CODEWITHSUNDEEP
pythonarraysnumpy
shiftyscales
shiftyscales

Reputation: 465

Converting one-dimensional structured array to 2D numpy array

I have a one-dimensional structured array e.g.([ (1,2), (3,4) ]) and want to convert it to 2D numpy array ([ [1,2], [3,4] ]). Right now I am using list comprehension and then np.asarray() 

list_of_lists = [list(elem) for elem in array_of_tuples]
array2D = np.asarray(list_of_lists)

This doesn't look very efficient - is there a better way? Thanks.

NOTE: the initial version of this question was mentioning "numpy array of tuples" instead of one-dimensional structured array. This might be useful for confused python newbies as me.

Upvotes: 1

Views: 408

Answers (1)

Warren Weckesser
Warren Weckesser

Reputation: 114781

In a comment, you stated that the array is one-dimensional, with dtype [('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('confidence', '<f4'), ('intensity', '<f4')]. All the fields in the structured array are the same type ('<f4'), so you can create a 2-d view using the view method:

In [13]: x
Out[13]: 
array([(1.0, 2.0, 3.0, 4.0, 5.0), (6.0, 7.0, 8.0, 9.0, 10.0)], 
      dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4'), ('confidence', '<f4'), ('intensity', '<f4')])

In [14]: x.view('<f4').reshape(len(x), -1)
Out[14]: 
array([[  1.,   2.,   3.,   4.,   5.],
       [  6.,   7.,   8.,   9.,  10.]], dtype=float32)

In this case, the view method gives a flattened result, so we have to reshape it into a 2-d array using the reshape method.

Upvotes: 3

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