选择或作业np.ix_
使用索引或布尔数组/掩码
1. With indexing-arrays
一个选择
我们可以用np.ix_ https://docs.scipy.org/doc/numpy/reference/generated/numpy.ix_.html获取可相互广播的索引数组元组,以产生更高维的索引组合。因此,当该元组用于索引输入数组时,将为我们提供相同的高维数组。因此,要根据两个因素进行选择1D
索引数组,那就是 -
x_indexed = x[np.ix_(row_indices,col_indices)]
B、作业
我们可以使用相同的符号将标量或可广播数组分配到这些索引位置。因此,以下作业适用于 -
x[np.ix_(row_indices,col_indices)] = # scalar or broadcastable array
2. With masks
我们还可以使用布尔数组/掩码np.ix_
,类似于索引数组的使用方式。这可以再次用于从输入数组中选择一个块,也可以对其进行赋值。
一个选择
因此,随着row_mask
and col_mask
布尔数组分别作为行和列选择的掩码,我们可以使用以下内容进行选择 -
x[np.ix_(row_mask,col_mask)]
B、作业
以下适用于作业 -
x[np.ix_(row_mask,col_mask)] = # scalar or broadcastable array
样品运行
1. 使用np.ix_
with indexing-arrays
输入数组和索引数组 -
In [221]: x
Out[221]:
array([[17, 39, 88, 14, 73, 58, 17, 78],
[88, 92, 46, 67, 44, 81, 17, 67],
[31, 70, 47, 90, 52, 15, 24, 22],
[19, 59, 98, 19, 52, 95, 88, 65],
[85, 76, 56, 72, 43, 79, 53, 37],
[74, 46, 95, 27, 81, 97, 93, 69],
[49, 46, 12, 83, 15, 63, 20, 79]])
In [222]: row_indices
Out[222]: [4, 2, 5, 4, 1]
In [223]: col_indices
Out[223]: [1, 2]
索引数组的元组np.ix_
-
In [224]: np.ix_(row_indices,col_indices) # Broadcasting of indices
Out[224]:
(array([[4],
[2],
[5],
[4],
[1]]), array([[1, 2]]))
做出选择 -
In [225]: x[np.ix_(row_indices,col_indices)]
Out[225]:
array([[76, 56],
[70, 47],
[46, 95],
[76, 56],
[92, 46]])
As OP建议 https://stackoverflow.com/questions/35607818/index-a-2d-numpy-array-with-2-lists-of-indices#comment58900519_35608029,这实际上与执行相同老套使用 2D 数组版本进行广播row_indices
有它的元素/索引sent to axis=0
从而创建一个单例维度axis=1
从而允许广播col_indices
。因此,我们会有一个替代解决方案,如下所示 -
In [227]: x[np.asarray(row_indices)[:,None],col_indices]
Out[227]:
array([[76, 56],
[70, 47],
[46, 95],
[76, 56],
[92, 46]])
正如前面所讨论的,对于作业,我们只是这样做。
行、列索引数组 -
In [36]: row_indices = [1, 4]
In [37]: col_indices = [1, 3]
使用标量进行赋值 -
In [38]: x[np.ix_(row_indices,col_indices)] = -1
In [39]: x
Out[39]:
array([[17, 39, 88, 14, 73, 58, 17, 78],
[88, -1, 46, -1, 44, 81, 17, 67],
[31, 70, 47, 90, 52, 15, 24, 22],
[19, 59, 98, 19, 52, 95, 88, 65],
[85, -1, 56, -1, 43, 79, 53, 37],
[74, 46, 95, 27, 81, 97, 93, 69],
[49, 46, 12, 83, 15, 63, 20, 79]])
使用 2D 块(可广播数组)进行分配 -
In [40]: rand_arr = -np.arange(4).reshape(2,2)
In [41]: x[np.ix_(row_indices,col_indices)] = rand_arr
In [42]: x
Out[42]:
array([[17, 39, 88, 14, 73, 58, 17, 78],
[88, 0, 46, -1, 44, 81, 17, 67],
[31, 70, 47, 90, 52, 15, 24, 22],
[19, 59, 98, 19, 52, 95, 88, 65],
[85, -2, 56, -3, 43, 79, 53, 37],
[74, 46, 95, 27, 81, 97, 93, 69],
[49, 46, 12, 83, 15, 63, 20, 79]])
2. 使用np.ix_
with masks
输入数组 -
In [19]: x
Out[19]:
array([[17, 39, 88, 14, 73, 58, 17, 78],
[88, 92, 46, 67, 44, 81, 17, 67],
[31, 70, 47, 90, 52, 15, 24, 22],
[19, 59, 98, 19, 52, 95, 88, 65],
[85, 76, 56, 72, 43, 79, 53, 37],
[74, 46, 95, 27, 81, 97, 93, 69],
[49, 46, 12, 83, 15, 63, 20, 79]])
输入行、列掩码 -
In [20]: row_mask = np.array([0,1,1,0,0,1,0],dtype=bool)
In [21]: col_mask = np.array([1,0,1,0,1,1,0,0],dtype=bool)
做出选择 -
In [22]: x[np.ix_(row_mask,col_mask)]
Out[22]:
array([[88, 46, 44, 81],
[31, 47, 52, 15],
[74, 95, 81, 97]])
使用标量进行赋值 -
In [23]: x[np.ix_(row_mask,col_mask)] = -1
In [24]: x
Out[24]:
array([[17, 39, 88, 14, 73, 58, 17, 78],
[-1, 92, -1, 67, -1, -1, 17, 67],
[-1, 70, -1, 90, -1, -1, 24, 22],
[19, 59, 98, 19, 52, 95, 88, 65],
[85, 76, 56, 72, 43, 79, 53, 37],
[-1, 46, -1, 27, -1, -1, 93, 69],
[49, 46, 12, 83, 15, 63, 20, 79]])
使用 2D 块(可广播数组)进行分配 -
In [25]: rand_arr = -np.arange(12).reshape(3,4)
In [26]: x[np.ix_(row_mask,col_mask)] = rand_arr
In [27]: x
Out[27]:
array([[ 17, 39, 88, 14, 73, 58, 17, 78],
[ 0, 92, -1, 67, -2, -3, 17, 67],
[ -4, 70, -5, 90, -6, -7, 24, 22],
[ 19, 59, 98, 19, 52, 95, 88, 65],
[ 85, 76, 56, 72, 43, 79, 53, 37],
[ -8, 46, -9, 27, -10, -11, 93, 69],
[ 49, 46, 12, 83, 15, 63, 20, 79]])