numpy - how to add a value to every element in the first column of an array?

Question

I have an array like this:

array([('6506', 4.6725971801473496e-25, 0.99999999995088695),
       ('6601', 2.2452745388799898e-27, 0.99999999995270605),
       ('21801', 1.9849650921836601e-31, 0.99999999997999001), ...,
       ('45164194', 1.0413482803123399e-24, 0.99999999997453404),
       ('45164198', 1.09470356446595e-24, 0.99999999997635303),
       ('45164519', 3.7521365799080699e-24, 0.99999999997453404)], 
      dtype=[('pos', '|S100'), ('par1', '<f8'), ('par2', '<f8')])

And I want to turn it into this: (adding a prefix '2R' onto each value in the first column)

array([('2R:6506', 4.6725971801473496e-25, 0.99999999995088695),
       ('2R:6601', 2.2452745388799898e-27, 0.99999999995270605),
       ('2R:21801', 1.9849650921836601e-31, 0.99999999997999001), ...,
       ('2R:45164194', 1.0413482803123399e-24, 0.99999999997453404),
       ('2R:45164198', 1.09470356446595e-24, 0.99999999997635303),
       ('2R:45164519', 3.7521365799080699e-24, 0.99999999997453404)], 
      dtype=[('pos', '|S100'), ('par1', '<f8'), ('par2', '<f8')])

I looked up some stuff about nditer (but I want to support earlier versions of numpy.) Also I'm reading one should avoid iteration.

Answer 1

Using numpy.core.defchararray.add:

>>> from numpy import array
>>> from numpy.core.defchararray import add
>>>
>>> xs = array([('6506', 4.6725971801473496e-25, 0.99999999995088695),
...             ('6601', 2.2452745388799898e-27, 0.99999999995270605),
...             ('21801', 1.9849650921836601e-31, 0.99999999997999001),
...             ('45164194', 1.0413482803123399e-24, 0.99999999997453404),
...             ('45164198', 1.09470356446595e-24, 0.99999999997635303),
...             ('45164519', 3.7521365799080699e-24, 0.99999999997453404)],
...            dtype=[('pos', '|S100'), ('par1', '<f8'), ('par2', '<f8')])
>>> xs['pos'] = add('2R:', xs['pos'])
>>> xs
array([('2R:6506', 4.67259718014735e-25, 0.999999999950887),
       ('2R:6601', 2.24527453887999e-27, 0.999999999952706),
       ('2R:21801', 1.98496509218366e-31, 0.99999999997999),
       ('2R:45164194', 1.04134828031234e-24, 0.999999999974534),
       ('2R:45164198', 1.09470356446595e-24, 0.999999999976353),
       ('2R:45164519', 3.75213657990807e-24, 0.999999999974534)],
      dtype=[('pos', 'S100'), ('par1', '<f8'), ('par2', '<f8')])

Answer 2

A simple (albeit perhaps not optimal) solution is just:

a = np.array([('6506', 4.6725971801473496e-25, 0.99999999995088695),
       ('6601', 2.2452745388799898e-27, 0.99999999995270605),
       ('21801', 1.9849650921836601e-31, 0.99999999997999001),
       ('45164194', 1.0413482803123399e-24, 0.99999999997453404),
       ('45164198', 1.09470356446595e-24, 0.99999999997635303),
       ('45164519', 3.7521365799080699e-24, 0.99999999997453404)],
      dtype=[('pos', '|S100'), ('par1', '<f8'), ('par2', '<f8')])


a['pos'] = [''.join(('2R:',x)) for x in a['pos']]

In [11]: a
Out[11]:
array([('2R:6506', 4.67259718014735e-25, 0.999999999950887),
       ('2R:6601', 2.24527453887999e-27, 0.999999999952706),
       ('2R:21801', 1.98496509218366e-31, 0.99999999997999),
       ('2R:45164194', 1.04134828031234e-24, 0.999999999974534),
       ('2R:45164198', 1.09470356446595e-24, 0.999999999976353),
       ('2R:45164519', 3.75213657990807e-24, 0.999999999974534)],
      dtype=[('pos', 'S100'), ('par1', '<f8'), ('par2', '<f8')])

While I like @falsetru's answer for using core numpy routines, surprisingly, list comprehension seems a bit faster:

In [19]: a = np.empty(20000, dtype=[('pos', 'S100'), ('par1', '<f8'), ('par2', '<f8')])

In [20]: %timeit a['pos'] = [''.join(('2R:',x)) for x in a['pos']]
100 loops, best of 3: 11.1 ms per loop

In [21]: %timeit a['pos'] = add('2R:', a['pos'])
100 loops, best of 3: 15.7 ms per loop

Definitely benchmark your own use case and hardware to see which makes more sense for your actual application though. One of the things I've learned is that in certain situations, basic python constructs can outperform numpy built-ins, depending on the task at hand.