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| = Python Performance Tips = This page is devoted to various tips and tricks that help improve the performance of your Python programs. Wherever the information comes from someone else, I've tried to identify the source. Python has has changed in some significant ways since I first wrote my "fast python" page in about 1996, which means that some of the orderings will have changed. I migratg it to the Python wiki in hopes others will help maintain it. '''Note:''' You should always test these tips with your application and the version of Python you intend to use and not just blindly accept that one method is faster than another. Also new since this was originally written are packages like [https://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/ Pyrex], [https://psyco.sourceforge.net/ Psyco], [https://www.scipy.org/site_content/weave Weave] and [https://pyinline.sourceforge.net/ PyInline], which can dramatically improve your application's performance by making it easier to push performance-critical code into C or machine language. == Contents == * [#datatype Choose the Right Data Structure (tbd)] * [#sorting Sorting] * [#stringcat String concatenation] * [#loops Loops] * [#dots Avoiding dots...] * [#local Local Variables] * [#initdict Initializing Dictionary Elements] * [#import Import Statement Overhead] * [#aggregate Data Aggregation] * [#periodic Doing Stuff Less Often] * [#notc Python is not C] * [#profiling Profiling Code] * [#profile Profile Module] * [#hotshot Hotshot Profiler (tbd)] * [#trace Trace Module] [[Anchor(datatype)]] == Choose the Right Data Structure == TBD. [[Anchor(sorting)]] == Sorting == From Guido van Rossum <guido@python.org> <mailto:guido@python.org> Sorting lists of basic Python objects is generally pretty efficient. The sort method for lists takes an optional comparison function as an argument that can be used to change the sorting behavior. This is quite convenient, though it can really slow down your sorts. An alternative way to speed up sorts is to construct a list of tuples whose first element is a sort key that will sort properly using the default comparison, and whose second element is the original list element. This is the so-called Schwartzian Transform <https://www.google.com/search?q=Schwartzian+Transform&ie=UTF-8&oe=UTF-8>. Suppose, for example, you have a list of tuples that you want to sort by the n-th field of each tuple. The following function will do that. def sortby(somelist, n): nlist = [(x[n], x) for x in somelist] nlist.sort() return [val for (key, val) in nlist] Matching the behavior of the current list sort method (sorting in place) is easily achieved as well: def sortby_inplace(somelist, n): somelist[:] = [(x[n], x) for x in somelist] somelist.sort() somelist[:] = [val for (key, val) in somelist] return Here's an example use: >>> somelist = [(1, 2, 'def'), (2, -4, 'ghi'), (3, 6, 'abc')] >>> somelist.sort() >>> somelist [(1, 2, 'def'), (2, -4, 'ghi'), (3, 6, 'abc')] >>> nlist = sortby(somelist, 2) >>> sortby_inplace(somelist, 2) >>> nlist == somelist True >>> nlist = sortby(somelist, 1) >>> sortby_inplace(somelist, 1) >>> nlist == somelist True [[Anchor(stringcat)]] == String Concatenation == Strings in Python are immutable. This fact frequently sneaks up and bites novice Python programmers on the rump. Immutability confers some advantages and disadvantages. In the plus column, strings can be used a keys in dictionaries and individual copies can be shared among multiple variable bindings. (Python automatically shares one- and two-character strings.) In the minus column, you can't say something like, "change all the 'a's to 'b's" in any given string. Instead, you have to create a new string with the desired properties. This continual copying can lead to significant inefficiencies in Python programs. Avoid this: s = "" for substring in list: s += substring Use |s = "".join(list)| instead. The former is a very common and catastrophic mistake when building large strings. Similarly, if you are generating bits of a string sequentially instead of: s = "" for x list: s += some_function(x) use slist = [some_function(elt) for elt in somelist] s = "".join(slist) Avoid: out = "<html>" + head + prologue + query + tail + "</html>" Instead, use out = "<html>%s%s%s%s</html>" % (head, prologue, query, tail) Even better, for readability (this has nothing to do with efficiency other than yours as a programmer), use dictionary substitution: out = "<html>%(head)s%(prologue)s%(query)s%(tail)s</html>" % locals() This last two are going to be much faster, especially when piled up over many CGI script executions, and easier to modify to boot. In addition, the slow way of doing things got slower in Python 2.0 with the addition of rich comparisons to the language. It now takes the Python virtual machine a lot longer to figure out how to concatenate two strings. (Don't forget that Python does all method lookup at runtime.) [[Anchor(loops)]] == Loops == Python supports a couple of looping constructs. The |for| statement is most commonly used. It loops over the elements of a sequence, assigning each to the loop variable. If the body of your loop is simple, the interpreter overhead of the |for| loop itself can be a substantial amount of the overhead. This is where the |map <https://python.domainunion.de/doc/lib/built-in-funcs.html>| function is handy. You can think of |map| as a |for| moved into C code. The only restriction is that the "loop body" of |map| must be a function call. Here's a straightforward example. Instead of looping over a list of words and converting them to upper case: newlist = [] for word in oldlist: newlist.append(word.upper()) you can use |map| to push the loop from the interpreter into compiled C code: newlist = map(str.upper, oldlist) List comprehensions were added to Python in version 2.0 as well. They provide a syntactically more compact way of writing the above for loop: newlist = [s.upper() for s in list] It's generally not any faster than the for loop version, however. Guido van Rossum wrote a much more detailed examination of loop optimization <https://python.domainunion.de/doc/essays/list2str.html> that is definitely worth reading. [[Anchor(dots)]] == Avoiding dots... == Suppose you can't use |map| or a list comprehension? You may be stuck with the for loop. The for loop example has another inefficiency. Both |newlist.append| and |word.upper| are function references that are reevaluated each time through the loop. The original loop can be replaced with: upper = str.upper newlist = [] append = newlist.append for word in list: append(upper(word)) This technique should be used with caution. It gets more difficult to maintain if the loop is large. Unless you are intimately familiar with that piece of code you will find yourself scanning up to check the definitions of |append| and |upper|. [[Anchor(local)]] == Local Variables == The final speedup available to us for the non-|map| version of the |for| loop is to use local variables wherever possible. If the above loop is cast as a function, append|| and |upper| become local variables. Python accesses local variables much more efficiently than global variables. def func(): upper = str.upper newlist = [] append = newlist.append for word in words: append(upper(word)) return newlist At the time I originally wrote this I was using a 100MHz Pentium running BSDI. I got the following times for converting the list of words in |/usr/share/dict/words| (38,470 words at that time) to upper case: Version Time (seconds) Basic loop 3.47 Eliminate dots 2.45 Local variable & no dots 1.79 Using |map| function 0.54 Eliminating the loop overhead by using |map| is often going to be the most efficient option. When the complexity of your loop precludes its use other techniques are available to speed up your loops, however. [[Anchor(initdict)]] == Initializing Dictionary Elements == Suppose you are building a dictionary of word frequencies and you've already broken your text up into a list of words. You might execute something like: wdict = {} has_key = wdict.has_key for word in words: if not has_key(word): wdict[word] = 0 wdict[word] = wdict[word] + 1 Except for the first time, each time a word is seen the |if| statement's test fails. If you are counting a large number of words, many will probably occur multiple times. In a situation where the initialization of a value is only going to occur once and the augmentation of that value will occur many times it is cheaper to use a |try| statement: wdict = {} for word in words: try: wdict[word] += 1 except KeyError: wdict[word] = 1 It's important to catch the expected KeyError exception, and not have a default |except| clause to avoid trying to recover from an exception you really can't handle by the statement(s) in the |try| clause. A third alternative became available with the release of Python 2.x. Dictionaries now have a get() method which will return a default value if the desired key isn't found in the dictionary. This simplifies the loop: wdict = {} for word in words: wdict[word] = wdict.get(word, 0) + 1 When I originally wrote this section, there were clear situations where one of the first two approaches was faster. It seems that all three approaches now exhibit similar performance (within about 10% of each other), more or less independent of the properties of the list of words. [[Anchor(import)]] == Import Statement Overhead == |import| statements can be executed just about anywhere. It's often useful to place them inside functions to restrict their visibility and/or reduce initial startup time. Although Python's interpreter is optimized to not import the same module multiple times, repeatedly executing an import statement can seriously affect performance in some circumstances. Consider the following two snippets of code (originally from Greg McFarlane, I believe - I found it unattributed in a comp.lang.python <news:comp.lang.python>/python-list@python.org <mailto:python-list@python.org> posting and later attributed to him in another source): def doit1(): import string ###### import statement inside function string.lower('Python') for num in range(100000): doit1() or: import string ###### import statement outside function def doit2(): string.lower('Python') for num in range(100000): doit2() |doit2| will run much faster than |doit1|, even though the reference to the string module is global in |doit2|. Here's a Python interpreter session run using Python 2.3 and the new |timeit| module, which shows how much faster the second is than the first: >>> def doit1(): ... import string ... string.lower('Python') ... >>> import string >>> def doit2(): ... string.lower('Python') ... >>> import timeit >>> t = timeit.Timer(setup='from __main__ import doit1', stmt='doit1()') >>> t.timeit() 11.479144930839539 >>> t = timeit.Timer(setup='from __main__ import doit2', stmt='doit2()') >>> t.timeit() 4.6661689281463623 String methods were introduced to the language in Python 2.0. These provide a version that avoids the import completely and runs even faster: def doit3(): 'Python'.lower() for num in range(100000): doit3() Here's the proof from |timeit|: >>> def doit3(): ... 'Python'.lower() ... >>> t = timeit.Timer(setup='from __main__ import doit3', stmt='doit3()') >>> t.timeit() 2.5606080293655396 The above example is obviously a bit contrived, but the general principle holds. [[Anchor(aggregate)]] == Data Aggregation == Function call overhead in Python is relatively high, especially compared with the execution speed of a builtin function. This strongly suggests that where appropriate, functions should handle data aggregates. Here's a contrived example written in Python. import time x = 0 def doit1(i): global x x = x + i list = range(100000) t = time.time() for i in list: doit1(i) print "%.3f" % (time.time()-t) vs. import time x = 0 def doit2(list): global x for i in list: x = x + i list = range(100000) t = time.time() doit2(list) print "%.3f" % (time.time()-t) Here's the proof in the pudding using an interactive session: >>> t = time.time() >>> doit2(list) >>> print "%.3f" % (time.time()-t) 0.204 >>> t = time.time() >>> for i in list: ... doit1(i) ... >>> print "%.3f" % (time.time()-t) 0.758 Even written in Python, the second example runs about four times faster than the first. Had |doit| been written in C the difference would likely have been even greater (exchanging a Python |for| loop for a C |for| loop as well as removing most of the function calls). [[Anchor(periodic)]] == Doing Stuff Less Often == The Python interpreter performs some periodic checks. In particular, it decides whether or not to let another thread run and whether or not to run a pending call (typically a call established by a signal handler). Most of the time there's nothing to do, so performing these checks each pass around the interpreter loop can slow things down. There is a function in the |sys| module, |setcheckinterval|, which you can call to tell the interpreter how often to perform these periodic checks. Prior to the release of Python 2.3 it defaulted to 10. In 2.3 this was raised to 100. If you aren't running with threads and you don't expect to be catching many signals, setting this to a larger value can improve the interpreter's performance, sometimes substantially. [[Anchor(notc)]] == Python is not C == It is also not Perl, Java, C++ or Haskell. Be careful when transferring your knowledge of how other languages perform to Python. A simple example serves to demonstrate: % timeit.py -s 'x = 47' 'x * 2' 1000000 loops, best of 3: 0.574 usec per loop % timeit.py -s 'x = 47' 'x << 1' 1000000 loops, best of 3: 0.524 usec per loop % timeit.py -s 'x = 47' 'x + x' 1000000 loops, best of 3: 0.382 usec per loop Now consider the similar C programs (only the add version is shown): #include <stdio.h> int main (int argc, char **argv) { int i = 47; int loop; for (loop=0; loop<500000000; loop++) i + i; } and the execution times: % for prog in mult add shift ; do < for i in 1 2 3 ; do < echo -n "$prog: " < /usr/bin/time ./$prog < done < echo < done mult: 6.12 real 5.64 user 0.01 sys mult: 6.08 real 5.50 user 0.04 sys mult: 6.10 real 5.45 user 0.03 sys add: 6.07 real 5.54 user 0.00 sys add: 6.08 real 5.60 user 0.00 sys add: 6.07 real 5.58 user 0.01 sys shift: 6.09 real 5.55 user 0.01 sys shift: 6.10 real 5.62 user 0.01 sys shift: 6.06 real 5.50 user 0.01 sys Note that there is a significant advantage in Python to adding a number to itself instead of multiplying it by two or shifting it left by one bit. In C on all modern computer architectures, each of the three arithmetic operations are translated into a single machine instruction which executes in one cycle, so it doesn't really matter which one you choose. A common "test" new Python programmers often perform is to translate the common Perl idiom while (<>) { print; } into Python code that looks something like #!/usr/bin/env python import fileinput for line in fileinput.input(): print line, and use it to conclude that Python must be much slower than Perl. As others have pointed out numerous times, Python is slower than Perl for some things and faster for others. Relative performance also often depends on your experience with the two languages. [[Anchor(profiling)]] == Profiling Code == The first step to speeding up your program is learning where the bottlenecks lie. It hardly makes sense to optimize code that is never executed or that already runs fast. I use two modules to help locate the hotspots in my code, profile and trace. In later examples I also use the |timeit| module, which is new in Python 2.3. [[Anchor(profile)]] === Profile Module === The profile module <https://python.domainunion.de/doc/current/lib/module-profile.html> is included as a standard module in the Python distribution. Using it to profile the execution of a set of functions is quite easy. Suppose your main function is called |main|, takes no arguments and you want to execute it under the control of the profile module. In its simplest form you just execute import profile profile.run('main()') When |main()| returns, the profile module will print a table of function calls and execution times. The output can be tweaked using the Stats class included with the module. In Python 2.4 profile will allow the time consumed by Python builtins and functions in extension modulesto be profiled as well. [[Anchor(hotshot)]] === Hotshot Module === New in Python 2.2, the hotshot package <https://python.domainunion.de/doc/current/lib/module-hotshot.html> is intended as a replacement for the profile module. The underlying module is written in C, so using hotshot should result in a much smaller performance hit, and thus a more accurate idea of how your application is performing. There is also a |hotshotmain.py| program in the distributions |Tools/scripts| directory which makes it easy to run your program under hotshot control from the command line. [[Anchor(trace)]] === Trace Module === The trace module is a spin-off of the profile module I wrote originally to perform some crude statement level test coverage. It's been heavily modified by several other people since I released my initial crude effort. As of Python 2.0 you should find trace.py in the Tools/scripts directory of the Python distribution. Starting with Python 2.3 it's in the standard library (the Lib directory). You can copy it to your local bin directory and set the execute permission, then execute it directly. It's easy to run from the command line to trace execution of whole scripts: % trace.py -t spam.py eggs There's no separate documentation, but you can execute "pydoc trace" to view the inline documentation. |
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