Converting a 2D numpy array to a structured array

Question

I\'m trying to convert a two-dimensional array into a structured array with named fields. I want each row in the 2D array to be a new record in the structured array. Unfortu

Answer 1

If the data starts as a list of tuples, then creating a structured array is straight forward:

In [228]: alist = [("Hello",2.5,3),("World",3.6,2)]
In [229]: dt = [("Col1","S8"),("Col2","f8"),("Col3","i8")]
In [230]: np.array(alist, dtype=dt)
Out[230]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '

The complication here is that the list of tuples has been turned into a 2d string array:

In [231]: arr = np.array(alist)
In [232]: arr
Out[232]: 
array([['Hello', '2.5', '3'],
       ['World', '3.6', '2']], 
      dtype='

We could use the well known zip* approach to 'transposing' this array - actually we want a double transpose:

In [234]: list(zip(*arr.T))
Out[234]: [('Hello', '2.5', '3'), ('World', '3.6', '2')]

zip has conveniently given us a list of tuples. Now we can recreate the array with desired dtype:

In [235]: np.array(_, dtype=dt)
Out[235]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '

The accepted answer uses fromarrays:

In [236]: np.rec.fromarrays(arr.T, dtype=dt)
Out[236]: 
rec.array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
          dtype=[('Col1', 'S8'), ('Col2', '

Internally, fromarrays takes a common recfunctions approach: create target array, and copy values by field name. Effectively it does:

In [237]: newarr = np.empty(arr.shape[0], dtype=dt)
In [238]: for n, v in zip(newarr.dtype.names, arr.T):
     ...:     newarr[n] = v
     ...:     
In [239]: newarr
Out[239]: 
array([(b'Hello',  2.5, 3), (b'World',  3.6, 2)], 
      dtype=[('Col1', 'S8'), ('Col2', '