Consider using literal syntax to create the data structure PTC-W0019
Performance
Minor
4 years ago
—
4 years old
unnecessary
dict call - write as literal 55 setup_kwargs = dict(zip_safe=0)
56else:
57 from distutils.core import setup
58 setup_kwargs = dict() 59
60
61storage_dirs = [ ('storage/ceres/dummy.txt', []), ('storage/whisper/dummy.txt',[]),unnecessary
list call - write as literal175
176 # No bucket of this size exists, create it
177 while nr_points > len(self.buckets):
178 self.buckets.append(list())179
180 # Remove existing metrics from its bucket
181 if nr_points > 1:unnecessary
list call - write as literal155 pop().
156 """
157 def __init__(self, cache):
158 self.buckets = list()159 super(BucketMaxStrategy, self).__init__(cache)
160
161 def choose_item(self):unnecessary
dict call - write as literal 19 elif size > 0:
20 return LRUCache(size)
21 else:
22 return dict() 23
24
25class RuleManager(object):
Description
Using the literal syntax can give minor performance bumps compared to using function calls to create dict, list and tuple.
In [1]: timeit.timeit(stmt="dict()", number=100000000)
Out[1]: 9.560388602000103
In [2]: timeit.timeit(stmt="{}", number=100000000)
Out[2]: 1.685333584000091
In [3]: timeit.timeit(stmt="tuple()", number=100000000)
Out[3]: 4.509182139000131
In [4]: timeit.timeit(stmt="()", number=100000000)
Out[4]: 0.5455615430000762
In [5]: timeit.timeit(stmt="list()", number=100000000)
Out[5]: 7.356000728000254
In [6]: timeit.timeit(stmt="[]", number=100000000)
Out[6]: 1.573771306999788
This is because here, the name dict must be looked up in the global scope in case it has been rebound.
Same goes for the other two types list() and tuple().