In Numpy you can subset certain columns by giving a list or integer. For example:
a = np.ones((10, 5))
a[:,2] or a[:,[1,3,4]]
But how to do exclusion ? Where it return all other columns except 2 or [1,3,4].
The reason is that I want to make all other columns zeros except one or a list of selected columns, for example:
a[:, exclude(1)] *= 0
I can generate a new zeros array with the same shape then just assign the specific column to the new variable. But I wonder if there is any more efficient way
Thanks
答案 0 :(得分:3)
One way is to generate the index list yourself:
>>> a[:,list(i for i in range(a.shape[1]) if i not in set((2,1,3,4)))]
array([[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.]])
or to exclude a single column (following your edit):
>>> a[:,list(i for i in range(a.shape[1]) if i != 1)]*= 0
or if you use this often, and want to use a function (which will not be called except
, since that is a Python keyword:
def exclude(size,*args):
return [i for i in range(size) if i not in set(args)] #Supports multiple exclusion
so now
a[:,exclude(a.shape[1],1)]
works.
@jdehesa mentions from Numpy 1.13 you can use
a[:, np.isin(np.arange(a.shape[1]), [2, 1, 3, 4], invert=True)]
as well for something within Numpy itself.
答案 1 :(得分:0)
np.delete
使用布尔掩码删除/选择项。
In [27]: arr = np.arange(24).reshape(3,8)
In [29]: mask = np.ones(arr.shape[1], bool)
In [30]: mask[[1,3,4]] = False
In [31]: mask
Out[31]: array([ True, False, True, False, False, True, True, True])
In [32]: arr[:,mask]
Out[32]:
array([[ 0, 2, 5, 6, 7],
[ 8, 10, 13, 14, 15],
[16, 18, 21, 22, 23]])
In [33]: arr[:,mask] *= 0
In [34]: arr
Out[34]:
array([[ 0, 1, 0, 3, 4, 0, 0, 0],
[ 0, 9, 0, 11, 12, 0, 0, 0],
[ 0, 17, 0, 19, 20, 0, 0, 0]])
很容易翻转诸如面具之类的东西:
In [35]: arr[:,~mask]
Out[35]:
array([[ 1, 3, 4],
[ 9, 11, 12],
[17, 19, 20]])