How to recover original indices for a flattened Numpy array?

Question

I\'ve got a multidimensional numpy array that I\'m trying to stick into a pandas data frame. I\'d like to flatten the array, and create a pandas index that reflects the pre-

Answer 1

My solution is based on this this answer by Divakar involving np.ogrid. This function should work for any array of any dimension.

def indices_merged_arr(arr):
    n = arr.ndim
    grid = np.ogrid[tuple(map(slice, arr.shape))]
    out = np.empty(arr.shape + (n+1,), dtype=arr.dtype)
    for i in range(n):
        out[...,i+1] = grid[i]
    out[...,0] = arr
    out.shape = (-1,n+1)
    return out

A = np.array([[[ 0.43793885,  0.40078139,  0.48078691,  0.05334248],
               [ 0.76331509,  0.82514441,  0.86169078,  0.86496111],
               [ 0.75572665,  0.80860943,  0.79995337,  0.63123724]],

              [[ 0.20648946,  0.57042315,  0.71777265,  0.34155005],
               [ 0.30843717,  0.39381407,  0.12623462,  0.93481552],
               [ 0.3267771 ,  0.64097038,  0.30405215,  0.57726629]]])

df = pd.DataFrame(indices_merged_arr(A), columns=list('Axyz'))
df

           A    x    y    z
0   0.437939  0.0  0.0  0.0
1   0.400781  0.0  0.0  1.0
2   0.480787  0.0  0.0  2.0
3   0.053342  0.0  0.0  3.0
4   0.763315  0.0  1.0  0.0
5   0.825144  0.0  1.0  1.0
6   0.861691  0.0  1.0  2.0
7   0.864961  0.0  1.0  3.0
8   0.755727  0.0  2.0  0.0
9   0.808609  0.0  2.0  1.0
10  0.799953  0.0  2.0  2.0
11  0.631237  0.0  2.0  3.0
12  0.206489  1.0  0.0  0.0
13  0.570423  1.0  0.0  1.0
14  0.717773  1.0  0.0  2.0
15  0.341550  1.0  0.0  3.0
16  0.308437  1.0  1.0  0.0
17  0.393814  1.0  1.0  1.0
18  0.126235  1.0  1.0  2.0
19  0.934816  1.0  1.0  3.0
20  0.326777  1.0  2.0  0.0
21  0.640970  1.0  2.0  1.0
22  0.304052  1.0  2.0  2.0
23  0.577266  1.0  2.0  3.0