Handling attributes of a class within a numpy array

The extra application of modifyvar to the 1st element results from vectorize trying to determine the type of array to return. Specifying the otypes gets around that problem:

vecfunc = np.vectorize(MyClass.modifyvar,otypes=[object])

With this 'inplace' modifier, you don't need to pay attention to what is returned:

vecfunc(myarray2)

is sufficient.

From the vectorize documentation:

The data type of the output of vectorized is determined by calling the function with the first element of the input. This can be avoided by specifying the otypes argument.

If you defined an add5 method like:

    def add5(self):        self.myvar1 += 5        return self.myvar1

then

vecfunc = np.vectorize(MyClass.add5,otypes=[int])vecfunc(myarray2)

would return a numeric array, and modify myarray2 at the same time:

array([15, 15, 15, 15, 15, 15, 15, 15, 15, 15])

to display the values I use:

[x.myvar1 for x in myarray2]

I really should define a vectorized 'print'.

This looks like one of the better applications of vectorize. It doesn't give you any compiled speed, but it does let you use the array notation and broadcasting while operating on your instances one by one. For example vecfunc(myarray2.reshape(2,5)) returns a (2,5) array of values.