Doc/howto/functional.rst

Functional Programming HOWTO
================================

**Version 0.30**

(This is a first draft.  Please send comments/error
reports/suggestions to amk@amk.ca.  This URL is probably not going to
be the final location of the document, so be careful about linking to
it -- you may want to add a disclaimer.)

In this document, we'll take a tour of Python's features suitable for
implementing programs in a functional style.  After an introduction to
the concepts of functional programming, we'll look at language
features such as iterators and generators and relevant library modules
such as ``itertools`` and ``functools``.


.. contents::

Introduction
----------------------

This section explains the basic concept of functional programming; if
you're just interested in learning about Python language features,
skip to the next section.

Programming languages support decomposing problems in several different 
ways:

* Most programming languages are **procedural**: 
  programs are lists of instructions that tell the computer what to
  do with the program's input.
  C, Pascal, and even Unix shells are procedural languages.

* In **declarative** languages, you write a specification that describes 
  the problem to be solved, and the language implementation figures out 
  how to perform the computation efficiently.  SQL is the declarative 
  language you're most likely to be familiar with; a SQL query describes
  the data set you want to retrieve, and the SQL engine decides whether to 
  scan tables or use indexes, which subclauses should be performed first,
  etc.

* **Object-oriented** programs manipulate  collections of objects.
  Objects have internal state and support methods that query or modify
  this internal state in some way. Smalltalk and Java are
  object-oriented languages.  C++ and Python are languages that
  support object-oriented programming, but don't force the use 
  of object-oriented features.

* **Functional** programming decomposes a problem into a set of functions.
  Ideally, functions only take inputs and produce outputs, and don't have any 
  internal state that affects the output produced for a given input.
  Well-known functional languages include the ML family (Standard ML,
  OCaml, and other variants) and Haskell.

The designers of some computer languages have chosen one approach to 
programming that's emphasized.  This often makes it difficult to
write programs that use a different approach.  Other languages are
multi-paradigm languages that support several different approaches.  Lisp,
C++, and Python are multi-paradigm; you can write programs or
libraries that are largely procedural, object-oriented, or functional
in all of these languages.  In a large program, different sections
might be written using different approaches; the GUI might be object-oriented
while the processing logic is procedural or functional, for example.

In a functional program, input flows through a set of functions. Each
function operates on its input and produces some output.  Functional
style frowns upon functions with side effects that modify internal
state or make other changes that aren't visible in the function's
return value.  Functions that have no side effects at all are 
called **purely functional**.
Avoiding side effects means not using data structures
that get updated as a program runs; every function's output 
must only depend on its input.

Some languages are very strict about purity and don't even have
assignment statements such as ``a=3`` or ``c = a + b``, but it's
difficult to avoid all side effects.  Printing to the screen or
writing to a disk file are side effects, for example.  For example, in
Python a ``print`` statement or a ``time.sleep(1)`` both return no
useful value; they're only called for their side effects of sending
some text to the screen or pausing execution for a second.

Python programs written in functional style usually won't go to the
extreme of avoiding all I/O or all assignments; instead, they'll
provide a functional-appearing interface but will use non-functional
features internally.  For example, the implementation of a function
will still use assignments to local variables, but won't modify global
variables or have other side effects.

Functional programming can be considered the opposite of
object-oriented programming.  Objects are little capsules containing
some internal state along with a collection of method calls that let
you modify this state, and programs consist of making the right set of
state changes.  Functional programming wants to avoid state changes as
much as possible and works with data flowing between functions.  In
Python you might combine the two approaches by writing functions that
take and return instances representing objects in your application
(e-mail messages, transactions, etc.).

Functional design may seem like an odd constraint to work under.  Why
should you avoid objects and side effects?  There are theoretical and
practical advantages to the functional style:

* Formal provability.
* Modularity.
* Composability.
* Ease of debugging and testing.

Formal provability
''''''''''''''''''''''

A theoretical benefit is that it's easier to construct a mathematical proof
that a functional program is correct.

For a long time researchers have been interested in finding ways to
mathematically prove programs correct.  This is different from testing
a program on numerous inputs and concluding that its output is usually
correct, or reading a program's source code and concluding that the
code looks right; the goal is instead a rigorous proof that a program
produces the right result for all possible inputs.

The technique used to prove programs correct is to write down 
**invariants**, properties of the input data and of the program's 
variables that are always true.  For each line of code, you then show 
that if invariants X and Y are true **before** the line is executed, 
the slightly different invariants X' and Y' are true **after**
the line is executed.  This continues until you reach the end of the
program, at which point the invariants should match the desired 
conditions on the program's output.

Functional programming's avoidance of assignments arose because 
assignments are difficult to handle with this technique; 
assignments can break invariants that were true before the assignment
without producing any new invariants that can be propagated onward.

Unfortunately, proving programs correct is largely impractical and not
relevant to Python software. Even trivial programs require proofs that
are several pages long; the proof of correctness for a moderately
complicated program would be enormous, and few or none of the programs
you use daily (the Python interpreter, your XML parser, your web
browser) could be proven correct.  Even if you wrote down or generated
a proof, there would then be the question of verifying the proof;
maybe there's an error in it, and you wrongly believe you've proved
the program correct.

Modularity
''''''''''''''''''''''

A more practical benefit of functional programming is that it forces
you to break apart your problem into small pieces.  Programs are more
modular as a result.  It's easier to specify and write a small
function that does one thing than a large function that performs a
complicated transformation.  Small functions are also easier to read
and to check for errors.


Ease of debugging and testing 
''''''''''''''''''''''''''''''''''

Testing and debugging a functional-style program is easier.

Debugging is simplified because functions are generally small and
clearly specified.  When a program doesn't work, each function is an
interface point where you can check that the data are correct.  You
can look at the intermediate inputs and outputs to quickly isolate the
function that's responsible for a bug.

Testing is easier because each function is a potential subject for a
unit test.  Functions don't depend on system state that needs to be
replicated before running a test; instead you only have to synthesize
the right input and then check that the output matches expectations.



Composability
''''''''''''''''''''''

As you work on a functional-style program, you'll write a number of
functions with varying inputs and outputs.  Some of these functions
will be unavoidably specialized to a particular application, but
others will be useful in a wide variety of programs.  For example, a
function that takes a directory path and returns all the XML files in
the directory, or a function that takes a filename and returns its
contents, can be applied to many different situations.

Over time you'll form a personal library of utilities.  Often you'll
assemble new programs by arranging existing functions in a new
configuration and writing a few functions specialized for the current
task.



Iterators
-----------------------

I'll start by looking at a Python language feature that's an important
foundation for writing functional-style programs: iterators.

An iterator is an object representing a stream of data; this object
returns the data one element at a time.  A Python iterator must
support a method called ``__next__()`` that takes no arguments and always
returns the next element of the stream.  If there are no more elements
in the stream, ``__next__()`` must raise the ``StopIteration`` exception.
Iterators don't have to be finite, though; it's perfectly reasonable
to write an iterator that produces an infinite stream of data.
The built-in ``next()`` function is normally used to call the iterator's
``__next__()`` method.

The built-in ``iter()`` function takes an arbitrary object and tries
to return an iterator that will return the object's contents or
elements, raising ``TypeError`` if the object doesn't support
iteration.  Several of Python's built-in data types support iteration,
the most common being lists and dictionaries.  An object is called 
an **iterable** object if you can get an iterator for it.

You can experiment with the iteration interface manually::

    >>> L = [1,2,3]
    >>> it = iter(L)
    >>> print it
    <iterator object at 0x8116870>
    >>> next(it)
    1
    >>> next(it)
    2
    >>> next(it)
    3
    >>> next(it)
    Traceback (most recent call last):
      File "<stdin>", line 1, in ?
    StopIteration
    >>>      

Python expects iterable objects in several different contexts, the 
most important being the ``for`` statement.  In the statement ``for X in Y``,
Y must be an iterator or some object for which ``iter()`` can create 
an iterator.  These two statements are equivalent::

        for i in iter(obj):
            print i

        for i in obj:
            print i

Iterators can be materialized as lists or tuples by using the
``list()`` or ``tuple()`` constructor functions::

    >>> L = [1,2,3]
    >>> iterator = iter(L)
    >>> t = tuple(iterator)
    >>> t
    (1, 2, 3)

Sequence unpacking also supports iterators: if you know an iterator 
will return N elements, you can unpack them into an N-tuple::

    >>> L = [1,2,3]
    >>> iterator = iter(L)
    >>> a,b,c = iterator
    >>> a,b,c
    (1, 2, 3)

Built-in functions such as ``max()`` and ``min()`` can take a single
iterator argument and will return the largest or smallest element.
The ``"in"`` and ``"not in"`` operators also support iterators: ``X in
iterator`` is true if X is found in the stream returned by the
iterator.  You'll run into obvious problems if the iterator is
infinite; ``max()``, ``min()``, and ``"not in"`` will never return, and
if the element X never appears in the stream, the ``"in"`` operator
won't return either.

Note that you can only go forward in an iterator; there's no way to
get the previous element, reset the iterator, or make a copy of it.
Iterator objects can optionally provide these additional capabilities,
but the iterator protocol only specifies the ``__next__()`` method.
Functions may therefore consume all of the iterator's output, and if
you need to do something different with the same stream, you'll have
to create a new iterator.



Data Types That Support Iterators
'''''''''''''''''''''''''''''''''''

We've already seen how lists and tuples support iterators.  In fact,
any Python sequence type, such as strings, will automatically support
creation of an iterator.

Calling ``iter()`` on a dictionary returns an iterator that will loop
over the dictionary's keys::

    >>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,
    ...      'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
    >>> for key in m:
    ...     print key, m[key]
    Mar 3
    Feb 2
    Aug 8
    Sep 9
    May 5
    Jun 6
    Jul 7
    Jan 1
    Apr 4
    Nov 11
    Dec 12
    Oct 10

Note that the order is essentially random, because it's based on the
hash ordering of the objects in the dictionary.

Applying ``iter()`` to a dictionary always loops over the keys, but
dictionaries have methods that return other iterators.  If you want to
iterate over keys, values, or key/value pairs, you can explicitly call
the ``iterkeys()``, ``itervalues()``, or ``iteritems()`` methods to
get an appropriate iterator.

The ``dict()`` constructor can accept an iterator that returns a
finite stream of ``(key, value)`` tuples::

    >>> L = [('Italy', 'Rome'), ('France', 'Paris'), ('US', 'Washington DC')]
    >>> dict(iter(L))
    {'Italy': 'Rome', 'US': 'Washington DC', 'France': 'Paris'}

Files also support iteration by calling the ``readline()``
method until there are no more lines in the file.  This means you can
read each line of a file like this::

    for line in file:
        # do something for each line
        ...

Sets can take their contents from an iterable and let you iterate over
the set's elements::

    S = set((2, 3, 5, 7, 11, 13))
    for i in S:
        print i



Generator expressions and list comprehensions
----------------------------------------------------

Two common operations on an iterator's output are 1) performing some
operation for every element, 2) selecting a subset of elements that
meet some condition.  For example, given a list of strings, you might
want to strip off trailing whitespace from each line or extract all
the strings containing a given substring.

List comprehensions and generator expressions (short form: "listcomps"
and "genexps") are a concise notation for such operations, borrowed
from the functional programming language Haskell
(http://www.haskell.org).  You can strip all the whitespace from a
stream of strings with the following code::

        line_list = ['  line 1\n', 'line 2  \n', ...]

        # Generator expression -- returns iterator
        stripped_iter = (line.strip() for line in line_list)

        # List comprehension -- returns list
        stripped_list = [line.strip() for line in line_list]

You can select only certain elements by adding an ``"if"`` condition::

        stripped_list = [line.strip() for line in line_list
                         if line != ""]

With a list comprehension, you get back a Python list;
``stripped_list`` is a list containing the resulting lines, not an
iterator.  Generator expressions return an iterator that computes the
values as necessary, not needing to materialize all the values at
once.  This means that list comprehensions aren't useful if you're
working with iterators that return an infinite stream or a very large
amount of data.  Generator expressions are preferable in these
situations.

Generator expressions are surrounded by parentheses ("()") and list
comprehensions are surrounded by square brackets ("[]").  Generator
expressions have the form::

    ( expression for expr in sequence1 
                 if condition1
                 for expr2 in sequence2
                 if condition2
                 for expr3 in sequence3 ...
                 if condition3
                 for exprN in sequenceN
                 if conditionN )

Again, for a list comprehension only the outside brackets are
different (square brackets instead of parentheses).

The elements of the generated output will be the successive values of
``expression``.  The ``if`` clauses are all optional; if present,
``expression`` is only evaluated and added to the result when
``condition`` is true.

Generator expressions always have to be written inside parentheses,
but the parentheses signalling a function call also count.  If you
want to create an iterator that will be immediately passed to a
function you can write::

        obj_total = sum(obj.count for obj in list_all_objects())

The ``for...in`` clauses contain the sequences to be iterated over.
The sequences do not have to be the same length, because they are
iterated over from left to right, **not** in parallel.  For each
element in ``sequence1``, ``sequence2`` is looped over from the
beginning.  ``sequence3``  is then looped over for each 
resulting pair of elements from ``sequence1`` and ``sequence2``.

To put it another way, a list comprehension or generator expression is
equivalent to the following Python code::

    for expr1 in sequence1:
        if not (condition1):
            continue   # Skip this element
        for expr2 in sequence2:
            if not (condition2):
                continue    # Skip this element
            ...
            for exprN in sequenceN:
                 if not (conditionN):
                     continue   # Skip this element

                 # Output the value of 
                 # the expression.

This means that when there are multiple ``for...in`` clauses but no
``if`` clauses, the length of the resulting output will be equal to
the product of the lengths of all the sequences.  If you have two
lists of length 3, the output list is 9 elements long::

    seq1 = 'abc'
    seq2 = (1,2,3)
    >>> [ (x,y) for x in seq1 for y in seq2]
    [('a', 1), ('a', 2), ('a', 3), 
     ('b', 1), ('b', 2), ('b', 3), 
     ('c', 1), ('c', 2), ('c', 3)]

To avoid introducing an ambiguity into Python's grammar, if
``expression`` is creating a tuple, it must be surrounded with
parentheses.  The first list comprehension below is a syntax error,
while the second one is correct::

    # Syntax error
    [ x,y for x in seq1 for y in seq2]
    # Correct
    [ (x,y) for x in seq1 for y in seq2]


Generators
-----------------------

Generators are a special class of functions that simplify the task of
writing iterators.  Regular functions compute a value and return it,
but generators return an iterator that returns a stream of values.

You're doubtless familiar with how regular function calls work in
Python or C.  When you call a function, it gets a private namespace
where its local variables are created.  When the function reaches a
``return`` statement, the local variables are destroyed and the
value is returned to the caller.  A later call to the same function
creates a new private namespace and a fresh set of local
variables. But, what if the local variables weren't thrown away on
exiting a function?  What if you could later resume the function where
it left off?  This is what generators provide; they can be thought of
as resumable functions.

Here's the simplest example of a generator function::

    def generate_ints(N):
        for i in range(N):
            yield i

Any function containing a ``yield`` keyword is a generator function;
this is detected by Python's bytecode compiler which compiles the
function specially as a result.

When you call a generator function, it doesn't return a single value;
instead it returns a generator object that supports the iterator
protocol.  On executing the ``yield`` expression, the generator
outputs the value of ``i``, similar to a ``return``
statement.  The big difference between ``yield`` and a
``return`` statement is that on reaching a ``yield`` the
generator's state of execution is suspended and local variables are
preserved.  On the next call ``next(generator)``,
the function will resume executing.  

Here's a sample usage of the ``generate_ints()`` generator::

    >>> gen = generate_ints(3)
    >>> gen
    <generator object at 0x8117f90>
    >>> next(gen)
    0
    >>> next(gen)
    1
    >>> next(gen)
    2
    >>> next(gen)
    Traceback (most recent call last):
      File "stdin", line 1, in ?
      File "stdin", line 2, in generate_ints
    StopIteration

You could equally write ``for i in generate_ints(5)``, or
``a,b,c = generate_ints(3)``.

Inside a generator function, the ``return`` statement can only be used
without a value, and signals the end of the procession of values;
after executing a ``return`` the generator cannot return any further
values.  ``return`` with a value, such as ``return 5``, is a syntax
error inside a generator function.  The end of the generator's results
can also be indicated by raising ``StopIteration`` manually, or by
just letting the flow of execution fall off the bottom of the
function.

You could achieve the effect of generators manually by writing your
own class and storing all the local variables of the generator as
instance variables.  For example, returning a list of integers could
be done by setting ``self.count`` to 0, and having the
``__next__()`` method increment ``self.count`` and return it.
However, for a moderately complicated generator, writing a
corresponding class can be much messier.

The test suite included with Python's library, ``test_generators.py``,
contains a number of more interesting examples.  Here's one generator
that implements an in-order traversal of a tree using generators
recursively.

::

    # A recursive generator that generates Tree leaves in in-order.
    def inorder(t):
        if t:
            for x in inorder(t.left):
                yield x

            yield t.label

            for x in inorder(t.right):
                yield x

Two other examples in ``test_generators.py`` produce
solutions for the N-Queens problem (placing N queens on an NxN
chess board so that no queen threatens another) and the Knight's Tour
(finding a route that takes a knight to every square of an NxN chessboard
without visiting any square twice).



Passing values into a generator
''''''''''''''''''''''''''''''''''''''''''''''

In Python 2.4 and earlier, generators only produced output.  Once a
generator's code was invoked to create an iterator, there was no way to
pass any new information into the function when its execution is
resumed.  You could hack together this ability by making the
generator look at a global variable or by passing in some mutable object
that callers then modify, but these approaches are messy.

In Python 2.5 there's a simple way to pass values into a generator.
``yield`` became an expression, returning a value that can be assigned
to a variable or otherwise operated on::

    val = (yield i)

I recommend that you **always** put parentheses around a ``yield``
expression when you're doing something with the returned value, as in
the above example.  The parentheses aren't always necessary, but it's
easier to always add them instead of having to remember when they're
needed.

(PEP 342 explains the exact rules, which are that a
``yield``-expression must always be parenthesized except when it
occurs at the top-level expression on the right-hand side of an
assignment.  This means you can write ``val = yield i`` but have to
use parentheses when there's an operation, as in ``val = (yield i)
+ 12``.)

Values are sent into a generator by calling its
``send(value)`` method.  This method resumes the 
generator's code and the ``yield`` expression returns the specified
value.  If the regular ``__next__()`` method is called, the
``yield`` returns ``None``.

Here's a simple counter that increments by 1 and allows changing the
value of the internal counter.

::

    def counter (maximum):
        i = 0
        while i < maximum:
            val = (yield i)
            # If value provided, change counter
            if val is not None:
                i = val
            else:
                i += 1

And here's an example of changing the counter:

    >>> it = counter(10)
    >>> print next(it)
    0
    >>> print next(it)
    1
    >>> print it.send(8)
    8
    >>> print next(it)
    9
    >>> print next(it)
    Traceback (most recent call last):
      File ``t.py'', line 15, in ?
        print next(it)
    StopIteration

Because ``yield`` will often be returning ``None``, you
should always check for this case.  Don't just use its value in
expressions unless you're sure that the ``send()`` method
will be the only method used resume your generator function.

In addition to ``send()``, there are two other new methods on
generators:

* ``throw(type, value=None, traceback=None)`` is used to raise an exception inside the
  generator; the exception is raised by the ``yield`` expression
  where the generator's execution is paused.

* ``close()`` raises a ``GeneratorExit``
  exception inside the generator to terminate the iteration.  
  On receiving this
  exception, the generator's code must either raise
  ``GeneratorExit`` or ``StopIteration``; catching the 
  exception and doing anything else is illegal and will trigger
  a ``RuntimeError``.  ``close()`` will also be called by 
  Python's garbage collector when the generator is garbage-collected.

  If you need to run cleanup code when a ``GeneratorExit`` occurs,
  I suggest using a ``try: ... finally:`` suite instead of 
  catching ``GeneratorExit``.

The cumulative effect of these changes is to turn generators from
one-way producers of information into both producers and consumers.

Generators also become **coroutines**, a more generalized form of
subroutines.  Subroutines are entered at one point and exited at
another point (the top of the function, and a ``return``
statement), but coroutines can be entered, exited, and resumed at
many different points (the ``yield`` statements).  


Built-in functions
----------------------------------------------

Let's look in more detail at built-in functions often used with iterators.

Two Python's built-in functions, ``map()`` and ``filter()``, are
somewhat obsolete; they duplicate the features of list comprehensions
but return actual lists instead of iterators.  

``map(f, iterA, iterB, ...)`` returns a list containing ``f(iterA[0],
iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ...``.  

::

    def upper(s):
        return s.upper()
    map(upper, ['sentence', 'fragment']) =>
      ['SENTENCE', 'FRAGMENT']

    [upper(s) for s in ['sentence', 'fragment']] =>
      ['SENTENCE', 'FRAGMENT']

As shown above, you can achieve the same effect with a list
comprehension.  The ``itertools.imap()`` function does the same thing
but can handle infinite iterators; it'll be discussed later, in the section on 
the ``itertools`` module.

``filter(predicate, iter)`` returns a list 
that contains all the sequence elements that meet a certain condition,
and is similarly duplicated by list comprehensions.
A **predicate** is a function that returns the truth value of
some condition; for use with ``filter()``, the predicate must take a 
single value.  

::

    def is_even(x):
        return (x % 2) == 0

    filter(is_even, range(10)) =>
      [0, 2, 4, 6, 8]

This can also be written as a list comprehension::

    >>> [x for x in range(10) if is_even(x)]
    [0, 2, 4, 6, 8]

``filter()`` also has a counterpart in the ``itertools`` module,
``itertools.ifilter()``, that returns an iterator and 
can therefore handle infinite sequences just as ``itertools.imap()`` can.

``reduce(func, iter, [initial_value])`` doesn't have a counterpart in
the ``itertools`` module because it cumulatively performs an operation
on all the iterable's elements and therefore can't be applied to
infinite iterables.  ``func`` must be a function that takes two elements
and returns a single value.  ``reduce()`` takes the first two elements
A and B returned by the iterator and calculates ``func(A, B)``.  It
then requests the third element, C, calculates ``func(func(A, B),
C)``, combines this result with the fourth element returned, and
continues until the iterable is exhausted.  If the iterable returns no
values at all, a ``TypeError`` exception is raised.  If the initial
value is supplied, it's used as a starting point and
``func(initial_value, A)`` is the first calculation.

::

    import operator
    reduce(operator.concat, ['A', 'BB', 'C']) =>
      'ABBC'
    reduce(operator.concat, []) =>
      TypeError: reduce() of empty sequence with no initial value
    reduce(operator.mul, [1,2,3], 1) =>
      6
    reduce(operator.mul, [], 1) =>
      1

If you use ``operator.add`` with ``reduce()``, you'll add up all the 
elements of the iterable.  This case is so common that there's a special
built-in called ``sum()`` to compute it::

    reduce(operator.add, [1,2,3,4], 0) =>
      10
    sum([1,2,3,4]) =>
      10
    sum([]) =>
      0

For many uses of ``reduce()``, though, it can be clearer to just write
the obvious ``for`` loop::

    # Instead of:
    product = reduce(operator.mul, [1,2,3], 1)

    # You can write:
    product = 1
    for i in [1,2,3]:
        product *= i


``enumerate(iter)`` counts off the elements in the iterable, returning
2-tuples containing the count and each element.

::

    enumerate(['subject', 'verb', 'object']) =>
      (0, 'subject'), (1, 'verb'), (2, 'object')

``enumerate()`` is often used when looping through a list 
and recording the indexes at which certain conditions are met::

    f = open('data.txt', 'r')
    for i, line in enumerate(f):
        if line.strip() == '':
            print 'Blank line at line #%i' % i

``sorted(iterable, [cmp=None], [key=None], [reverse=False)`` 
collects all the elements of the iterable into a list, sorts 
the list, and returns the sorted result.  The ``cmp``, ``key``, 
and ``reverse`` arguments are passed through to the 
constructed list's ``.sort()`` method.

::

    import random
    # Generate 8 random numbers between [0, 10000)
    rand_list = random.sample(range(10000), 8)
    rand_list =>
      [769, 7953, 9828, 6431, 8442, 9878, 6213, 2207]
    sorted(rand_list) =>
      [769, 2207, 6213, 6431, 7953, 8442, 9828, 9878]
    sorted(rand_list, reverse=True) =>
      [9878, 9828, 8442, 7953, 6431, 6213, 2207, 769]

(For a more detailed discussion of sorting, see the Sorting mini-HOWTO
in the Python wiki at http://wiki.python.org/moin/HowTo/Sorting.)

The ``any(iter)`` and ``all(iter)`` built-ins look at 
the truth values of an iterable's contents.  ``any()`` returns 
True if any element in the iterable is a true value, and ``all()`` 
returns True if all of the elements are true values::

    any([0,1,0]) =>
      True
    any([0,0,0]) =>
      False
    any([1,1,1]) =>
      True
    all([0,1,0]) =>
      False
    all([0,0,0]) => 
      False
    all([1,1,1]) =>
      True


Small functions and the lambda statement
----------------------------------------------

When writing functional-style programs, you'll often need little
functions that act as predicates or that combine elements in some way.

If there's a Python built-in or a module function that's suitable, you
don't need to define a new function at all::

        stripped_lines = [line.strip() for line in lines]
        existing_files = filter(os.path.exists, file_list)

If the function you need doesn't exist, you need to write it.  One way
to write small functions is to use the ``lambda`` statement.  ``lambda``
takes a number of parameters and an expression combining these parameters,
and creates a small function that returns the value of the expression::

        lowercase = lambda x: x.lower()

        print_assign = lambda name, value: name + '=' + str(value)

        adder = lambda x, y: x+y

An alternative is to just use the ``def`` statement and define a
function in the usual way::

        def lowercase(x):
            return x.lower()

        def print_assign(name, value):
            return name + '=' + str(value)

        def adder(x,y):
            return x + y

Which alternative is preferable?  That's a style question; my usual
course is to avoid using ``lambda``.

One reason for my preference is that ``lambda`` is quite limited in
the functions it can define.  The result has to be computable as a
single expression, which means you can't have multiway
``if... elif... else`` comparisons or ``try... except`` statements.
If you try to do too much in a ``lambda`` statement, you'll end up
with an overly complicated expression that's hard to read.  Quick,
what's the following code doing?

::

    total = reduce(lambda a, b: (0, a[1] + b[1]), items)[1]

You can figure it out, but it takes time to disentangle the expression
to figure out what's going on.  Using a short nested
``def`` statements makes things a little bit better::

    def combine (a, b):
        return 0, a[1] + b[1]

    total = reduce(combine, items)[1]

But it would be best of all if I had simply used a ``for`` loop::

     total = 0
     for a, b in items:
         total += b

Or the ``sum()`` built-in and a generator expression::

     total = sum(b for a,b in items)

Many uses of ``reduce()`` are clearer when written as ``for`` loops.

Fredrik Lundh once suggested the following set of rules for refactoring 
uses of ``lambda``:

1) Write a lambda function.
2) Write a comment explaining what the heck that lambda does.
3) Study the comment for a while, and think of a name that captures
   the essence of the comment.
4) Convert the lambda to a def statement, using that name.
5) Remove the comment.

I really like these rules, but you're free to disagree that this 
lambda-free style is better.


The itertools module
-----------------------

The ``itertools`` module contains a number of commonly-used iterators
as well as functions for combining several iterators.  This section
will introduce the module's contents by showing small examples.

The module's functions fall into a few broad classes:

* Functions that create a new iterator based on an existing iterator.
* Functions for treating an iterator's elements as function arguments.
* Functions for selecting portions of an iterator's output.
* A function for grouping an iterator's output.

Creating new iterators
''''''''''''''''''''''

``itertools.count(n)`` returns an infinite stream of
integers, increasing by 1 each time.  You can optionally supply the
starting number, which defaults to 0::

        itertools.count() =>
          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
        itertools.count(10) =>
          10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...

``itertools.cycle(iter)`` saves a copy of the contents of a provided
iterable and returns a new iterator that returns its elements from
first to last.  The new iterator will repeat these elements infinitely.

::

        itertools.cycle([1,2,3,4,5]) =>
          1, 2, 3, 4, 5, 1, 2, 3, 4, 5, ...

``itertools.repeat(elem, [n])`` returns the provided element ``n``
times, or returns the element endlessly if ``n`` is not provided.

::

    itertools.repeat('abc') =>
      abc, abc, abc, abc, abc, abc, abc, abc, abc, abc, ...
    itertools.repeat('abc', 5) =>
      abc, abc, abc, abc, abc

``itertools.chain(iterA, iterB, ...)`` takes an arbitrary number of
iterables as input, and returns all the elements of the first
iterator, then all the elements of the second, and so on, until all of
the iterables have been exhausted.

::

    itertools.chain(['a', 'b', 'c'], (1, 2, 3)) =>
      a, b, c, 1, 2, 3

``itertools.izip(iterA, iterB, ...)`` takes one element from each iterable
and returns them in a tuple::

    itertools.izip(['a', 'b', 'c'], (1, 2, 3)) =>
      ('a', 1), ('b', 2), ('c', 3)

It's similiar to the built-in ``zip()`` function, but doesn't
construct an in-memory list and exhaust all the input iterators before
returning; instead tuples are constructed and returned only if they're
requested.  (The technical term for this behaviour is 
`lazy evaluation <http://en.wikipedia.org/wiki/Lazy_evaluation>`__.)

This iterator is intended to be used with iterables that are all of
the same length.  If the iterables are of different lengths, the
resulting stream will be the same length as the shortest iterable.

::

    itertools.izip(['a', 'b'], (1, 2, 3)) =>
      ('a', 1), ('b', 2)

You should avoid doing this, though, because an element may be taken
from the longer iterators and discarded.  This means you can't go on
to use the iterators further because you risk skipping a discarded
element.

``itertools.islice(iter, [start], stop, [step])`` returns a stream
that's a slice of the iterator.  With a single ``stop`` argument, 
it will return the first ``stop``
elements.  If you supply a starting index, you'll get ``stop-start``
elements, and if you supply a value for ``step``, elements will be
skipped accordingly.  Unlike Python's string and list slicing, you
can't use negative values for ``start``, ``stop``, or ``step``.

::

    itertools.islice(range(10), 8) =>
      0, 1, 2, 3, 4, 5, 6, 7
    itertools.islice(range(10), 2, 8) =>
      2, 3, 4, 5, 6, 7
    itertools.islice(range(10), 2, 8, 2) =>
      2, 4, 6

``itertools.tee(iter, [n])`` replicates an iterator; it returns ``n``
independent iterators that will all return the contents of the source
iterator.  If you don't supply a value for ``n``, the default is 2.
Replicating iterators requires saving some of the contents of the source
iterator, so this can consume significant memory if the iterator is large
and one of the new iterators is consumed more than the others.

::

        itertools.tee( itertools.count() ) =>
           iterA, iterB

        where iterA ->
           0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...

        and   iterB ->
           0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...


Calling functions on elements
'''''''''''''''''''''''''''''

Two functions are used for calling other functions on the contents of an
iterable.

``itertools.imap(f, iterA, iterB, ...)`` returns 
a stream containing ``f(iterA[0], iterB[0]), f(iterA[1], iterB[1]),
f(iterA[2], iterB[2]), ...``::

    itertools.imap(operator.add, [5, 6, 5], [1, 2, 3]) =>
      6, 8, 8

The ``operator`` module contains a set of functions 
corresponding to Python's operators.  Some examples are 
``operator.add(a, b)`` (adds two values), 
``operator.ne(a, b)`` (same as ``a!=b``),
and 
``operator.attrgetter('id')`` (returns a callable that
fetches the ``"id"`` attribute).

``itertools.starmap(func, iter)`` assumes that the iterable will 
return a stream of tuples, and calls ``f()`` using these tuples as the 
arguments::

    itertools.starmap(os.path.join, 
                      [('/usr', 'bin', 'java'), ('/bin', 'python'),
                       ('/usr', 'bin', 'perl'),('/usr', 'bin', 'ruby')])
    =>
      /usr/bin/java, /bin/python, /usr/bin/perl, /usr/bin/ruby


Selecting elements
''''''''''''''''''

Another group of functions chooses a subset of an iterator's elements
based on a predicate.

``itertools.ifilter(predicate, iter)`` returns all the elements for
which the predicate returns true::

    def is_even(x):
        return (x % 2) == 0

    itertools.ifilter(is_even, itertools.count()) =>
      0, 2, 4, 6, 8, 10, 12, 14, ...

``itertools.ifilterfalse(predicate, iter)`` is the opposite, 
returning all elements for which the predicate returns false::

    itertools.ifilterfalse(is_even, itertools.count()) =>
      1, 3, 5, 7, 9, 11, 13, 15, ...

``itertools.takewhile(predicate, iter)`` returns elements for as long
as the predicate returns true.  Once the predicate returns false, 
the iterator will signal the end of its results.

::

    def less_than_10(x):
        return (x < 10)

    itertools.takewhile(less_than_10, itertools.count()) =>
      0, 1, 2, 3, 4, 5, 6, 7, 8, 9

    itertools.takewhile(is_even, itertools.count()) =>
      0

``itertools.dropwhile(predicate, iter)`` discards elements while the
predicate returns true, and then returns the rest of the iterable's
results.

::

    itertools.dropwhile(less_than_10, itertools.count()) =>
      10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...

    itertools.dropwhile(is_even, itertools.count()) =>
      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...


Grouping elements
'''''''''''''''''

The last function I'll discuss, ``itertools.groupby(iter,
key_func=None)``, is the most complicated.  ``key_func(elem)`` is a
function that can compute a key value for each element returned by the
iterable.  If you don't supply a key function, the key is simply each
element itself.

``groupby()`` collects all the consecutive elements from the
underlying iterable that have the same key value, and returns a stream
of 2-tuples containing a key value and an iterator for the elements
with that key.  

::

    city_list = [('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL'), 
                 ('Anchorage', 'AK'), ('Nome', 'AK'),
                 ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ'), 
                 ...
                ]

    def get_state ((city, state)):
        return state

    itertools.groupby(city_list, get_state) =>
      ('AL', iterator-1),
      ('AK', iterator-2),
      ('AZ', iterator-3), ...

    where
    iterator-1 =>
      ('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL')
    iterator-2 => 
      ('Anchorage', 'AK'), ('Nome', 'AK')
    iterator-3 =>
      ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ')

``groupby()`` assumes that the underlying iterable's contents will
already be sorted based on the key.  Note that the returned iterators
also use the underlying iterable, so you have to consume the results
of iterator-1 before requesting iterator-2 and its corresponding key.


The functools module
----------------------------------------------

The ``functools`` module in Python 2.5 contains some higher-order
functions.  A **higher-order function** takes one or more functions as
input and returns a new function.  The most useful tool in this module
is the ``partial()`` function.

For programs written in a functional style, you'll sometimes want to
construct variants of existing functions that have some of the
parameters filled in.  Consider a Python function ``f(a, b, c)``; you
may wish to create a new function ``g(b, c)`` that's equivalent to
``f(1, b, c)``; you're filling in a value for one of ``f()``'s parameters.  
This is called "partial function application".

The constructor for ``partial`` takes the arguments ``(function, arg1,
arg2, ... kwarg1=value1, kwarg2=value2)``.  The resulting object is
callable, so you can just call it to invoke ``function`` with the
filled-in arguments.

Here's a small but realistic example::

    import functools

    def log (message, subsystem):
        "Write the contents of 'message' to the specified subsystem."
        print '%s: %s' % (subsystem, message)
        ...

    server_log = functools.partial(log, subsystem='server')
    server_log('Unable to open socket')


The operator module
-------------------

The ``operator`` module was mentioned earlier.  It contains a set of
functions corresponding to Python's operators.  These functions 
are often useful in functional-style code because they save you 
from writing trivial functions that perform a single operation.

Some of the functions in this module are:

* Math operations: ``add()``, ``sub()``, ``mul()``, ``div()``, ``floordiv()``,
  ``abs()``, ...
* Logical operations: ``not_()``, ``truth()``.
* Bitwise operations: ``and_()``, ``or_()``, ``invert()``.
* Comparisons: ``eq()``, ``ne()``, ``lt()``, ``le()``, ``gt()``, and ``ge()``.
* Object identity: ``is_()``, ``is_not()``.

Consult `the operator module's documentation <http://docs.python.org/lib/module-operator.html>`__ for a complete
list.



The functional module
---------------------

Collin Winter's `functional module <http://oakwinter.com/code/functional/>`__ 
provides a number of more
advanced tools for functional programming. It also reimplements
several Python built-ins, trying to make them more intuitive to those
used to functional programming in other languages.

This section contains an introduction to some of the most important
functions in ``functional``; full documentation can be found at `the
project's website <http://oakwinter.com/code/functional/documentation/>`__.

``compose(outer, inner, unpack=False)``

The ``compose()`` function implements function composition.
In other words, it returns a wrapper around the ``outer`` and ``inner`` callables, such
that the return value from ``inner`` is fed directly to ``outer``.  That is,

::

        >>> def add(a, b):
        ...     return a + b
        ...
        >>> def double(a):
        ...     return 2 * a
        ...
        >>> compose(double, add)(5, 6)
        22

is equivalent to

::

        >>> double(add(5, 6))
        22
                    
The ``unpack`` keyword is provided to work around the fact that Python functions are not always
`fully curried <http://en.wikipedia.org/wiki/Currying>`__.
By default, it is expected that the ``inner`` function will return a single object and that the ``outer``
function will take a single argument. Setting the ``unpack`` argument causes ``compose`` to expect a
tuple from ``inner`` which will be expanded before being passed to ``outer``. Put simply,

::

        compose(f, g)(5, 6)
                    
is equivalent to::

        f(g(5, 6))
                    
while

::

        compose(f, g, unpack=True)(5, 6)
                    
is equivalent to::

        f(*g(5, 6))

Even though ``compose()`` only accepts two functions, it's trivial to
build up a version that will compose any number of functions. We'll
use ``reduce()``, ``compose()`` and ``partial()`` (the last of which
is provided by both ``functional`` and ``functools``).

::

        from functional import compose, partial
        
        multi_compose = partial(reduce, compose)
        
    
We can also use ``map()``, ``compose()`` and ``partial()`` to craft a
version of ``"".join(...)`` that converts its arguments to string::

        from functional import compose, partial
        
        join = compose("".join, partial(map, str))


``flip(func)``
                    
``flip()`` wraps the callable in ``func`` and  
causes it to receive its non-keyword arguments in reverse order.

::

        >>> def triple(a, b, c):
        ...     return (a, b, c)
        ...
        >>> triple(5, 6, 7)
        (5, 6, 7)
        >>>
        >>> flipped_triple = flip(triple)
        >>> flipped_triple(5, 6, 7)
        (7, 6, 5)

``foldl(func, start, iterable)``
                    
``foldl()`` takes a binary function, a starting value (usually some kind of 'zero'), and an iterable.
The function is applied to the starting value and the first element of the list, then the result of
that and the second element of the list, then the result of that and the third element of the list,
and so on.

This means that a call such as::

        foldl(f, 0, [1, 2, 3])

is equivalent to::

        f(f(f(0, 1), 2), 3)

    
``foldl()`` is roughly equivalent to the following recursive function::

        def foldl(func, start, seq):
            if len(seq) == 0:
                return start

            return foldl(func, func(start, seq[0]), seq[1:])

Speaking of equivalence, the above ``foldl`` call can be expressed in terms of the built-in ``reduce`` like
so::

        reduce(f, [1, 2, 3], 0)


We can use ``foldl()``, ``operator.concat()`` and ``partial()`` to
write a cleaner, more aesthetically-pleasing version of Python's
``"".join(...)`` idiom::

        from functional import foldl, partial
        from operator import concat
        
        join = partial(foldl, concat, "")


Revision History and Acknowledgements
------------------------------------------------

The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Ian Bicking, Nick Coghlan, Nick Efford, Raymond Hettinger,
Jim Jewett, Mike Krell, Leandro Lameiro, Jussi Salmela, 
Collin Winter, Blake Winton.

Version 0.1: posted June 30 2006.

Version 0.11: posted July 1 2006.  Typo fixes.

Version 0.2: posted July 10 2006.  Merged genexp and listcomp
sections into one.  Typo fixes.

Version 0.21: Added more references suggested on the tutor mailing list.

Version 0.30: Adds a section on the ``functional`` module written by
Collin Winter; adds short section on the operator module; a few other
edits.


References
--------------------

General
'''''''''''''''

**Structure and Interpretation of Computer Programs**, by 
Harold Abelson and Gerald Jay Sussman with Julie Sussman.
Full text at http://mitpress.mit.edu/sicp/.
In this classic textbook of computer science,  chapters 2 and 3 discuss the
use of sequences and streams to organize the data flow inside a
program.  The book uses Scheme for its examples, but many of the
design approaches described in these chapters are applicable to
functional-style Python code.

http://www.defmacro.org/ramblings/fp.html: A general 
introduction to functional programming that uses Java examples
and has a lengthy historical introduction.

http://en.wikipedia.org/wiki/Functional_programming:
General Wikipedia entry describing functional programming.

http://en.wikipedia.org/wiki/Coroutine:
Entry for coroutines.

http://en.wikipedia.org/wiki/Currying:
Entry for the concept of currying.

Python-specific
'''''''''''''''''''''''''''

http://gnosis.cx/TPiP/:
The first chapter of David Mertz's book :title-reference:`Text Processing in Python` 
discusses functional programming for text processing, in the section titled
"Utilizing Higher-Order Functions in Text Processing".

Mertz also wrote a 3-part series of articles on functional programming
for IBM's DeveloperWorks site; see 
`part 1 <http://www-128.ibm.com/developerworks/library/l-prog.html>`__,
`part 2 <http://www-128.ibm.com/developerworks/library/l-prog2.html>`__, and
`part 3 <http://www-128.ibm.com/developerworks/linux/library/l-prog3.html>`__,


Python documentation
'''''''''''''''''''''''''''

http://docs.python.org/lib/module-itertools.html:
Documentation for the ``itertools`` module.

http://docs.python.org/lib/module-operator.html:
Documentation for the ``operator`` module.

http://www.python.org/dev/peps/pep-0289/:
PEP 289: "Generator Expressions"

http://www.python.org/dev/peps/pep-0342/
PEP 342: "Coroutines via Enhanced Generators" describes the new generator
features in Python 2.5.

.. comment

    Topics to place
    -----------------------------

    XXX os.walk()

    XXX Need a large example.

    But will an example add much?  I'll post a first draft and see
    what the comments say.

.. comment

    Original outline:
    Introduction
            Idea of FP
                    Programs built out of functions
                    Functions are strictly input-output, no internal state
            Opposed to OO programming, where objects have state

            Why FP?
                    Formal provability
                            Assignment is difficult to reason about
                            Not very relevant to Python
                    Modularity
                            Small functions that do one thing
                    Debuggability:
                            Easy to test due to lack of state
                            Easy to verify output from intermediate steps
                    Composability
                            You assemble a toolbox of functions that can be mixed

    Tackling a problem
            Need a significant example

    Iterators
    Generators
    The itertools module
    List comprehensions
    Small functions and the lambda statement
    Built-in functions
            map
            filter
            reduce

.. comment

    Handy little function for printing part of an iterator -- used
    while writing this document.

    import itertools
    def print_iter(it):
         slice = itertools.islice(it, 10)
         for elem in slice[:-1]:
             sys.stdout.write(str(elem))
             sys.stdout.write(', ')
        print elem[-1]