No need for explicit for loops. Based on your second attempt:

# Setup
my_series = pd.Series([np.nan, np.nan, ['A', 'B']])

# Solution
np.where(my_series.isnull(), 'do A', 'do B')                                              

# Output
array(['do A', 'do A', 'do B'], dtype='<U4')

You are applying the isna method to a float object which, as the error states, doesn't have such a method:

>>> import pandas as pd
>>> pd.Series([1, np.nan]).apply(lambda x: x.isna())
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/ubuntu/documents/assets/envs/venv/lib/python3.6/site-packages/pandas/core/series.py", line 4212, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/lib.pyx", line 2403, in pandas._libs.lib.map_infer
  File "<stdin>", line 1, in <lambda>
AttributeError: 'float' object has no attribute 'isna'

You could instead use np.isnan to test whether a float is nan, like so:

>>> pd.Series([1, np.nan]).apply(lambda x: True if not np.isnan(x) else False)
0     True
1    False
dtype: bool

So your function would look like this:

def func(df):
    try:
        test = np.isnan(df['check1']) and np.isnan(df['check2'])
    except Exception as e:
        if 'not supported for the input types' in str(e):
            test = False
        else:
            raise
    return df['colb'] if test else df

You might consider using some other variable name for func besides df as apply applies functions row-wise, not necessarily on an entire dataframe.

You could also use {:.3%} instead of {:.3f}%.

It will transform the value into percentages automatically. That means "{:.3%}".format(0.3) will print "30%" while you have to write "{:.3f}%".format(0.3 * 100) to get "30%" as well.