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 numpy as np  a = np.array([(1.5, 2.5, (1.0,2.0)), (3.,4.,(4.,5.)), (1.,3.,(2.,6.))],         dtype=[('x',float), ('y',float), ('value',float,(2,2))])  print a[['x','y']] #[(1.5, 2.5) (3.0, 4.0) (1.0, 3.0)]  print a[['x','y']].dtype #[('x', '<f4') ('y', '<f4')]) 

But the problem is that it seems to be a copy rather than a view:

b = a[['x','y']] b[0] = (9.,9.)  print b #[(9.0, 9.0) (3.0, 4.0) (1.0, 3.0)]  print a[['x','y']] #[(1.5, 2.5) (3.0, 4.0) (1.0, 3.0)] 

If I only select one column, it's a view:

c = x['y'] c[0] = 99.  print c #[ 99.  4.   3. ]  print a['y'] #[ 99.  4.   3. ] 

Is there any way I can get the view behavior for more than one column at once?

I have two workarounds, one is to just loop through the columns, the other is to create a hierarchical dtype, so that the one column actually returns a structured array with the two (or more) fields that I want. Unfortunately, zip also returns a copy, so I can't do:

x = a['x']; y = a['y'] z = zip(x,y) z[0] = (9.,9.) 

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)] 

Answer 2

Building on @HYRY's answer, you could also use ndarray's method getfield:

def fields_view(array, fields):     return array.getfield(numpy.dtype(         {name: array.dtype.fields[name] for name in fields}     ))