Dumb implementation of monads in Python.
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A monad is just a monoid in the category of endofunctors, what's the problem? -Philip Wadler

Just playing around with implementing monads in Python.

There's also a little metaclass muckery thrown in for good measure!

Don't take it too seriously, this is just a toy project.

I was good enough to include a test suite though.

## Quick Examples

Currently Maybe, Either, List, Reader, State and Writer are all implemented in either full or partial fashion. I've also included an applicative dictionary instance named Map.

pynads will also export Reader aliased to Function and R, but these resolve to the original Reader class.

###A Truly 10+ Developer Example

Every Python library needs to be able to tackle FizzBuzz! Which is universally considered the premier live coding test for interviews.

from itertools import repeat
from functools import partial

pairs = ((3, 'fizz'), (5, 'buzz'))

def fizzer(n, pairs):
fizzed = ''.join([buzz for fizz, buzz in pairs if n and not n % fizz]) or n
return Writer(n+1, {n:fizzed})

multibind(Writer.unit(1), *repeat(partial(fizzer, pairs=pairs), 15))
# Writer(16, {1:1, 2:2, 3:'fizz', 4:4, 5:'buzz', 6:'fizz', 7:7, 8:8,
#             9:'fizz', 10:'buzz', 11:11, 12:'fizz', 13:13, 14:14
#             15:'fizzbuzz'})

Interviewer tried to one up you and asked for multiplies of 7 to evaluate to bazz? Modify the pairs tuple. Extensible and short.

You can't out fizzbuzz this.

###Putting values in Context

Maybe.unit(4) # Returns Just(4)

Or use a functional approach:

unit(4, Maybe)

Either.unit(4)      # Right 4
List.unit(4)        # List 4
Writer.unit(4)      # Writer (4, Mempty)
Map.unit(('a', 4))  # Map({'a': 4})
State.unit(4)       # State(lambda s: (4, s))

The Maybe monad abuses __new__ to perform a check when instantiating an object. You can pass a checker function to determine if a Just or Nothing should be returned. By default, if the value being passed is None you get a Nothing.

>>> # only even Justs
>>> is_even = lambda x: not x%2
>>> Maybe(3, checker=is_even)
... Nothing
>>> Maybe(4, checker=is_even)

Shamelessly, this is lifted from PyMonad.

List makes no promises of splatting iterables when using the unit method:

List.unit("hello")   # List("hello")
List.unit({'a': 4})  # List({'a': 4})
List.unit([4, 5])    # List([4, 5])

Reader's unit method puts a value into a closure that ignores any input and returns the original value.

print(r(None)) # boom, 4 pops out!

Kinda similar to Reader, State's unit method creates a lambda that awaits some input but returns a tuple of (original_value, passed_value)

s = State.unit(4)
print(s(None)) # out pops (4, None)

###Bind, Shove, >>=, et. al

Use a bastardized version of Haskell's >>=:

Just(4) >> (lambda v: Maybe(v+2))

Chain them together with excessive parens:

Maybe.unit(4) >> (lambda v: v+2) >> (lambda v: Nothing)

Tired of guarding against Nones?

from random import randint

x = randint(1,10)
return x if x%2 else None

inc = lambda x: Just(x+1)

Now you can focus what your fail condition is, rather than how to handle it!

Use the bind assignment operator:

m = Just(2)
m <<= lambda x: Just(x+2) # now it's Just 4

When using Reader and pulling from the environment is needed, nesting lambdas is necessary, formatted for readability:

>>> from operators import itemgetter as read
>>> comp = R(read('a')) >> (lambda a:
R.unit(a+b)             ))
>>> comp({'a': 10, 'b': 7})
... 17

If you've got more binds than editor columns, you can use pynads.funcs.multibind to chain them all together.

from itertools import repeat
multibind(Just(0), *repeat(lambda x: Just(x+2), 3))

###Functors There are here too! Functor is more a protocol, but also serves as an interface as well. A class must implement the fmap method to fulfill the protocol or interface.

class FMapDict(dict, Functor):
def fmap(self, f):
return FMapDict((k, f(v)) for k,v in self.items())

Note: FMapDict is actually implemented at pynads.concrete.map.Map as both a functor and an applicative functor.

Two ways to use!:

fmd = FMapDict((i,i) for i in range(10))
fmd.fmap(lambda x: x+1)
fmap(lambda x: x+1, fmd)

I lied, there's three! In Haskell there's <\$> that serves as an infix fmap. Of course it'll make an appearance here, too!

(lambda x: x+1) % fmd

% was chosen because if you squint really hard, ignore the angle brackets, then % and <\$> kinda look similar. It's also on the same precedence level as *, forcing Python to evaluate it first without extra parens.

###Applicatives We got applicatives as well!

Just(lambda x: x+2).apply(Just(2))

But wait, there's an operator!

Just(lambda x: x+2) * Just(2)

from random import randint

x = randint(1,10)
return Maybe(x if x%2 else None)

add_two = lambda x: lambda y: x+y

Got a curried function (or just a bunch of nested single argument lambdas)?!

fake_curry = lambda x: lambda y: lambda z: x+y+z

multiapply(Just(fake_curry), Just(1), Just(2), Just(3))

###Monoids! Sure, why not. pynads has an abstract Monoid base class at pynads.abc.monoid.Monoid (but just from pynads import Monoid).

The Monoid abstract base class allows joining together monoids with the + operator (or sum or whatever) by defining an mappend method which + will delegate to. In addition, the monoid must define an mempty value which represents its "empty" or "zero" value.

This class also provides a default implementation of mconcat which uses functools.reduce and the mappend method defined in the instance. Subclasses of Monoid are welcome to replace mconcat with their own versions.

But that doesn't stop pynads from mappending and mconcatting its way through built in types. int, float, complex, set, frozenset, bool, list, str, dict... pynads don't care, it'll figure out how to handle it. And it doesn't stop there, it can determine how to mappend a few non-built in types as well like decimal.Decimal or your class that extends from collections.abc.Set. Yes, yes, it uses type checking but it tries its best to do it against ABCs rather than hard types.

###Mempty With monoids comes mempty! And pynads has a pretty nifty trick up its sleeve when it comes to dealing with mempty. Haskell allows us to use mempty as a placeholder value. And so does pynads with the special Mempty singleton (didn't need to be a singleton though, just taking your RAM into consideration). Mempty just stands around and waits for a real monoid to show up. That means if you try to use mappend or mconcat on it, Mempty goes, "Here, let me step out of the way...". If you try to mconcat with all values set to Mempty, you get a Mempty back ready to step out of the way again.

On top of all that, it reminds me Memphis when I type out Mempty and just makes me yearn for real BBQ.

##Coming Soon! Despite this being a toy implementation of Haskell things in Python, I've taken quite a shine to it. Currently, these are the things in the works:

• do notation (by abusing the coroutine protocol and decorators)
• Expanded notes section so if something seems really odd, you can see my thought process and the notes I've taken.
• Continuation Monad -- maybe, I'm not sure if I want to tackle this
• Monad Transformers -- once I fully apperciate them

##Sources Of course, I didn't come up with this idea on my own. Many implementations of Haskell-style monads in Python exist. And many sources have informed my understanding of how each monad operates in Haskell. I doubt I'll list all of them here as some were found by desperate Google searching and I've forgotten to record them somewhere.