Extract Python 'native' Values From Numpy Structured Array

I have a structured numpy array. The numpy structure matches the type google.protobuf.Timestamp. I need to extract the seconds int64 and the nanos int32 from each element of said s

Solution 1:

So

In [112]: x_values
Out[112]: 
array([(  0,  0), ( 50,  5), (100, 10)], 
      dtype=[('seconds', '<i8'), ('nanos', '<i4')])

I don't usually recommend using nditer unless you need special behavior. Simple iteration on the array (rows if 2d) is usually all you need. But to better understand what is happening, lets compare the iteration methods:

In [114]: for elem in np.nditer(x_values):
     ...:     print(elem, elem.dtype)
     ...:     print(type(elem))   
(0, 0) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.ndarray'>
(50, 5) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.ndarray'>
(100, 10) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.ndarray'>

In [115]: for elem in x_values:
     ...:     print(elem, elem.dtype)
     ...:     print(type(elem))
(0, 0) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.void'>
(50, 5) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.void'>
(100, 10) [('seconds', '<i8'), ('nanos', '<i4')]
<class 'numpy.void'>

Same except the type is different, np.ndarray v. np.void. It's easier to modify the nditer variable.

Do the same but looking at one field:

In [119]: for elem in np.nditer(x_values):
     ...:     print(elem['seconds'], type(elem['seconds']))   
0 <class 'numpy.ndarray'>
50 <class 'numpy.ndarray'>
100 <class 'numpy.ndarray'>

In [120]: for elem in x_values:
     ...:     print(elem['seconds'], type(elem['seconds']))
0 <class 'numpy.int64'>
50 <class 'numpy.int64'>
100 <class 'numpy.int64'>

I don't have the protobuf code, but I suspect

ts2.seconds = elem['seconds']

will work better with the 2nd iteration, the one that produces np.int64 values. Or add elem['seconds'].item().