np.isnan can be applied to NumPy arrays of native dtype (such as np.float64):

In [99]: np.isnan(np.array([np.nan, 0], dtype=np.float64))
Out[99]: array([ True, False], dtype=bool)

but raises TypeError when applied to object arrays:

In [96]: np.isnan(np.array([np.nan, 0], dtype=object))
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

Since you have Pandas, you could use pd.isnull instead -- it can accept NumPy arrays of object or native dtypes:

In [97]: pd.isnull(np.array([np.nan, 0], dtype=float))
Out[97]: array([ True, False], dtype=bool)

In [98]: pd.isnull(np.array([np.nan, 0], dtype=object))
Out[98]: array([ True, False], dtype=bool)

Note that None is also considered a null value in object arrays.

You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())

Change your code to:

for element in my_series:
    if type(element) == float and pd.isna(element):
        print('do A')
    else:
        print('do B')

Edit following the comment by Peter

I on purpose didn't change the original concept of processing the source Series in a loop. It looks like both print instructions are rather "placeholders", to be replaced with one piece of code for NaN values and another for other values.

The isin function was added in NumPy 1.13:

New np.isin function, improves on in1d.

You're probably using an older version.