Question

I would like to take an existing array with several named fields, and create a new array (or change it in place) with one field with a hierarchical dtype equal to the original dtype. That is,

newarray = np.array(oldarray, dtype=[('old',oldarray.dtype)])

such that newarray['old'] is identical in shape and structure to oldarray

Here’s an example:

In [1]: import numpy as np

In [2]: dt = np.dtype([('name',np.str_,2),('val',np.float_)])

In [3]: constants = np.array([('pi',3.14),('e',2.72)],dtype=dt)

In [4]: constants
Out[4]: 
array([('pi', 3.14), ('e', 2.72)], 
      dtype=[('name', '|S2'), ('val', '<f8')])

In [5]: numbers = constants.astype([('constants',dt)])

But this gives me all zeros:

In [6]: numbers
Out[6]: 
array([(('', 0.0),), (('', 0.0),)], 
      dtype=[('constants', [('name', '|S2'), ('val', '<f8')])])

I have the same problem if I try to make a copy:

In [7]: numbers = np.array(constants,dtype=[('constants',dt)])

In [8]: numbers
Out[8]: 
array([(('', 0.0),), (('', 0.0),)], 
      dtype=[('constants', [('name', '|S2'), ('val', '<f8')])])

Also: Does anybody know why this is happening?

Answer 1

You can take a view of the original array with the new dtype (http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.view.html):

>>> import numpy as np
>>> dt = np.dtype([('name',np.str_,2),('val',np.float_)])
>>> constants = np.array([('pi',3.14),('e',2.72)],dtype=dt)
>>> 
>>> numbers = constants.view([('constants',dt)])
>>> 
>>> numbers['constants']
array([('pi', 3.14), ('e', 2.72)], 
      dtype=[('name', '|S2'), ('val', '<f8')])

Be aware that the resulting array numbers is a view of the origingal array, so changes in one of them will also affect the other.