Maintain strings when converting python list into numpy structured array

Question

I have a data structure that looks like this:

data = [ ('a', 1.0, 2.0),
         ('b', 2.0, 4.0),
         ('c', 3.0, 6.0) ]

I want to convert it into a structured array using numpy. However, when I try the following, I keep the floats but I lose the string information:

import numpy
x = numpy.array(data, dtype=[('label', str), ('x', float), ('y', float)])
print x

Resulting in:

>>> [('', 1.0, 2.0) ('', 2.0, 4.0) ('', 3.0, 6.0)]

Could anyone explain why this happens, and how I might keep the string information?

Answer 1

You can see the problem if you print out the array and look carefully:

>>> numpy.array(data, dtype=[('label', str), ('x', float), ('y', float)])
array([('', 1.0, 2.0), ('', 2.0, 4.0), ('', 3.0, 6.0)], 
      dtype=[('label', '|S0'), ('x', '<f8'), ('y', '<f8')])

The first field has a data type of '|S0' -- a zero width string field. Make the string field longer -- here's a 2-char string field:

>>> numpy.array(data, dtype=[('label', 'S2'), ('x', float), ('y', float)])
array([('a', 1.0, 2.0), ('b', 2.0, 4.0), ('c', 3.0, 6.0)], 
      dtype=[('label', '|S2'), ('x', '<f8'), ('y', '<f8')])

You can also do it this way, as documented here :

>>> numpy.array(data, dtype=[('label', (str, 2)), ('x', float), ('y', float)])
array([('a', 1.0, 2.0), ('b', 2.0, 4.0), ('c', 3.0, 6.0)], 
      dtype=[('label', '|S2'), ('x', '<f8'), ('y', '<f8')])