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Python NumPy (Page: 4)
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 NumPy creating;
 NumPy array reshaping;
 Images with NumPy;
 NumPy copy;
 NumPy mask;
 Geometry;
NumPy shallow copy (vew)
To make view of NumPy or "view" use method .view(). It will create view to the original array, or shallow copy. It will be a different structure but the elements will be the same.
import numpy as np
t1 = np.array([1, 2, 3, 4, 5, 6])
v1 = t1.view()
print("id, v1 =", id(v1), v1) # id, v1 = 140499941424864 [1 2 3 4 5 6]
print("id, t1 =", id(t1), t1) # id, t1 = 140499941424384 [1 2 3 4 5 6]
print("v1 is t1:", v1 is t1) # False
print("v1.base is t1:", v1.base is t1) # True
v1[2]='33'
print("v1 =", v1) # v1 = [ 1 2 33 4 5 6]
print("t1 =", t1) # t1 = [ 1 2 33 4 5 6]
v1.shape = 2,3
print("v1 =", v1) # v1 = [[ 1 2 33] [ 4 5 6]]
print("t1 =", t1) # t1 = [ 1 2 33 4 5 6]
v1[1][0] = '44'
print("v1 =", v1) # v1 = [[ 1 2 33] [ 44 5 6]]
print("t1 =", t1) # t1 = [ 1 2 33 44 5 6]
NumPy deep copy
To make a proper, deep copy of NumPy arrray use methof .copy()  which is a bit different from copy.copy() method. This numpy.copy() method will create fully independent copy.
import numpy as np
t2 = np.array([1, 2, 3, 4, 5, 6])
v2 = t2.copy()
print("v2.base is t2:", v2.base is t2) # False
v2[2] = 33
print("v2 =", v2) # v2 = [ 1 2 33 4 5 6]
print("t2 =", t2) # t2 = [ 1 2 3 4 5 6]
t3 = np.array([[1, 2], [3, 4], [5, 6]])
v3 = t3.copy()
v3[1][1] = 44
print("v3 =", v3) # v3 = [[ 1 2] [ 3 44] [ 5 6]]
print("t3 =", t3) # t3 = [[1 2] [3 4] [5 6]]
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Published: 20211004 11:48:19 Updated: 20211114 08:41:55

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