modify numpy array to return `nan` out of bounds?

Question

Is there anyway to create a numpy array that returns np.nan when indexed out of bounds? Eg

x = np.array([1,2,3])
x[1] # 2
x[-2] # np.nan
x[5] # np.nan

The closest thing I found was np.pad.

I know I could write a wrapper class, but I was wondering if there's any efficient numpy way to do it.

x[-2] is valid right? Numpy supports negative indices. Would you like to override this feature? — Vishnu Dasu
– Vishnu Dasu, Commented Jun 7, 2019 at 11:58
This seems like something that scipy people would have to add to numpy. I don't think numpy has this option. You can though use try and except, if index not found, return np.nan or something like that. That's the only thing I can think of right now. — Amit Amola
– Amit Amola, Commented Jun 7, 2019 at 12:07
Ah I understand now, you want to disable negative indexing. In that case I think you need a wrapper, exactly like this answer and in __getitem__ you can then also check if the index is out of bounds manually or with a try/except. I don't think you can monkeypatch a np.ndarray. — Jeppe
– Jeppe, Commented Jun 7, 2019 at 13:21

hpaulj · Accepted Answer · 2019-06-07 16:06:05Z

In [360]: x = np.array([1,2,3])                                                                        
In [361]: x[1]                                                                                         
Out[361]: 2

np.take lets you index with mode control. The default is to raise an error if the index is out of bounds (see the docs for other options):

In [363]: np.take(x,1)                                                                                 
Out[363]: 2
In [364]: np.take(x,-2)                                                                                
Out[364]: 2
In [365]: np.take(x,5)                                                                                 
----
IndexError: index 5 is out of bounds for size 3

You could write a little function that wraps this in a try/except, returning np.nan in case of IndexError.

Keep in mind that np.nan is a float, while your example array is integer dtype.

Tommy-Xavier Robillard · Accepted Answer · 2019-06-07 18:13:52Z

-1

You have many choices:

Just insert a try/except in your code.
Create a class that inherit from np.array and redefine the indexing operator to implement the behavior
write a simple function that does the try/except

I like the class one.

answered Jun 7, 2019 at 18:13

Tommy-Xavier Robillard

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