Python：是“除键错误之外”比“if dict in dict”更快 [英] Python: is "Except keyerror" faster than "if key in dict"?
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问题描述
编辑：
我从我可以更清楚的问题。
对于代码拼写错误，是的，应该使用适当的python操作符来添加。
关于输入数据，我刚刚选择了一个随机数列表，这是一个常见的样本。在我的情况下，我使用的是一个dict，我期望很多keyerrors，大概95％的密钥将不存在，而存在的几个密钥将包含数据簇。
我对一般的讨论感兴趣，不管输入的数据是什么，但是运行时间的样本当然是有趣的。
我的标准方法将像许多其他帖子一样写出如
list =（100个随机数）
d = {}
列表中的x：
如果x中的x：
d [x] + = 1
else ：
d [x] = 1
但是我刚刚想到这样更快，因为我们不必检查字典是否包含密钥。我们只是假设它是，如果不是，我们处理这个。
有没有什么区别，或者比我更聪明吗？
list =（100个随机数）
d = {}
在列表中的x：
try：
d [x] + = 1
除了KeyError：
d [x] = 1
与数组中的索引相同的方法，超出界限，负指数等。
解决方案
您的声明绝对是错误的取决于输入。
如果您有一组多样化的键，并且经常碰到，除了块，性能不好。如果尝试块占优势， try / except 成语可以在较小的列表上执行。
这是一个基准测试，显示了几种方法来做同样的事情：
从__future__导入print_function
import timeit
import random
import collection
def f1（）：
d = {}
for x in tgt：
如果x中的x：
d [x] + = 1
else：
d [x] = 1
return d
def f2（）
d = {}
为tgt中的x
尝试：
d [x] + = 1
除了KeyError：
d [x] = 1
return d
def f3（）：
d = {} fromkeys（tgt，0）
for t in tgt：
d [x] + = 1
return d
def f4（）：
d = collections.defaultdict（int）
for t in tgt：
d [x] + = 1
return d
def f5（）：
return collections.Counter（tgt）
def f6（）：
d = {}
for t in tgt：
d [x] = d.setdefault（x，0）+1
返回d
def f7（）：
d = {}
为tgt中的x
d [x] = d.get（x，0）+1
return d
def cmpthese（funcs，c = 10000，rate = True，micro = False）：
生成Perl样式函数基准
def pprint_table（table）：
Perl style table output
def format_field（field，fmt ='{：，。0f}'）：
如果type ）是str：return field
如果type（field）是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 table in table]）
col_paddings = [get_max_col_w（table，i）for我在范围内（len（table [0]））]
for i，row in enumerate（table）：
＃left col
row_tab = [r ow [0] .ljust（col_paddings [0]）]
＃其余的cols
row_tab + = [format_field（row [j]）。rjust（col_paddings [j]）for j in range ，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 = [['']]
如果rate：table [0] .append / sec'）
如果micro：table [0] .append（'usec / pass'）
表[0] .extend（最快）
为e最快：
tmp = [e]
如果rate：
tmp.append（'{：，}'。format（int（round（float（c）/ results [e]））））
如果micro：
tmp.append（'{：。3f}'。format（1000000 * results [e] / float（c）））
for x in fastest ：
如果x == e：tmp.append（' - '）
else：tmp.append（'{：。1％}'。format（（results [x] -results [e ]）/ results [e]））
table.append（tmp）
pprint_table（table）
如果__name __ =='__ main__'：
import sys
print（sys.version）
for j in [100,1000 ]：$（$）
for [（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（）
基于 timeit 的基准函数，其功能从最慢到最快，它们之间的百分比差异。
以下是Python 3的结果：
3.4.1 （默认，2014年5月19日，13:10:29）
[GCC 4.2.1兼容的苹果LLVM 5.1（clang-503.0.40）]
100 rand ints之间（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 4 8,491 23.1％1.1％-0.5％-28.4％-35.8％-46.2％
f4 48,737 23.7％1.6％0.5％-2.8.0％-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（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（0，500）之间：
=====
/秒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％ -
Python 2：
2.7.6（默认，2013年12月1日，13:26:15）
[GCC 4.2 .1兼容的Apple LLVM 5.0（clang-500.2.79）]
100 rand ints（0，5）之间：
=====
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 42,367 71.8％ - -1.2％-16.4％-23.9％-34.0％-40.2％
f6 43,582 73.8％1.2％ - -15.4％-23.0％-33.2％-39.5％ b $ b 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 int （0，500）之间：
=====
rate / sec f2 f5 f4 f6 f7 f3 f1
f2 13,122 - - 399％-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方法几乎总是在最轻的 -
Counter方法是Python 2中最慢的，但是到目前为止，Python 3.4最快>
- c $ c
c c c c通常
如果dict中的键值版本是可预测的，无论大小或密钥数量如何，都是最佳/最快的之一
{}。fromkeys（tgt，0）是非常可预测的
defaultdict版本是最快的较大的列表较小的列表将较长的安装时间通过太少的元素进行分摊。
Edit 2: It was suggested that this is a copy of a similar question. I'd disagree since my question focuses on speed, while the other question asks what is more "readable" or "better" (without defining better). While the questions are similar, there is a big difference in the discussion/answers given.
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.
My standard approach would be like so many other posts to write something like
list = (100 random numbers)
d = {}
for x in list:
if x in d:
d[x]+=1
else:
d[x]=1
But I just came to think of this being faster, since we dont have to check if the dictionary contains the key. We just assume it does, and if not, we handle that. Is there any difference or is Python smarter than I am?
list = (100 random numbers)
d = {}
for x in list:
try:
d[x]+=1
except KeyError:
d[x] = 1
The same approach with indexes in an array, out of bounds, negative indexes etc.
解决方案
Your claim is absolutely false depends on the input.
If you have a diverse set of keys, and hits the except block often, the performance is not good. If the try block is dominant the try/except idiom can be performant on smaller lists.
Here is a benchmark showing several ways to do the same thing:
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()
I have included a small benchmark function based on timeit that prints the functions from Slowest to Fastest with a percent difference between them.
Here is the results for Python 3:
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% --
And 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% --
So -- it depends.
Conclusions:
TheCountermethod is almost always among the slowest- The
Countermethod is among the slowest on Python 2 but by far the fastest on Python 3.4 - The
try/exceptversion is usually among the slowest - The
if key in dictversion is predictably one of the best/fastest regardless of the size or key count - The
{}.fromkeys(tgt, 0)is very predictable - The
defaultdictversion is fastest on larger lists. Smaller lists the longer setup time is amortized over too few elements.
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