ndarry:多維數(shù)組對(duì)象
數(shù)據(jù)切片是原始數(shù)組的視圖闯睹，數(shù)據(jù)不會(huì)被復(fù)制库说，試圖上的任何修改都會(huì)直接反映到源數(shù)據(jù)上：
a = np.arange(10)
a
Out[11]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
b = a[5:8]
b
Out[13]: array([5, 6, 7])
b[2] = 1
b
Out[15]: array([5, 6, 1])
a
Out[16]: array([0, 1, 2, 3, 4, 5, 6, 1, 8, 9])
b[:] = 23
a
Out[18]: array([ 0, 1, 2, 3, 4, 23, 23, 23, 8, 9])
如果想得到切片的副本，需要顯式的進(jìn)行復(fù)制
c = a[2:4].copy()
多維數(shù)據(jù)讀取情況
arr1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
arr1[1]
Out[30]: array([4, 5, 6])
arr1[0,2]
Out[31]: 3
arr1[0][2]
Out[32]: 3
Numpy數(shù)組中的元素索引
多維數(shù)組情況
arr2 = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
arr2
Out[34]:
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
arr2[0]
Out[35]:
array([[1, 2, 3],
[4, 5, 6]])
arr2.shape
Out[39]: (2, 2, 3)
二維數(shù)組切片
