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diff --git a/Doc/howto/functional.rst b/Doc/howto/functional.rst index d241f1a..0f4c4e4 100644 --- a/Doc/howto/functional.rst +++ b/Doc/howto/functional.rst @@ -3,7 +3,7 @@ ******************************** :Author: A. M. Kuchling -:Release: 0.31 +:Release: 0.32 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 @@ -15,9 +15,9 @@ concepts of functional programming, we'll look at language features such as 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. +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 on :ref:`functional-howto-iterators`. Programming languages support decomposing problems in several different ways: @@ -173,6 +173,8 @@ new programs by arranging existing functions in a new configuration and writing a few functions specialized for the current task. +.. _functional-howto-iterators: + Iterators ========= @@ -670,7 +672,7 @@ indexes at which certain conditions are met:: :func:`sorted(iterable, key=None, reverse=False) <sorted>` collects all the elements of the iterable into a list, sorts the list, and returns the sorted -result. The *key*, and *reverse* arguments are passed through to the +result. The *key* and *reverse* arguments are passed through to the constructed list's :meth:`~list.sort` method. :: >>> import random @@ -836,7 +838,8 @@ Another group of functions chooses a subset of an iterator's elements based on a predicate. :func:`itertools.filterfalse(predicate, iter) <itertools.filterfalse>` is the -opposite, returning all elements for which the predicate returns false:: +opposite of :func:`filter`, returning all elements for which the predicate +returns false:: itertools.filterfalse(is_even, itertools.count()) => 1, 3, 5, 7, 9, 11, 13, 15, ... @@ -864,6 +867,77 @@ iterable's results. :: itertools.dropwhile(is_even, itertools.count()) => 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ... +:func:`itertools.compress(data, selectors) <itertools.compress>` takes two +iterators and returns only those elements of *data* for which the corresponding +element of *selectors* is true, stopping whenever either one is exhausted:: + + itertools.compress([1,2,3,4,5], [True, True, False, False, True]) => + 1, 2, 5 + + +Combinatoric functions +---------------------- + +The :func:`itertools.combinations(iterable, r) <itertools.combinations>` +returns an iterator giving all possible *r*-tuple combinations of the +elements contained in *iterable*. :: + + itertools.combinations([1, 2, 3, 4, 5], 2) => + (1, 2), (1, 3), (1, 4), (1, 5), + (2, 3), (2, 4), (2, 5), + (3, 4), (3, 5), + (4, 5) + + itertools.combinations([1, 2, 3, 4, 5], 3) => + (1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5), + (2, 3, 4), (2, 3, 5), (2, 4, 5), + (3, 4, 5) + +The elements within each tuple remain in the same order as +*iterable* returned them. For example, the number 1 is always before +2, 3, 4, or 5 in the examples above. A similar function, +:func:`itertools.permutations(iterable, r=None) <itertools.permutations>`, +removes this constraint on the order, returning all possible +arrangements of length *r*:: + + itertools.permutations([1, 2, 3, 4, 5], 2) => + (1, 2), (1, 3), (1, 4), (1, 5), + (2, 1), (2, 3), (2, 4), (2, 5), + (3, 1), (3, 2), (3, 4), (3, 5), + (4, 1), (4, 2), (4, 3), (4, 5), + (5, 1), (5, 2), (5, 3), (5, 4) + + itertools.permutations([1, 2, 3, 4, 5]) => + (1, 2, 3, 4, 5), (1, 2, 3, 5, 4), (1, 2, 4, 3, 5), + ... + (5, 4, 3, 2, 1) + +If you don't supply a value for *r* the length of the iterable is used, +meaning that all the elements are permuted. + +Note that these functions produce all of the possible combinations by +position and don't require that the contents of *iterable* are unique:: + + itertools.permutations('aba', 3) => + ('a', 'b', 'a'), ('a', 'a', 'b'), ('b', 'a', 'a'), + ('b', 'a', 'a'), ('a', 'a', 'b'), ('a', 'b', 'a') + +The identical tuple ``('a', 'a', 'b')`` occurs twice, but the two 'a' +strings came from different positions. + +The :func:`itertools.combinations_with_replacement(iterable, r) <itertools.combinations_with_replacement>` +function relaxes a different constraint: elements can be repeated +within a single tuple. Conceptually an element is selected for the +first position of each tuple and then is replaced before the second +element is selected. :: + + itertools.combinations_with_replacement([1, 2, 3, 4, 5], 2) => + (1, 1), (1, 2), (1, 3), (1, 4), (1, 5), + (2, 2), (2, 3), (2, 4), (2, 5), + (3, 3), (3, 4), (3, 5), + (4, 4), (4, 5), + (5, 5) + Grouping elements ----------------- @@ -986,6 +1060,17 @@ write the obvious :keyword:`for` loop:: for i in [1,2,3]: product *= i +A related function is `itertools.accumulate(iterable, func=operator.add) <itertools.accumulate`. +It performs the same calculation, but instead of returning only the +final result, :func:`accumulate` returns an iterator that also yields +each partial result:: + + itertools.accumulate([1,2,3,4,5]) => + 1, 3, 6, 10, 15 + + itertools.accumulate([1,2,3,4,5], operator.mul) => + 1, 2, 6, 24, 120 + The operator module ------------------- @@ -1159,51 +1244,6 @@ 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 - -.. comment - Handy little function for printing part of an iterator -- used while writing this document. @@ -1214,5 +1254,3 @@ features in Python 2.5. sys.stdout.write(str(elem)) sys.stdout.write(', ') print(elem[-1]) - - |