Efficient String Concatenation in Python. An assessment of the performance of several methods. Introduction. Building long strings in the Python progamming language can sometimes result. In this article I investigate the computational performance of various string concatenation methods.
In. Python the string object is immutable - each time a string is assigned to. As the strings you are manipulating become large this proces becomes increasingly. What other methods are available and how does their performance compare?
I. decided to test several different approaches to constructing very long strings. For this comparison I required a test problem that calls for. Use the concatenate. Using backticks (``) around num. You can accomplish.
I stuck with the backticks for all my methods. If you. need to do lots of concatenations, this is not the right way to go about. Method 2: Mutable. String classdef method. User. String import Mutable.
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String. might think that an append operator on a mutable string would not reallocate. In the test this method performed even worse than method. Examining the source code for User.
String. py I found that the storage for. Mutable. String is just a class member of type string and indeed Mutable. String. doesn't even override the . Concatenations using this class.
4 Writing Structured Programs. By now you will have a sense of the capabilities of the Python programming language for processing natural language.
Mutable. String class methods. Method 3: Character arrraysdef method.
Arrays are. mutable in python, so they can be modified in place without copying the existing. In this case we're not interested in changing existing array. We just want to add new array elements at the end of the array. Method 4: Build a list of strings, then join itdef method. Not too many languages.
If you find that offensive. Obviously it's easy to append to a file - you simply.
There is a similar. String. IO, but that's implemented in python whereas c. String. IO. is in C. It should be pretty speedy. Using this object we can build our string.
Interestingly enough string objects in Java are immutable just like python. This is a bit more powerful.
String. IO or the array approach, because it supports. Method 6: List comprehensionsdef method. I'll spoil the surprise and tell you it's also the fastest. It's extremely compact, and. Create a list of numbers using a list comprehension. Couldn't be simpler than that. This is really. just an abbreviated version of Method 4, and it consumes pretty much the same.
It's faster though because we don't have to call the list. Results. I wanted to look at both the length.
Python. interpreter during the computation. Although memory is cheap, there are a. The python program. For example in a shared web.
Typically the kernel will kill a process whose allocated memory exceeds. That would. unfortunate for a long- lived server process. So in those cases keeping memory.
The other reason is that when. Then performance.
It doesn't matter if you find the fastest algorithm. If we. use an algorithm that uses less memory, the chances of paging are reduced and. I tried each method of the methods as a separate test using it's own python process. I ran these tests.
Python. 2. 2. 1 on a 4. MHz PII Celeron under Free. BSD 4. 9. Results: Twenty thousand integers. In the first test 2. This is a much more serious test and we start to see. They copy the entire source string on each append operation, so their. O(n^2). It would take many minutes to concatenate a.
Comparing the results on this test to our previous graph, notice that. Clearly python. is doing a great job of storing the array efficiently and garbage. The performance of Method 4 is more than twenty times better than naive appending in the 2. We can guess that if we. Method 5 would surpass Method.
Notice also the differences in process sizes. At the end of the. Method 6 the interpreter is using 2.
B of memory, eight. Methods 3 and 5 uses less than half. Conclusions. I would use Method 6 in most real programs. It's fast and it's easy to understand.
Sometimes that's just not convenient. In those cases you can pick between Method.
Method 4 wins for flexibility. You can use all of the normal slice operations. It's uses less memory than either of the. If you're. doing a lot of string appending c. String. IO is the way to go. Measurement techniques.
Measuring the time taken to execute each method was relatively easy. I used. the Python library timing module to measure elapsed time. I didn't attempt. CPU time used by the Python process as opposed to other processes.
I don't think this would make much. Measuring memory used was a little trickier. Python doesn't currently provide. I instead used the Unix. Since process size varies. I wanted to measure the maximum allocated memory. To do that. I ran the 'ps' process right as the computation finishes.
The call to ps. I ran code slightly modified. My implementation of ps. I don't think this. Armin Rigo has recently. I find this argument compelling from a language design. I have no idea how hard. Future Work. I'd love to do a comparison of other programming languages on this same.
Revisions. 2. 00. Edited and re- ordered sections. Original version.