unsupported operand type(s) for *: 'numpy.ndarray' and 'numpy.float64'

Question

long time reader, first time writer.

I searched around on google and stack overflow, but wasn't really able to find a general answer to this question.

I am getting an "unsupported operand type(s) for *: 'numpy.ndarray' and 'numpy.float64'" error in python 2.7.3 using numpy 1.6.2.

The error comes from multiplying a numpy array and a numpy float, but it doesn't happen every time.

For example:

x = np.tan(1) # numpy.float64
y = np.array([0,1,2,3]) # numpy.ndarray
np.multiply(x,y) # works no problem

Or

x = np.tan(np.abs(np.multiply(-31,41)))  # numpy.float64
y = np.square(np.add(np.divide(np.zeros(100),42),(-27)**40)) # numpy.ndarray
np.multiply(x,y) # works no problem

Both work

Now for the problem children:

np.multiply(np.square(np.add(np.divide(np.zeros(100),42),-27)**40)),
np.tan(np.abs(np.multiply(-31,41))))

or, with x defined as above:

np.multiply(np.square(np.add(np.divide(np.zeros(100),42),(-27)**40)),x)

both produce the error: NotImplemented

I know the random functions and numbers seem odd, but conceptually this still should work, as it worked when both were set to variables individually.

Why does this happen? How can I fix it in a general sense?

Thanks so much! Jason

Answer 1

I suspect that the problem here is that NumPy cannot store Python long values in its arrays. As soon as you try to do this, it switches the data type of the array to object. Arithmetic operations on the array then become trickier because NumPy can no longer do the arithmetic itself.

>>> np.array(27**40)
array(1797010299914431210413179829509605039731475627537851106401L, dtype=object)
>>> np.array(27**40) * np.tan(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for *: 'numpy.ndarray' and 'numpy.float64'

Oddly enough, swapping the order of the arguments can sometimes work:

>>> np.tan(1) * np.array(27**40)
2.7986777223711575e+57

In this second case, the type of the result is a Python float, not a NumPy array.

The fix is to avoid creating long values in NumPy arrays, and use floats instead:

>>> np.array(27.0**40)
array(1.797010299914431e+57)
>>> np.array(27.0**40) * np.tan(1)
2.7986777223711575e+57
>>> np.multiply(np.square(np.add(np.divide(np.zeros(10),42),(-27.0)**40)),np.tan(1))
array([  5.02925269e+114,   5.02925269e+114,   5.02925269e+114,
         5.02925269e+114,   5.02925269e+114,   5.02925269e+114,
         5.02925269e+114,   5.02925269e+114,   5.02925269e+114,
         5.02925269e+114])

If you do get an error like this is the future, the first thing to do is to check the dtype of the array being multiplied. Does it contain NumPy values or Python objects?

Answer 2

Is this a quiz? I do not understand why the question is so obfuscated... Everything boils down to this simple fact.

Given

>>> x = 10**100
>>> type(x)
<type 'long'>
>>> y = np.float64(1)

we have

>>> y.__mul__(x)
1e+100
>>> y.__rmul__(x)
NotImplemented

This is the bug (or feature, I do not know) since it should be y.__mul__(x) == y.__rmul__(x) (at least for these particular values of x and y).

Python long does not know how to handle multiplication with numpy.float64 (but this is correct.)

>>> x.__mul__(y)
NotImplemented
>>> x.__rmul__(y)
NotImplemented

So y*x evaluates to y.__mul__(x) and gives the expected result. On the contrary x*y is first tried as x.__mul__(y) (not implemented, OK) than as y.__rmul__(x) (not implemented but a bug.).

As already pointed out, we can have nd.arrays of arbitrary objects, and everything becomes clear.

Edit

This bug has been corrected (probably in numpy ver. 1.7):

>>> np.version.version
'1.13.1'
>>> x = 10**100
>>> y = np.float64(1)
>>> x.__mul__(y)
NotImplemented
>>> x.__rmul__(y)
NotImplemented
>>> y.__mul__(x)
1e+100
>>> y.__rmul__(x)
1e+100
>>> x*y
1e+100
>>> y*x
1e+100