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Implementation of missing features to enjoy functional programming in Python

Project description enjoy FP in Python

Despite the fact that Python is not pure-functional programming language, it’s multi-paradigm PL and it gives you enough freedom to take credits from functional programming approach. There are theoretical and practical advantages to the functional style:

  • Formal provability
  • Modularity
  • Composability
  • Ease of debugging and testing library provides you with missing “batteries” to get maximum from functional approach even in mostly-imperative program.

More about functional approach from my Pycon UA 2012 talks: Functional Programming with Python.

Scala-style lambdas definition

from fn import _
from fn.op import zipwith
from itertools import repeat

assert list(map(_ * 2, range(5))) == [0,2,4,6,8]
assert list(filter(_ < 10, [9,10,11])) == [9]
assert list(zipwith(_ + _)([0,1,2], repeat(10))) == [10,11,12]

More examples of using _ you can find in test cases declaration (attributes resolving, method calling, slicing).

Attention! If you work in interactive python shell, your should remember that _ means “latest output” and you’ll get unpredictable results. In this case, you can do something like from fn import _ as X (and then write functions like X * 2).

If you are not sure, what your function is going to do, you can print it:

from fn import _

print (_ + 2) # "(x1) => (x1 + 2)"
print (_ + _ * _) # "(x1, x2, x3) => (x1 + (x2 * x3))"

_ will fail with ArityError (TypeError subclass) on inaccurate number of passed arguments. This is one more restrictions to ensure that you did everything right:

>>> from fn import _
>>> (_ + _)(1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "fn/", line 82, in __call__
    raise ArityError(self, self._arity, len(args))
fn.underscore.ArityError: (_ + _) expected 2 arguments, got 1

Streams and infinite sequences declaration

Lazy-evaluated scala-style streams. Basic idea: evaluate each new element “on demand” and share calculated elements between all created iterators. Stream object supports << operator that means pushing new elements when it’s necessary.

Simplest cases:

from fn import Stream

s = Stream() << [1,2,3,4,5]
assert list(s) == [1,2,3,4,5]
assert s[1] == 2
assert list(s[0:2]) == [1,2]

s = Stream() << range(6) << [6,7]
assert list(s) == [0,1,2,3,4,5,6,7]

def gen():
    yield 1
    yield 2
    yield 3

s = Stream() << gen << (4,5)
assert list(s) == [1,2,3,4,5]

Lazy-evaluated stream is useful for infinite sequences, i.e. fibonacci sequence can be calculated as:

from fn import Stream
from fn.iters import take, drop, map
from operator import add

f = Stream()
fib = f << [0, 1] << map(add, f, drop(1, f))

assert list(take(10, fib)) == [0,1,1,2,3,5,8,13,21,34]
assert fib[20] == 6765
assert list(fib[30:35]) == [832040,1346269,2178309,3524578,5702887]

Trampolines decorator

fn.recur.tco is a workaround for dealing with TCO without heavy stack utilization. Let’s start from simple example of recursive factorial calculation:

def fact(n):
    if n == 0: return 1
    return n * fact(n-1)

This variant works, but it’s really ugly. Why? It will utilize memory too heavy cause of recursive storing all previous values to calculate final result. If you will execute this function with big n (more then sys.getrecursionlimit()) CPython will fail with

>>> import sys
>>> fact(sys.getrecursionlimit() * 2)
... many many lines of stacktrace ...
RuntimeError: maximum recursion depth exceeded

Which is good, cause it prevents you from terrible mistakes in your code.

How can we optimize this solution? Answer is simple, lets transform function to use tail call:

def fact(n, acc=1):
    if n == 0: return acc
    return fact(n-1, acc*n)

Why this variant is better? Cause you don’t need to remember previous values to calculate final result. More about tail call optimization on Wikipedia. But… Python interpreter will execute this function the same way as previous one, so you won’t win nothing.

fn.recur.tco gives you mechanism to write “optimized a bit” tail call recursion (using “trampoline” approach):

from fn import recur

def fact(n, acc=1):
    if n == 0: return False, acc
    return True, (n-1, acc*n)

@recur.tco is a decorator that execute your function in while loop and check output:

  • (False, result) means that we finished
  • (True, args, kwargs) means that we need to call function again with other arguments
  • (func, args, kwargs) to switch function to be executed inside while loop

The last variant is really useful, when you need to switch callable inside evaluation loop. Good example for such situation is recursive detection if given number is odd or even:

>>> from fn import recur
>>> @recur.tco
... def even(x):
...     if x == 0: return False, True
...     return odd, (x-1,)
>>> @recur.tco
... def odd(x):
...     if x == 0: return False, False
...     return even, (x-1,)
>>> print even(100000)

Attention: be careful with mutable/immutable data structures processing.

High-level operations with functions

fn.F is a useful function wrapper to provide easy-to-use partial application and functions composition.

from fn import F, _
from operator import add, mul

# F(f, *args) means partial application
# same as functools.partial but returns fn.F instance
assert F(add, 1)(10) == 11

# F << F means functions composition,
# so (F(f) << g)(x) == f(g(x))
f = F(add, 1) << F(mul, 100)
assert list(map(f, [0, 1, 2])) == [1, 101, 201]
assert list(map(F() << str << (_ ** 2) << (_ + 1), range(3))) == ["1", "4", "9"]

It also give you move readable in many cases “pipe” notation to deal with functions composition:

from fn import F, _
from fn.iters import filter, range

func = F() >> (filter, _ < 6) >> sum
assert func(range(10)) == 15

You can find more examples for compositions usage in fn._ implementation source code.

fn.op.apply executes given function with given positional arguments in list (or any other iterable). fn.op.flip returns you function that will reverse arguments order before apply.

from fn.op import apply, flip
from operator import add, sub

assert apply(add, [1, 2]) == 3
assert flip(sub)(20,10) == -10
assert list(map(apply, [add, mul], [(1,2), (10,20)])) == [3, 200]

fn.op.foldl and fn.op.foldr are folding operators. Each accepts function with arity 2 and returns function that can be used to reduce iterable to scalar: from left-to-right and from right-to-left in case of foldl and foldr respectively.

from fn import op, _

folder = op.foldr(_ * _, 1)
assert 6 == op.foldl(_ + _)([1,2,3])
assert 6 == folder([1,2,3])

Use case specific for right-side folding is:

from fn.op import foldr, call

assert 100 == foldr(call, 0 )([lambda s: s**2, lambda k: k+10])
assert 400 == foldr(call, 10)([lambda s: s**2, lambda k: k+10])

Itertools recipes

fn.uniform provides you with “unification” of lazy functionality for few functions to work the same way in Python 2+/3+:

  • map (returns itertools.imap in Python 2+)
  • filter (returns itertools.ifilter in Python 2+)
  • reduce (returns functools.reduce in Python 3+)
  • zip (returns itertools.izip in Python 2+)
  • range (returns xrange in Python 2+)
  • filterfalse (returns itertools.ifilterfalse in Python 2+)
  • zip_longest (returns itertools.izip_longest in Python 2+)
  • accumulate (backported to Python < 3.3)

fn.iters is high-level recipes to work with iterators. Most of them taken from Python docs and adopted to work both with Python 2+/3+. Such recipes as drop, takelast, droplast, splitat, splitby I have already submitted as docs patch which is review status just now.

  • take, drop
  • takelast, droplast
  • head, tail
  • consume
  • nth
  • padnone, ncycles
  • repeatfunc
  • grouper, powerset, pairwise
  • roundrobin
  • partition, splitat, splitby
  • flatten
  • iter_except

More information about use cases you can find in docstrings for each function in source code and in test cases.

Functional style for error-handling

fn.monad.Option represents optional values, each instance of Option can be either instance of Full or Empty. It provides you with simple way to write long computation sequences and get rid of many if/else blocks. See usage examples below.

Assume that you have Request class that gives you parameter value by its name. To get uppercase notation for non-empty striped value:

class Request(dict):
    def parameter(self, name):
        return self.get(name, None)

r = Request(testing="Fixed", empty="   ")
param = r.parameter("testing")
if param is None:
    fixed = ""
    param = param.strip()
    if len(param) == 0:
        fixed = ""
        fixed = param.upper()

Hmm, looks ugly.. Update code with fn.monad.Option:

from operator import methodcaller
from fn.monad import optionable

class Request(dict):
    def parameter(self, name):
        return self.get(name, None)

r = Request(testing="Fixed", empty="   ")
fixed = r.parameter("testing")

fn.monad.Option.or_call is good method for trying several variant to end computation. I.e. use have Request class with optional attributes type, mimetype, url. You need to evaluate “request type” using at least one attribute:

from fn.monad import Option

request = dict(url="face.png", mimetype="PNG")
tp = Option \
        .from_value(request.get("type", None)) \ # check "type" key first
        .or_call(from_mimetype, request) \ # or.. check "mimetype" key
        .or_call(from_extension, request) \ # or... get "url" and check extension


To install, simply:

$ pip install fn

Or, if you absolutely must:

$ easy_install fn

You can also build library from source

$ git clone
$ cd
$ python install

Work in progress


  • fn.monad.Either to deal with error logging
  • C-accelerator for most modules

Ideas to think about:

  • Curried function builder to simplify lambda arg1: lambda arg2: ...
  • Scala-style for-yield loop to simplify long map/filter blocks


  1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
  2. Fork the repository on Github to start making your changes to the master branch (or branch off of it).
  3. Write a test which shows that the bug was fixed or that the feature works as expected.

How to find me

  • Twitter: @kachayev
  • Email: kachayev <at>



  • added example of using tail call optimization with changing callable


  • fixed @23 about flipping of underscore function
  • added special uniform module
  • fixed @22 (underscore functions representation)
  • adjustments to unary operators processing in underscore


  • prelimitary implementation of recur.tco to deal with recursive functions
  • iters.flatten is reimplemented to work with different iterators


  • iters.accumulate - backported version for Python < 3.3
  • first implementation for monad.Option with tests and README samples


  • fn.Stream slice is another fn.Stream
  • fn.Stream got new public method cursor to get position on next evaluated element


  • Update documentation with special fn._ use cases for interactive shells
  • Move zipwith from fn.iters to fn.op
  • fn._ dump to string


  • Added 22 itertools recipes to fn.iters
  • Documentation is converted to RST


  • Unit tests coverage for
  • _StreamIterator works fine both in Python 2/3


  • Finished underscore module functionality
  • Test cases for all implemented modules/functions
  • Update in Readme file with several fixes
  • Get rid of F.flip classmethod in pref. for simple building blocks
  • Optimized version for fn.op.flip operator


  • Simplest Stream implementation
  • Code samples for streams, labdas (_) and functions compositions
  • Plan, contribute section in readme file


  • Full list of ideas on paper
  • Repository is created
  • Initial commit

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