CODEWITHSUNDEEP
pythonnumpy
gboffi
gboffi

Reputation: 25023

Adding columns to a structured Numpy array

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).

PS I'd prefer not to pandas.

Upvotes: 0

Views: 2819

Answers (1)

Eric
Eric

Reputation: 97565

You can use np.lib.recfunctions:

import numpy.lib.recfunctions as rfn

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

Upvotes: 5

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