基础索引与切片
2019-01-14 本文已影响8人
庵下桃花仙
In [11]: arr = np.arange(10)
In [12]: arr
Out[12]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [13]: arr[5]
Out[13]: 5
In [14]: arr[5: 8]
Out[14]: array([5, 6, 7])
In [15]: arr[5: 8]
Out[15]: array([5, 6, 7])
In [16]: arr[5: 8] = 12
In [17]: arr
Out[17]: array([ 0, 1, 2, 3, 4, 12, 12, 12, 8, 9])
与 Python 内建列表不同。数组的切片是原数组的视图,意味着数据并不是被复制了,任何对于视图的修改都会反映到原数组上。
In [22]: arr_slice = arr[5: 8]
In [23]: arr_slice
Out[23]: array([12, 12, 12])
In [24]: arr_slice[1] = 12345
In [25]: arr
Out[25]:
array([ 0, 1, 2, 3, 4, 12, 12345, 12, 8,
9])
In [26]: arr_slice[:] = 64
In [27]: arr
Out[27]: array([ 0, 1, 2, 3, 4, 64, 64, 64, 8, 9])
>>> a = [1, 2, 3, 4, 5]
>>> a[0] = 6
>>> a
[6, 2, 3, 4, 5]
>>> arr = a[0]
>>> arr
6
>>> arr = 12345
>>> arr
12345
>>> a
[6, 2, 3, 4, 5]
因为 numpy 适合处理很大的数组,如果持续复制数据,会引起内存问题。实在想要一份拷贝的话,要显式地复制数组 arr[5: 8].copy()
在二维数组中,每个索引值对应的元素是一个一维数组。
In [28]: arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
In [29]: arr2d
Out[29]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [30]: arr2d[2]
Out[30]: array([7, 8, 9])
In [31]: arr2d[0][2]
Out[31]: 3
In [32]: arr2d[0, 2]
Out[32]: 3
在多维数组中,可以省略后续索引值,返回降低一个维度的数组。
In [2]: import numpy as np
In [3]: arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
In [4]: arr3d
Out[4]:
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
In [5]: arr3d[0]
Out[5]:
array([[1, 2, 3],
[4, 5, 6]])
In [6]: old_values = arr3d[0].copy()
In [7]: arr3d[0] = 42
In [8]: arr3d
Out[8]:
array([[[42, 42, 42],
[42, 42, 42]],
[[ 7, 8, 9],
[10, 11, 12]]])
In [9]: arr3d[0] = old_values
In [10]: arr3d
Out[10]:
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
In [11]: arr3d[1, 0]
Out[11]: array([7, 8, 9])
数组的切片索引
In [12]: arr = np.arange(10)
In [13]: arr[5: 8] = 64
In [14]: arr
Out[14]: array([ 0, 1, 2, 3, 4, 64, 64, 64, 8, 9])
In [15]: arr[1: 6]
Out[15]: array([ 1, 2, 3, 4, 64])
In [16]: arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
In [17]: arr2d
Out[17]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [18]: arr2d[:2]
Out[18]:
array([[1, 2, 3],
[4, 5, 6]])
In [19]: arr2d[:2, 1:]
Out[19]:
array([[2, 3],
[5, 6]])
In [20]: arr2d[1, :2]
Out[20]: array([4, 5])
In [21]: arr2d[:2, 2]
Out[21]: array([3, 6])
In [22]: arr2d[:, :1]
Out[22]:
array([[1],
[4],
[7]])
In [23]: arr2d[:2, 1:] = 0
In [24]: arr2d
Out[24]:
array([[1, 0, 0],
[4, 0, 0],
[7, 8, 9]])