Array operations on heterogeneous numpy arrays

Question

I need an efficient heterogeneous array in which the first element is an int and the rest are floats. After creating it, however, basic array operations are exploding.

A = np.zeros(1, dtype='i4, f4, f4')
B = np.array([3,3,3])
A + B
TypeError: invalid type promotion

Answer 1

With a structured array like this, operations that call for iteration over the fields generally don't work.

Even adding A to itself does not work:

In [476]: A = np.zeros(1, dtype='i4, f4, f4')

In [477]: A+A
...
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype([('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4')]) ....

In other words, there's a way of adding an int to an int, a float to an int, but not a way of adding a A element to another element.

An element of A is a tuple or a numpy.void (depending on how you access it)

In [478]: A.item()
Out[478]: (0, 0.0, 0.0)

In [479]: type(A.item())
Out[479]: tuple

In [480]: type(A[0])
Out[480]: numpy.void

To work across the fields of a structured array you usually have to iterate over the field names.

In [493]: B=np.arange(3)

In [494]: for i,name in enumerate(A.dtype.names):
   A[name] = A[name]+B[i]
   .....: 

In [495]: A
Out[495]: 
array([(0, 1.0, 2.0)], 
      dtype=[('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4')])

If all the fields have the same type, e.g. dtype='i4, i4, i4', then it is possible to view the structured array as a homogeneous dtype, and perform regular math on it. But with your mix of floats and ints, that isn't possible.