You have to create a new dtype that contains the new field.
For example, here's a
:
In [86]: a
Out[86]:
array([(1, [-112.01268501699997, 40.64249414272372]),
(2, [-111.86145708699996, 40.4945008710162])],
dtype=[('i', '<i8'), ('loc', '<f8', (2,))])
a.dtype.descr
is [('i', '<i8'), ('loc', '<f8', (2,))]
; i.e. a list of field types. We'll create a new dtype by adding ('USNG', 'S100')
to the end of that list:
In [87]: new_dt = np.dtype(a.dtype.descr + [('USNG', 'S100')])
Now create a new structured array, b
. I used zeros
here, so the string fields will start out with the value ''
. You could also use empty
. The strings will then contain garbage, but that won't matter if you immediately assign values to them.
In [88]: b = np.zeros(a.shape, dtype=new_dt)
Copy over the existing data from a
to b
:
In [89]: b['i'] = a['i']
In [90]: b['loc'] = a['loc']
Here's b
now:
In [91]: b
Out[91]:
array([(1, [-112.01268501699997, 40.64249414272372], ''),
(2, [-111.86145708699996, 40.4945008710162], '')],
dtype=[('i', '<i8'), ('loc', '<f8', (2,)), ('USNG', 'S100')])
Fill in the new field with some data:
In [93]: b['USNG'] = ['FOO', 'BAR']
In [94]: b
Out[94]:
array([(1, [-112.01268501699997, 40.64249414272372], 'FOO'),
(2, [-111.86145708699996, 40.4945008710162], 'BAR')],
dtype=[('i', '<i8'), ('loc', '<f8', (2,)), ('USNG', 'S100')])