askewchan - 2 months ago 6

Python Question

I can see several columns (

`fields`

`numpy`

`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`

`zip`

`x = a['x']; y = a['y']`

z = zip(x,y)

z[0] = (9.,9.)

Answer

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)]
```

Source (Stackoverflow)

Comments