CODEWITHSUNDEEP
arrayspython-3.xnumpyindexing
Mustafa Süve
Mustafa Süve

Reputation: 151

Numpy - Indexing one dimension of a multidimensional array

I have an numpy array like this with shape (6, 2, 4):

x = np.array([[[0, 3, 2, 0],
               [1, 3, 1, 1]],

              [[3, 2, 3, 3],
               [0, 3, 2, 0]],

              [[1, 0, 3, 1],
               [3, 2, 3, 3]],

              [[0, 3, 2, 0],
               [1, 3, 2, 2]],

              [[3, 0, 3, 1],
               [1, 0, 1, 1]],

              [[1, 3, 1, 1],
               [3, 1, 3, 3]]])

And I have choices array like this:

choices = np.array([[1, 1, 1, 1],
                    [0, 1, 1, 0],
                    [1, 1, 1, 1],
                    [1, 0, 0, 0],
                    [1, 0, 1, 1],
                    [0, 0, 0, 1]])

How can I use choices array to index only the middle dimension with size 2 and get a new numpy array with shape (6, 4) in the most efficient way possible?

The result would be this:

[[1 3 1 1]
 [3 3 2 3]
 [3 2 3 3]
 [1 3 2 0]
 [1 0 1 1]
 [1 3 1 3]]

I've tried to do it by x[:, choices, :] but this doesn't return what I want. I also tried to do x.take(choices, axis=1) but no luck.

Upvotes: 3

Views: 5380

Answers (2)

Paje
Paje

Reputation: 31

as I recently had this issue, found @divakar's answer useful, but still wanted a general functions for that (independent of number of dims etc.), here it is :

def take_indices_along_axis(array, choices, choice_axis):
    """
    array is N dim
    choices are integer of N-1 dim
       with valuesbetween 0 and array.shape[choice_axis] - 1
    choice_axis is the axis along which you want to take indices
    """
    nb_dims = len(array.shape)
    list_indices = []
    for this_axis, this_axis_size in enumerate(array.shape):
        if this_axis == choice_axis:
            # means this is the axis along which we want to choose
            list_indices.append(choices)
            continue
        # else, we want arange(this_axis), but reshaped to match the purpose
        this_indices = np.arange(this_axis_size)
        reshape_target = [1 for _ in range(nb_dims)]
        reshape_target[this_axis] = this_axis_size # replace the corresponding axis with the right range
        del reshape_target[choice_axis] # remove the choice_axis
        list_indices.append(
            this_indices.reshape(tuple(reshape_target))
        )
    tuple_indices = tuple(list_indices)
    return array[tuple_indices]

# test it !
array = np.random.random(size=(10, 10, 10, 10))
choices = np.random.randint(10, size=(10, 10, 10))
assert take_indices_along_axis(array, choices, choice_axis=0)[5, 5, 5] == array[choices[5, 5, 5], 5, 5, 5]
assert take_indices_along_axis(array, choices, choice_axis=2)[5, 5, 5] == array[5, 5, choices[5, 5, 5], 5]

Upvotes: -1

Divakar
Divakar

Reputation: 221504

Use np.take_along_axis to index along the second axis -

In [16]: np.take_along_axis(x,choices[:,None],axis=1)[:,0]
Out[16]: 
array([[1, 3, 1, 1],
       [3, 3, 2, 3],
       [3, 2, 3, 3],
       [1, 3, 2, 0],
       [1, 0, 1, 1],
       [1, 3, 1, 3]])

Or with explicit integer-array indexing -

In [22]: m,n = choices.shape

In [23]: x[np.arange(m)[:,None],choices,np.arange(n)]
Out[23]: 
array([[1, 3, 1, 1],
       [3, 3, 2, 3],
       [3, 2, 3, 3],
       [1, 3, 2, 0],
       [1, 0, 1, 1],
       [1, 3, 1, 3]])

Upvotes: 9

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