详解Python list 与 NumPy.ndarry 切片之间的区别
实例代码：
# list 切片返回的是不原数据，对新数据的修改不会影响原数据 In [45]: list1 = [1, 2, 3, 4, 5] In [46]: list2 = list1[:3] In [47]: list2 Out[47]: [1, 2, 3] In [49]: list2[1] = 1999 # 原数据没变 In [50]: list1 Out[50]: [1, 2, 3, 4, 5] In [51]: list2 Out[51]: [1, 1999, 3] # 而 NumPy.ndarry 的切片返回的是原数据 In [52]: arr = np.array([1, 2, 3, 4, 5]) In [53]: arr Out[53]: array([1, 2, 3, 4, 5]) In [54]: arr1 = arr[:3] In [55]: arr1 Out[55]: array([1, 2, 3]) In [56]: arr1[0] = 9 In [57]: arr1 Out[57]: array([9, 2, 3]) # 修改了原数据 In [58]: arr Out[58]: array([9, 2, 3, 4, 5]) # 若希望得到原数据的副本， 可以用 copy() In [59]: arr2 = arr[:3].copy() In [60]: arr2 Out[60]: array([9, 2, 3]) In [61]: arr2[1] = 99282 In [62]: arr2 Out[62]: array([ 9, 99282, 3]) # 原数据没被修改 In [63]: arr Out[63]: array([9, 2, 3, 4, 5])