[Numpy-discussion] Bug in numpy std, etc. with other data structures?

[Wes McKinney]

On Sat, Sep 17, 2011 at 8:36 PM,  <josef.pktd at gmail.com> wrote:
> On Sat, Sep 17, 2011 at 5:12 PM, Wes McKinney <wesmckinn at gmail.com> wrote:
>> On Sat, Sep 17, 2011 at 4:48 PM, Skipper Seabold <jsseabold at 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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>>
>> 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.
>
> I'm a bit worried about the different ddof defaults in cases like
> this. Essentially we will not be able to rely on ddof=0 anymore.
> Different defaults on axis are easy to catch, but having the same
> function call return sometimes ddof=0 and sometimes ddof=1 might make
> for some fun debugging.
>
> Josef
>
>
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Can we lobby for default ddof=1 in NumPy 2.0? Breaking with a
convention like this doesn't make much sense to me.