= Addressing Array Columns by Name =
There are two very closely related ways to access array columns by name: recarrays and structured arrays.  Structured arrays are just ndarrays with a complicated data type:

{{{
#!python numbers=disable
In [1]: from numpy import *
In [2]: ones(3, dtype=dtype([('foo', int), ('bar', float)]))
Out[2]:
array([(1, 1.0), (1, 1.0), (1, 1.0)],
      dtype=[('foo', '<i4'), ('bar', '<f8')])
In [3]: r = _
In [4]: r['foo']
Out[4]: array([1, 1, 1])
}}}
recarray is a subclass of ndarray that just adds attribute access to structured arrays:

{{{
#!python numbers=disable
In [10]: r2 = r.view(recarray)
In [11]: r2
Out[11]:
recarray([(1, 1.0), (1, 1.0), (1, 1.0)],
      dtype=[('foo', '<i4'), ('bar', '<f8')])
In [12]: r2.foo
Out[12]: array([1, 1, 1])
}}}
One downside of recarrays is that the attribute access feature slows down all field accesses, even the r['foo'] form, because it sticks a bunch of pure Python code in the middle. Much code won't notice this, but if you end up having to iterate over an array of records, this will be a hotspot for you.

Structured arrays are sometimes confusingly called record arrays.

 . - lightly adapted from a Robert Kern post of Thu, 26 Jun 2008 15:25:11 -0500
= Converting to regular arrays and reshaping =
A little script showing how to efficiently reformat structured arrays into normal ndarrays.

Based on: [http://old.nabble.com/printing-structured-arrays-td27794203.html printing structured arrays].

{{{
#!python numbers=disable

import numpy as np

data = [ (1, 2), (3, 4.1), (13, 77) ]
dtype = [('x', float), ('y', float)]

print('\n ndarray')
nd = np.array(data)
print nd

print ('\n structured array')

struct_1dtype = np.array(data, dtype=dtype)
print struct_1dtype

print('\n structured to ndarray')
struct_1dtype_float = struct_1dtype.view(np.ndarray).reshape(len(struct_1dtype), -1) 
print struct_1dtype_float

print('\n structured to float: alternative ways')
struct_1dtype_float_alt = struct_1dtype.view((np.float, len(struct_1dtype.dtype.names)))
print struct_1dtype_float_alt

# with heterogeneous dtype.
struct_diffdtype = np.array([(1.0, 'string1', 2.0), (3.0, 'string2', 4.1)],
dtype=[('x', float),('str_var', 'a7'),('y',float)])
print('\n structured array with different dtypes')
print struct_diffdtype
struct_diffdtype_nd = struct_diffdtype[['str_var', 'x', 'y']].view(np.ndarray).reshape(len(struct_diffdtype), -1) 


print('\n structured array with different dtypes to reshaped ndarray')
print struct_diffdtype_nd


print('\n structured array with different dtypes to reshaped float array ommiting string columns')
struct_diffdtype_float = struct_diffdtype[['x', 'y']].view(float).reshape(len(struct_diffdtype),-1)
print struct_diffdtype_float

}}}