选择某些行(满足条件)，但仅选择Python/Numpy中的某些列

问题描述：

我有一个包含4列的numpy数组，并且想要选择第1列，第3列和第4列，其中第二列的值满足特定条件(即固定值).我尝试首先仅选择行，但通过以下方式选择所有4列:

I have an numpy array with 4 columns and want to select columns 1, 3 and 4, where the value of the second column meets a certain condition (i.e. a fixed value). I tried to first select only the rows, but with all 4 columns via:

I = A[A[:,1] == i]

有效.然后我进一步尝试了(类似于我非常了解的matlab):

which works. Then I further tried (similarly to matlab which I know very well):

I = A[A[:,1] == i, [0,2,3]]

不起作用.怎么做?

示例数据:

 >>> A = np.array([[1,2,3,4],[6,1,3,4],[3,2,5,6]])
 >>> print A
 [[1 2 3 4]
  [6 1 3 4]
  [3 2 5 6]]
 >>> i = 2

 # I want to get the columns 1, 3 and 4 for every row which has the value i in the second column. In this case, this would be row 1 and 3 with columns 1, 3 and 4:
 [[1 3 4]
  [3 5 6]]

我现在正在使用这个:

I = A[A[:,1] == i]
I = I[:, [0,2,3]]

但是我认为必须有一种更好的方法...(我习惯于MATLAB)

But I thought that there had to be a nicer way of doing it... (Im used to MATLAB)

答

>>> a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
>>> a
array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

>>> a[a[:,0] > 3] # select rows where first column is greater than 3
array([[ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])

>>> a[a[:,0] > 3][:,np.array([True, True, False, True])] # select columns
array([[ 5,  6,  8],
       [ 9, 10, 12]])

# fancier equivalent of the previous
>>> a[np.ix_(a[:,0] > 3, np.array([True, True, False, True]))]
array([[ 5,  6,  8],
       [ 9, 10, 12]])

有关晦涩的np.ix_()的说明，请参见 https://stackoverflow.com/a/13599843/4323

For an explanation of the obscure np.ix_(), see https://stackoverflow.com/a/13599843/4323

最后，我们可以通过给出列号列表而不是乏味的布尔掩码来简化:

Finally, we can simplify by giving the list of column numbers instead of the tedious boolean mask:

>>> a[np.ix_(a[:,0] > 3, (0,1,3))]
array([[ 5,  6,  8],
       [ 9, 10, 12]])

选择某些行(满足条件)，但仅选择Python/Numpy中的某些列

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