Adding columns to a structured Numpy array

Question

I have the following data in geo.dat

id  lon  lat inhab  name
 1   9.  45.   100  Ciriè
 2  10.  45.    60  Acquanegra

and I get it in a ndarray

import numpy as np
data = np.genfromtxt('geo.dat', dtype=None, names=True)

so far, so good, I have a data structure that I can address by column name

print(data['name'][1]) #>>> Acquanegra

Next step, and question — I have a function that takes in input two vectors of geographical coordinates ( data['LON'] and data['LAT'] of course) and returns two arrays x and y of projected positions on a map (this works ok).

I can live with separate vectors x and y but I'd like to augment data with two new columns, data['x'] and data['y'] . My naive attempt

data['x'], data['y'] = convert(data['LON'], data['LAT'])

raised a ValueError: no field of name x , teaching me that data has some traits of a dictionary but a dictionary is not.

Is it possible to do as I want? tia

Please consider that .hstack() doesn't work with structured arrays aka record arrays, most previous answers work only for homogeneous arrays (the exception is mentioned in below comment by Warren).

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PS I'd prefer not to pandas .

Answer 1

You can use np.lib.recfunctions :

import numpy.lib.recfunctions as rfn

data = rfn.append_fields(data, ['x', 'y'], [x, y])