В одну сторону с masking
, созданным с помощью broadcatsed-comparison
-
In [43]: a # input array
Out[43]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [44]: remove_idx # indices to be removed from each row
Out[44]: array([1, 0, 2])
In [45]: n = a.shape[1]
In [46]: a[remove_idx[:,None]!=np.arange(n)].reshape(-1,n-1)
Out[46]:
array([[1, 3],
[5, 6],
[7, 8]])
Другой подход на основе mask
с маской, созданной с помощью array-assignment
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In [47]: mask = np.ones(a.shape,dtype=bool)
In [48]: mask[np.arange(len(remove_idx)), remove_idx] = 0
In [49]: a[mask].reshape(-1,a.shape[1]-1)
Out[49]:
array([[1, 3],
[5, 6],
[7, 8]])
Другой сnp.delete
-
In [64]: m,n = a.shape
In [66]: np.delete(a.flat,remove_idx+n*np.arange(m)).reshape(m,-1)
Out[66]:
array([[1, 3],
[5, 6],
[7, 8]])