How to return a view of several columns in numpy structured array

Question

I can see several columns (fields) at once in a numpy structured array by indexing with a list of the field names, for example

import n         


        

Answer 1

You can create a dtype object contains only the fields that you want, and use numpy.ndarray() to create a view of original array:

import numpy as np
strc = np.zeros(3, dtype=[('x', int), ('y', float), ('z', int), ('t', "i8")])

def fields_view(arr, fields):
    dtype2 = np.dtype({name:arr.dtype.fields[name] for name in fields})
    return np.ndarray(arr.shape, dtype2, arr, 0, arr.strides)

v1 = fields_view(strc, ["x", "z"])
v1[0] = 10, 100

v2 = fields_view(strc, ["y", "z"])
v2[1:] = [(3.14, 7)]

v3 = fields_view(strc, ["x", "t"])

v3[1:] = [(1000, 2**16)]

print(strc)

here is the output:

[(10, 0.0, 100, 0L) (1000, 3.14, 7, 65536L) (1000, 3.14, 7, 65536L)]