a & b & c is equivalent to
import functools
print(functools.reduce(lambda x,y: x & y, [a, b, c]))
which yields
0 False
1 False
2 True
dtype: bool
Unlike my original answer below (suggesting np.logical_and.reduce), I am confident functools.reduce(lambda x,y: x & y, [a, b, c]) will faithfully return the same Series as a & b & c.
(In Python2.7, reduce is a builtin function. functools.reduce is the same function as reduce. In Python3, reduce was removed from the builtins and only functools.reduce remains. So to future-proof your code, use functools.reduce.)
Edit: Using np.logical_and.reduce([logic]) may not work in all situations. Here is a counterexample:
import pandas as pd
import numpy as np
x = pd.Series([True,True,False,False], index=[1,2,3,4])
y = pd.Series([True,True,False,False], index=[1,2,3,4])
print(x & y)
prints
1 True
2 True
3 False
4 False
dtype: bool
but np.logical_and.reduce([x,y]) raises a ValueError
print(np.logical_and.reduce([x,y]))
File "/data1/unutbu/.virtualenvs/dev/local/lib/python2.7/site-packages/pandas-0.13.0_98_gd9b0c1f-py2.7-linux-i686.egg/pandas/core/generic.py", line 665, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
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