Converting a row from an astropy table to a numpy array

Question

I have a numpy table

<Table length=3>
  a     b  
int64 int64
----- -----
    1     3
    2     5
    4     7

And I would like to convert a row to a numpy array. But when I try, I end up with an array with no dimensions

In: np.array(mytable[0]).shape
Out: ()

and if I do

myrow = mytable[0]
myrow[0]

I get the error

IndexError: too many indices for array

Is there something like t[0].values I could do, that would return array([1, 3]) ?

Tom Aldcroft · Accepted Answer · 2020-02-08 12:31:48Z

2

The Table.Row object provides an iterator over the values, so you could do:

>>> np.array(list(t[0]))
array([1, 3])

answered Feb 8, 2020 at 12:31

Tom Aldcroft

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user3483203 · Accepted Answer · 2020-02-07 14:59:43Z

1

When you slice a row from a table in Astropy and convert to an ndarray, you get a 0D structured array back, which is the shape attribute is empty. For a general solution, numpy provides a structured_to_unstructured method that will work well for more than just a single row slice as well.

>>> np.lib.recfunctions.structured_to_unstructured(np.array(t[0]))
array([1, 3])

>>> np.lib.recfunctions.structured_to_unstructured(np.array(t[1:]))
array([[2, 5],
       [4, 7]])

answered Feb 7, 2020 at 14:59

user3483203

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