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merge 3.2

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Benjamin Peterson 2012-10-12 12:04:32 -04:00
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Doc/glossary.rst
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@ -365,7 +365,7 @@ Glossary
iterator
An object representing a stream of data. Repeated calls to the iterator's
:meth:`__next__` method (or passing it to the built-in function
:meth:`~iterator.__next__` method (or passing it to the built-in function
:func:`next`) return successive items in the stream. When no more data
are available a :exc:`StopIteration` exception is raised instead. At this
point, the iterator object is exhausted and any further calls to its

353
Doc/howto/functional.rst
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@ -181,26 +181,26 @@ 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.
:meth:`~iterator.__next__` that takes no arguments and always returns the next
element of the stream. If there are no more elements in the stream,
:meth:`~iterator.__next__` must raise the :exc:`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 :func:`iter` function takes an arbitrary object and tries to return
an iterator that will return the object's contents or elements, raising
:exc:`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.
dictionaries. An object is called :term:`iterable` if you can get an iterator
for it.
You can experiment with the iteration interface manually:
>>> L = [1,2,3]
>>> it = iter(L)
>>> it
>>> it #doctest: +ELLIPSIS
<...iterator object at ...>
>>> it.__next__()
>>> it.__next__() # same as next(it)
1
>>> next(it)
2
@ -213,9 +213,9 @@ You can experiment with the iteration interface manually:
>>>
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::
important being the :keyword:`for` statement. In the statement ``for X in Y``,
Y must be an iterator or some object for which :func:`iter` can create an
iterator. These two statements are equivalent::
for i in iter(obj):
@ -246,16 +246,16 @@ Built-in functions such as :func:`max` and :func:`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()``
problems if the iterator is infinite; :func:`max`, :func:`min`
will never return, and if the element X never appears in the stream, the
``"in"`` and ``"not in"`` operators 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.
only specifies the :meth:`~iterator.__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.
@ -267,15 +267,11 @@ sequence type, such as strings, will automatically support creation of an
iterator.
Calling :func:`iter` on a dictionary returns an iterator that will loop over the
dictionary's keys:
.. not a doctest since dict ordering varies across Pythons
::
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:
>>> for key in m: #doctest: +SKIP
... print(key, m[key])
Mar 3
Feb 2
@ -296,7 +292,7 @@ ordering of the objects in the dictionary.
Applying :func:`iter` to a dictionary always loops over the keys, but
dictionaries have methods that return other iterators. If you want to iterate
over values or key/value pairs, you can explicitly call the
:meth:`values` or :meth:`items` methods to get an appropriate iterator.
:meth:`~dict.values` or :meth:`~dict.items` methods to get an appropriate iterator.
The :func:`dict` constructor can accept an iterator that returns a finite stream
of ``(key, value)`` tuples:
@ -305,9 +301,9 @@ of ``(key, value)`` tuples:
>>> 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::
Files also support iteration by calling the :meth:`~io.TextIOBase.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
@ -410,12 +406,9 @@ 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:
.. doctest::
:options: +NORMALIZE_WHITESPACE
>>> seq1 = 'abc'
>>> seq2 = (1,2,3)
>>> [(x,y) for x in seq1 for y in seq2]
>>> [(x, y) for x in seq1 for y in seq2] #doctest: +NORMALIZE_WHITESPACE
[('a', 1), ('a', 2), ('a', 3),
('b', 1), ('b', 2), ('b', 3),
('c', 1), ('c', 2), ('c', 3)]
@ -425,9 +418,9 @@ 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]
[x, y for x in seq1 for y in seq2]
# Correct
[ (x,y) for x in seq1 for y in seq2]
[(x, y) for x in seq1 for y in seq2]
Generators
@ -448,15 +441,13 @@ is what generators provide; they can be thought of as resumable functions.
Here's the simplest example of a generator function:
.. testcode::
>>> def generate_ints(N):
... for i in range(N):
... yield i
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 :term:`bytecode` compiler which compiles the function
specially as a result.
Any function containing a :keyword:`yield` keyword is a generator function;
this is detected by Python's :term:`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
@ -464,12 +455,13 @@ 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 to the
generator's ``.__next__()`` method, the function will resume executing.
generator's :meth:`~generator.__next__` method, the function will resume
executing.
Here's a sample usage of the ``generate_ints()`` generator:
>>> gen = generate_ints(3)
>>> gen
>>> gen #doctest: +ELLIPSIS
<generator object generate_ints at ...>
>>> next(gen)
0
@ -491,17 +483,19 @@ 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
:exc:`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.
0, and having the :meth:`~iterator.__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
The test suite included with Python's library,
:source:`Lib/test/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. ::
@ -544,23 +538,23 @@ 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
(: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``.
Values are sent into a generator by calling its :meth:`send(value)
<generator.send>` method. This method resumes the generator's code and the
``yield`` expression returns the specified value. If the regular
:meth:`~generator.__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.
.. testcode::
def counter (maximum):
def counter(maximum):
i = 0
while i < maximum:
val = (yield i)
@ -572,16 +566,16 @@ the internal counter.
And here's an example of changing the counter:
>>> it = counter(10)
>>> next(it)
>>> it = counter(10) #doctest: +SKIP
>>> next(it) #doctest: +SKIP
0
>>> next(it)
>>> next(it) #doctest: +SKIP
1
>>> it.send(8)
>>> it.send(8) #doctest: +SKIP
8
>>> next(it)
>>> next(it) #doctest: +SKIP
9
>>> next(it)
>>> next(it) #doctest: +SKIP
Traceback (most recent call last):
File "t.py", line 15, in ?
it.next()
@ -589,20 +583,23 @@ And here's an example of changing the counter:
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.
:meth:`~generator.send` method will be the only method used resume your
generator function.
In addition to ``send()``, there are two other new methods on generators:
In addition to :meth:`~generator.send`, there are two other 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.
* :meth:`throw(type, value=None, traceback=None) <generator.throw>` 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 :exc:`GeneratorExit` exception inside the generator to
terminate the iteration. On receiving this exception, the generator's code
must either raise :exc:`GeneratorExit` or :exc:`StopIteration`; catching the
exception and doing anything else is illegal and will trigger a
:exc:`RuntimeError`. ``close()`` will also be called by Python's garbage
collector when the generator is garbage-collected.
* :meth:`~generator.close` raises a :exc:`GeneratorExit` exception inside the
generator to terminate the iteration. On receiving this exception, the
generator's code must either raise :exc:`GeneratorExit` or
:exc:`StopIteration`; catching the exception and doing anything else is
illegal and will trigger a :exc:`RuntimeError`. :meth:`~generator.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 :exc:`GeneratorExit` occurs, I suggest
using a ``try: ... finally:`` suite instead of catching :exc:`GeneratorExit`.
@ -624,13 +621,12 @@ Let's look in more detail at built-in functions often used with iterators.
Two of Python's built-in functions, :func:`map` and :func:`filter` duplicate the
features of generator expressions:
``map(f, iterA, iterB, ...)`` returns an iterator over the sequence
:func:`map(f, iterA, iterB, ...) <map>` returns an iterator over the sequence
``f(iterA[0], iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ...``.
>>> def upper(s):
... return s.upper()
>>> list(map(upper, ['sentence', 'fragment']))
['SENTENCE', 'FRAGMENT']
>>> [upper(s) for s in ['sentence', 'fragment']]
@ -638,11 +634,11 @@ features of generator expressions:
You can of course achieve the same effect with a list comprehension.
``filter(predicate, iter)`` returns an iterator over 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 :func:`filter`, the predicate must take a single
value.
:func:`filter(predicate, iter) <filter>` returns an iterator over 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 :func:`filter`, the predicate must take a
single value.
>>> def is_even(x):
... return (x % 2) == 0
@ -657,8 +653,8 @@ This can also be written as a list comprehension:
[0, 2, 4, 6, 8]
``enumerate(iter)`` counts off the elements in the iterable, returning 2-tuples
containing the count and each element. ::
:func:`enumerate(iter) <enumerate>` counts off the elements in the iterable,
returning 2-tuples containing the count and each element. ::
>>> for item in enumerate(['subject', 'verb', 'object']):
... print(item)
@ -674,29 +670,28 @@ indexes at which certain conditions are met::
if line.strip() == '':
print('Blank line at line #%i' % i)
``sorted(iterable, [key=None], [reverse=False])`` 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 constructed list's
``.sort()`` method. ::
: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
constructed list's :meth:`~list.sort` method. ::
>>> import random
>>> # Generate 8 random numbers between [0, 10000)
>>> rand_list = random.sample(range(10000), 8)
>>> rand_list
>>> rand_list #doctest: +SKIP
[769, 7953, 9828, 6431, 8442, 9878, 6213, 2207]
>>> sorted(rand_list)
>>> sorted(rand_list) #doctest: +SKIP
[769, 2207, 6213, 6431, 7953, 8442, 9828, 9878]
>>> sorted(rand_list, reverse=True)
>>> sorted(rand_list, reverse=True) #doctest: +SKIP
[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.)
(For a more detailed discussion of sorting, see the :ref:`sortinghowto`.)
The ``any(iter)`` and ``all(iter)`` built-ins look at the truth values of an
iterable's contents. :func:`any` returns True if any element in the iterable is
a true value, and :func:`all` returns True if all of the elements are true
values:
The :func:`any(iter) <any>` and :func:`all(iter) <all>` built-ins look at the
truth values of an iterable's contents. :func:`any` returns True if any element
in the iterable is a true value, and :func:`all` returns True if all of the
elements are true values:
>>> any([0,1,0])
True
@ -712,7 +707,7 @@ values:
True
``zip(iterA, iterB, ...)`` takes one element from each iterable and
:func:`zip(iterA, iterB, ...) <zip>` takes one element from each iterable and
returns them in a tuple::
zip(['a', 'b', 'c'], (1, 2, 3)) =>
@ -752,42 +747,44 @@ The module's functions fall into a few broad classes:
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::
:func:`itertools.count(n) <itertools.count>` 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. ::
:func:`itertools.cycle(iter) <itertools.cycle>` 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. ::
:func:`itertools.repeat(elem, [n]) <itertools.repeat>` 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. ::
:func:`itertools.chain(iterA, iterB, ...) <itertools.chain>` 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.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``. ::
:func:`itertools.islice(iter, [start], stop, [step]) <itertools.islice>` 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
@ -796,9 +793,10 @@ negative values for ``start``, ``stop``, or ``step``. ::
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
:func:`itertools.tee(iter, [n]) <itertools.tee>` 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. ::
@ -816,19 +814,21 @@ consumed more than the others. ::
Calling functions on elements
-----------------------------
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).
The :mod:`operator` module contains a set of functions corresponding to Python's
operators. Some examples are :func:`operator.add(a, b) <operator.add>` (adds
two values), :func:`operator.ne(a, b) <operator.ne>` (same as ``a != b``), and
:func:`operator.attrgetter('id') <operator.attrgetter>`
(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::
:func:`itertools.starmap(func, iter) <itertools.starmap>` assumes that the
iterable will return a stream of tuples, and calls *func* using these tuples as
the arguments::
itertools.starmap(os.path.join,
[('/usr', 'bin', 'java'), ('/bin', 'python'),
('/usr', 'bin', 'perl'),('/usr', 'bin', 'ruby')])
[('/bin', 'python'), ('/usr', 'bin', 'java'),
('/usr', 'bin', 'perl'), ('/usr', 'bin', 'ruby')])
=>
/usr/bin/java, /bin/python, /usr/bin/perl, /usr/bin/ruby
/bin/python, /usr/bin/java, /usr/bin/perl, /usr/bin/ruby
Selecting elements
@ -837,20 +837,18 @@ Selecting elements
Another group of functions chooses a subset of an iterator's elements based on a
predicate.
``itertools.filterfalse(predicate, iter)`` is the opposite, returning all
elements for which the predicate returns false::
:func:`itertools.filterfalse(predicate, iter) <itertools.filterfalse>` is the
opposite, returning all elements for which the predicate returns false::
itertools.filterfalse(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.
::
:func:`itertools.takewhile(predicate, iter) <itertools.takewhile>` 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)
return x < 10
itertools.takewhile(less_than_10, itertools.count()) =>
0, 1, 2, 3, 4, 5, 6, 7, 8, 9
@ -858,10 +856,9 @@ signal the end of its results.
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.
::
:func:`itertools.dropwhile(predicate, iter) <itertools.dropwhile>` 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, ...
@ -873,14 +870,14 @@ returns true, and then returns the rest of the iterable's results.
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.
The last function I'll discuss, :func:`itertools.groupby(iter, key_func=None)
<itertools.groupby>`, 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.
:func:`~itertools.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.
::
@ -890,7 +887,7 @@ value and an iterator for the elements with that key.
...
]
def get_state (city_state):
def get_state(city_state):
return city_state[1]
itertools.groupby(city_list, get_state) =>
@ -906,9 +903,9 @@ value and an iterator for the elements with that key.
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
:func:`~itertools.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.
@ -926,33 +923,34 @@ 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.
The constructor for :func:`~functools.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."
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')
``functools.reduce(func, iter, [initial_value])`` cumulatively performs an
operation on all the iterable's elements and, therefore, can't be applied to
infinite iterables. (Note it is not in :mod:`builtins`, but in the
:mod:`functools` module.) ``func`` must be a function that takes two elements
and returns a single value. :func:`functools.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 :exc:`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. ::
:func:`functools.reduce(func, iter, [initial_value]) <functools.reduce>`
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. :func:`functools.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 :exc:`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, functools
>>> functools.reduce(operator.concat, ['A', 'BB', 'C'])
@ -978,8 +976,8 @@ built-in called :func:`sum` to compute it:
>>> sum([])
0
For many uses of :func:`functools.reduce`, though, it can be clearer to just write the
obvious :keyword:`for` loop::
For many uses of :func:`functools.reduce`, though, it can be clearer to just
write the obvious :keyword:`for` loop::
import functools
# Instead of:
@ -1023,28 +1021,23 @@ need to define a new function at all::
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)
small functions is to use the :keyword:`lambda` statement. ``lambda`` takes a
number of parameters and an expression combining these parameters, and creates
an anonymous function that returns the value of the expression::
adder = lambda x, y: x+y
print_assign = lambda name, value: name + '=' + str(value)
An alternative is to just use the ``def`` statement and define a function in the
usual way::
def lowercase(x):
return x.lower()
def adder(x, y):
return x + y
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``.
@ -1053,9 +1046,7 @@ 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?
::
hard to read. Quick, what's the following code doing? ::
import functools
total = functools.reduce(lambda a, b: (0, a[1] + b[1]), items)[1]
@ -1065,7 +1056,7 @@ out what's going on. Using a short nested ``def`` statements makes things a
little bit better::
import functools
def combine (a, b):
def combine(a, b):
return 0, a[1] + b[1]
total = functools.reduce(combine, items)[1]
@ -1085,12 +1076,12 @@ Many uses of :func:`functools.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
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.
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
about whether this lambda-free style is better.

2
Doc/library/2to3.rst
View File

@ -23,7 +23,7 @@ Using 2to3
also located in the :file:`Tools/scripts` directory of the Python root.
2to3's basic arguments are a list of files or directories to transform. The
directories are to recursively traversed for Python sources.
directories are recursively traversed for Python sources.
Here is a sample Python 2.x source file, :file:`example.py`::

22
Doc/library/concurrent.futures.rst
View File

@ -42,12 +42,13 @@ Executor Objects
Equivalent to ``map(func, *iterables)`` except *func* is executed
asynchronously and several calls to *func* may be made concurrently. The
returned iterator raises a :exc:`TimeoutError` if :meth:`__next__()` is
called and the result isn't available after *timeout* seconds from the
original call to :meth:`Executor.map`. *timeout* can be an int or a
float. If *timeout* is not specified or ``None``, there is no limit to
the wait time. If a call raises an exception, then that exception will
be raised when its value is retrieved from the iterator.
returned iterator raises a :exc:`TimeoutError` if
:meth:`~iterator.__next__` is called and the result isn't available
after *timeout* seconds from the original call to :meth:`Executor.map`.
*timeout* can be an int or a float. If *timeout* is not specified or
``None``, there is no limit to the wait time. If a call raises an
exception, then that exception will be raised when its value is
retrieved from the iterator.
.. method:: shutdown(wait=True)
@ -364,10 +365,11 @@ Module Functions
different :class:`Executor` instances) given by *fs* that yields futures as
they complete (finished or were cancelled). Any futures that completed
before :func:`as_completed` is called will be yielded first. The returned
iterator raises a :exc:`TimeoutError` if :meth:`__next__` is called and the
result isn't available after *timeout* seconds from the original call to
:func:`as_completed`. *timeout* can be an int or float. If *timeout* is not
specified or ``None``, there is no limit to the wait time.
iterator raises a :exc:`TimeoutError` if :meth:`~iterator.__next__` is
called and the result isn't available after *timeout* seconds from the
original call to :func:`as_completed`. *timeout* can be an int or float.
If *timeout* is not specified or ``None``, there is no limit to the wait
time.
.. seealso::

8
Doc/library/dis.rst
View File

@ -660,10 +660,10 @@ the more significant byte last.
.. opcode:: FOR_ITER (delta)
``TOS`` is an :term:`iterator`. Call its :meth:`__next__` method. If this
yields a new value, push it on the stack (leaving the iterator below it). If
the iterator indicates it is exhausted ``TOS`` is popped, and the byte code
counter is incremented by *delta*.
``TOS`` is an :term:`iterator`. Call its :meth:`~iterator.__next__` method.
If this yields a new value, push it on the stack (leaving the iterator below
it). If the iterator indicates it is exhausted ``TOS`` is popped, and the
byte code counter is incremented by *delta*.
.. opcode:: LOAD_GLOBAL (namei)

21
Doc/library/functions.rst
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@ -348,10 +348,10 @@ are always available. They are listed here in alphabetical order.
.. function:: enumerate(iterable, start=0)
Return an enumerate object. *iterable* must be a sequence, an
:term:`iterator`, or some other object which supports iteration. The
:meth:`__next__` method of the iterator returned by :func:`enumerate` returns a
tuple containing a count (from *start* which defaults to 0) and the
values obtained from iterating over *iterable*.
:term:`iterator`, or some other object which supports iteration.
The :meth:`~iterator.__next__` method of the iterator returned by
:func:`enumerate` returns a tuple containing a count (from *start* which
defaults to 0) and the values obtained from iterating over *iterable*.
>>> seasons = ['Spring', 'Summer', 'Fall', 'Winter']
>>> list(enumerate(seasons))
@ -683,9 +683,10 @@ are always available. They are listed here in alphabetical order.
starting at ``0``). If it does not support either of those protocols,
:exc:`TypeError` is raised. If the second argument, *sentinel*, is given,
then *object* must be a callable object. The iterator created in this case
will call *object* with no arguments for each call to its :meth:`__next__`
method; if the value returned is equal to *sentinel*, :exc:`StopIteration`
will be raised, otherwise the value will be returned.
will call *object* with no arguments for each call to its
:meth:`~iterator.__next__` method; if the value returned is equal to
*sentinel*, :exc:`StopIteration` will be raised, otherwise the value will
be returned.
One useful application of the second form of :func:`iter` is to read lines of
a file until a certain line is reached. The following example reads a file
@ -779,9 +780,9 @@ are always available. They are listed here in alphabetical order.
.. function:: next(iterator[, default])
Retrieve the next item from the *iterator* by calling its :meth:`__next__`
method. If *default* is given, it is returned if the iterator is exhausted,
otherwise :exc:`StopIteration` is raised.
Retrieve the next item from the *iterator* by calling its
:meth:`~iterator.__next__` method. If *default* is given, it is returned
if the iterator is exhausted, otherwise :exc:`StopIteration` is raised.
.. function:: object()

9
Doc/library/stdtypes.rst
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@ -779,9 +779,9 @@ specific sequence types, dictionaries, and other more specialized forms. The
specific types are not important beyond their implementation of the iterator
protocol.
Once an iterator's :meth:`__next__` method raises :exc:`StopIteration`, it must
continue to do so on subsequent calls. Implementations that do not obey this
property are deemed broken.
Once an iterator's :meth:`~iterator.__next__` method raises
:exc:`StopIteration`, it must continue to do so on subsequent calls.
Implementations that do not obey this property are deemed broken.
.. _generator-types:
@ -792,7 +792,8 @@ Generator Types
Python's :term:`generator`\s provide a convenient way to implement the iterator
protocol. If a container object's :meth:`__iter__` method is implemented as a
generator, it will automatically return an iterator object (technically, a
generator object) supplying the :meth:`__iter__` and :meth:`__next__` methods.
generator object) supplying the :meth:`__iter__` and :meth:`~generator.__next__`
methods.
More information about generators can be found in :ref:`the documentation for
the yield expression <yieldexpr>`.

8
Doc/reference/datamodel.rst
View File

@ -600,9 +600,9 @@ Callable types
A function or method which uses the :keyword:`yield` statement (see section
:ref:`yield`) is called a :dfn:`generator function`. Such a function, when
called, always returns an iterator object which can be used to execute the
body of the function: calling the iterator's :meth:`__next__` method will
cause the function to execute until it provides a value using the
:keyword:`yield` statement. When the function executes a
body of the function: calling the iterator's :meth:`iterator__next__`
method will cause the function to execute until it provides a value
using the :keyword:`yield` statement. When the function executes a
:keyword:`return` statement or falls off the end, a :exc:`StopIteration`
exception is raised and the iterator will have reached the end of the set of
values to be returned.
@ -1189,7 +1189,7 @@ Basic customization
builtin: print
Called by the :func:`format` built-in function (and by extension, the
:meth:`format` method of class :class:`str`) to produce a "formatted"
:meth:`str.format` method of class :class:`str`) to produce a "formatted"
string representation of an object. The ``format_spec`` argument is
a string that contains a description of the formatting options desired.
The interpretation of the ``format_spec`` argument is up to the type

23
Doc/reference/expressions.rst
View File

@ -294,13 +294,13 @@ for comprehensions, except that it is enclosed in parentheses instead of
brackets or curly braces.
Variables used in the generator expression are evaluated lazily when the
:meth:`__next__` method is called for generator object (in the same fashion as
normal generators). However, the leftmost :keyword:`for` clause is immediately
evaluated, so that an error produced by it can be seen before any other possible
error in the code that handles the generator expression. Subsequent
:keyword:`for` clauses cannot be evaluated immediately since they may depend on
the previous :keyword:`for` loop. For example: ``(x*y for x in range(10) for y
in bar(x))``.
:meth:`~generator.__next__` method is called for generator object (in the same
fashion as normal generators). However, the leftmost :keyword:`for` clause is
immediately evaluated, so that an error produced by it can be seen before any
other possible error in the code that handles the generator expression.
Subsequent :keyword:`for` clauses cannot be evaluated immediately since they
may depend on the previous :keyword:`for` loop. For example: ``(x*y for x in
range(10) for y in bar(x))``.
The parentheses can be omitted on calls with only one argument. See section
:ref:`calls` for the detail.
@ -394,10 +394,11 @@ is already executing raises a :exc:`ValueError` exception.
Starts the execution of a generator function or resumes it at the last
executed :keyword:`yield` expression. When a generator function is resumed
with a :meth:`__next__` method, the current :keyword:`yield` expression
always evaluates to :const:`None`. The execution then continues to the next
:keyword:`yield` expression, where the generator is suspended again, and the
value of the :token:`expression_list` is returned to :meth:`next`'s caller.
with a :meth:`~generator.__next__` method, the current :keyword:`yield`
expression always evaluates to :const:`None`. The execution then continues
to the next :keyword:`yield` expression, where the generator is suspended
again, and the value of the :token:`expression_list` is returned to
:meth:`next`'s caller.
If the generator exits without yielding another value, a :exc:`StopIteration`
exception is raised.

18
Doc/tutorial/classes.rst
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@ -737,11 +737,11 @@ using a :keyword:`for` statement::
This style of access is clear, concise, and convenient. The use of iterators
pervades and unifies Python. Behind the scenes, the :keyword:`for` statement
calls :func:`iter` on the container object. The function returns an iterator
object that defines the method :meth:`__next__` which accesses elements in the
container one at a time. When there are no more elements, :meth:`__next__`
raises a :exc:`StopIteration` exception which tells the :keyword:`for` loop to
terminate. You can call the :meth:`__next__` method using the :func:`next`
built-in function; this example shows how it all works::
object that defines the method :meth:`~iterator.__next__` which accesses
elements in the container one at a time. When there are no more elements,
:meth:`__next__` raises a :exc:`StopIteration` exception which tells the
:keyword:`for` loop to terminate. You can call the :meth:`__next__` method
using the :func:`next` built-in function; this example shows how it all works::
>>> s = 'abc'
>>> it = iter(s)
@ -761,8 +761,8 @@ built-in function; this example shows how it all works::
Having seen the mechanics behind the iterator protocol, it is easy to add
iterator behavior to your classes. Define an :meth:`__iter__` method which
returns an object with a :meth:`__next__` method. If the class defines
:meth:`__next__`, then :meth:`__iter__` can just return ``self``::
returns an object with a :meth:`~iterator.__next__` method. If the class
defines :meth:`__next__`, then :meth:`__iter__` can just return ``self``::
class Reverse:
"""Iterator for looping over a sequence backwards."""
@ -819,8 +819,8 @@ easy to create::
Anything that can be done with generators can also be done with class based
iterators as described in the previous section. What makes generators so
compact is that the :meth:`__iter__` and :meth:`__next__` methods are created
automatically.
compact is that the :meth:`__iter__` and :meth:`~generator.__next__` methods
are created automatically.
Another key feature is that the local variables and execution state are
automatically saved between calls. This made the function easier to write and

4
Doc/whatsnew/3.0.rst
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@ -771,7 +771,7 @@ Operators And Special Methods
respectively).
* :pep:`3114`: the standard :meth:`next` method has been renamed to
:meth:`__next__`.
:meth:`~iterator.__next__`.
* The :meth:`__oct__` and :meth:`__hex__` special methods are removed
-- :func:`oct` and :func:`hex` use :meth:`__index__` now to convert
@ -807,7 +807,7 @@ Builtins
To get the old behavior of :func:`input`, use ``eval(input())``.
* A new built-in function :func:`next` was added to call the
:meth:`__next__` method on an object.
:meth:`~iterator.__next__` method on an object.
* The :func:`round` function rounding strategy and return type have
changed. Exact halfway cases are now rounded to the nearest even

7
Lib/platform.py
View File

@ -595,8 +595,13 @@ def win32_ver(release='',version='',csd='',ptype=''):
release = '7'
else:
release = '2008ServerR2'
elif min == 2:
if product_type == VER_NT_WORKSTATION:
release = '8'
else:
release = 'post2008Server'
release = '2012Server'
else:
release = 'post2012Server'
else:
if not release:

4
Lib/test/test_runpy.py
View File

@ -565,10 +565,10 @@ class RunPathTestCase(unittest.TestCase, CodeExecutionMixin):
with open(filename, 'w', encoding='latin1') as f:
f.write("""
#coding:latin1
"non-ASCII: h\xe9"
s = "non-ASCII: h\xe9"
""")
result = run_path(filename)
self.assertEqual(result['__doc__'], "non-ASCII: h\xe9")
self.assertEqual(result['s'], "non-ASCII: h\xe9")
def test_main():

1
Lib/test/test_timeit.py
View File

@ -250,6 +250,7 @@ class TestTimeit(unittest.TestCase):
s = self.run_main(seconds_per_increment=60.0, switches=['-r-5'])
self.assertEqual(s, "10 loops, best of 1: 60 sec per loop\n")
@unittest.skipIf(sys.flags.optimize >= 2, "need __doc__")
def test_main_help(self):
s = self.run_main(switches=['-h'])
# Note: It's not clear that the trailing space was intended as part of