On Sat, Sep 17, 2011 at 10:50 PM, Bruce Southey <bsout...@gmail.com> wrote:
> On Sat, Sep 17, 2011 at 4:12 PM, Wes McKinney <wesmck...@gmail.com> wrote:
>> On Sat, Sep 17, 2011 at 4:48 PM, Skipper Seabold <jsseab...@gmail.com> wrote:
>>> Just ran into this. Any objections for having numpy.std and other
>>> functions in core/fromnumeric.py call asanyarray before trying to use
>>> the array's method? Other data structures like pandas and larry define
>>> their own std method, for instance, and this doesn't allow them to
>>> pass through. I'm inclined to say that the issue is with numpy, though
>>> maybe the data structures shouldn't shadow numpy array methods while
>>> altering the signature. I dunno.
>>>
>>> df = pandas.DataFrame(np.random.random((10,5)))
>>>
>>> np.std(df,axis=0)
>>> <snip>
>>> TypeError: std() got an unexpected keyword argument 'dtype'
>>>
>>> np.std(np.asanyarray(df),axis=0)
>>> array([ 0.30883352, 0.3133324 , 0.26517361, 0.26389029, 0.20022444])
>>>
>>> Though I don't think this would work with larry yet.
>>>
>>> Pull request: https://github.com/numpy/numpy/pull/160
>>>
>>> Skipper
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>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
> numpy.std() does accepts array-like which obvious means that
> np.std([1,2,3,5]) works making asanyarray call a total waste of cpu
> time. Clearly pandas is not array-like input (as Wes points out below)
> so an error is correct. Doing this type of 'fix' will have unintended
> consequences when other non-numpy objects are incorrectly passed to
> numpy functions. Rather you should determine why 'array-like' failed
> here IF you think a pandas object is either array-like or a numpy
> object.
No, the reason it is failing is because np.std takes the
EAFP/duck-typing approach:
try:
std = a.std
except AttributeError:
return _wrapit(a, 'std', axis, dtype, out, ddof)
return std(axis, dtype, out, ddof)
Indeed DataFrame has an std method but it doesn't have the same
function signature as ndarray.std.
>
>>
>> Note I've no real intention of making DataFrame fully ndarray-like--
>> but it's nice to be able to type:
>>
>> df.std(axis=0)
>> df.std(axis=1)
>> np.sqrt(df)
>>
>> etc. which works the same as ndarray. I suppose the
>> __array__/__array_wrap__ interface is there largely as a convenience.
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>>
>
> I consider that the only way pandas or any other numpy-derivative to
> overcome this is get into numpy/scipy. After all Travis opened the
> discussion for Numpy 3 which you could still address.
>
> Bruce
> PS Good luck on the ddof thing given the past discussions on it!
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