How slow is Python's string concatenation vs. str.join?

Question

As a result of the comments in my answer on this thread, I wanted to know what the speed difference is between the += operator and ''.join()

So what is the speed comparison between the two?

Answer 1

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).

Answer 2

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()