gotchas where Numpy differs from straight python?

Question

Folks,

is there a collection of gotchas where Numpy differs from python, points that have puzzled and cost time ?

"The horror of that moment I shall never never forget !"
"You will, though," the Queen said, "if you don't make a memorandum of it."

For example, NaNs are always trouble, anywhere. If you can explain this without running it, give yourself a point --

from numpy import array, NaN, isnan  pynan = float("nan") print pynan is pynan, pynan is NaN, NaN is NaN a = (0, pynan) print a, a[1] is pynan, any([aa is pynan for aa in a])  a = array(( 0, NaN )) print a, a[1] is NaN, isnan( a[1] ) 

(I'm not knocking numpy, lots of good work there, just think a FAQ or Wiki of gotchas would be useful.)

Edit: I was hoping to collect half a dozen gotchas (surprises for people learning Numpy).
Then, if there are common gotchas or, better, common explanations, we could talk about adding them to a community Wiki (where ?) It doesn't look like we have enough so far.

Answer 1

Because __eq__ does not return a bool, using numpy arrays in any kind of containers prevents equality testing without a container-specific work around.

Example:

>>> import numpy >>> a = numpy.array(range(3)) >>> b = numpy.array(range(3)) >>> a == b array([ True,  True,  True], dtype=bool) >>> x = (a, 'banana') >>> y = (b, 'banana') >>> x == y Traceback (most recent call last):   File "<stdin>", line 1, in <module> ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() 

This is a horrible problem. For example, you cannot write unittests for containers which use TestCase.assertEqual() and must instead write custom comparison functions. Suppose we write a work-around function special_eq_for_numpy_and_tuples. Now we can do this in a unittest:

x = (array1, 'deserialized') y = (array2, 'deserialized') self.failUnless( special_eq_for_numpy_and_tuples(x, y) ) 

Now we must do this for every container type we might use to store numpy arrays. Furthermore, __eq__ might return a bool rather than an array of bools:

>>> a = numpy.array(range(3)) >>> b = numpy.array(range(5)) >>> a == b False 

Now each of our container-specific equality comparison functions must also handle that special case.

Maybe we can patch over this wart with a subclass?

>>> class SaneEqualityArray (numpy.ndarray): ...   def __eq__(self, other): ...     return isinstance(other, SaneEqualityArray) and self.shape == other.shape and (numpy.ndarray.__eq__(self, other)).all() ...  >>> a = SaneEqualityArray( (2, 3) ) >>> a.fill(7) >>> b = SaneEqualityArray( (2, 3) ) >>> b.fill(7) >>> a == b True >>> x = (a, 'banana') >>> y = (b, 'banana') >>> x == y True >>> c = SaneEqualityArray( (7, 7) ) >>> c.fill(7) >>> a == c False 

That seems to do the right thing. The class should also explicitly export elementwise comparison, since that is often useful.