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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]

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"]

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]

Itertools recipes

fn.iters module consists from two parts. First one is “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)

Second part of module 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

  • Maybe
  • Either

TODO: Implementation, code samples

Trampolines decorator

Workaround for dealing with TCO without heavy stack utilization.

TODO: Implementation, code samples and documented theory.


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 (!)


  • Trampolines decorator
  • Error handling (Maybe, Either from Haskell, Option from Scala etc)
  • Add to fn.iters module foldl, foldr, findelem, findindex
  • C-accelerator for most modules

Ideas to think about:

  • “Pipeline” notation for composition (back-order): F() >> list >> partial(map, int)
  • 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.



  • iters.accumulate - backported version for Python < 3.3


  • 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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