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Showing content from https://github.com/pandas-dev/pandas/issues/13538 below:

bug in bool type series logical AND operation · Issue #13538 · pandas-dev/pandas · GitHub

s1=pd.Series([True,False,True,True])
s2=pd.Series([True,True,False])

s1.index=pd.MultiIndex.from_tuples( [(0, 2), (1, 1), (1, 2), (2, 1)],names=['st', 'at'])
s2.index=pd.Index([0,1,2], name='st')

ds1=pd.DataFrame(s1)
ds2=pd.DataFrame(s2)

s3=pd.Series([True,False,True])
s4=pd.Series([True,True,False])

st  at
0   2      True
1   1     False
1   2      True
2   1     False
dtype: bool

For series, * and & give different result:

In: s1 & s2
Out: 
st  at
0   2     False
1   1     False
1   2     False
2   1     False
dtype: bool

In: s1 * s2
Out: 
st  at
0   2      True
1   1     False
1   2      True
2   1     False
dtype: bool

BUT for dataframes they give the same:

In: ds1 * ds2
Out: 
           0
st at       
0  2    True
1  1   False
1  2    True
2  1   False

In: ds1 & ds2
Out: 
           0
st at       
0  2    True
1  1   False
1  2    True
2  1   False

and last for series with single index, also both s3 & s4 or s3 * s4 give:

0     True
1    False
2    False
dtype: bool

pandas: 0.18.1

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