How to do logical operation between DataFrame and Series?

Question

Suppose I have a bool DataFrame df and a bool Series x with the same index, and I want to do logical operation between df and x per column. Is there any short and fast way like DataFrame.sub compare to using DataFrame.apply ?

In [31]: df
Out[31]: 
       x      y      z      u
A  False  False   True   True
B   True   True   True   True
C   True  False  False  False

In [32]: x
Out[32]: 
A     True
B    False
C     True
dtype: bool

In [33]: r = df.apply(lambda col: col & x) # Any other way ??

In [34]: r
Out[34]: 
       x      y      z      u
A  False  False   True   True
B  False  False  False  False
C   True  False  False  False

Answer 1

Use mul , but need cast to int and then to bool , because UserWarning :

print (df.astype(int).mul(x.values, axis=0).astype(bool))
       x      y      z      u
A  False  False   True   True
B  False  False  False  False
C   True  False  False  False

Similar solution:

print (df.mul(x.astype(int), axis=0).astype(bool))
       x      y      z      u
A  False  False   True   True
B  False  False  False  False
C   True  False  False  False

---

print (df.mul(x.values, axis=0))
       x      y      z      u
A  False  False   True   True
B  False  False  False  False
C   True  False  False  False

C:\\Anaconda3\\lib\\site-packages\\pandas\\computation\\expressions.py:181: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead unsupported[op_str]))

Another numpy solution with np.logical_and :

print (pd.DataFrame(np.logical_and(df.values, x.values[:, None]), 
                                   index=df.index, 
                                   columns=df.columns))

       x      y      z      u
A  False  False   True   True
B  False  False  False  False
C   True  False  False  False