使用 array-initialization 和 broadcasted-assignment 在后续步骤中分配索引和数组值-

Using array-initialization and then broadcasted-assignment for assigning indices and the array values in subsequent steps -

def indices_merged_arr(arr):
    m,n = arr.shape
    I,J = np.ogrid[:m,:n]
    out = np.empty((m,n,3), dtype=arr.dtype)
    out[...,0] = I
    out[...,1] = J
    out[...,2] = arr
    out.shape = (-1,3)
    return out

请注意，我们正在避免使用 np.indices(arr.shape)，这可能会减慢速度.

Note that we are avoiding the use of np.indices(arr.shape), which could have slowed things down.

样品运行 -

In [10]: arr = np.array([[1, 3, 7], [4, 9, 8]])

In [11]: indices_merged_arr(arr)
Out[11]: 
array([[0, 0, 1],
       [0, 1, 3],
       [0, 2, 7],
       [1, 0, 4],
       [1, 1, 9],
       [1, 2, 8]])

性能

arr = np.random.randn(100000, 2)

%timeit df = pd.DataFrame(np.hstack((np.indices(arr.shape).reshape(2, arr.size).T,\
                                arr.reshape(-1, 1))), columns=['x', 'y', 'value'])
100 loops, best of 3: 4.97 ms per loop

%timeit pd.DataFrame(indices_merged_arr_divakar(arr), columns=['x', 'y', 'value'])
100 loops, best of 3: 3.82 ms per loop

%timeit pd.DataFrame(indices_merged_arr_eric(arr), columns=['x', 'y', 'value'], dtype=np.float32)
100 loops, best of 3: 5.59 ms per loop

注意:时间包括转换为 pandas 数据帧，这是此解决方案的最终用例.

Note: Timings include conversion to pandas dataframe, that is the eventual use case for this solution.