[Numpy-discussion] ufunc oddities

Robert Kern robert.kern@gmail....
Sat May 24 21:48:27 CDT 2008


On Sat, May 24, 2008 at 9:46 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
> On Sat, May 24, 2008 at 7:36 PM, Robert Kern <robert.kern@gmail.com> wrote:
>> On Sat, May 24, 2008 at 9:28 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
>>> I think it's interesting how python and numpy bools behave differently.
>>>
>>>>> x = np.array([True, True], dtype=bool)
>>>>> x[0] + x[1]
>>>   True
>>>>> x[0] & x[1]
>>>   True
>>>>>
>>>>> x = [True, True]
>>>>> x[0] + x[1]
>>>   2
>>>>> x[0] & x[1]
>>>   True
>>
>> The difference arises straightforwardly from the principle that numpy
>> tries not to upcast when you do an operation on two arrays of the same
>> dtype; True+True==True is of somewhat more use than True+True==False.
>> Python bools are just ints subclasses to give a nice string
>> representation.
>
> Sounds like there is no perfect solution. I like it the way it is but
> these are differences I never noticed.
>
>>> x = np.array([True, True], dtype=bool)
>>> x.sum()
>   2

Yes, the default accumulator dtype for integer types is at least the
size of the native int type, so we don't have the situation of
"bool+bool=bool".

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
 -- Umberto Eco


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