Third parameter of np.r_? (numpy)

问题

I'm looking over the docs and I still can't figure out how the third parameter operates.

 np.r_['0,2,0', [1,2,3], [4,5,6]]

output:

array([[1],
       [2],
       [3],
       [4],
       [5],

np.r_['1,2,0', [1,2,3], [4,5,6]]

output:

array([[1, 4],
       [2, 5],
       [3, 6]])

The first parameter is the axis, second is the number of dimensions and third according to the docs means " which axis should contain the start of the arrays which are less than the specified number of dimensions"

Here are the docs:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.r_.html

Thank you.

回答1:

Maybe a simple example can clear things up:

b=np.arange(3)
np.r_['0,2,0', b, b]
# array([[0],
#        [1],
#        [2],
#        [0],
#        [1],
#        [2]])
np.r_['0,2,1', b, b]
# array([[0, 1, 2],
#        [0, 1, 2]])

We are concatenating b a 1d array with itself. The second number specifies that it should be made 2d before it gets stacked on itself as specified by the first number. Now there are two ways to make a shape (3,) array 2d: either make it (3, 1) (first example) or make it (1, 3) (second example). The third number specifies where the first original dimension (i.e. 3) goes in the 2d array.

回答2:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.r_.html Negative integers specify where in the new shape tuple the last dimension of upgraded arrays should be placed, so the default is ‘-1’.

what does this sentence mean?

np.r_['0,2,-5', [1,2,3],[4,5,6] ] # ValueError: all the input array dimensions except for the concatenation axis must match exactly
np.r_['0,2,-6', [1,2,3],[4,5,6] ] # array([[1],[2],[3],[4],[5],[6]])

来源：https://stackoverflow.com/questions/43106345/third-parameter-of-np-r-numpy

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