Combining logic statements AND in numpy array

Question

What would be the way to select elements when two conditions are `True` in a matrix? In R, it is basically possible to combine vectors of booleans. So what I'm aiming for: ``` A = np.array([2,2,2,2,2]) A < 3 and A > 1 # A < 3 & A > 1 does not work either ``` Evals to: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() It should eval to: ``` array([True,True,True,True,True]) ``` My workaround usually is to sum these boolean vectors and equate to 2, but there must be a better way. What is it?

Accepted Answer

you could just use `&`, eg: ``` x = np.arange(10) (x<8) & (x>2) ``` gives ``` array([False, False, False, True, True, True, True, True, False, False], dtype=bool) ``` A few details: - This works because `&` is shorthand for the numpy ufunc `bitwise_and`, which for the `bool` type is the same as `logical_and`. That is, this could also be spelled out as `bitwise_and(less(x,8), greater(x,2))` - You need the parentheses because in numpy `&` has higher precedence than `<` and `>` - `and` does not work because it is ambiguous for numpy arrays, so rather than guess, numpy raise the exception.

Combining logic statements AND in numpy array

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