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authorAndrew Kuchling <amk@amk.ca>2013-05-20 14:14:53 (GMT)
committerAndrew Kuchling <amk@amk.ca>2013-05-20 14:14:53 (GMT)
commit2a9c8e8cd316f7463585e8d1ed76b0bfa6a77f5c (patch)
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#17955: minor updates to Functional howto
* Describe compress() and accumulate() * Add a subsection on combinatoric functions. * Add a forward link to skip the theoretical discussion in the first section. * Clarify what filterfalse() is the opposite of. * Remove the old outline and some notes at the end. * Various small edits.
-rw-r--r--Doc/howto/functional.rst144
1 files changed, 91 insertions, 53 deletions
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])
-
-