Actually, they are different.
Put a dict.{iter}items() in an O(k^N) algorithm and make it a hundred thousand
entries, and you will feel the difference.
Dict uses hashing to get a value from the dict and this is why it's O(1).
10.08.2012, в 1:21, Tim Chase написал(а):
> On 08/09/12 15:41, Roman Vashkevich wrote:
>> 10.08.2012, в 0:35, Tim Chase написал(а):
>>> On 08/09/12 15:22, Roman Vashkevich wrote:
>>>>> {(4, 5): 1, (5, 4): 1, (4, 4): 2, (2, 3): 1, (4, 3): 2}
>>>>> and i want to print to a file without the brackets comas and semicolon in
>>>>> order to obtain something like this?
>>>>> 4 5 1
>>>>> 5 4 1
>>>>> 4 4 2
>>>>> 2 3 1
>>>>> 4 3 2
>>>>
>>>> for key in dict:
>>>> print key[0], key[1], dict[key]
>>>
>>> This might read more cleanly with tuple unpacking:
>>>
>>> for (edge1, edge2), cost in d.iteritems(): # or .items()
>>> print edge1, edge2, cost
>>>
>>> (I'm making the assumption that this is a edge/cost graph...use
>>> appropriate names according to what they actually mean)
>>
>> dict.items() is a list - linear access time whereas with 'for
>> key in dict:' access time is constant:
>> http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html#use-in-where-possible-1
>
> That link doesn't actually discuss dict.{iter}items()
>
> Both are O(N) because you have to touch each item in the dict--you
> can't iterate over N entries in less than O(N) time. For small
> data-sets, building the list and then iterating over it may be
> faster faster; for larger data-sets, the cost of building the list
> overshadows the (minor) overhead of a generator. Either way, the
> iterate-and-fetch-the-associated-value of .items() & .iteritems()
> can (should?) be optimized in Python's internals to the point I
> wouldn't think twice about using the more readable version.
>
> -tkc
>
>
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