On Thu, Jul 18, 2013 at 10:49 PM, Yaroslav Halchenko <lists@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