Not trolling here. Just curious.
The first example in the article demonstrates what I mean by "computation composition". "mdiv(2.0, 3.0)" returns a Maybe, not a float, and before using it as a float the inner value must be unpacked. That can be done without the monad operators, but is verbose and error-prone:
mdiv :: Float -> Float -> Float
mdiv a b = if b == 0
then Nothing
else Just (a / b)
with_maybe :: Maybe Float
with_maybe = case mdiv 2.0 2.0 of
Nothing -> Nothing
Just val1 -> case mdiv 3.0 0.0 of
Nothing -> Nothing
Just val2 -> case mdiv val1 val2 of
Nothing -> Nothing
Just val3 -> Just val3
with_maybe :: Maybe Float
with_maybe =
mdiv 2.0 2.0 >>= (\val1 ->
mdiv 3.0 0.0 >>= (\val2 ->
mdiv val1 val2 >>= (\val3 ->
Just val3)))
with_maybe :: Maybe Float
with_maybe = do
val1 <- mdiv 2.0 2.0
val2 <- mdiv 3.0 0.0
val3 <- mdiv val1 val2
Just val3The author claims we should use monads in Python code. However, he leaves the explanation for another day. Hopefully it will be quite enlightening.
I can't really see what Monads offer that Python is already well equipped to handle. We have weak-typing and exceptions. These are tools enough to call a function who may give us a random value of any type and we go off and start using it. If something blows up, we beg for forgiveness and can even try again... or whatever we need to do.
Then again, perhaps I'm missing the point as well.