I wondered what the performance would be and tested the following code:
#!/usr/bin/env python3
import random
import time
random.seed( hash('Testing Keys') )
lt_calls = 0
key_calls = 0
class MyObject:
def __init__( self, value1, value2 ):
self.value1 = value1
self.value2 = value2
def __lt__(self, other):
global lt_calls
lt_calls +=1
if self.value1 < other.value1:
return True
else:
return self.value2 < other.value2
def key(self):
global key_calls
key_calls +=1
return self.value1, self.value2
lt_list = []
random
for value1 in reversed(range(10000)):
value2 = value1 - 50
lt_list.append( MyObject( value1, value2 ) )
random.shuffle( lt_list )
key_list = lt_list[:]
print( len(lt_list) )
s = time.time()
key_list.sort( key=MyObject.key )
e = time.time() - s
print( 'key %.6fs %6d calls' % (e, key_calls) )
s = time.time()
lt_list.sort()
e = time.time() - s
print( ' lt %.6fs %6d calls' % (e, lt_calls) )
it outputs this for my with python 3.6.0
10000
key 0.010628s 10000 calls
lt 0.053690s 119886 calls
It seems that providing a key is ~5 times faster the depending on __lt__.
(I even used a short circuit to se if __lt__ could beat key).
I often have more then one way to sort an object. Its easy for me to provide a
set
of key functions that meet the needs of each sort context. I'm not sure what
extra
value the __sort_key__ would offer over providing a key method as I did.
Barry
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