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the npy file I used ⬆️ https://github.com/mangomangomango0820/DataAnalysis/blob/master/NumPy/NumPyEx/NumPy_Ex1_3Dscatterplt.npy after loading the npy file，

data = np.load('NumPy_Ex1_3Dscatterplt.npy')
[([   2,    2, 1920,  480],) ([   1,    3, 1923,  480],)
 ......
 ([   3,    3, 1923,  480],)]
⬆️ data.shape, (69,)
⬆️ data.shape, (69,)
⬆️ data.dtype, [('f0', '<i8', (4,))]
⬆️ type(data), <class 'numpy.ndarray'>
⬆️ type(data[0]), <class 'numpy.void'>
you can see for each row, e.g. data[0]，its type is <class 'numpy.void'>
I wish to get a ndarray based on the data above, looking like this ⬇️
[[   2    2 1920  480]
 [   3    3 1923  480]]
the way I did is ⬇️
all = np.array([data[i][0] for i in range(data.shape[0])])
[[   2    2 1920  480]
 [   3    3 1923  480]]
                welcome other anwers ( if there's any smarter method I do not know and you would like to share
– missmango
                Mar 5, 2022 at 5:14
Your data is a structured array, with a compound dtype.
https://numpy.org/doc/stable/user/basics.rec.html
I can recreate it with:
In [130]: dt = np.dtype([("f0", "<i8", (4,))])
In [131]: x = np.array(
     ...:     [([2, 2, 1920, 480],), ([1, 3, 1923, 480],), ([3, 3, 1923, 480],)], dtype=dt
     ...: )
In [132]: x
Out[132]: 
array([([   2,    2, 1920,  480],), ([   1,    3, 1923,  480],),
       ([   3,    3, 1923,  480],)], dtype=[('f0', '<i8', (4,))])
This is 1d array onr field, and the field itself contains 4 elements.
Fields are accessed by name:
In [133]: x["f0"]
Out[133]: 
array([[   2,    2, 1920,  480],
       [   1,    3, 1923,  480],
       [   3,    3, 1923,  480]])
This has integer dtype with shape (3,4).
Accessing fields by name applies to more complex structured arrays as well.
Using the tolist approach from the other answer:
In [134]: x.tolist()
Out[134]: 
[(array([   2,    2, 1920,  480]),),
 (array([   1,    3, 1923,  480]),),
 (array([   3,    3, 1923,  480]),)]
In [135]: np.array(x.tolist())           # (3,1,4) shape
Out[135]: 
array([[[   2,    2, 1920,  480]],
       [[   1,    3, 1923,  480]],
       [[   3,    3, 1923,  480]]])
In [136]: np.vstack(x.tolist())          # (3,4) shape
Out[136]: 
array([[   2,    2, 1920,  480],
       [   1,    3, 1923,  480],
       [   3,    3, 1923,  480]])
The documentation also suggests using:
In [137]: import numpy.lib.recfunctions as rf
In [138]: rf.structured_to_unstructured(x)
Out[138]: 
array([[   2,    2, 1920,  480],
       [   1,    3, 1923,  480],
       [   3,    3, 1923,  480]])
An element of a structured array displays as a tuple, though the type is a generic np.void
There is an older class recarray, that is similar, but with an added way of accessing fields
In [146]: y=x.view(np.recarray)
In [147]: y
Out[147]: 
rec.array([([   2,    2, 1920,  480],), ([   1,    3, 1923,  480],),
           ([   3,    3, 1923,  480],)],
          dtype=[('f0', '<i8', (4,))])
In [148]: y.f0
Out[148]: 
array([[   2,    2, 1920,  480],
       [   1,    3, 1923,  480],
       [   3,    3, 1923,  480]])
In [149]: type(y[0])
Out[149]: numpy.record
I often refer to elements of structured arrays as records.
(69, 1, 4)
In case if you dont like the extra 1 dimension in between, you can squeeze like this
data_sqz = data_clean.squeeze()
print(data_sqz)
print(data_sqz.shape)
Output
 [   3    3 1923  480]]
(69, 4)
                If you use vstack instead of np.array you won't need the squeeze (not that it's expensive).
– hpaulj
                Mar 5, 2022 at 8:29
        
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