Modernize the Sorting HowTo guide (gh-115479)

Doc/howto/sorting.rst

@@ -4,7 +4,6 @@ Sorting HOW TO
 **************
 
 :Author: Andrew Dalke and Raymond Hettinger
-:Release: 0.1
 
 
 Python lists have a built-in :meth:`list.sort` method that modifies the list
@@ -56,7 +55,7 @@ For example, here's a case-insensitive string comparison:
 
 .. doctest::
 
-    >>> sorted("This is a test string from Andrew".split(), key=str.lower)
+    >>> sorted("This is a test string from Andrew".split(), key=str.casefold)
     ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
 
 The value of the *key* parameter should be a function (or other callable) that
@@ -97,10 +96,14 @@ The same technique works for objects with named attributes. For example:
     >>> sorted(student_objects, key=lambda student: student.age)   # sort by age
     [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
 
-Operator Module Functions
-=========================
+Objects with named attributes can be made by a regular class as shown
+above, or they can be instances of :class:`~dataclasses.dataclass` or
+a :term:`named tuple`.
 
-The key-function patterns shown above are very common, so Python provides
+Operator Module Functions and Partial Function Evaluation
+=========================================================
+
+The :term:`key function` patterns shown above are very common, so Python provides
 convenience functions to make accessor functions easier and faster. The
 :mod:`operator` module has :func:`~operator.itemgetter`,
 :func:`~operator.attrgetter`, and a :func:`~operator.methodcaller` function.
@@ -128,6 +131,24 @@ sort by *grade* then by *age*:
     >>> sorted(student_objects, key=attrgetter('grade', 'age'))
     [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
 
+The :mod:`functools` module provides another helpful tool for making
+key-functions.  The :func:`~functools.partial` function can reduce the
+`arity <https://en.wikipedia.org/wiki/Arity>`_ of a multi-argument
+function making it suitable for use as a key-function.
+
+.. doctest::
+
+    >>> from functools import partial
+    >>> from unicodedata import normalize
+
+    >>> names = 'Zoë Åbjørn Núñez Élana Zeke Abe Nubia Eloise'.split()
+
+    >>> sorted(names, key=partial(normalize, 'NFD'))
+    ['Abe', 'Åbjørn', 'Eloise', 'Élana', 'Nubia', 'Núñez', 'Zeke', 'Zoë']
+
+    >>> sorted(names, key=partial(normalize, 'NFC'))
+    ['Abe', 'Eloise', 'Nubia', 'Núñez', 'Zeke', 'Zoë', 'Åbjørn', 'Élana']
+
 Ascending and Descending
 ========================
 
@@ -200,6 +221,8 @@ This idiom is called Decorate-Sort-Undecorate after its three steps:
 
 For example, to sort the student data by *grade* using the DSU approach:
 
+.. doctest::
+
     >>> decorated = [(student.grade, i, student) for i, student in enumerate(student_objects)]
     >>> decorated.sort()
     >>> [student for grade, i, student in decorated]               # undecorate
@@ -282,7 +305,11 @@ Odds and Ends
     [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
 
   However, note that ``<`` can fall back to using :meth:`~object.__gt__` if
-  :meth:`~object.__lt__` is not implemented (see :func:`object.__lt__`).
+  :meth:`~object.__lt__` is not implemented (see :func:`object.__lt__`
+  for details on the mechanics).  To avoid surprises, :pep:`8`
+  recommends that all six comparison methods be implemented.
+  The :func:`~functools.total_ordering` decorator is provided to make that
+  task easier.
 
 * Key functions need not depend directly on the objects being sorted. A key
   function can also access external resources. For instance, if the student grades
@@ -295,3 +322,24 @@ Odds and Ends
     >>> newgrades = {'john': 'F', 'jane':'A', 'dave': 'C'}
     >>> sorted(students, key=newgrades.__getitem__)
     ['jane', 'dave', 'john']
+
+Partial Sorts
+=============
+
+Some applications require only some of the data to be ordered.  The standard
+library provides several tools that do less work than a full sort:
+
+* :func:`min` and :func:`max` return the smallest and largest values,
+  respectively.  These functions make a single pass over the input data and
+  require almost no auxiliary memory.
+
+* :func:`heapq.nsmallest` and :func:`heapq.nlargest` return
+  the *n* smallest and largest values, respectively.  These functions
+  make a single pass over the data keeping only *n* elements in memory
+  at a time.  For values of *n* that are small relative to the number of
+  inputs, these functions make far fewer comparisons than a full sort.
+
+* :func:`heapq.heappush` and :func:`heapq.heappop` create and maintain a
+  partially sorted arrangement of data that keeps the smallest element
+  at position ``0``.  These functions are suitable for implementing
+  priority queues which are commonly used for task scheduling.