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.

nan != nan. That's just how equality comparisons on nan are defined. It was decided that this result is more convenient for numerical algorithms than the alternative. This is specifically why isnan exists.

numpy.log10 is a "ufunc", and the method Series.apply(func) has a special test for numpy ufuncs which makes test.apply(log10) equivalent to np.log10(test). This means test, a Pandas Series instance, is passed to log10. The data type of test is object, which means that the elements in test can be arbitrary Python objects. np.log10 doesn't know how to handle such a collection of objects (it doesn't "know" that those objects are, in fact, all np.float64 instances), so it attempts to dispatch the calculation to the individual elements in the Series. To do that, it expects the elements themselves to have a log10 method. That's when the error occurs: the elements in the Series (in this case, np.float64 instances) do not have a log10 method.

A couple alternative expression that should do what you want are np.log10(test.astype(np.float64)) or test.astype(np.float64).apply(np.log10). The essential part is that test.astype(np.float64) converts the data type of the Series object from object to np.float64.

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())