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Quoting Steven D'Aprano on c.l.p.
"But add a call to replace, and things are very different:
[steve@ando ~]$ python2.7 -m timeit -s "s = 'b'*1000" "s.replace('b', 'a')"
100000 loops, best of 3: 9.3 usec per loop
[steve@ando ~]$ python3.2 -m timeit -s "s = 'b'*1000" "s.replace('b', 'a')"
100000 loops, best of 3: 5.43 usec per loop
[steve@ando ~]$ python3.3 -m timeit -s "s = 'b'*1000" "s.replace('b', 'a')"
100000 loops, best of 3: 18.3 usec per loop
Three times slower, even for pure-ASCII strings. I get comparable results for Unicode. Notice how slow Python 2.7 is:
[steve@ando ~]$ python2.7 -m timeit -s "s = u'你'*1000" "s.replace(u'你', u'a')"
10000 loops, best of 3: 65.6 usec per loop
[steve@ando ~]$ python3.2 -m timeit -s "s = '你'*1000" "s.replace('你', 'a')"
100000 loops, best of 3: 2.79 usec per loop
[steve@ando ~]$ python3.3 -m timeit -s "s = '你'*1000" "s.replace('你', 'a')"
10000 loops, best of 3: 23.7 usec per loop
Even with the performance regression, it is still over twice as fast as
Python 2.7.
Nevertheless, I think there is something here. The consequences are nowhere near as dramatic as jmf claims, but it does seem that replace() has taken a serious performance hit. Perhaps it is unavoidable, but perhaps not.
If anyone else can confirm similar results, I think this should be raised as a performance regression.
Quoting Serhiy Storchaka in response.
"Yes, I confirm, it's a performance regression. It should be avoidable.
Almost any PEP393 performance regression can be avoided. At least for
such corner case. Just no one has yet optimized this case."

My results aren't quite as dramatic as yours, but there does appear to be a regression:
$ ./python -V
Python 2.7.3+
$ ./python -m timeit -s "s = 'b'*1000" "s.replace('b', 'a')"
100000 loops, best of 3: 16.5 usec per loop
$ ./python -V
Python 3.3.0rc3+
$ ./python -m timeit -s "s = 'b'*1000" "s.replace('b', 'a')"
10000 loops, best of 3: 22.7 usec per loop

Python 3.3 is 2x faster than Python 3.2 to replace a character with
another if the string only contains the character 3 times. This is not
acceptable, Python 3.3 must be as slow as Python 3.2!
$ python3.2 -m timeit "ch='é'; sp=' '*1000; s = ch+sp+ch+sp+ch;
after='à'; s.replace(ch, after)"
100000 loops, best of 3: 3.62 usec per loop
$ python3.3 -m timeit "ch='é'; sp=' '*1000; s = ch+sp+ch+sp+ch;
after='à'; s.replace(ch, after)"
1000000 loops, best of 3: 1.36 usec per loop
$ python3.2 -m timeit "ch='€'; sp=' '*1000; s = ch+sp+ch+sp+ch;
after='Ł'; s.replace(ch, after)"
100000 loops, best of 3: 3.15 usec per loop
$ python3.2 -m timeit "ch='€'; sp=' '*1000; s = ch+sp+ch+sp+ch;
after='Ł'; s.replace(ch, after)"
1000000 loops, best of 3: 1.91 usec per loop
More seriously, I changed the algorithm of str.replace(before, after)
when before and after are only one character: changeset c802bfc8acfc.
The code is now using the heavily optimized findchar() function.
PyUnicode_READ() is slow and should be avoided when possible:
PyUnicode_READ() macro is expanded to 2 if, whereas findchar() uses
directly pointer of the right type (Py_UCS1*, Py_UCS2* or Py_UCS4*).
In Python 3.2, the code looks like:
 for (i = 0; i < u->length; i++) {
 if (u->str[i] == u1) {
 if (--maxcount < 0)
 break;
 u->str[i] = u2;
 }
 }
In Python 3.3, the code looks like:
 pos = findchar(sbuf, PyUnicode_KIND(self), slen, u1, 1);
 if (pos < 0)
 goto nothing;
 ...
 while (--maxcount)
 {
 pos++;
 src += pos * PyUnicode_KIND(self);
 slen -= pos;
 index += pos;
 pos = findchar(src, PyUnicode_KIND(self), slen, u1, 1);
 if (pos < 0)
 break;
 PyUnicode_WRITE(rkind, PyUnicode_DATA(u), index + pos, u2);
 }

> The code is now using the heavily optimized findchar() function.
I compared performances of the two methods: dummy loop vs find. Results with a string of 100,000 characters:
 * Replace 100% (rewrite all characters): find is 12.5x slower than a loop
 * Replace 50%: find is 3.3x slower
 * Replace only 2 characters (0.001%): find is 10.4x faster
In practice, I bet that the most common case is to replace only a few characters. Replace all characters is a rare usecase.
Use attached "unicode.patch" on Python 3.4 with the following commands to reproduce my benchmark:
python -m timeit -s "a='a'; b='b'; text=a*100000" "text.replace(a, b)"
python -m timeit -s "a='a'; b='b'; text=(a+' ')*(100000//2)" "text.replace(a, b)"
python -m timeit -s "a='a'; b='b'; text=a+' '*100000+a" "text.replace(a, b)"
--
An option is to use the find method, and then switch to the dummy loop method if there are too much characters to replace. I don't know if it's necessary to develop such complex algorithm. It would be better to have a benchmark extracted from a real world application like a template engine.

> I compared performances of the two methods: dummy loop vs find.
You can hybridize them. First just compare chars and if not match then use 
memcmp(). This speed up the case of repeated chars.

> You can hybridize them. First just compare chars and if not match then use
> memcmp(). This speed up the case of repeated chars.
Oh, you're patch is simple and it's amazing fast! I compare unicode with
Python 2.7, 3.2, 3.4 and 3.4 patched, and bytes with 2.7. Using your patch,
Python 3.4 is the fastest implemented in most cases.
Common platform:
CPU model: Intel(R) Core(TM) i5 CPU 661 @ 3.33GHz
Bits: int=32, long=32, long long=64, pointer=32
Platform: Linux-3.2.0-31-generic-pae-i686-with-debian-wheezy-sid
Platform of campaign 2.7-bytes:
Python unicode implementation: UTF-16
Python version: 2.7.3+ (2.7:19d37c8d1882+, Oct 9 2012, 14:37:36) [GCC 4.6.3]
CFLAGS: -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall
-Wstrict-prototypes
SCM: hg revision=ad51ed93377c tag=tip branch=default date="2012-10-11 00:11
-0700"
Date: 2012年10月11日 14:41:49
Platform of campaign 2.7-unicode:
Python unicode implementation: UTF-16
Python version: 2.7.3+ (2.7:19d37c8d1882+, Oct 9 2012, 14:37:36) [GCC 4.6.3]
CFLAGS: -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall
-Wstrict-prototypes
SCM: hg revision=ad51ed93377c tag=tip branch=default date="2012-10-11 00:11
-0700"
Date: 2012年10月11日 14:42:55
Platform of campaign 3.2-wide:
Python unicode implementation: UCS-4
Python version: 3.2.3+ (3.2:f7615ee43318, Sep 27 2012, 15:00:15) [GCC 4.6.3]
CFLAGS: -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes
SCM: hg revision=ad51ed93377c tag=tip branch=default date="2012-10-11 00:11
-0700"
Date: 2012年10月11日 14:41:30
Platform of campaign 3.4:
Python unicode implementation: PEP 393
Python version: 3.4.0a0 (default:ad51ed93377c, Oct 11 2012, 14:40:51) [GCC
4.6.3]
CFLAGS: -Wno-unused-result -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes
SCM: hg revision=ad51ed93377c tag=tip branch=default date="2012-10-11 00:11
-0700"
Date: 2012年10月11日 14:40:52
Platform of campaign 3.4-patch:
Date: 2012年10月11日 14:40:25
Python version: 3.4.0a0 (default:ad51ed93377c+, Oct 11 2012, 14:33:04) [GCC
4.6.3]
CFLAGS: -Wno-unused-result -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes
SCM: hg revision=ad51ed93377c+ tag=tip branch=default date="2012-10-11
00:11 -0700"
Python unicode implementation: PEP 393
----------------+-----------------+-----------------+-----------------+-----------------+----------------
Tests | 2.7-bytes | 2.7-unicode | 3.2-wide | 3.4 | 3.4-patch
----------------+-----------------+-----------------+-----------------+-----------------+----------------
all | 7.83 ms (+552%) | 2.05 ms (+71%) | 3.45 ms (+188%) | 15 ms (+1152%) |
1.2 ms (*)
replace 50% | 4.14 ms (+135%) | 1.76 ms (*) | 3.17 ms (+81%) | 7.76 ms
(+342%) | 4.18 ms (+138%)
replace 10% | 1.21 ms (*) | 1.52 ms (+26%) | 3.01 ms (+150%) | 2.01 ms
(+67%) | 1.23 ms
replace 1% | 490 us | 1.55 ms (+217%) | 2.94 ms (+501%) | 589 us (+20%) |
489 us (*)
replace 2 chars | 398 us | 1.47 ms (+271%) | 2.89 ms (+632%) | 398 us | 395
us (*)
----------------+-----------------+-----------------+-----------------+-----------------+----------------
Total | 14.1 ms (+88%) | 8.34 ms (+11%) | 15.5 ms (+106%) | 25.8 ms (+244%)
| 7.49 ms (*)
----------------+-----------------+-----------------+-----------------+-----------------+----------------
**
Compare 3.2, 3.4 and 3.4 patched:
----------------+-------------+-----------------+---------------
Tests | 3.2-wide | 3.4 | 3.4-patch
----------------+-------------+-----------------+---------------
all | 3.45 ms (*) | 15 ms (+335%) | 1.2 ms (-65%)
replace 50% | 3.17 ms (*) | 7.76 ms (+145%) | 4.18 ms (+32%)
replace 10% | 3.01 ms (*) | 2.01 ms (-33%) | 1.23 ms (-59%)
replace 1% | 2.94 ms (*) | 589 us (-80%) | 489 us (-83%)
replace 2 chars | 2.89 ms (*) | 398 us (-86%) | 395 us (-86%)
----------------+-------------+-----------------+---------------
Total | 15.5 ms (*) | 25.8 ms (+67%) | 7.49 ms (-52%)
----------------+-------------+-----------------+---------------
The patch should be completed to optimize also other Unicode kinds.

> The patch should be completed to optimize also other Unicode kinds.
I'm working on it.
Here are benchmark scripts which I use. First tests regular strings (replace 
every n-th char), second tests random strings (replace 1/n of total randomly 
distributed chars).

The performance numbers are very nice, but the patch needs a comment about the optimization, IMO.

After much experimentation, I suggest the new patch.
Benchmark results (time of replacing 1 of n character (ch1 to ch2) in 100000-
char string).
Py3.2 Py3.3 patch n ch1 ch2 fill
231 (-13%) 3025 (-93%) 200 1 'a' 'b' 'c'
626 (-18%) 2035 (-75%) 511 2 'a' 'b' 'c'
444 (-26%) 957 (-66%) 327 5 'a' 'b' 'c'
349 (-30%) 530 (-54%) 243 10 'a' 'b' 'c'
306 (-40%) 300 (-38%) 185 20 'a' 'b' 'c'
280 (-54%) 169 (-23%) 130 50 'a' 'b' 'c'
273 (-62%) 123 (-15%) 105 100 'a' 'b' 'c'
265 (-70%) 82 (-4%) 79 1000 'a' 'b' 'c'
230 (+4%) 3012 (-92%) 239 1 '\u010a' '\u010b' '\u010c'
624 (-17%) 1907 (-73%) 518 2 '\u010a' '\u010b' '\u010c'
442 (-16%) 962 (-62%) 370 5 '\u010a' '\u010b' '\u010c'
347 (-5%) 566 (-42%) 330 10 '\u010a' '\u010b' '\u010c'
305 (-10%) 357 (-23%) 275 20 '\u010a' '\u010b' '\u010c'
285 (-26%) 241 (-12%) 212 50 '\u010a' '\u010b' '\u010c'
280 (-33%) 190 (-2%) 187 100 '\u010a' '\u010b' '\u010c'
263 (-41%) 170 (-8%) 156 1000 '\u010a' '\u010b' '\u010c'
3355 (-85%) 3309 (-85%) 498 1 '\U0001000a' '\U0001000b' '\U0001000c'
2290 (-65%) 2267 (-65%) 800 2 '\U0001000a' '\U0001000b' '\U0001000c'
1598 (-62%) 1279 (-52%) 612 5 '\U0001000a' '\U0001000b' '\U0001000c'
1313 (-60%) 950 (-45%) 519 10 '\U0001000a' '\U0001000b' '\U0001000c'
1195 (-61%) 824 (-44%) 464 20 '\U0001000a' '\U0001000b' '\U0001000c'
1055 (-59%) 640 (-32%) 434 50 '\U0001000a' '\U0001000b' '\U0001000c'
982 (-55%) 549 (-20%) 439 100 '\U0001000a' '\U0001000b' '\U0001000c'
941 (-56%) 473 (-12%) 417 1000 '\U0001000a' '\U0001000b' '\U0001000c'
On other platforms other numbers are possible. Especially I'm interested in 
the results on Windows and on 64-bit. For the test I used the script 
replacebench2.py, and compared the results with the help of script 
https://bitbucket.org/storchaka/cpython-stuff/raw/default/bench/bench-diff.py 
.

I going speed up other cases for replace(), but for now I have only this patch. Is it good? Should I apply it to 3.3 as there is a 3.3 regression?

As __ap__ says, it would be nice to have a comment.

64-bit linux results:
3.2 3.3 patch
133 (-28%) 1343 (-93%) 96 1 'a' 'b' 'c'
414 (-9%) 704 (-47%) 375 2 'a' 'b' 'c'
319 (-8%) 491 (-40%) 293 3 'a' 'b' 'c'
253 (-7%) 384 (-39%) 235 4 'a' 'b' 'c'
216 (-8%) 320 (-38%) 199 5 'a' 'b' 'c'
192 (-9%) 278 (-37%) 175 6 'a' 'b' 'c'
175 (-10%) 247 (-36%) 157 7 'a' 'b' 'c'
162 (-11%) 223 (-35%) 144 8 'a' 'b' 'c'
153 (-14%) 204 (-35%) 132 9 'a' 'b' 'c'
145 (-15%) 188 (-35%) 123 10 'a' 'b' 'c'
108 (-36%) 112 (-38%) 69 20 'a' 'b' 'c'
86 (-59%) 53 (-34%) 35 50 'a' 'b' 'c'
78 (-71%) 31 (-26%) 23 100 'a' 'b' 'c'
73 (-84%) 12 (+0%) 12 1000 'a' 'b' 'c'
81 (-88%) 10 (+0%) 10 10000 'a' 'b' 'c'
133 (-23%) 1470 (-93%) 103 1 '\u010a' '\u010b' '\u010c'
414 (-10%) 799 (-54%) 371 2 '\u010a' '\u010b' '\u010c'
319 (-5%) 576 (-47%) 303 3 '\u010a' '\u010b' '\u010c'
254 (-1%) 461 (-46%) 251 4 '\u010a' '\u010b' '\u010c'
216 (+2%) 391 (-44%) 220 5 '\u010a' '\u010b' '\u010c'
193 (+4%) 341 (-41%) 200 6 '\u010a' '\u010b' '\u010c'
175 (+5%) 303 (-39%) 184 7 '\u010a' '\u010b' '\u010c'
163 (+6%) 275 (-37%) 172 8 '\u010a' '\u010b' '\u010c'
153 (+6%) 252 (-36%) 162 9 '\u010a' '\u010b' '\u010c'
145 (+7%) 235 (-34%) 155 10 '\u010a' '\u010b' '\u010c'
108 (-1%) 133 (-20%) 107 20 '\u010a' '\u010b' '\u010c'
86 (-27%) 66 (-5%) 63 50 '\u010a' '\u010b' '\u010c'
79 (-44%) 44 (+0%) 44 100 '\u010a' '\u010b' '\u010c'
74 (-66%) 24 (+4%) 25 1000 '\u010a' '\u010b' '\u010c'
75 (-71%) 22 (+0%) 22 10000 '\u010a' '\u010b' '\u010c'
1687 (-91%) 1362 (-89%) 150 1 '\U0001000a' '\U0001000b' '\U0001000c'
1146 (-58%) 817 (-41%) 479 2 '\U0001000a' '\U0001000b' '\U0001000c'
919 (-61%) 627 (-43%) 358 3 '\U0001000a' '\U0001000b' '\U0001000c'
802 (-63%) 521 (-44%) 294 4 '\U0001000a' '\U0001000b' '\U0001000c'
729 (-64%) 446 (-42%) 259 5 '\U0001000a' '\U0001000b' '\U0001000c'
678 (-65%) 394 (-40%) 237 6 '\U0001000a' '\U0001000b' '\U0001000c'
643 (-66%) 350 (-37%) 220 7 '\U0001000a' '\U0001000b' '\U0001000c'
617 (-66%) 313 (-34%) 207 8 '\U0001000a' '\U0001000b' '\U0001000c'
598 (-67%) 283 (-30%) 198 9 '\U0001000a' '\U0001000b' '\U0001000c'
581 (-67%) 258 (-27%) 189 10 '\U0001000a' '\U0001000b' '\U0001000c'
511 (-71%) 152 (-3%) 148 20 '\U0001000a' '\U0001000b' '\U0001000c'
472 (-76%) 89 (+28%) 114 50 '\U0001000a' '\U0001000b' '\U0001000c'
461 (-78%) 68 (+47%) 100 100 '\U0001000a' '\U0001000b' '\U0001000c'
452 (-81%) 48 (+81%) 87 1000 '\U0001000a' '\U0001000b' '\U0001000c'
452 (-81%) 46 (+85%) 85 10000 '\U0001000a' '\U0001000b' '\U0001000c'

64-bit windows results:
3.3 patched
925 (-90%) 97 1 'a' 'b' 'c'
881 (-54%) 405 2 'a' 'b' 'c'
623 (-51%) 308 3 'a' 'b' 'c'
482 (-48%) 252 4 'a' 'b' 'c'
396 (-44%) 223 5 'a' 'b' 'c'
344 (-40%) 208 6 'a' 'b' 'c'
306 (-38%) 190 7 'a' 'b' 'c'
276 (-37%) 173 8 'a' 'b' 'c'
254 (-36%) 162 9 'a' 'b' 'c'
241 (-35%) 156 10 'a' 'b' 'c'
158 (-24%) 120 20 'a' 'b' 'c'
107 (-12%) 94 50 'a' 'b' 'c'
87 (+7%) 93 100 'a' 'b' 'c'
70 (+3%) 72 1000 'a' 'b' 'c'
63 (+8%) 68 10000 'a' 'b' 'c'
1332 (-92%) 106 1 '\u010a' '\u010b' '\u010c'
1137 (-60%) 459 2 '\u010a' '\u010b' '\u010c'
836 (-58%) 347 3 '\u010a' '\u010b' '\u010c'
660 (-56%) 288 4 '\u010a' '\u010b' '\u010c'
567 (-55%) 256 5 '\u010a' '\u010b' '\u010c'
503 (-51%) 245 6 '\u010a' '\u010b' '\u010c'
455 (-47%) 242 7 '\u010a' '\u010b' '\u010c'
414 (-44%) 231 8 '\u010a' '\u010b' '\u010c'
387 (-41%) 227 9 '\u010a' '\u010b' '\u010c'
365 (-35%) 237 10 '\u010a' '\u010b' '\u010c'
256 (-21%) 201 20 '\u010a' '\u010b' '\u010c'
185 (-9%) 168 50 '\u010a' '\u010b' '\u010c'
186 (-19%) 150 100 '\u010a' '\u010b' '\u010c'
137 (-1%) 136 1000 '\u010a' '\u010b' '\u010c'
131 (+3%) 135 10000 '\u010a' '\u010b' '\u010c'
1346 (-88%) 160 1 '\U0001000a' '\U0001000b' '\U0001000c'
1247 (-62%) 469 2 '\U0001000a' '\U0001000b' '\U0001000c'
965 (-64%) 352 3 '\U0001000a' '\U0001000b' '\U0001000c'
845 (-64%) 303 4 '\U0001000a' '\U0001000b' '\U0001000c'
720 (-65%) 251 5 '\U0001000a' '\U0001000b' '\U0001000c'
655 (-65%) 230 6 '\U0001000a' '\U0001000b' '\U0001000c'
604 (-58%) 256 7 '\U0001000a' '\U0001000b' '\U0001000c'
570 (-62%) 214 8 '\U0001000a' '\U0001000b' '\U0001000c'
546 (-63%) 203 9 '\U0001000a' '\U0001000b' '\U0001000c'
515 (-63%) 190 10 '\U0001000a' '\U0001000b' '\U0001000c'
404 (-61%) 157 20 '\U0001000a' '\U0001000b' '\U0001000c'
339 (-62%) 130 50 '\U0001000a' '\U0001000b' '\U0001000c'
308 (-60%) 122 100 '\U0001000a' '\U0001000b' '\U0001000c'
284 (-54%) 130 1000 '\U0001000a' '\U0001000b' '\U0001000c'
281 (-60%) 113 10000 '\U0001000a' '\U0001000b' '\U0001000c'

> As __ap__ says, it would be nice to have a comment.
Oh, I thought I had already done this.

str_replace_1char.patch: why not implementing replace_1char_inplace() in stringlib, with one version per character type (UCS1, UCS2, UCS4)?
I prefer unicode_2.patch algorithm because it's simpler: only one loop (vs two loops for str_replace_1char.patch, with a threshold of 10 different characters).
Why do you changed your algorithm? Is str_replace_1char.patch algorithm more efficient than unicode_2.patch algorithm? Is the speedup really interesting?

> str_replace_1char.patch: why not implementing replace_1char_inplace() in
> stringlib, with one version per character type (UCS1, UCS2, UCS4)?
Because there are no benefits to do it. All three versions (UCS1, UCS2, and 
UCS4) have no any common code. The best implementation used for every kind of 
strings. For UCS1 it uses fast memchr() (findchar() has some overhead here), 
for UCS2 it uses findchar(), and for UCS4 it uses a dumb loop, because 
findchar() will be too ineffective here.
> I prefer unicode_2.patch algorithm because it's simpler: only one loop (vs
> two loops for str_replace_1char.patch, with a threshold of 10 different
> characters).
Yes, UCS1-implementation in str_replace_1char.patch is more complicated, but 
it is faster for more input strings. memchr() is more effective than a simple 
loop when the replaceable characters are rare. But when they meet often, a 
simple cycle is more efficient. The "attempts" counter determines how many 
characters will be checked before using memchr(). This speeds up the 
replacement in strings with frequent replacements, but a little slow down the 
replacement in strings with rare replacements. 10 is a compromise. 
str_replace_1char.patch speed up not only case when *each* character replaced, 
but when 1/2, 1/3, 1/5,... characters replaced.
> Why do you changed your algorithm? Is str_replace_1char.patch algorithm
> more efficient than unicode_2.patch algorithm? Is the speedup really
> interesting?
You can run benchmarks and compare results. str_replace_1char.patch provides 
not the best performance, but most stable results for wide sort of strings, 
and has no regressions comparing with 3.2.

How can we move this issu forward? I still prefer unicode_2.patch over str_replace_1char.patch because the code is simpler and so easier to maintain.
str_replace_1char.patch has a "bug": replace_1char() does not use "pos" for the latin1 path.

My experiments last September, before this was filed, showed that str.find (index) had most of the relative slowdown of str.replace. I assumed at that time that .replace used .find or .index to find substrings to replace, so that the fix for .replace would include speeding up .find/index. Looking at the patches, I see that I was wrong; I guess because .replace copies as it goes. I will open a separate issue sometime unless .find gets fixed here.
For both .find and .replace, the regression was worse on Windows than on linux, so the patches should be tested on Windows also. If I can get vc++ express 2008 installed and working on my current substitute machine. I will give them a try.

Here is an updated patch. Some comments added (I will be grateful for help in the improvement of these comments), an implementation moved to stringlib (a new file Objects/stringlib/replace.h added).
unicode_2.patch optimizes only too special case and I consider this is not worth the effort. str_replace_1char*.patch cover a wider area and designed to be faster than 3.2 and 3.3 in most cases and not to be significant slower in corner cases.

str_replace_1char_2.patch looks good to me. Just one nit: please add a reference to this issue in the comment (in replace.h).

New changeset d396e0716bf4 by Serhiy Storchaka in branch 'default':
Issue #16061: Speed up str.replace() for replacing 1-character strings.
http://hg.python.org/cpython/rev/d396e0716bf4 

Thanks to Ezio Melotti and Daniel Shahaf for their great help in correcting my clumsy wording.