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Issue13443 - Remove the functional module examples from 2.7 (as module is 

maintained only till 2.5 and tests had failures with 2.7) and update the links
int the howto document.
This commit is contained in:
Senthil Kumaran 2011-12-22 23:44:53 +08:00
parent 7d4d074c99
commit a9091d2611
1 changed files with 12 additions and 138 deletions
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Doc/howto/functional.rst
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@ -44,15 +44,14 @@ Programming languages support decomposing problems in several different ways:
functional languages include the ML family (Standard ML, OCaml, and other
variants) and Haskell.
The designers of some computer languages choose to emphasize one
particular approach to programming. 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
The designers of some computer languages choose to emphasize one particular
approach to programming. 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
@ -1115,132 +1114,6 @@ Some of the functions in this module are:
Consult the operator module's documentation 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
=====================================
@ -1296,9 +1169,10 @@ 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>`__,
`part 1 <http://www.ibm.com/developerworks/linux/library/l-prog/index.html>`__,
`part 2 <http://www.ibm.com/developerworks/linux/library/l-prog2/index.html>`__, and
`part 3 <http://www.ibm.com/developerworks/linux/library/l-prog3/index.html>`__,
Python documentation