Numpy Vertical Function :'float' Object Is Not Subscriptable

I have a numpy arrary: import numpy as np pval=np.array([[0., 0.,0., 0., 0.,0., 0., 0.], [0., 0., 0., 0., 0.,0., 0., 0.]]) And a vectorized function: def getnpx(age

Solution 1:

I don't see why you are trying to use frompyfunc. That's for passing array arguments to a function that only takes scalar inputs.

In [97]: pval=np.array([[0., 0.,0., 0., 0.,0., 0., 0.],
    ...:               [0., 0., 0., 0., 0.,0., 0., 0.]])

In the first case you use global pval, and use just 1 age value. No need to frompyfunc:

In[98]: pval[0]+1Out[98]: array([1., 1., 1., 1., 1., 1., 1., 1.])

And if you want to pass pval as argument, just do:

In [99]: def foo(mt,age):
    ...:     return mt[0]+age
    ...: 
In [100]: foo(pval,1)
Out[100]: array([1., 1., 1., 1., 1., 1., 1., 1.])

You gave a link to an earlier question that I answered. The sticky point in that case was that your function returned an array that could vary in size. I showed how to use it with a list comprehension. I also showed how to tweak vectorize so it would happy returning an object dtype result. Alternatively use frompyfunc to return that object. In all those cases the function argument was a scalar, a single number.

If your goal is to add a different age to each row of pval, just do:

In [102]: pval + np.array([[1],[2]])
Out[102]: 
array([[1., 1., 1., 1., 1., 1., 1., 1.],
       [2., 2., 2., 2., 2., 2., 2., 2.]])