Why Is It That The Numpy Array Column Data Type Does Not Get Updated?

nd2values[:,[1]]=nd2values[:,[1]].astype(int) nd2values outputs array([['021fd159b55773fba8157e2090fe0fe2', '1', '881f83d2dee3f18c7d1751659406144e', '012059d397c0b

Solution 1:

A structured array alternative:

A copy-n-paste from the question gives me a (6,5) array with U32 dtype:

In[96]: arr.shapeOut[96]: (6, 5)

define a compound dtype:

In [99]: dt = np.dtype([('f0','U32'),('f1',int),('f2','U32'),('f3','U32'),('f4','U1')])

Input to a structured array should be a list of tuples:

In [100]: arrS = np.array([tuple(x) for x in arr], dt)
In [101]: arrS
Out[101]: 
array([('021fd159b55773fba8157e2090fe0fe2', 1, '881f83d2dee3f18c7d1751659406144e', '012059d397c0b7e5a30a5bb89c0b075e', 'A'),
       ('021fd159b55773fba8157e2090fe0fe2', 1, 'cec898a1d355dbfbad8c760615fde1af', '012059d397c0b7e5a30a5bb89c0b075e', 'A'),
       ('021fd159b55773fba8157e2090fe0fe2', 1, 'a99f44bbff39e352191a870e17f04537', '012059d397c0b7e5a30a5bb89c0b075e', 'A'),
       ('fdeb2950c4d5209d449ebd2d6afac11e', 4, '4f4e47023263931e1445dc97f7dae941', '3cd0b15957ceb80f5125bef8bd1bbea7', 'A'),
       ('fdeb2950c4d5209d449ebd2d6afac11e', 4, '021dabc5d7a1404ec8ad34fe8ca4b5e3', '3cd0b15957ceb80f5125bef8bd1bbea7', 'A'),
       ('fdeb2950c4d5209d449ebd2d6afac11e', 4, 'f79a2b5e6190ac3c534645e806f1b611', '3cd0b15957ceb80f5125bef8bd1bbea7', 'A')],
      dtype=[('f0', '<U32'), ('f1', '<i8'), ('f2', '<U32'), ('f3', '<U32'), ('f4', '<U1')])

One field can be accessed by name:

In[102]: arrS['f1']Out[102]: array([1, 1, 1, 4, 4, 4])

Solution 2:

The assignement is casting your ints to the type of the array. To be able to hold all kind of objects in an array set the dtype to object.

nd2values = nd2values.astype(object)

then

nd2values[:,[1]]=nd2values[:,[1]].astype(int)