[Numpy-discussion] the mean, var, std of non-arrays
Skipper Seabold
jsseabold at gmail.com
Thu Jul 18 23:18:49 EDT 2013
On Thu, Jul 18, 2013 at 10:49 PM, Yaroslav Halchenko
<lists at onerussian.com>wrote:
> Hi everyone,
>
> Some of my elderly code stopped working upon upgrades of numpy and
> upcoming pandas: https://github.com/pydata/pandas/issues/4290 so I have
> looked at the code of
>
> 2481 def mean(a, axis=None, dtype=None, out=None, keepdims=False):
> 2482 """
> ...
> 2489 Parameters
> 2490 ----------
> 2491 a : array_like
> 2492 Array containing numbers whose mean is desired. If `a` is
> not an
> 2493 array, a conversion is attempted.
> ...
> 2555 """
> 2556 if type(a) is not mu.ndarray:
> 2557 try:
> 2558 mean = a.mean
> 2559 return mean(axis=axis, dtype=dtype, out=out)
> 2560 except AttributeError:
> 2561 pass
> 2562
> 2563 return _methods._mean(a, axis=axis, dtype=dtype,
> 2564 out=out, keepdims=keepdims)
>
> here 'array_like'ness is checked by a having mean function. Then it is
> assumed
> that it has the same definition as ndarray, including dtype keyword
> argument.
>
> Not sure anyways if my direct numpy.mean application to pandas DataFrame is
> "kosher" -- initially I just assumed that any argument is asanyarray'ed
> first
> -- but I think here catching TypeError for those incompatible .mean's
> would not
> hurt either. What do you think? Similar logic applies to mean cousins
> (var,
> std, ...?) decorated around _methods implementations.
Related? From a while ago.
https://github.com/numpy/numpy/pull/160
Skipper
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