On Tue, Jul 13, 2010 at 11:54 AM, John Reid <j.r...@mail.cryst.bbk.ac.uk> wrote: > Hi, > > I have some arrays of various shapes in which I need to set any NaNs to > 0. I have been doing the following: > > a[numpy.where(numpy.isnan(a)] = 0. > > > > as you can see here: > > In [20]: a=numpy.ones(2) > > In [21]: a[1]=numpy.log(-1) > > In [22]: a > Out[22]: array([ 1., NaN]) > > In [23]: a[numpy.where(numpy.isnan(a))]=0. > > In [24]: a > Out[24]: array([ 1., 0.]) > > > > Unfortunately, I've just discovered that when a.shape == () this doesn't > work at all. For example: > > In [41]: a=numpy.array((1.)) > > In [42]: a.shape > Out[42]: () > > In [43]: a[numpy.where(numpy.isnan(a))]=0. > > In [44]: a > Out[44]: array(0.0) > > > > > > but if the shape is (1,), everything is ok: > > In [47]: a=numpy.ones(1) > > In [48]: a.shape > Out[48]: (1,) > > In [49]: a[numpy.where(numpy.isnan(a))]=0. > > In [50]: a > Out[50]: array([ 1.]) > > > > What's the difference between the 2 arrays with different shapes? > > If I pass a scalar into numpy.asarray() why do I get an array of shape > () back? In my case this has caused a subtle bug. > > Is there a better way to set NaNs in an array to 0?
You could make use of np.atleast_1d, and then everything would be canonicalized: In [33]: a = np.array(np.nan) In [34]: a Out[34]: array(nan) In [35]: a1d = np.atleast_1d(a) In [36]: a1d Out[36]: array([ NaN]) In [37]: a Out[37]: array(nan) In [38]: a1d.base is a Out[38]: True In [39]: a1d[np.isnan(a1d)] = 0. In [40]: a1d Out[40]: array([ 0.]) In [41]: a Out[41]: array(0.0) So Keith's nan_replace would be: In [42]: def nan_replace(a, fill=0.0): ....: a_ = np.atleast_1d(a) ....: a_[np.isnan(a_)] = fill ....: > > Thanks for any tips, > John. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion