[英]Filtering rows of numpy array based on whether row elements are in another array
I have an array group
which is Nx2:我有一个数组
group
,它是 Nx2:
array([[ 1, 6],
[ 1, 0],
[ 2, 1],
...,
[40196, 40197],
[40196, 40198],
[40196, 40199]], dtype=uint32)
and another array selection
which is (M,):另一个数组
selection
是 (M,):
array([3216, 3217, 3218, ..., 8039])
I want to create a new array containing all the rows of group
where both elements are in selection
.我想创建一个新数组,其中包含两个元素都在
selection
的group
所有行。 This is how I did it:我是这样做的:
np.array([(i,j) for (i,j) in group if i in selection and j in selection])
This works, but I know there must be a more efficient way that takes advantage of some numpy function.这有效,但我知道必须有一种更有效的方法来利用一些 numpy 函数。
You can use np.isin
to get a boolean array of the same shape as group
that says whether an element is in selection
.您可以使用
np.isin
获取与group
形状相同的布尔数组,该数组表示元素是否在selection
。 Then, to check whether both of the entries in rows are in selection
, you can use all
with axis=1
, which will give a 1D boolean array that says which rows to keep.然后,要检查 rows 中的两个条目是否都在
selection
,您可以将all
与axis=1
,这将给出一个一维布尔数组,说明要保留哪些行。 We finally index with it:我们最终用它索引:
group[np.isin(group, selection).all(axis=1)]
Sample:样本:
>>> group
array([[ 1, 6],
[ 1, 0],
[ 2, 1],
[40196, 40197],
[40196, 40198],
[40196, 40199]])
>>> selection
array([ 1, 2, 3, 4, 5, 6, 40196, 40199])
>>> np.isin(group, selection)
array([[ True, True],
[ True, False],
[ True, True],
[ True, False],
[ True, False],
[ True, True]])
>>> np.isin(group, selection).all(axis=1)
array([ True, False, True, False, False, True])
>>> group[np.isin(group, selection).all(axis=1)]
array([[ 1, 6],
[ 2, 1],
[40196, 40199]])
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