On Wed, Aug 24, 2011 at 8:19 PM, Mark Wiebe <mwwi...@gmail.com> wrote: > On Fri, Aug 19, 2011 at 11:37 AM, Bruce Southey <bsout...@gmail.com> wrote: >> >> Hi, >> <snip> >> >> 2) Can the 'skipna' flag be added to the methods? >> >>> a.sum(skipna=True) >> Traceback (most recent call last): >> File "<stdin>", line 1, in <module> >> TypeError: 'skipna' is an invalid keyword argument for this function >> >>> np.sum(a,skipna=True) >> nan > > I've added this now, as well. I think that finishes up the changes you > suggested in this email which felt right to me. > Cheers, > Mark > >> >> <snip> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
Sorry I haven't had a chance to have a tinker yet. My initial observations: - I haven't decided whether this is a problem: In [50]: arr = np.arange(100) In [51]: arr[5:10] = np.NA --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/wesm/<ipython-input-51-7e07a94409e9> in <module>() ----> 1 arr[5:10] = np.NA ValueError: Cannot set NumPy array values to NA values without first enabling NA support in the array I assume when you flip the maskna switch that a mask is created? - Performance with skipna is a bit disappointing: In [52]: arr = np.random.randn(1e6) In [54]: arr.flags.maskna = True In [56]: arr[::2] = np.NA In [58]: timeit arr.sum(skipna=True) 100 loops, best of 3: 7.31 ms per loop this goes down to 2.12 ms if there are no NAs present. but: In [59]: import bottleneck as bn In [60]: arr = np.random.randn(1e6) In [61]: arr[::2] = np.nan In [62]: timeit bn.nansum(arr) 1000 loops, best of 3: 1.17 ms per loop do you have a sense if this gap can be closed? I assume you've been, as you should, focused on a correct implementation as opposed with squeezing out performance. best, Wes _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
