I want to convert NumPy structured array with columns of the same (np.float) type to unstructured array in Numpy 1.16.0.
Previously I did it like this:
array = np.ones((100,), dtype=[('user', np.object), ('item', np.float), ('value', np.float)])
array[['item','value']].view((np.float, 2))
In 1.16.0 the structured_to_unstructured func appeared at numpy.lib.recfunctions.
But for a view from array with object columns both new structured_to_unstructured and old view-way throws TypeError:
Cannot change data-type for object array.
It works OK for views from a structured array without object columns at all, but crashes if view with only numeric columns made from array containing object field.
With 1.16 there was a major change in the handling of multifield views. You need to use rf.repack_fields to get earlier behavior.
In [277]: import numpy.lib.recfunctions as rf
In [287]: arr = np.ones(3, dtype='O,f,f')
In [288]: arr
Out[288]:
array([(1, 1., 1.), (1, 1., 1.), (1, 1., 1.)],
dtype=[('f0', 'O'), ('f1', '<f4'), ('f2', '<f4')])
In [289]: rf.structured_to_unstructured(arr[['f1','f2']])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-289-8700aa9aacb4> in <module>
----> 1 rf.structured_to_unstructured(arr[['f1','f2']])
/usr/local/lib/python3.6/dist-packages/numpy/lib/recfunctions.py in structured_to_unstructured(arr, dtype, copy, casting)
969 with suppress_warnings() as sup: # until 1.16 (gh-12447)
970 sup.filter(FutureWarning, "Numpy has detected")
--> 971 arr = arr.view(flattened_fields)
972
973 # next cast to a packed format with all fields converted to new dtype
/usr/local/lib/python3.6/dist-packages/numpy/core/_internal.py in _view_is_safe(oldtype, newtype)
492
493 if newtype.hasobject or oldtype.hasobject:
--> 494 raise TypeError("Cannot change data-type for object array.")
495 return
496
TypeError: Cannot change data-type for object array.
repacking before conversion:
In [290]: rf.structured_to_unstructured(rf.repack_fields(arr[['f1','f2']]))
Out[290]:
array([[1., 1.],
[1., 1.],
[1., 1.]], dtype=float32)
Multifield view preserves the underlying data layout. Notice the use of offsets in this display. The object field is still present, just not displayed.
In [291]: arr[['f1','f2']]
Out[291]:
array([(1., 1.), (1., 1.), (1., 1.)],
dtype={'names':['f1','f2'], 'formats':['<f4','<f4'], 'offsets':[8,12], 'itemsize':16})
repack makes a copy that does not include the object field:
In [292]: rf.repack_fields(arr[['f1','f2']])
Out[292]: array([(1., 1.), (1., 1.), (1., 1.)], dtype=[('f1', '<f4'), ('f2', '<f4')])
The view approach has problems even if all fields are float:
In [301]: arr = np.ones(3, dtype='f,f,f')
In [302]: arr[['f1','f2']].view(('f',2))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-302-68433a44bcfe> in <module>
----> 1 arr[['f1','f2']].view(('f',2))
ValueError: Changing the dtype to a subarray type is only supported if the total itemsize is unchanged
In [303]: arr[['f1','f2']]
Out[303]:
array([(1., 1.), (1., 1.), (1., 1.)],
dtype={'names':['f1','f2'], 'formats':['<f4','<f4'], 'offsets':[4,8], 'itemsize':12})
In [304]: rf.repack_fields(arr[['f1','f2']]).view(('f',2))
Out[304]:
array([[1., 1.],
[1., 1.],
[1., 1.]], dtype=float32)