how to replace NaN values in one dataframe with values from another

Question

I have two dataframes. If the values in the index exist in both dataframes then I need to update the NaN values in the first one with the integer value in the second, but only if the value in the second is equal to 1. Below is any example of what I'm trying to do.

df1

index   A     B     C   
aaa    NaN    1    NaN
bbb    NaN   NaN    10
ccc     3    NaN    6
ddd    NaN   NaN    NaN
eee    NaN   NaN    NaN

df2

index   A     B     C   
aaa     1     1     NaN
bbb    NaN   NaN    10
eee    NaN    1     NaN

df1 (after the procedure)

index   A     B     C   
aaa    *1*    1    NaN
bbb    NaN   NaN    10
ccc     3    NaN    6
ddd    NaN   NaN    NaN
eee    NaN   *1*    NaN

The values in df1 (after the procedure) with asterisks around them are the only ones that should change. All other values in df1 (after the procedure) should remain untouched. This is an extension of a previous question I asked and while the answer I got works great I can't seem to extend it to accomplish this last task. I'm still trying to fully understand how it works. I'm new to python. For quick refrence this is the code I have so far that I am trying to extend. Thanks for any help.

df1 = df1.set_index('values')
df2 = df2.set_index('values')

cols = [*df1.columns]
for col in cols:
    #Update to df1
    df1[col].update(df2.loc[df2[col].isnull(), col].fillna('-'))
    df1[col].replace('-', np.NaN, inplace = True)

    #Update to df2, sum if they both have numbers
    df2[col].update(df2.loc[~df2[col].isnull(), col] + df1.loc[~df1[col].isnull(), col])