On Tue, Oct 1, 2013 at 2:39 PM, Jeff <jeffreb...@gmail.com> wrote: > Just built from numpy master looks ok > > In [7]: pd.__version__ > Out[7]: '0.12.0' > > In [8]: np.__version__ > Out[8]: '1.9.0.dev-74abfa2' > > In [9]: s = pandas.Series([1.0, np.nan]) > > In [10]: np.nansum(s) > Out[10]: 1.0 > > In [11]: type(np.nansum(s)) > Out[11]: numpy.float64
Yeah, I can confirm this... With pandas 0.12.0, this code: np.nansum(pandas.Series([1.0, np.nan])) works correctly using current master (74abfa27), but is broken with current maintenance/1.8.x (f5fe91b9). -n > On Tuesday, October 1, 2013 6:41:26 AM UTC-4, Nathaniel Smith wrote: >> >> On Tue, Oct 1, 2013 at 3:27 AM, Charles R Harris >> <charles...@gmail.com> wrote: >> >>> >> On Mon, Sep 30, 2013 at 5:12 PM, Christoph Gohlke <cgo...@uci.edu >> >>> >> <mailto:cgo...@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 > > -- > You received this message because you are subscribed to the Google Groups > "PyData" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to pydata+unsubscr...@googlegroups.com. > For more options, visit https://groups.google.com/groups/opt_out. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
