Adding a field to a structured numpy array

Question

What is the cleanest way to add a field to a structured numpy array? Can it be done destructively, or is it necessary to create a new array and copy over the existing fields? Are the contents of each field stored contiguously in memory so that such copying can be done efficiently?

Answer 1

If you're using numpy 1.3, there's also numpy.lib.recfunctions.append_fields().

For many installations, you'll need to import numpy.lib.recfunctions to access this. import numpy will not allow one to see the numpy.lib.recfunctions

Answer 2

import numpy  def add_field(a, descr):     """Return a new array that is like "a", but has additional fields.      Arguments:       a     -- a structured numpy array       descr -- a numpy type description of the new fields      The contents of "a" are copied over to the appropriate fields in     the new array, whereas the new fields are uninitialized.  The     arguments are not modified.      >>> sa = numpy.array([(1, 'Foo'), (2, 'Bar')], \                          dtype=[('id', int), ('name', 'S3')])     >>> sa.dtype.descr == numpy.dtype([('id', int), ('name', 'S3')])     True     >>> sb = add_field(sa, [('score', float)])     >>> sb.dtype.descr == numpy.dtype([('id', int), ('name', 'S3'), \                                        ('score', float)])     True     >>> numpy.all(sa['id'] == sb['id'])     True     >>> numpy.all(sa['name'] == sb['name'])     True     """     if a.dtype.fields is None:         raise ValueError, "`A' must be a structured numpy array"     b = numpy.empty(a.shape, dtype=a.dtype.descr + descr)     for name in a.dtype.names:         b[name] = a[name]     return b