@unutbu's answer is spot on, just wanted to add a warning that your mask needs to be dtype bool, not 'object'. Ie your mask can't have ever had any nan's. See here - even if your mask is nan-free now, it will remain 'object' type.
The inverse of an 'object' series won't throw an error, instead you'll get a garbage mask of ints that won't work as you expect.
In[1]: df = pd.DataFrame({'A':[True, False, np.nan], 'B':[True, False, True]})
In[2]: df.dropna(inplace=True)
In[3]: df['A']
Out[3]:
0 True
1 False
Name: A, dtype object
In[4]: ~df['A']
Out[4]:
0 -2
0 -1
Name: A, dtype object
After speaking with colleagues about this one I have an explanation: It looks like pandas is reverting to the bitwise operator:
In [1]: ~True
Out[1]: -2
As @geher says, you can convert it to bool with astype before you inverse with ~
~df['A'].astype(bool)
0 False
1 True
Name: A, dtype: bool
(~df['A']).astype(bool)
0 True
1 True
Name: A, dtype: bool