Gabriel - 10 months ago 41

Python Question

I want to do something similar to what was asked here NumPy array, change the values that are NOT in a list of indices, but not quite the same.

Consider a

`numpy`

`> a = np.array([0.2, 5.6, 88, 12, 1.3, 6, 8.9])`

I know I can access its elements via a list of indexes, like:

`> indxs = [1, 2, 5]`

> a[indxs]

array([ 5.6, 88. , 6. ])

But I also need to access those elements which are

`indxs`

`> a[not in indxs]`

> array([0.2, 12, 1.3, 8.9])

What is the proper way to do this?

Answer Source

```
In [170]: a = np.array([0.2, 5.6, 88, 12, 1.3, 6, 8.9])
In [171]: idx=[1,2,5]
In [172]: a[idx]
Out[172]: array([ 5.6, 88. , 6. ])
In [173]: np.delete(a,idx)
Out[173]: array([ 0.2, 12. , 1.3, 8.9])
```

`delete`

is more general than you really need, using different strategies depending on the inputs. I think in this case it uses the boolean mask approach (timings should be similar).

```
In [175]: mask=np.ones_like(a, bool)
In [176]: mask
Out[176]: array([ True, True, True, True, True, True, True], dtype=bool)
In [177]: mask[idx]=False
In [178]: mask
Out[178]: array([ True, False, False, True, True, False, True], dtype=bool)
In [179]: a[mask]
Out[179]: array([ 0.2, 12. , 1.3, 8.9])
```