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# dpifke/counter_test.py

Created Mar 29, 2012
Performance comparison of defaultdict vs. Counter and tuple vs. namedtuple in Python
 #!/usr/bin/python # # Copyright (c) 2012 Dave Pifke. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # # This is a simple performance test of different methods for counting the # number of occurrences of a series of values. def values(): """ Returns a tuple containing four random values: an integer between 0 and 512, a boolean, an integer between 0 and 256, and a boolean, respectively. """ from random import randint return (randint(0, 512), bool(randint(0, 1)), randint(0, 256), bool(randint(0 , 1))) def nested_defaultdict(n): """ Returns a series of nested defaultdict objects, four deep. The value of the innermost dict is the number of occurrences of the keys that got us there. """ from collections import defaultdict from functools import partial counts = defaultdict( partial(defaultdict, partial(defaultdict, partial(defaultdict, int)))) for i in range(n): a, b, c, d = values() counts[a][b][c][d] += 1 return counts def tuple_defaultdict(n): """ Returns a defaultdict where the key is a tuple of the input values and the value is the number of occurrences. """ from collections import defaultdict counts = defaultdict(int) for i in range(n): a, b, c, d = values() counts[(a, b, c, d)] += 1 return counts def namedtuple_defaultdict(n): """ Returns a defaultdict where the key is a namedtuple of the input values and the value is the number of occurrences. """ from collections import namedtuple, defaultdict counts = defaultdict(int) Key = namedtuple('Key', 'a b c d') for i in range(n): a, b, c, d = values() counts[Key(a, b, c, d)] += 1 return counts def tuple_counter_update(n): """ Returns a Counter, keyed using a tuple. Uses Counter.update(). """ from collections import Counter counts = Counter() for i in range(n): a, b, c, d = values() counts.update((a, b, c, d)) return counts def tuple_counter_incr(n): """ Returns a Counter, keyed using a tuple. Uses Counter()[value] += 1. """ from collections import Counter counts = Counter() for i in range(n): a, b, c, d = values() counts[(a, b, c, d)] += 1 return counts def namedtuple_counter_update(n): """ Returns a Counter, keyed using a namedtuple. Uses Counter.update() """ from collections import namedtuple, Counter counts = Counter() Key = namedtuple('Key', 'a b c d') for i in range(n): a, b, c, d = values() counts.update(Key(a, b, c, d)) return counts def namedtuple_counter_incr(n): """ Returns a Counter, keyed using a namedtuple. Uses Counter()[value] += 1. """ from collections import namedtuple, Counter counts = Counter() Key = namedtuple('Key', 'a b c d') for i in range(n): a, b, c, d = values() counts[Key(a, b, c, d)] += 1 return counts if __name__ == '__main__': from timeit import Timer funcs = [nested_defaultdict, tuple_defaultdict, namedtuple_defaultdict, tuple_counter_update, tuple_counter_incr, namedtuple_counter_update, namedtuple_counter_incr] # Credit to Raymond Hettinger for the following: setup = 'from __main__ import %s' % ', '.join([x.__name__ for x in funcs]) for func in funcs: stmt = '%s(%d)' % (func.__name__, 1000) print(func.__name__, min(Timer(stmt, setup).repeat(7, 20))) # eof

### dpifke commented Mar 29, 2012

 Using CPython, tuples are slightly more efficient than nesting and namedtuples, and Counter is significantly slower than defaultdict: ``````dave@anarchy:~\$ python --version Python 2.7.2+ dave@anarchy:~\$ python counter_test.py nested_defaultdict 0.205599069595 tuple_defaultdict 0.188335895538 namedtuple_defaultdict 0.219454050064 tuple_counter 0.295751094818 namedtuple_counter 0.336565971375 `````` Using PyPy, namedtuples perform significantly worse than regular tuples, but Counter and defaultdict are about equivalent: ``````dave@anarchy:~\$ pypy --version Python 2.7.2 (2346207d99463f299f09f3e151c9d5fa9158f71b, Feb 15 2012, 17:02:59) [PyPy 1.8.0 with GCC 4.6.1] dave@anarchy:~\$ pypy counter_test.py nested_defaultdict 0.031476020813 tuple_defaultdict 0.0274238586426 namedtuple_defaultdict 0.159631967545 tuple_counter 0.0281150341034 namedtuple_counter 0.686085939407 `````` Interesting that both the best and the worst results are using PyPy.

### dpifke commented Mar 30, 2012

 ...and using CPython 3.2 (after making some syntax changes): ``````dave@anarchy:~\$ python3 --version Python 3.2.2 dave@anarchy:~\$ python3 counter_test.py nested_defaultdict 0.29567694664001465 tuple_defaultdict 0.2673780918121338 namedtuple_defaultdict 0.3005399703979492 tuple_counter 0.3803229331970215 namedtuple_counter 0.4219701290130615 `````` Not sure why this is slower than 2.7 across the board, but it is.

### dpifke commented Mar 30, 2012

 Thinking that perhaps `Counter.update()` versus `counts[value] += 1` was an unfair comparison, I added timings for each. New results: ``````dave@anarchy:~\$ python counter_test.py ('nested_defaultdict', 0.20764708518981934) ('tuple_defaultdict', 0.18820714950561523) ('namedtuple_defaultdict', 0.21594595909118652) ('tuple_counter_update', 0.29559993743896484) ('tuple_counter_incr', 0.1953890323638916) ('namedtuple_counter_update', 0.33496999740600586) ('namedtuple_counter_incr', 0.22653508186340332) dave@anarchy:~\$ pypy counter_test.py ('nested_defaultdict', 0.032904863357543945) ('tuple_defaultdict', 0.02780008316040039) ('namedtuple_defaultdict', 0.1599569320678711) ('tuple_counter_update', 0.02878713607788086) ('tuple_counter_incr', 0.021777868270874023) ('namedtuple_counter_update', 0.6833841800689697) ('namedtuple_counter_incr', 0.14212298393249512) dave@anarchy:~\$ python3 counter_test.py nested_defaultdict 0.3038370609283447 tuple_defaultdict 0.27263712882995605 namedtuple_defaultdict 0.3053758144378662 tuple_counter_update 0.38199901580810547 tuple_counter_incr 0.28246116638183594 namedtuple_counter_update 0.4246039390563965 namedtuple_counter_incr 0.31453800201416016 `````` `update()` is slower across the board, which seems counter-intuitive.

### pswaminathan commented Jul 14, 2013

 Hi, thanks for performing these benchmarks. A Google search for performance of python defaultdict vs counter led me here. Out of curiosity, why did you choose to import `defaultdict` and `Counter` in the function instead of using it as a setup? I'd be curious to see what the differences there are, as importing them as a setup rather than as part of the function could have a significant impact on both the overall times and the relative times.