无法使用numpy展平数组

Question

I have an output which is as follows :

data = [array([1.        , 1.14112001, 0.7205845 , 1.41211849, 0.46342708,
        1.65028784, 0.24901275]),
 array([1.83665564, 0.09442164, 1.95637593, 0.01196838, 1.99991186,
        0.00822115, 1.96379539]),
 array([0.08347845, 1.85090352, 0.23174534, 1.67022918, 0.44121095,
        1.43616476])]

That is a list of 3 arrays. I have to flatten the array. This should be simple as data.flatten("F") But it does not work

The output should contain the first element of each array then the second and so on.

something like: 1,1.83665564,1.14112001,0.09442164,1.85090352 and so on. How can i do that

Update :

the output I am getting with given code as follows

array([array([1.        , 1.14112001, 0.7205845 , 1.41211849, 0.46342708,
       1.65028784, 0.24901275]),
       array([1.83665564, 0.09442164, 1.95637593, 0.01196838, 1.99991186,
       0.00822115, 1.96379539]),
       array([0.08347845, 1.85090352, 0.23174534, 1.67022918, 0.44121095,
       1.43616476])], dtype=object)

Answer 1

from numpy import array
data = [array([1.        , 1.14112001, 0.7205845 , 1.41211849, 0.46342708,
        1.65028784, 0.24901275]),
 array([1.83665564, 0.09442164, 1.95637593, 0.01196838, 1.99991186,
        0.00822115, 1.96379539]),
 array([0.08347845, 1.85090352, 0.23174534, 1.67022918, 0.44121095,
        1.43616476])]
max=len(max(data,key=len))
final_list=[]
for index in range (0,max):
        final_list.extend([a[index] for a in data if len(a)>index])
print(final_list)

Output:

[1.0, 1.83665564, 0.08347845, 1.14112001, 0.09442164, 1.85090352, 0.7205845, 1.95637593, 0.23174534, 1.41211849, 0.01196838, 1.67022918, 0.46342708, 1.99991186, 0.44121095, 1.65028784, 0.00822115, 1.43616476, 0.24901275, 1.96379539]

Answer 2

Don't know if best solution, but you can use np.resize to reshape all arrays to the same length and stack them vertically. Then use .T to flatten them column based, and use a mask to exclude the values created by np.resize :

l = max(x.size for x in data)
masks = [np.arange(l) < x.size for x in data]
np.vstack(np.resize(x, l) for x in data).T.flatten()[np.vstack(masks).T.flatten()]

>> array([1.        , 1.83665564, 0.08347845, 1.14112001, 0.09442164,
   1.85090352, 0.7205845 , 1.95637593, 0.23174534, 1.41211849,
   0.01196838, 1.67022918, 0.46342708, 1.99991186, 0.44121095,
   1.65028784, 0.00822115, 1.43616476, 0.24901275, 1.96379539])

Answer 3

Use itertools.zip_longest to get elements in different-length arrays in order (see this post ) and then use list comprehension to flatten the resulting tuples.

In Python 2 we should use itertools.izip_longest instead.

>>> from itertools import zip_longest
>>> from numpy import array

# three sample arrays make up a list from 1 to 11
>>> data = array([array([1,4,7,10]), array([2,5,8,11]), array([3,6,9])])
>>> temp = list(zip_longest(*data, fillvalue=None))
>>> temp
[(1, 2, 3), (4, 5, 6), (7, 8, 9), (10, 11, None)]

>>> [i for tup in temp for i in tup if i is not None]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]