You can use pandas.DataFrame.mask
to add virtually as many conditions as you need:
data = {'a': [1,2,3,4,5], 'b': [6,8,9,10,11]}
d = pd.DataFrame.from_dict(data, orient='columns')
c = {'c1': (2, 'Value1'), 'c2': (3, 'Value2'), 'c3': (5, d['b'])}
d['new'] = np.nan
for value in c.values():
d['new'].mask(d['a'] == value[0], value[1], inplace=True)
d['new'] = d['new'].fillna('Else')
d
Output:
a b new
0 1 6 Else
1 2 8 Value1
2 3 9 Value2
3 4 10 Else
4 5 11 11