On Tue, Oct 1, 2013 at 3:27 AM, Charles R Harris <charlesr.harris@gmail.com> wrote:
On Mon, Sep 30, 2013 at 5:12 PM, Christoph Gohlke <cgohlke@uci.edu <mailto:cgohlke@uci.edu>> wrote: > NumPy 1.8.0rc1 looks good. All tests pass on Windows and most 3rd party > packages test OK now. Thank you. > > A few tests still fail in the following packages when run with > numpy-MKL-1.8.0rc1-win-amd64-py3.3 compared to > numpy-MKL-1.7.1-win-amd64-py3.3: > > 1) Pandas 0.12.0 > > ``` >
====================================================================== > FAIL: test_nansum_buglet (pandas.tests.test_series.TestNanops) >
---------------------------------------------------------------------- > Traceback (most recent call last): > File "X:\Python33\lib\site-packages\pandas\tests\test_series.py", > line 254, in test_nansum_buglet > assert_almost_equal(result, 1) > File "X:\Python33\lib\site-packages\pandas\util\testing.py", line > 134, in assert_almost_equal > np.testing.assert_(isiterable(b)) > File "D:\Dev\Compile\Test\numpy-build\numpy\testing\utils.py", line > 44, in assert_ > raise AssertionError(msg) > AssertionError > ``` > > Possibly related: > > ``` > >>> import numpy as np > >>> from pandas import Series > >>> s = Series([0.0]) > >>> result = np.nansum(s) > >>> print(result) > Traceback (most recent call last): > File "<stdin>", line 1, in <module> > File "X:\Python33\lib\site-packages\pandas\core\base.py", line > 19, in > __str__ > return self.__unicode__() > File "X:\Python33\lib\site-packages\pandas\core\series.py", line > 1115, in __unicode__ > length=len(self) > 50, > TypeError: len() of unsized object > ``` [...]
The pandas test passes for current pandas dev, so it looks like a bug on their end that has been taken care of.
test_nansum_buglet (__main__.TestNanops) ... ok
I'm concerned about this. 0.12.0 is currently the latest pandas release, so even if it is a bug on their side, we're going to be converting it from a latent bug to a real bug when we release... CC'ing pydata, do you guys have any insight into what changed here? The code is: s = pandas.Series([1.0, np.nan]) result = np.nansum(s) With numpy 1.7.1, 'result' comes out as a np.float64. With numpy maintenance/1.8.x, 'result' comes out as a 0-d Series object. Series is a subclass of ndarray, but it's supposed to always be 1-d, so all kinds of stuff blows up as soon as you have a 0-d Series object. I'm not sure what changed in numpy's nansum; if I try this same test with a simple no-op ndarray subclass: class MyArray(np.ndarray): pass np.nansum(np.array([1.0, np.nan]).view(MyArray)) then 1.7.1 and maintenance/1.8.x both act the same, and both return a 0-d MyArray, so it's not just a question of whether we remembered to handle subclasses at all. -n