Py-ser - 1 year ago 61

Python Question

I have two lists of the same length that I can convert into array to play with the numpy.stats.pearsonr method. Now, some of the elements of these lists are

`nan`

Example: I have

`[1 2 nan 4 5 6 ]`

`[1 nan 3 nan 5 6]`

and in the end I need

`[1 5 6 ]`

`[1 5 6 ]`

(here the number are representative of the position/indices, not of the actual numbers I am dealing with). EDIT: The tricky part here is to have both lists/arrays without

`nan`

`nan`

Answer

The accepted answer to proposed duplicate gets you half-way there. Since you're using Numpy already you should make these into numpy arrays. Then you should generate an indexing expression, and then use it to index these 2 arrays. Here indices will be a new array of `bool`

of same shape where each element is `True`

iff not (respective element in x is `nan`

or respective element in y is `nan`

):

```
>>> x
array([ 1., 2., nan, 4., 5., 6.])
>>> y
array([ 1., nan, 3., nan, 5., 6.])
>>> indices = np.logical_not(np.logical_or(np.isnan(x), np.isnan(y)))
>>> x = x[indices]
>>> y = y[indices]
>>> x
array([ 1., 5., 6.])
>>> y
array([ 1., 5., 6.])
```

Notably, this works for any 2 arrays of same shape.

P.S., if you know that the element type in the operand arrays is boolean, as is the case for arrays returned from `isnan`

here, you can use `~`

instead of `logical_not`

and `|`

instead of `logical_or`

: `indices = ~(np.isnan(x) | np.isnan(y))`

Source (Stackoverflow)