I want change the numpy column data type, but when I to replace the original numpy column, the dtype will not change succesfully.
import numpy as np
arraylist =[(1526869384.273246, 0, 'a0'),
(1526869385.273246, 1, 'a1'),
(1526869386.273246, 2, 'a2'),
(1526869387.273246, 3, 'a3'),
(1526869388.273246, 4, 'a4'),
(1526869389.273246, 5, 'a5'),
(1526869390.273246, 6, 'a6'),
(1526869391.273246, 7, 'a7'),
(1526869392.273246, 8, 'a8'),
(1526869393.273246, 9, 'a9'),
(1526869384.273246, 0, 'a0'),
(1526869385.273246, 1, 'a1'),
(1526869386.273246, 2, 'a2'),
(1526869387.273246, 3, 'a3'),
(1526869388.273246, 4, 'a4'),
(1526869389.273246, 5, 'a5'),
(1526869390.273246, 6, 'a6'),
(1526869391.273246, 7, 'a7'),
(1526869392.273246, 8, 'a8'),
(1526869393.273246, 9, 'a9')]
array = np.array(arraylist)
array.dtype
dtype('<U32')
array[:,0]=array[:,0].astype("float64")
array[:,0].dtype
>>> dtype('<U32')
Event through I changed the dtype of the column, but why I want to replace the orignal column it's still u32?
np.arrayassigns the best commondtypeto the whole array, in this case, a string. Once created that dtype is fixed, and can't be changed by simple assignment. Consider structured arrays or object dtype arrays if you must mix floats and strings. But beware that those come with an increased processing cost.