Python 的字符串连接比 str.join 慢多少？

This is what silly programs are designed to test :)

Use plus

import time


if __name__ == '__main__':
start = time.clock()
for x in range (1, 10000000):
dog = "a" + "b"


end = time.clock()
print "Time to run Plusser = ", end - start, "seconds"

Output of:

Time to run Plusser =  1.16350010965 seconds

Now with join....

import time
if __name__ == '__main__':
start = time.clock()
for x in range (1, 10000000):
dog = "a".join("b")


end = time.clock()
print "Time to run Joiner = ", end - start, "seconds"

Output Of:

Time to run Joiner =  21.3877386651 seconds

So on python 2.6 on windows, I would say + is about 18 times faster than join :)

From: Efficient String Concatenation

Method 1:

def method1():
out_str = ''
for num in xrange(loop_count):
out_str += 'num'
return out_str

Method 4:

def method4():
str_list = []
for num in xrange(loop_count):
str_list.append('num')
return ''.join(str_list)

Now I realise they are not strictly representative, and the 4th method appends to a list before iterating through and joining each item, but it's a fair indication.

String join is significantly faster then concatenation.

Why? Strings are immutable and can't be changed in place. To alter one, a new representation needs to be created (a concatenation of the two).

My original code was wrong, it appears that + concatenation is usually faster (especially with newer versions of Python on newer hardware)

The times are as follows:

Iterations: 1,000,000

Python 3.3 on Windows 7, Core i7

String of len:   1 took:     0.5710     0.2880 seconds
String of len:   4 took:     0.9480     0.5830 seconds
String of len:   6 took:     1.2770     0.8130 seconds
String of len:  12 took:     2.0610     1.5930 seconds
String of len:  80 took:    10.5140    37.8590 seconds
String of len: 222 took:    27.3400   134.7440 seconds
String of len: 443 took:    52.9640   170.6440 seconds

Python 2.7 on Windows 7, Core i7

String of len:   1 took:     0.7190     0.4960 seconds
String of len:   4 took:     1.0660     0.6920 seconds
String of len:   6 took:     1.3300     0.8560 seconds
String of len:  12 took:     1.9980     1.5330 seconds
String of len:  80 took:     9.0520    25.7190 seconds
String of len: 222 took:    23.1620    71.3620 seconds
String of len: 443 took:    44.3620   117.1510 seconds

On Linux Mint, Python 2.7, some slower processor

String of len:   1 took:     1.8840     1.2990 seconds
String of len:   4 took:     2.8394     1.9663 seconds
String of len:   6 took:     3.5177     2.4162 seconds
String of len:  12 took:     5.5456     4.1695 seconds
String of len:  80 took:    27.8813    19.2180 seconds
String of len: 222 took:    69.5679    55.7790 seconds
String of len: 443 took:   135.6101   153.8212 seconds

And here is the code:

from __future__ import print_function
import time


def strcat(string):
newstr = ''
for char in string:
newstr += char
return newstr


def listcat(string):
chars = []
for char in string:
chars.append(char)
return ''.join(chars)


def test(fn, times, *args):
start = time.time()
for x in range(times):
fn(*args)
return "{:>10.4f}".format(time.time() - start)


def testall():
strings = ['a', 'long', 'longer', 'a bit longer',
'''adjkrsn widn fskejwoskemwkoskdfisdfasdfjiz  oijewf sdkjjka dsf sdk siasjk dfwijs''',
'''this is a really long string that's so long
it had to be triple quoted  and contains lots of
superflous characters for kicks and gigles
@!#(*_#)(*$(*!#@&)(*E\xc4\x32\xff\x92\x23\xDF\xDFk^%#$!)%#^(*#''',
'''I needed another long string but this one won't have any new lines or crazy characters in it, I'm just going to type normal characters that I would usually write blah blah blah blah this is some more text hey cool what's crazy is that it looks that the str += is really close to the O(n^2) worst case performance, but it looks more like the other method increases in a perhaps linear scale? I don't know but I think this is enough text I hope.''']


for string in strings:
print("String of len:", len(string), "took:", test(listcat, 1000000, string), test(strcat, 1000000, string), "seconds")


testall()

I rewrote the last answer, could jou please share your opinion on the way i tested?

import time


start1 = time.clock()
for x in range (10000000):
dog1 = ' and '.join(['spam', 'eggs', 'spam', 'spam', 'eggs', 'spam','spam', 'eggs', 'spam', 'spam', 'eggs', 'spam'])


end1 = time.clock()
print("Time to run Joiner = ", end1 - start1, "seconds")




start2 = time.clock()
for x in range (10000000):
dog2 = 'spam'+' and '+'eggs'+' and '+'spam'+' and '+'spam'+' and '+'eggs'+' and '+'spam'+' and '+'spam'+' and '+'eggs'+' and '+'spam'+' and '+'spam'+' and '+'eggs'+' and '+'spam'


end2 = time.clock()
print("Time to run + = ", end2 - start2, "seconds")

NOTE: This example is written in Python 3.5, where range() acts like the former xrange()

The output i got:

Time to run Joiner =  27.086106206103153 seconds
Time to run + =  69.79100515996426 seconds

Personally i prefer ''.join([]) over the 'Plusser way' because it's cleaner and more readable.

The existing answers are very well-written and researched, but here's another answer for the Python 3.6 era, since now we have literal string interpolation (AKA, f-strings):

>>> import timeit
>>> timeit.timeit('f\'{"a"}{"b"}{"c"}\'', number=1000000)
0.14618930302094668
>>> timeit.timeit('"".join(["a", "b", "c"])', number=1000000)
0.23334730707574636
>>> timeit.timeit('a = "a"; a += "b"; a += "c"', number=1000000)
0.14985873899422586

Test performed using CPython 3.6.5 on a 2012 Retina MacBook Pro with an Intel Core i7 at 2.3 GHz.

This is by no means any formal benchmark, but it looks like using f-strings is roughly as performant as using += concatenation; any improved metrics or suggestions are, of course, welcome.

If I expect well, for a list with k string, with n characters in total, time complexity of join should be O(nlogk) while time complexity of classic concatenation should be O(nk).

That would be the same relative costs as merging k sorted list (efficient method is O(nlkg), while the simple one, akin to concatenation is O(nk) ).

If I say it algorithmically, if you choose [ += ] then it generates a new object and it will be O(n)**2. But if you use [ .join ] then it will be O(n).