python flatten an array of array

Question

I have an array of array, something like that:

array([[array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
      dtype=int64)],
       [array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
      dtype=int64)],
       [array([46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285,
       17616, 25146, 32645, 41276], dtype=int64)],
       ...,
       [array([24534,  8230, 14267,  9352,  3543, 29397,   900, 32398, 34262,
       37646, 11930, 37173], dtype=int64)],
       [array([25157], dtype=int64)],
       [array([ 8859, 20850, 19322,  8075], dtype=int64)]], dtype=object)

what I want is

     array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668,33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665,46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285,17616, 25146, 32645, 41276
               ...,
24534,  8230, 14267,  9352,  3543, 29397,   900, 32398, 34262,
               37646, 11930, 37173,25157 8859, 20850, 19322,  8075, dtype=object)

I have searched some solution for that, but seems that all of them are for np.array or list, which are not work for array.

    functools.reduce(operator.iconcat, orders2.values.tolist(), [])
[array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
       dtype=int64),
 array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
       dtype=int64),
 array([46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285,
        17616, 25146, 32645, 41276], dtype=int64),...
    orders2.values.flatten()
array([array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
      dtype=int64),
       array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
      dtype=int64),

I couldnt even convert the array to list

[sub.tolist() for sub in orders2.values]
    [array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
           dtype=int64),
     array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
           dtype=int64),
     array([46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285,
            17616, 25146, 32645, 41276], dtype=int64),...
        orders2.values.flatten()
    array([array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
          dtype=int64),
           array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
          dtype=int64),...

I find it is hard to get some information about array class ,everything is list or np.array

What do you mean by array here? It sure looks like you have a numpy array of arrays. If so, why isn't there a [numpy] tag? There is a builtin array module but I don't think supports this kind of structure. — hpaulj
– hpaulj, Commented Jul 8, 2019 at 4:37
Another way to put the question, what module imports did you do? — hpaulj
– hpaulj, Commented Jul 8, 2019 at 4:46
Can you improve this question by giving important information like which libraries you're using as well as trimming down the irrelevant data to the minimal data needed to convey your question? — Daniel
– Daniel, Commented Sep 10, 2022 at 20:18

U13-Forward · Accepted Answer · 2019-07-08 03:09:25Z

Use a list comprehension, then convert back to array:

>>> arr = array([[array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668],
      dtype='int64')],
       [array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665],
      dtype='int64')],
       [array([46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285,
       17616, 25146, 32645, 41276], dtype='int64')],
       [array([24534,  8230, 14267,  9352,  3543, 29397,   900, 32398, 34262,
       37646, 11930, 37173], dtype='int64')],
       [array([25157], dtype='int64')],
       [array([ 8859, 20850, 19322,  8075], dtype='int64')]], dtype=object)
>>> array([x for i in arr.tolist() for x in i[0].tolist()])
array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668,
       33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665, 46842,
       26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285, 17616,
       25146, 32645, 41276, 24534,  8230, 14267,  9352,  3543, 29397,
         900, 32398, 34262, 37646, 11930, 37173, 25157,  8859, 20850,
       19322,  8075])
>>>

hpaulj · Accepted Answer · 2019-07-08 05:17:09Z

In [141]: array=np.array; 
     ...: arr = array([[array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668], 
     ...:       dtype='int64')], 
     ...:        [array([33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665], 
     ...:       dtype='int64')], 
     ...:        [array([46842, 26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285, 
     ...:        17616, 25146, 32645, 41276], dtype='int64')], 
     ...:        [array([24534,  8230, 14267,  9352,  3543, 29397,   900, 32398, 34262, 
     ...:        37646, 11930, 37173], dtype='int64')], 
     ...:        [array([25157], dtype='int64')], 
     ...:        [array([ 8859, 20850, 19322,  8075], dtype='int64')]], dtype=object)                           
In [142]: np.concatenate(arr.ravel())                                                                           
Out[142]: 
array([33120, 28985,  9327, 45918, 30035, 17794, 40141,  1819, 43668,
       33754, 24838, 17704, 21903, 17668, 46667, 17461, 32665, 46842,
       26434, 39758, 27761, 10054, 21351, 22598, 34862, 40285, 17616,
       25146, 32645, 41276, 24534,  8230, 14267,  9352,  3543, 29397,
         900, 32398, 34262, 37646, 11930, 37173, 25157,  8859, 20850,
       19322,  8075])

The shape is 2d:

In [143]: arr.shape                                                                                             
Out[143]: (6, 1)

arr.ravel() makes it 1d (6,), np.concatenate joins a list (or iterable) of arrays.

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