Re: Why does numpy.array(a[0],b[0]) have this meaning?
Rick Giuly wrote: Hello All, Case 1 This generates an error, which makes sense because the argument should be a list of numbers: numpy.array(10,10) Case 2 This does not generate an error and the result is an array with a single element: a = numpy.array([10]) b = numpy.array([10]) numpy.array(a[0],b[0]) The only different I see here between the numpy.array call in the cases is that a[0] is a numpy int32 10 is an int Why would this minor difference in integer types cause a totally different result for the two cases - or is something else causing the difference in results? The second argument is for a dtype. Basically, we'll accept anything there that can be coerced to a dtype using numpy.dtype(). For some reason, we have an undocumented feature where dtype(some_array_or_numpy_scalar) will return the dtype of that value. Plain Python ints and floats don't have a dtype attached to them, so we raise an exception. If you have more numpy questions, please join us on the numpy-discussion mailing list. http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
Re: Why does numpy.array(a[0],b[0]) have this meaning?
On Sun, 02 Nov 2008 23:26:24 -0800, Rick Giuly wrote: > Case 1 > This generates an error, which makes sense because the argument should > be a list of numbers: > numpy.array(10,10) > > Case 2 > This does not generate an error and the result is an array with a single > element: > a = numpy.array([10]) > b = numpy.array([10]) > numpy.array(a[0],b[0]) > > The only different I see here between the numpy.array call in the cases > is that > a[0] is a numpy int32 > 10 is an int > > Why would this minor difference in integer types cause a totally > different result for the two cases - or is something else causing the > difference in results? >From the `numpy.array` docstring: Inputs: object - an array, any object exposing the array interface, any object whose __array__ method returns an array, or any (nested) sequence. And `numpy.int32` instances have an `__array__()` method: In [225]: ten = numpy.int32(10) In [226]: ten.__array__() Out[226]: array(10) Ciao, Marc 'BlackJack' Rintsch -- http://mail.python.org/mailman/listinfo/python-list
Re: Why does numpy.array(a[0],b[0]) have this meaning?
just a shot in the dark. second arg to numpy.array is of type see numpy.array.__doc__ -nop On Mon, Nov 3, 2008 at 1:26 AM, Rick Giuly <[EMAIL PROTECTED]> wrote: > Hello All, > > Case 1 > This generates an error, which makes sense because the argument should > be a list of numbers: > numpy.array(10,10) > > Case 2 > This does not generate an error and the result is an array with a > single element: > a = numpy.array([10]) > b = numpy.array([10]) > numpy.array(a[0],b[0]) > > The only different I see here between the numpy.array call in the > cases is that > a[0] is a numpy int32 > 10 is an int > > Why would this minor difference in integer types cause a totally > different result for the two cases - or is something else causing the > difference in results? > > > -rick > > P.S. > I am aware that numpy.array([10,10]) will work, but I'm trying to > understand what is going on syntactically/semantically in the two > cases above. > > > -- > http://mail.python.org/mailman/listinfo/python-list > -- http://mail.python.org/mailman/listinfo/python-list
Why does numpy.array(a[0],b[0]) have this meaning?
Hello All, Case 1 This generates an error, which makes sense because the argument should be a list of numbers: numpy.array(10,10) Case 2 This does not generate an error and the result is an array with a single element: a = numpy.array([10]) b = numpy.array([10]) numpy.array(a[0],b[0]) The only different I see here between the numpy.array call in the cases is that a[0] is a numpy int32 10 is an int Why would this minor difference in integer types cause a totally different result for the two cases - or is something else causing the difference in results? -rick P.S. I am aware that numpy.array([10,10]) will work, but I'm trying to understand what is going on syntactically/semantically in the two cases above. -- http://mail.python.org/mailman/listinfo/python-list
