Speedy string concatenation in Python

August 12, 2016 . By Reuven

As many people know, one of the mantras of the Python programming language is, “There should be one, and only one, way to do it.”  (Use “import this” in your Python interactive shell to see the full list.)  However, there are often times when you could accomplish something in any of several ways. In such cases, it’s not always obvious which is the best one.

A student of mine recently e-mailed me, asking which is the most efficient way to concatenate strings in Python.

The results surprised me a bit — and gave me an opportunity to show her (and others) how to test such things.  I’m far from a benchmarking expert, but I do think that what I found gives some insights into concatenation.

First of all, let’s remember that Python provides us with several ways to concatenate strings.  We can use the + operator, for example:

>> 'abc' + 'def'

We can also use the % operator, which can do much more than just concatenation, but which is a legitimate option:

>>> "%s%s" % ('abc', 'def')

And as I’ve mentioned in previous blog posts, we also have a more modern way to do this, with the str.format method:

>>> '{0}{1}'.format('abc', 'def')

As with the % operator, str.format is far more powerful than simple concatenation requires. But I figured that this would give me some insights into the relative speeds.

Now, how do we time things? In Jupyter (aka IPython), we can use the magic “timeit” command to run code.  I thus wrote four functions, each of which concatenates in a different way. I purposely used global variables (named “x” and “y”) to contain the original strings, and a local variable “z” in which to put the result.  The result was then returned from the function.  (We’ll play a bit with the values and definitions of “x” and “y” in a little bit.)

def concat1(): 
    z = x + y 
    return z 

 def concat2(): 
    z = "%s%s" % (x, y) 
    return z 

def concat3(): 
    z = "{}{}".format(x, y) 
    return z 

def concat4(): 
    z = "{0}{1}".format(x, y) 
    return z

I should note that concat3 and concat4 are almost identical, in that they both use str.format. The first uses the implicit locations of the parameters, and the second uses the explicit locations.  I decided that if I’m already benchmarking string concatenation, I might as well also find out if there’s any difference in speed when I give the parameters’ iindexes.

I then defined the two global variables:

x = 'abc' 
y = 'def'

Finally, I timed running each of these functions:

%timeit concat1()
%timeit concat2()
%timeit concat3()
%timeit concat4()

The results were as follows:

  • concat1: 153ns/loop
  • concat2: 275ns/loop
  • concat3: 398ns/loop
  • concat4: 393ns/loop

From this benchmark, we can see that concat1, which uses +, is significantly faster than any of the others.  Which is a bit sad, given how much I love to use str.format — but it also means that if I’m doing tons of string processing, I should stick to +, which might have less power, but is far faster.

The thing is, the above benchmark might be a bit problematic, because we’re using short strings.  Very short strings in Python are “interned,” meaning that they are defined once and then kept in a table so that they need not be allocated and created again.  After all, since strings are immutable, why would we create “abc” more than once?  We can just reference the first “abc” that we created.

This might mess up our benchmark a bit.  And besides, it’s good to check with something larger. Fortunately, we used global variables — so by changing those global variables’ definitions, we can run our benchmark and be sure that no interning is taking place:

x = 'abc' * 10000 
y = 'def' * 10000

Now, when we benchmark our functions again, here’s what we get:

  • concat1: 2.64µs/loop
  • concat2: 3.09µs/loop
  • concat3: 3.33µs/loop
  • concat4: 3.48µs/loop

Each loop took a lot longer — but we see that our + operator is still the fastest.  The difference isn’t as great, but it’s still pretty obvious and significant.

What about if we no longer use global variables, and if we allocate the strings within our function?  Will that make a difference?  Almost certainly not, but it’s worth a quick investigation:

def concat1(): 
     x = 'abc' * 10000 
     y = 'def' * 10000 
     z = x + y 
     return z 

def concat2(): 
     x = 'abc' * 10000 
     y = 'def' * 10000 
     z = "%s%s" % (x, y) 
     return z 

def concat3(): 
     x = 'abc' * 10000 
     y = 'def' * 10000 
     z = "{}{}".format(x, y) 
     return z 

def concat4(): 
     x = 'abc' * 10000 
     y = 'def' * 10000 
     z = "{0}{1}".format(x, y) 
     return z 

And our final results are:

  • concat1: 4.89µs/loop
  • concat2: 5.78µs/loop
  • concat3: 6.22µs/loop
  • concat4: 6.19µs/loop

Once again, we see that + is the big winner here, but (again) but less of a margin than was the case with the short strings.  str.format is clearly shorter.  And we can see that in all of these tests, the difference between “{0}{1}” and “{}{}” in str.format is basically zero.

Upon reflection, this shouldn’t be a surprise. After all, + is a pretty simple operator, whereas % and str.format do much more.  Moreover, str.format is a method, which means that it’ll have greater overhead.

Now, there are a few more tests that I could have run — for example, with more than two strings.  But I do think that this demonstrates to at least some degree that + is the fastest way to achieve concatenation in Python.  Moreover, it shows that we can do simple benchmarking quickly and easily, conducting experiments that help us to understand which is the best way to do something in Python.

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  • «Premature optimization is the root of all evil (or at least most of it) in programming»

    Hello, mr. Lerner. I want to criticize your article, no hard feelings.

    A way to choose string concatenation method (or other such thing) should be never based on benchmarks:

    1) Interpreter implementation changes — all your results are invalid.

    2) Ugly, non-idiomatic way to do something makes your project readability worse. More and more such «optimizations» — worse and worse project readability. Less readability — more bugs and more time to modify.

    3) In real life you’ll never have problems because of «not fastest» way to concatenate strings. Run profiler in any non-synthetic project and you’ll see anything: I/O, bad DB architecture, O(n^2) complexity — anything, but not a string concatenation. String concatenation — is just not a place where real bottleneck can be.

    All above is not something I invented myself: all this written years ago in a wonderful book I sure you know — McConnell’s «Code Complete» (see chapter 25).

    Event if you’re agree with things above, I think article should be absolutely clear that a correct way to concatenate strings is only one — idiomatic way, beautiful way («+» in our case). All optimization — later, with profiler and only if you really need it.

    Thanks for reading.
    Sorry if I might to seem rough, I didn’t want it (english is not my native language).

    • All of your points are more than reasonable and welcome, except for the first — I wasn’t aiming to do any optimization. I was just curious to know which concatenation method was the fastest. When it comes to my own code, clarity is the most important factor, and I tell this to my students all of the time, in no small part because of the points you made here.

      I actually like to use str.format, much more than +, which I find fairly ugly. That’s probably why my student was curious about the relative speeds; I tend to use str.format extensively in my classes, and she was wondering if, given a large program with lots of concatenation, it would make a difference.

      Every time I try to do benchmarking, I’m humbled anew. You would think that I would have learned my lesson by now!

      • Glad to hear! I was confused after last words:

        «Moreover, it shows that we can do simple benchmarking quickly and easily, conducting experiments that help us to understand which is the best way to do something in Python»

        Some people may think that to do benchmarking is a method to choose the best way to do something, while it’s not.

  • You’re measuring the fastest way to concatenate two long strings. That particular example isn’t especially indicative of the fastest way to concatenate many strings, which is probably the actual information your student was seeking. In my experience, the usual times one needs to concatenate a small number of strings is user input or output, which tends to be rare, overall. On the other hand, concatenating many strings is common when processing significant quantities of data from external sources, an often time-consuming task.

    And when it comes to joining large numbers of strings, there’s a clear winner, and it’s not ‘+’:

    n [15]: strings = [‘abc’] * 100000

    In [16]: %timeit reduce(lambda x, y: x + y, strings)
    1 loop, best of 3: 1.02 s per loop

    In [17]: %timeit reduce(lambda x, y: ‘{0}{1}’.format(x, y), strings)
    10 loops, best of 3: 23.7 ms per loop

    In [18]: %timeit ”.join(strings)
    1000 loops, best of 3: 1.11 ms per loop

    • Very good point, although I’d argue that your tests here didn’t actually compare things with +. Doing so makes the difference quite obvious and stark, and confirms precisely what you wrote:

      In [2]: strings = [‘abc’] * 100000

      In [3]: %timeit reduce(lambda x, y: x + y, strings)
      1 loop, best of 3: 808 ms per loop

  • Your tests are kind of weird. I see several funny things with your first approach. First, you’re not only doing your operations (+, %, format(), etc), you’re also calling functions, so you can’t really know how much of the measured time corresponds to the operation and how much to the function call. True, the overhead is constant for all the tests, but then the proportion between the different speeds is off. Second, and this can be a question of personal taste, you’re using global variables instead of parameters. And you don’t explain which version of python/ipython you’re using.

    And then you ask: “What about if we no longer use global variables, and if we allocate the strings within our function?”. This really makes no sense, because you’re adding the execution of the str() * int() operator to all the functions. Now you’re measuring something completely different.

    This what I got in my system, with a surprise:

    $ ipython3
    Python 3.5.2+ (default, Aug 5 2016, 08:07:14)
    IPython 2.4.1 — An enhanced Interactive Python.

    In [1]: x= “abc”*1000
    In [2]: y= “xyz”*1000
    In [3]: z= x+y

    # this is to show why your numbers change so much
    # it takes more time than the fastest method I found.
    In [4]: %timeit x= “abc”*1000
    1000000 loops, best of 3: 251 ns per loop

    # and a function call:
    In [5]: def func_call (x):
    ….: return x

    In [6]: %timeit func_call (z)
    10000000 loops, best of 3: 87.7 ns per loop

    In [7]: %timeit x+y
    1000000 loops, best of 3: 279 ns per loop

    In [8]: %timeit “%s%s” % (x, y)
    1000000 loops, best of 3: 414 ns per loop

    In [9]: %timeit “{}{}”.format(x, y)
    1000000 loops, best of 3: 457 ns per loop

    In [10]: %timeit “{0}{1}”.format(x, y)
    10000000 loops, best of 3: 145 ns per loop (!!!)

    In [11]: %timeit ”.join ((x, y))
    1000000 loops, best of 3: 357 ns per loop

    There you go. Now it’s the seemingly most complex call who’s the fastest. God knows what optimizations are running behind these numbers.

    • Wow, thanks so much for this.

      I must admit that I’ve always (for reasons I’ve never really thought about) used %timeit with functions. It never occurred to me to just time plain ol’ code, in part because (as you indicated) the function-call overhead would be uniform. But your point about the function-call overhead being so much greater than the actual operation is a very good one.

      I was using Python 2 for my tests; I should have used Python 3, and clearly should have stated what version I was using. Moreover, a comparison between versions would seem appropriate.

      I’m also rather surprised that the numbered use of str.format is the fastest. I’m not surprised that it is faster than {} {}; my guess is that if you’re explicit, then Python doesn’t need to do as much work.

      In short, I greatly appreciate your comment. I knew that I’d mess up somewhere with the benchmarking, and am glad that you took the time to indicate where and how I made some mistakes.

  • It may be interesting to experiment and see what happens if you’re concatenating nore than two strings. (And if you do this, definitely add str.join() to the alternatives.)

  • David Hancock says:

    Unrelated note: The math captcha has two minor annoyances: (1) every time I’ve composed a reply, it’s taken a couple minutes. I didn’t use timeit() 😉 The math captcha expired. (2) Sometimes the appearance of one of the number (spelled out) makes it look like the captcha want a spelled-out number. This doesn’t work.

  • David Hancock says:

    Nice benchmarks — thank you! I added a concat5 that uses automatic concatenation of raw strings. It won’t work with the global variables.

    def concat5():
    z = “abc” “xyz”
    return z

    Across 1 million loops, this was reliably twice as fast as the + operator.

    • Wow, twice as fast as +? Egad! I thought about testing that as well, but decided against it because it didn’t involve variables.

      But wow, maybe I should have.

      I also often tell people not to use the implicit concatenation in their programs, because it’s hard to read such programs — and because I’m not sure how useful it really is. (Maybe there’s an example of where it’s a great idea?)

      But yes, thanks for that!

      • David Hancock says:

        I agree with your stance, just striving for completeness of the test. There’s even a PEP (rejected) to remove the implicit concatenation: http://legacy.python.org/dev/peps/pep-3126/. The only use cases that made sense at all were commenting REs (but verbose mode is better) and readable SQL strings (triple-quoting is nearly as good).

        The ”.join(x, y) approach in another comment seems nearly tied for second place with concat2:

        dhancock@air:~ $ time python strcat.py
        concat1: 0.176830053329
        concat2: 0.310678958893
        concat3: 0.43902015686
        concat4: 0.488086938858
        concat5: 0.113254070282 # implicit concatenation
        concat6: 0.296023130417 # ”.join(x,y)

    • Mark Lawrence says:

      Good grief, please don’t rely on rubbish like this dreadful post. Of course automatic concatenation is faster than using the + operator, its’s done at compile time, not run time!!!

  • Thanks for this.

    I wonder what the below performance would be:

    z = ”.join((x,y))

    Which I use a lot when joining strings. Especially when more than two are involved.

    • I’m going to guess that str.join is going to be slower than +, because it’s a method call. I’ll try to cook up a quick benchmark and post it here, unless someone beats me to it.

      • Mark Lawrence says:

        Please don’t waste your and other peoples’ time, as it is quite clear that you haven’t got the faintest idea what you’re talking about when it comes to Python.

        • I’m not quite sure how to respond to this comment, other than to say that I’ve been using Python for 20 years, teaching it for about 10, and writing about it for nearly that long. I make tons of mistakes, and very often learn from the people with whom I interact, or are nice enough to point out the flaws in my thinking. I’d be delighted to get such feedback from you, too.

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