- -7.1% -23.4% -59.4%
f3 7,661 51.7% 41.0% 11.5% 7.6% -- -17.6% -56.3%
f4 9,297 84.0% 71.1% 35.3% 30.6% 21.3% -- -47.0%
f5 17,531 247.1% 222.6% 155.1% 146.3% 128.8% 88.6% --
1000 rand ints between (0, 500):
=====
rate/sec f2 f4 f6 f7 f3 f1 f5
f2 3,985 -- -11.0% -13.6% -14.8% -25.7% -40.4% -66.9%
f4 4,479 12.4% -- -2.9% -4.3% -16.5% -33.0% -62.8%
f6 4,613 15.8% 3.0% -- -1.4% -14.0% -31.0% -61.6%
f7 4,680 17.4% 4.5% 1.4% -- -12.7% -30.0% -61.1%
f3 5,361 34.5% 19.7% 16.2% 14.6% -- -19.8% -55.4%
f1 6,683 67.7% 49.2% 44.9% 42.8% 24.6% -- -44.4%
f5 12,028 201.8% 168.6% 160.7% 157.0% 124.3% 80.0% --
和Python 2：
2.7.6 (default, Dec 1 2013, 13:26:15)
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.2.79)]
100 rand ints between (0, 5):
=====
rate/sec f5 f7 f6 f2 f1 f3 f4
f5 24,955 -- -41.8% -42.5% -51.3% -55.7% -61.6% -65.2%
f7 42,867 71.8% -- -1.2% -16.4% -23.9% -34.0% -40.2%
f6 43,382 73.8% 1.2% -- -15.4% -23.0% -33.2% -39.5%
f2 51,293 105.5% 19.7% 18.2% -- -9.0% -21.0% -28.5%
f1 56,357 125.8% 31.5% 29.9% 9.9% -- -13.2% -21.4%
f3 64,924 160.2% 51.5% 49.7% 26.6% 15.2% -- -9.5%
f4 71,709 187.3% 67.3% 65.3% 39.8% 27.2% 10.5% --
100 rand ints between (0, 50):
=====
rate/sec f2 f5 f7 f6 f4 f3 f1
f2 22,439 -- -4.7% -45.1% -45.5% -50.7% -63.3% -64.5%
f5 23,553 5.0% -- -42.4% -42.8% -48.3% -61.5% -62.8%
f7 40,878 82.2% 73.6% -- -0.7% -10.2% -33.2% -35.4%
f6 41,164 83.4% 74.8% 0.7% -- -9.6% -32.7% -34.9%
f4 45,525 102.9% 93.3% 11.4% 10.6% -- -25.6% -28.0%
f3 61,167 172.6% 159.7% 49.6% 48.6% 34.4% -- -3.3%
f1 63,261 181.9% 168.6% 54.8% 53.7% 39.0% 3.4% --
100 rand ints between (0, 500):
=====
rate/sec f2 f5 f4 f6 f7 f3 f1
f2 13,122 -- -39.9% -56.2% -63.2% -63.8% -75.8% -80.0%
f5 21,837 66.4% -- -27.1% -38.7% -39.8% -59.6% -66.7%
f4 29,945 128.2% 37.1% -- -16.0% -17.4% -44.7% -54.3%
f6 35,633 171.6% 63.2% 19.0% -- -1.7% -34.2% -45.7%
f7 36,257 176.3% 66.0% 21.1% 1.8% -- -33.0% -44.7%
f3 54,113 312.4% 147.8% 80.7% 51.9% 49.2% -- -17.5%
f1 65,570 399.7% 200.3% 119.0% 84.0% 80.8% 21.2% --
1000 rand ints between (0, 5):
=====
rate/sec f5 f7 f6 f1 f2 f3 f4
f5 2,787 -- -37.7% -38.4% -53.3% -59.9% -60.4% -67.0%
f7 4,477 60.6% -- -1.1% -25.0% -35.6% -36.3% -47.0%
f6 4,524 62.3% 1.1% -- -24.2% -34.9% -35.6% -46.5%
f1 5,972 114.3% 33.4% 32.0% -- -14.1% -15.0% -29.3%
f2 6,953 149.5% 55.3% 53.7% 16.4% -- -1.1% -17.7%
f3 7,030 152.2% 57.0% 55.4% 17.7% 1.1% -- -16.8%
f4 8,452 203.3% 88.8% 86.8% 41.5% 21.6% 20.2% --
1000 rand ints between (0, 50):
=====
rate/sec f5 f7 f6 f2 f1 f3 f4
f5 2,667 -- -37.8% -38.7% -53.0% -55.9% -61.1% -65.3%
f7 4,286 60.7% -- -1.5% -24.5% -29.1% -37.5% -44.2%
f6 4,351 63.1% 1.5% -- -23.4% -28.0% -36.6% -43.4%
f2 5,677 112.8% 32.4% 30.5% -- -6.1% -17.3% -26.1%
f1 6,045 126.6% 41.0% 39.0% 6.5% -- -11.9% -21.4%
f3 6,862 157.3% 60.1% 57.7% 20.9% 13.5% -- -10.7%
f4 7,687 188.2% 79.3% 76.7% 35.4% 27.2% 12.0% --
1000 rand ints between (0, 500):
=====
rate/sec f2 f5 f7 f6 f4 f3 f1
f2 2,018 -- -16.1% -44.1% -46.2% -53.4% -61.8% -63.0%
f5 2,405 19.1% -- -33.4% -35.9% -44.5% -54.4% -55.9%
f7 3,609 78.8% 50.1% -- -3.8% -16.7% -31.6% -33.8%
f6 3,753 85.9% 56.1% 4.0% -- -13.4% -28.9% -31.2%
f4 4,334 114.7% 80.2% 20.1% 15.5% -- -17.9% -20.5%
f3 5,277 161.5% 119.5% 46.2% 40.6% 21.8% -- -3.2%
f1 5,454 170.2% 126.8% 51.1% 45.3% 25.8% 3.3% --
所以 – 这取决于.
结论：
> Counter方法几乎总是最慢的
> Counter方法是Python 2中最慢的方法,但到目前为止在Python 3.4上是最快的
> try / except版本通常是最慢的版本
>无论大小或密钥数如何,dict版本中的if键可以预测为最佳/最快的密钥
> {..keyskeys(tgt,0)是非常可预测的
> defaultdict版本在较大的列表上最快.较小的列表表示较长的设置时间是通过太少的元素分摊的.