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

As hpaulj pointed out in a comment, I could add indexing=='ij' to the meshgrid call:

A = np.random.rand(2,3,4)
dimnames = ['z', 'y', 'x']
ranges   = [ np.arange(x) for x in A.shape ]
ix       = [ x.flatten()  for x in np.meshgrid(*ranges, indexing='ij') ]
for name, col in zip(dimnames, ix):
    df[name] = col
df = df.set_index(dimnames).squeeze()

# Compare the results
for ix, val in df.iteritems():
    print ix, val == A[ix]
(0, 0, 0) True
(0, 0, 1) True
(0, 0, 2) True
(0, 0, 3) True
(0, 1, 0) True
(0, 1, 1) True
(0, 1, 2) True
(0, 1, 3) True
(0, 2, 0) True
(0, 2, 1) True
(0, 2, 2) True
(0, 2, 3) True
(1, 0, 0) True
(1, 0, 1) True
(1, 0, 2) True
(1, 0, 3) True
(1, 1, 0) True
(1, 1, 1) True
(1, 1, 2) True
(1, 1, 3) True
(1, 2, 0) True
(1, 2, 1) True
(1, 2, 2) True
(1, 2, 3) True