I have a given array:
array = [(u'Andrew', -3, 3, 100.032) (u'Bob', -4, 4, 103.323) (u'Joe', -5, 5, 154.324)]
that is generated from another process (that I cannot control) of taking a CSV table and it outputs this numpy array. I now need to assign the dtypes of the columns to do further analysis.
How can I do this?
Thank you
Is this what you need ?
new_array = np.array(array, dtype = [("name", object),
("N1", int),
("N2", int),
("N3", float)])
where name and N1-3 are column names I gave.
It gives :
array([(u'Andrew', -3, 3, 100.032), (u'Bob', -4, 4, 103.323),
(u'Joe', -5, 5, 154.324)],
dtype=[('name', 'O'), ('N1', '<i8'), ('N2', '<i8'), ('N3', '<f8')])
You can sort on "N1" for instance :
new_array.sort(order="N1")
new_array
array([(u'Joe', -5, 5, 154.324), (u'Bob', -4, 4, 103.323),
(u'Andrew', -3, 3, 100.032)],
dtype=[('name', 'O'), ('N1', '<i8'), ('N2', '<i8'), ('N3', '<f8')])
Hope this helps.
recarr = np.rec.fromrecords(array)
Optionally set field names:
recarr = np.rec.fromrecords(array, names="name, idata, idata2, fdata")
