I have a 48x365 element numpy array where each element is a list containing 3 integers. I want to be able to turn it into a 1x17520 array with all the lists intact as elements. Using
np.reshape(-1)
seems to break the elements into three separate integers and makes a 1x52560 array. So I either need a new way of rearranging the original array or a way of grouping the elements in the new np.reshape array (which are still in order) back into lists of 3.
Thanks for your help.
Is there a reason you can't do it explicitly? As in:
>>> a = numpy.arange(17520 * 3).reshape(48, 365, 3)
>>> a.reshape((17520,3))
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
...,
[52551, 52552, 52553],
[52554, 52555, 52556],
[52557, 52558, 52559]])
You could also do it with -1, it just has to be paired with another arg of the appropriate size.
>>> a.reshape((17520,-1))
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
...,
[52551, 52552, 52553],
[52554, 52555, 52556],
[52557, 52558, 52559]])
or
>>> a.reshape((-1,3))
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
...,
[52551, 52552, 52553],
[52554, 52555, 52556],
[52557, 52558, 52559]])
It occurred to me a bit later that you could also create a record array -- this might be appropriate in some situations:
a = numpy.recarray((17520,), dtype=[('x', int), ('y', int), ('z', int)])
This can be reshaped in the original way you tried, i.e. reshape(-1). Still, as larsmans' comment says, just treating your data as a 3d array is easiest.
