Python 通过布尔掩码数组选择numpy数组的元素
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Select elements of numpy array via boolean mask array
提问by JohnDoe
I have a boolean mask array aof length n:
我有一个a长度为布尔值的掩码数组n：
a = np.array([True, True, True, False, False])
I have a 2d array with ncolumns:
我有一个带n列的二维数组：
b = np.array([[1,2,3,4,5], [1,2,3,4,5]])
I want a new array which contains only the "True"-values, for example
例如，我想要一个仅包含“真”值的新数组
c = ([[1,2,3], [1,2,3]])
c = a * bdoes not work because it contains also "0" for the false columns what I don't want
c = a * b不起作用，因为它也包含我不想要的假列的“0”
c = np.delete(b, a, 1) does not work
Any suggestions?
有什么建议？
回答by DSM
You probably want something like this:
你可能想要这样的东西：
>>> a = np.array([True, True, True, False, False])
>>> b = np.array([[1,2,3,4,5], [1,2,3,4,5]])
>>> b[:,a]
array([[1, 2, 3],
[1, 2, 3]])
Note that for this kind of indexing to work, it needs to be an ndarray, like you were using, not a list, or it'll interpret the Falseand Trueas 0and 1and give you those columns:
请注意，要使这种索引工作，它需要是ndarray，就像您使用的那样，而不是 a list，否则它会将Falseand解释True为0and1并为您提供这些列：
>>> b[:,[True, True, True, False, False]]
array([[2, 2, 2, 1, 1],
[2, 2, 2, 1, 1]])
回答by Magi
You can use numpy.mamodule and use np.ma.masked_arrayfunction to do so.
您可以使用numpy.mamodule 和 use np.ma.masked_arrayfunction 来做到这一点。
>>> x = np.array([1, 2, 3, -1, 5])
>>> mx = ma.masked_array(x, mask=[0, 0, 0, 1, 0])
masked_array(data=[1, 2, 3, --, 5], mask=[False, False, False, True, False], fill_value=999999)
回答by Raimi bin Karim
Hope I'm not too late! Here's your array:
希望我还不算太晚！这是你的数组：
X = np.array([[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5]])
Let's create an array of zeros of the same shape as X:
让我们创建一个形状相同的零数组X：
mask = np.zeros_like(X)
# array([[0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0]])
Then, specify the columns that you want to mask out or hide with a 1. In this case, we want the last 2 columns to be masked out.
然后，使用1. 在这种情况下，我们希望将最后 2 列屏蔽掉。
mask[:, -2:] = 1
# array([[0, 0, 0, 1, 1],
# [0, 0, 0, 1, 1]])
Create a masked array:
创建一个屏蔽数组：
X_masked = np.ma.masked_array(X, mask)
# masked_array(data=[[1, 2, 3, --, --],
# [1, 2, 3, --, --]],
# mask=[[False, False, False, True, True],
# [False, False, False, True, True]],
# fill_value=999999)
We can then do whatever we want with X_masked, like taking the sum of each column (along axis=0):
然后我们可以对 做任何我们想做的事情X_masked，比如计算每列的总和（沿着axis=0）：
np.sum(X_masked, axis=0)
# masked_array(data=[2, 4, 6, --, --],
# mask=[False, False],
# fill_value=1e+20)
Great thing about this is that X_maskedis just a view of X, not a copy.
很棒的一点是这X_masked只是一个视图X，而不是副本。
X_masked.base is X
# True
