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? Thanks for any tips, John.