CODEWITHSUNDEEP
pythonpython-3.x
gaussclb
gaussclb

Reputation: 1247

How to distinguish np.ndarray or None?

There is the code:

import numpy as np

def check(x):
    if x == None:
        print('x is None')

check(np.array([1,2]))

x can be None or np.ndarray, so I want to check whether x is None or np.ndarray, but if I pass np.ndarray into check, it will raise a error, because np.ndarray==None should use np.any() or np.all(). So what should I do?

Upvotes: 2

Views: 872

Answers (3)

Ilja Everilä
Ilja Everilä

Reputation: 52949

The reason it raises the value error is that with a recent enough version of Numpy the __eq__ override of arrays does an elementwise object comparison even when comparing to None.

With Numpy 1.12.1:

In [2]: if np.array([1,2,3]) == None:
   ...:     print('Equals None')
   ...: else:
   ...:     print('Does not equal None')
   ...:     
/home/user/Work/SO/bin/ipython:1: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  #!/home/user/Work/SO/bin/python3
Does not equal None

and with Numpy 1.13.1:

In [1]: if np.array([1,2,3]) == None:
   ...:     print('Equals None')
   ...: else:
   ...:     print('Does not equal None')
   ...:     
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-06133f6732e1> in <module>()
----> 1 if np.array([1,2,3]) == None:
      2     print('Equals None')
      3 else:
      4     print('Does not equal None')
      5 

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

The error is quite self explanatory. Numpy has a different view on truthiness of an array compared to how plain Python sequences behave.

In order to check if an object is the singleton value None, you should use identity comparison as also explained here:

def check(x):
    if x is None:
        print('x is None')

    else:
        print('x is not None')

Upvotes: 0

Brad Solomon
Brad Solomon

Reputation: 40908

Checking for ndarray type

Preferred over type(x): use isinstance.

From PEP 8:

Object type comparisons should always use isinstance() instead of comparing types directly.

In your example: use if isinstance(x, np.ndarray):

Checking if x is None

Option 1: Use elif x is None:. This explicitly checks that x is None.

Option 2: Use elif not x:. This takes advantage of the "Falsiness" of None, but it also would evaluate to True if x is other "Falsey" values such as np.nan, 0, or an empty data structure.

Upvotes: 1

Kian
Kian

Reputation: 1350

Try this one:

import numpy as np

def check(x):
    if type(x) is np.ndarray:
        print('x is numpy.ndarray')
    else:
        raise ValueError("x is None")

check(np.array([1, 2]))

Upvotes: 1

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