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

Bruce Southey bsouthey at gmail.com
Sat Sep 17 22:50:56 EDT 2011

On Sat, Sep 17, 2011 at 4:12 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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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

> 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.

PS Good luck on the ddof thing given the past discussions on it!

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