Numpy copy & deep copy

1、 '='的赋值方式会带有关联性

>>> import numpy as np
>>> a = np.arange(4)
>>> b = a
>>> c = a
>>> d = b

>>> a[0] = 11
>>> print(a)
[11  1  2  3]
#改变a的第一个值，b、c、d的第一个值也会同时改变
>>> b is a
True
>>> c is a
True
>>> d is a
True

#同样更改d的值，a、b、c也会改变
>>> d[1:3] = [22, 33]
>>> print(a)
[11 22 33  3]
>>> print(b)
[11 22 33  3]
>>> print(c)
[11 22 33  3]

2、copy()的赋值方式没有关联性

>>> b = a.copy()    # deep copy
>>> print(b)
[11 22 33  3]
>>> a[3] = 44
>>> print(a)
[11 22 33 44]
>>> print(b)#此时a与b已经没有关联
[11 22 33  3]