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I have loaded a .csv file in python with numpy.genfromtxt . Now it returns a 1 dimensional numpy.ndarray with in that array, numpy.void objects which are actually just arrays of integers. However I would like to convert these from type numpy.void to numpy.array . To clarify:

>>> print(train_data.shape)
(42000,)
>>> print(type(train_data[0]))
<class 'numpy.void'>
>>> print(train_data[0])
(9, 0, 0)
So here the array (9, 0, 0) which has type numpy.void should be a numpy.array.
How can I convert all values from train_data to be numpy arrays?
Efficiency is also somewhat important because I am working with a lot of data.
Some more code
>>> with open('filename.csv', 'rt') as raw_training_data:
>>>     train_data = numpy.genfromtxt(raw_training_data, delimiter=',', names=True, dtype=numpy.integer)
>>> print(train_data.dtype)
[('label', '<i4'), ('pixel0', '<i4'), ('pixel1', '<i4')]
>>> print(type(train_data))
<class 'numpy.ndarray'>
                You should show the genfromtxt call.  What's train_data.dtype?  My guess it is a structured array.  It is 1d with multiple fields, which are accessed by field name.  Whether it is easy to convert to 2d numeric dtype will depend on the field dtypes.
– hpaulj
                Oct 30, 2018 at 16:18
                train_data['label'] is the first field, etc.  If you want a 2d array with 3 columns, try skip_header=1 instead of names=True.  Since the fields are all i4 we could convert this after loading, but loading in the desired format will be simpler.
– hpaulj
                Oct 30, 2018 at 16:34
                Does this answer your question? How to slice a numpy.ndarray made up of numpy.void numbers?
– Behdad Abdollahi Moghadam
                Mar 2, 2022 at 9:02
                Sorry if I was unclear. I meant converting all 'void' arrays in the normal array. So by doing something creating an array with all numpy arrays in it.
– Tristan
                Oct 30, 2018 at 15:01
I know it is too late to answer this. But found a solution for a similar problem I had, thanks to the solution provided in this question.
If you can convert the train_data to list and then convert it to an numpy array, that would do the job for you. 
print(np.array(train_data.tolist()).shape)
        
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