Logical operation on two columns of a dataframe

Question

In pandas, I'd like to create a computed column that's a boolean operation on two other columns.

In pandas, it's easy to add together two numerical columns. I'd like to do something similar with logical operator AND. Here's my first try:

In [1]: d = pandas.DataFrame([{'foo':True, 'bar':True}, {'foo':True, 'bar':False}, {'foo':False, 'bar':False}])  In [2]: d Out[2]:       bar    foo 0   True   True 1  False   True 2  False  False  In [3]: d.bar and d.foo   ## can't ... ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). 

So I guess logical operators don't work quite the same way as numeric operators in pandas. I tried doing what the error message suggests and using bool():

In [258]: d.bar.bool() and d.foo.bool()  ## spoiler: this doesn't work either ... ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). 

I found a way that works by casting the boolean columns to int, adding them together and evaluating as a boolean.

In [4]: (d.bar.apply(int) + d.foo.apply(int)) > 0  ## Logical OR Out[4]:  0     True 1     True 2    False dtype: bool  In [5]: (d.bar.apply(int) + d.foo.apply(int)) > 1  ## Logical AND Out[5]:  0     True 1    False 2    False dtype: bool 

This is convoluted. Is there a better way?

Answer 1

Yes there is a better way! Just use the & element-wise logical and operator:

d.bar & d.foo  0     True 1    False 2    False dtype: bool 

Answer 2

Also, there exists another one you could just multiply for AND or add for OR. Without the conversion and extra comparison as you had done.

AND operation:

d.foo * d.bar 

OR operation:

d.foo + d.bar