email

The usability of the email package in Python 3 has been mostly fixed by the extensive efforts of R. David Murray. The problem was that emails are typically read and stored in the form of bytes rather than str text, and they may contain multiple encodings within a single email. So, the email package had to be extended to parse and generate email messages in bytes format.

• New functions message_from_bytes() and message_from_binary_file(), and new classes BytesFeedParser and BytesParser allow binary message data to be parsed into model objects.

• Given bytes input to the model, get_payload() will by default decode a message body that has a Content-Transfer-Encoding of 8bit using the charset specified in the MIME headers and return the resulting string.

• Given bytes input to the model, Generator will convert message bodies that have a Content-Transfer-Encoding of 8bit to instead have a 7bit Content-Transfer-Encoding.

  Headers with unencoded non-ASCII bytes are deemed to be RFC 2047-encoded using the unknown-8bit character set.

• A new class BytesGenerator produces bytes as output, preserving any unchanged non-ASCII data that was present in the input used to build the model, including message bodies with a Content-Transfer-Encoding of 8bit.

• The smtplib SMTP class now accepts a byte string for the msg argument to the sendmail() method, and a new method, send_message() accepts a Message object and can optionally obtain the from_addr and to_addrs addresses directly from the object.

(Proposed and implemented by R. David Murray, issue 4661 and issue 10321.)

elementtree

The xml.etree.ElementTree package and its xml.etree.cElementTree counterpart have been updated to version 1.3.

Several new and useful functions and methods have been added:

• xml.etree.ElementTree.fromstringlist() which builds an XML document from a sequence of fragments
• xml.etree.ElementTree.register_namespace() for registering a global namespace prefix
• xml.etree.ElementTree.tostringlist() for string representation including all sublists
• xml.etree.ElementTree.Element.extend() for appending a sequence of zero or more elements
• xml.etree.ElementTree.Element.iterfind() searches an element and subelements
• xml.etree.ElementTree.Element.itertext() creates a text iterator over an element and its subelements
• xml.etree.ElementTree.TreeBuilder.end() closes the current element
• xml.etree.ElementTree.TreeBuilder.doctype() handles a doctype declaration

Two methods have been deprecated:

• xml.etree.ElementTree.getchildren() use list(elem) instead.
• xml.etree.ElementTree.getiterator() use Element.iter instead.

For details of the update, see Introducing ElementTree on Fredrik Lundh’s website.

(Contributed by Florent Xicluna and Fredrik Lundh, issue 6472.)

functools

• The functools module includes a new decorator for caching function calls. functools.lru_cache() can save repeated queries to an external resource whenever the results are expected to be the same.

  For example, adding a caching decorator to a database query function can save database accesses for popular searches:

  >>> import functools
  >>> @functools.lru_cache(maxsize=300)
  >>> def get_phone_number(name):
          c = conn.cursor()
          c.execute('SELECT phonenumber FROM phonelist WHERE name=?', (name,))
          return c.fetchone()[0]
  

  >>> for name in user_requests:
          get_phone_number(name)        # cached lookup
  

  To help with choosing an effective cache size, the wrapped function is instrumented for tracking cache statistics:

  >>> get_phone_number.cache_info()
  CacheInfo(hits=4805, misses=980, maxsize=300, currsize=300)
  

  If the phonelist table gets updated, the outdated contents of the cache can be cleared with:

  >>> get_phone_number.cache_clear()
  

  (Contributed by Raymond Hettinger and incorporating design ideas from Jim Baker, Miki Tebeka, and Nick Coghlan; see recipe 498245, recipe 577479, issue 10586, and issue 10593.)

• The functools.wraps() decorator now adds a __wrapped__ attribute pointing to the original callable function. This allows wrapped functions to be introspected. It also copies __annotations__ if defined. And now it also gracefully skips over missing attributes such as __doc__ which might not be defined for the wrapped callable.

  In the above example, the cache can be removed by recovering the original function:

  >>> get_phone_number = get_phone_number.__wrapped__    # uncached function
  

  (By Nick Coghlan and Terrence Cole; issue 9567, issue 3445, and issue 8814.)

• To help write classes with rich comparison methods, a new decorator functools.total_ordering() will use existing equality and inequality methods to fill in the remaining methods.

  For example, supplying __eq__ and __lt__ will enable total_ordering() to fill-in __le__, __gt__ and __ge__:

  @total_ordering
  class Student:
      def __eq__(self, other):
          return ((self.lastname.lower(), self.firstname.lower()) ==
                  (other.lastname.lower(), other.firstname.lower()))
      def __lt__(self, other):
          return ((self.lastname.lower(), self.firstname.lower()) <
                  (other.lastname.lower(), other.firstname.lower()))
  

  With the total_ordering decorator, the remaining comparison methods are filled in automatically.

  (Contributed by Raymond Hettinger.)

• To aid in porting programs from Python 2, the functools.cmp_to_key() function converts an old-style comparison function to modern key function:

  >>> # locale-aware sort order
  >>> sorted(iterable, key=cmp_to_key(locale.strcoll))
  

  For sorting examples and a brief sorting tutorial, see the Sorting HowTo tutorial.

  (Contributed by Raymond Hettinger.)

itertools

• The itertools module has a new accumulate() function modeled on APL’s scan operator and Numpy’s accumulate function:

  >>> from itertools import accumulate
  >>> list(accumulate([8, 2, 50]))
  [8, 10, 60]
  

  >>> prob_dist = [0.1, 0.4, 0.2, 0.3]
  >>> list(accumulate(prob_dist))      # cumulative probability distribution
  [0.1, 0.5, 0.7, 1.0]
  

  For an example using accumulate(), see the examples for the random module.

  (Contributed by Raymond Hettinger and incorporating design suggestions from Mark Dickinson.)

collections

• The collections.Counter class now has two forms of in-place subtraction, the existing -= operator for saturating subtraction and the new subtract() method for regular subtraction. The former is suitable for multisets which only have positive counts, and the latter is more suitable for use cases that allow negative counts:

  >>> tally = Counter(dogs=5, cat=3)
  >>> tally -= Counter(dogs=2, cats=8)    # saturating subtraction
  >>> tally
  Counter({'dogs': 3})
  

  >>> tally = Counter(dogs=5, cats=3)
  >>> tally.subtract(dogs=2, cats=8)      # regular subtraction
  >>> tally
  Counter({'dogs': 3, 'cats': -5})
  

  (Contributed by Raymond Hettinger.)

• The collections.OrderedDict class has a new method move_to_end() which takes an existing key and moves it to either the first or last position in the ordered sequence.

  The default is to move an item to the last position. This is equivalent of renewing an entry with od[k] = od.pop(k).

  A fast move-to-end operation is useful for resequencing entries. For example, an ordered dictionary can be used to track order of access by aging entries from the oldest to the most recently accessed.

  >>> d = OrderedDict.fromkeys(['a', 'b', 'X', 'd', 'e'])
  >>> list(d)
  ['a', 'b', 'X', 'd', 'e']
  >>> d.move_to_end('X')
  >>> list(d)
  ['a', 'b', 'd', 'e', 'X']
  

  (Contributed by Raymond Hettinger.)

• The collections.deque class grew two new methods count() and reverse() that make them more substitutable for list objects:

  >>> d = deque('simsalabim')
  >>> d.count('s')
  2
  >>> d.reverse()
  >>> d
  deque(['m', 'i', 'b', 'a', 'l', 'a', 's', 'm', 'i', 's'])
  

  (Contributed by Raymond Hettinger.)

threading

The threading module has a new Barrier synchronization class for making multiple threads wait until all of them have reached a common barrier point. Barriers are useful for making sure that a task with multiple preconditions does not run until all of the predecessor tasks are complete.

Barriers can work with an arbitrary number of threads. This is a generalization of a Rendezvous which is defined for only two threads.

Implemented as a two-phase cyclic barrier, Barrier objects are suitable for use in loops. The separate filling and draining phases assure that all threads get released (drained) before any one of them can loop back and re-enter the barrier. The barrier fully resets after each cycle.

Example of using barriers:

from threading import Barrier, Thread

def get_votes(site):
    ballots = conduct_election(site)
    all_polls_closed.wait()        # do not count until all polls are closed
    totals = summarize(ballots)
    publish(site, totals)

all_polls_closed = Barrier(len(sites))
for site in sites:
    Thread(target=get_votes, args=(site,)).start()

In this example, the barrier enforces a rule that votes cannot be counted at any polling site until all polls are closed. Notice how a solution with a barrier is similar to one with threading.Thread.join(), but the threads stay alive and continue to do work (summarizing ballots) after the barrier point is crossed.

If any of the predecessor tasks can hang or be delayed, a barrier can be created with an optional timeout parameter. Then if the timeout period elapses before all the predecessor tasks reach the barrier point, all waiting threads are released and a BrokenBarrierError exception is raised:

def get_votes(site):
    ballots = conduct_election(site)
    try:
        all_polls_closed.wait(timeout = midnight - time.now())
    except BrokenBarrierError:
        lockbox = seal_ballots(ballots)
        queue.put(lockbox)
    else:
        totals = summarize(ballots)
        publish(site, totals)

In this example, the barrier enforces a more robust rule. If some election sites do not finish before midnight, the barrier times-out and the ballots are sealed and deposited in a queue for later handling.

See Barrier Synchronization Patterns for more examples of how barriers can be used in parallel computing. Also, there is a simple but thorough explanation of barriers in The Little Book of Semaphores, section 3.6.

(Contributed by Kristján Valur Jónsson with an API review by Jeffrey Yasskin in issue 8777.)

datetime and time

• The datetime module has a new type timezone that implements the tzinfo interface by returning a fixed UTC offset and timezone name. This makes it easier to create timezone-aware datetime objects:

  >>> from datetime import datetime, timezone
  
  >>> datetime.now(timezone.utc)
  datetime.datetime(2010, 12, 8, 21, 4, 2, 923754, tzinfo=datetime.timezone.utc)
  
  >>> datetime.strptime("01/01/2000 12:00 +0000", "%m/%d/%Y %H:%M %z")
  datetime.datetime(2000, 1, 1, 12, 0, tzinfo=datetime.timezone.utc)
  

• Also, timedelta objects can now be multiplied by float and divided by float and int objects. And timedelta objects can now divide one another.

• The datetime.date.strftime() method is no longer restricted to years after 1900. The new supported year range is from 1000 to 9999 inclusive.

• Whenever a two-digit year is used in a time tuple, the interpretation has been governed by time.accept2dyear. The default is True which means that for a two-digit year, the century is guessed according to the POSIX rules governing the %y strptime format.

  Starting with Py3.2, use of the century guessing heuristic will emit a DeprecationWarning. Instead, it is recommended that time.accept2dyear be set to False so that large date ranges can be used without guesswork:

  >>> import time, warnings
  >>> warnings.resetwarnings()      # remove the default warning filters
  
  >>> time.accept2dyear = True      # guess whether 11 means 11 or 2011
  >>> time.asctime((11, 1, 1, 12, 34, 56, 4, 1, 0))
  Warning (from warnings module):
    ...
  DeprecationWarning: Century info guessed for a 2-digit year.
  'Fri Jan  1 12:34:56 2011'
  
  >>> time.accept2dyear = False     # use the full range of allowable dates
  >>> time.asctime((11, 1, 1, 12, 34, 56, 4, 1, 0))
  'Fri Jan  1 12:34:56 11'
  

  Several functions now have significantly expanded date ranges. When time.accept2dyear is false, the time.asctime() function will accept any year that fits in a C int, while the time.mktime() and time.strftime() functions will accept the full range supported by the corresponding operating system functions.

(Contributed by Alexander Belopolsky and Victor Stinner in issue 1289118, issue 5094, issue 6641, issue 2706, issue 1777412, issue 8013, and issue 10827.)

math

The math module has been updated with six new functions inspired by the C99 standard.

The isfinite() function provides a reliable and fast way to detect special values. It returns True for regular numbers and False for Nan or Infinity:

>>> [isfinite(x) for x in (123, 4.56, float('Nan'), float('Inf'))]
[True, True, False, False]

The expm1() function computes e**x-1 for small values of x without incurring the loss of precision that usually accompanies the subtraction of nearly equal quantities:

>>> expm1(0.013671875)   # more accurate way to compute e**x-1 for a small x
0.013765762467652909

The erf() function computes a probability integral or Gaussian error function. The complementary error function, erfc(), is 1 - erf(x):

>>> erf(1.0/sqrt(2.0))   # portion of normal distribution within 1 standard deviation
0.682689492137086
>>> erfc(1.0/sqrt(2.0))  # portion of normal distribution outside 1 standard deviation
0.31731050786291404
>>> erf(1.0/sqrt(2.0)) + erfc(1.0/sqrt(2.0))
1.0

The gamma() function is a continuous extension of the factorial function. See http://en.wikipedia.org/wiki/Gamma_function for details. Because the function is related to factorials, it grows large even for small values of x, so there is also a lgamma() function for computing the natural logarithm of the gamma function:

>>> gamma(7.0)           # six factorial
720.0
>>> lgamma(801.0)        # log(800 factorial)
4551.950730698041

(Contributed by Mark Dickinson.)

abc

The abc module now supports abstractclassmethod() and abstractstaticmethod().

These tools make it possible to define an abstract base class that requires a particular classmethod() or staticmethod() to be implemented:

class Temperature(metaclass=abc.ABCMeta):
    @abc.abstractclassmethod
    def from_fahrenheit(cls, t):
        ...
    @abc.abstractclassmethod
    def from_celsius(cls, t):
        ...

(Patch submitted by Daniel Urban; issue 5867.)

io

The io.BytesIO has a new method, getbuffer(), which provides functionality similar to memoryview(). It creates an editable view of the data without making a copy. The buffer’s random access and support for slice notation are well-suited to in-place editing:

>>> REC_LEN, LOC_START, LOC_LEN = 34, 7, 11

>>> def change_location(buffer, record_number, location):
        start = record_number * REC_LEN + LOC_START
        buffer[start: start+LOC_LEN] = location

>>> import io

>>> byte_stream = io.BytesIO(
    b'G3805  storeroom  Main chassis    '
    b'X7899  shipping   Reserve cog     '
    b'L6988  receiving  Primary sprocket'
)
>>> buffer = byte_stream.getbuffer()
>>> change_location(buffer, 1, b'warehouse  ')
>>> change_location(buffer, 0, b'showroom   ')
>>> print(byte_stream.getvalue())
b'G3805  showroom   Main chassis    '
b'X7899  warehouse  Reserve cog     '
b'L6988  receiving  Primary sprocket'

(Contributed by Antoine Pitrou in issue 5506.)

reprlib

When writing a __repr__() method for a custom container, it is easy to forget to handle the case where a member refers back to the container itself. Python’s builtin objects such as list and set handle self-reference by displaying ”...” in the recursive part of the representation string.

To help write such __repr__() methods, the reprlib module has a new decorator, recursive_repr(), for detecting recursive calls to __repr__() and substituting a placeholder string instead:

>>> class MyList(list):
        @recursive_repr()
        def __repr__(self):
            return '<' + '|'.join(map(repr, self)) + '>'

>>> m = MyList('abc')
>>> m.append(m)
>>> m.append('x')
>>> print(m)
<'a'|'b'|'c'|...|'x'>

(Contributed by Raymond Hettinger in issue 9826 and issue 9840.)

logging

In addition to dictionary-based configuration described above, the logging package has many other improvements.

The logging documentation has been augmented by a basic tutorial, an advanced tutorial, and a cookbook of logging recipes. These documents are the fastest way to learn about logging.

The logging.basicConfig() set-up function gained a style argument to support three different types of string formatting. It defaults to “%” for traditional %-formatting, can be set to “{” for the new str.format() style, or can be set to “$” for the shell-style formatting provided by string.Template. The following three configurations are equivalent:

>>> from logging import basicConfig
>>> basicConfig(style='%', format="%(name)s -> %(levelname)s: %(message)s")
>>> basicConfig(style='{', format="{name} -> {levelname} {message}")
>>> basicConfig(style='$', format="$name -> $levelname: $message")

If no configuration is set-up before a logging event occurs, there is now a default configuration using a StreamHandler directed to sys.stderr for events of WARNING level or higher. Formerly, an event occurring before a configuration was set-up would either raise an exception or silently drop the event depending on the value of logging.raiseExceptions. The new default handler is stored in logging.lastResort.

The use of filters has been simplified. Instead of creating a Filter object, the predicate can be any Python callable that returns True or False.

There were a number of other improvements that add flexibility and simplify configuration. See the module documentation for a full listing of changes in Python 3.2.

csv

The csv module now supports a new dialect, unix_dialect, which applies quoting for all fields and a traditional Unix style with '\n' as the line terminator. The registered dialect name is unix.

The csv.DictWriter has a new method, writeheader() for writing-out an initial row to document the field names:

>>> import csv, sys
>>> w = csv.DictWriter(sys.stdout, ['name', 'dept'], dialect='unix')
>>> w.writeheader()
"name","dept"
>>> w.writerows([
        {'name': 'tom', 'dept': 'accounting'},
        {'name': 'susan', 'dept': 'Salesl'}])
"tom","accounting"
"susan","sales"

(New dialect suggested by Jay Talbot in issue 5975, and the new method suggested by Ed Abraham in issue 1537721.)

contextlib

There is a new and slightly mind-blowing tool ContextDecorator that is helpful for creating a context manager that does double duty as a function decorator.

As a convenience, this new functionality is used by contextmanager() so that no extra effort is needed to support both roles.

The basic idea is that both context managers and function decorators can be used for pre-action and post-action wrappers. Context managers wrap a group of statements using a with statement, and function decorators wrap a group of statements enclosed in a function. So, occasionally there is a need to write a pre-action or post-action wrapper that can be used in either role.

For example, it is sometimes useful to wrap functions or groups of statements with a logger that can track the time of entry and time of exit. Rather than writing both a function decorator and a context manager for the task, the contextmanager() provides both capabilities in a single definition:

from contextlib import contextmanager
import logging

logging.basicConfig(level=logging.INFO)

@contextmanager
def track_entry_and_exit(name):
    logging.info('Entering: {}'.format(name))
    yield
    logging.info('Exiting: {}'.format(name))

Formerly, this would have only been usable as a context manager:

with track_entry_and_exit('widget loader'):
    print('Some time consuming activity goes here')
    load_widget()

Now, it can be used as a decorator as well:

@track_entry_and_exit('widget loader')
def activity():
    print('Some time consuming activity goes here')
    load_widget()

Trying to fulfill two roles at once places some limitations on the technique. Context managers normally have the flexibility to return an argument usable by a with statement, but there is no parallel for function decorators.

In the above example, there is not a clean way for the track_entry_and_exit context manager to return a logging instance for use in the body of enclosed statements.

(Contributed by Michael Foord in issue 9110.)

decimal and fractions

Mark Dickinson crafted an elegant and efficient scheme for assuring that different numeric datatypes will have the same hash value whenever their actual values are equal (issue 8188):

assert hash(Fraction(3, 2)) == hash(1.5) == \
       hash(Decimal("1.5")) == hash(complex(1.5, 0))

Some of the hashing details are exposed through a new attribute, sys.hash_info, which describes the bit width of the hash value, the prime modulus, the hash values for infinity and nan, and the multiplier used for the imaginary part of a number:

>>> sys.hash_info
sys.hash_info(width=64, modulus=2305843009213693951, inf=314159, nan=0, imag=1000003)

An early decision to limit the inter-operability of various numeric types has been relaxed. It is still unsupported (and ill-advised) to have implicit mixing in arithmetic expressions such as Decimal('1.1') + float('1.1') because the latter loses information in the process of constructing the binary float. However, since existing floating point value can be converted losslessly to either a decimal or rational representation, it makes sense to add them to the constructor and to support mixed-type comparisons.

• The decimal.Decimal constructor now accepts float objects directly so there in no longer a need to use the from_float() method (issue 8257).
• Mixed type comparisons are now fully supported so that Decimal objects can be directly compared with float and fractions.Fraction (issue 2531 and issue 8188).

Similar changes were made to fractions.Fraction so that the from_float() and from_decimal() methods are no longer needed (issue 8294):

>>> Decimal(1.1)
Decimal('1.100000000000000088817841970012523233890533447265625')
>>> Fraction(1.1)
Fraction(2476979795053773, 2251799813685248)

Another useful change for the decimal module is that the Context.clamp attribute is now public. This is useful in creating contexts that correspond to the decimal interchange formats specified in IEEE 754 (see issue 8540).

(Contributed by Mark Dickinson and Raymond Hettinger.)

ftp

The ftplib.FTP class now supports the context management protocol to unconditionally consume socket.error exceptions and to close the FTP connection when done:

>>> from ftplib import FTP
>>> with FTP("ftp1.at.proftpd.org") as ftp:
        ftp.login()
        ftp.dir()

'230 Anonymous login ok, restrictions apply.'
dr-xr-xr-x   9 ftp      ftp           154 May  6 10:43 .
dr-xr-xr-x   9 ftp      ftp           154 May  6 10:43 ..
dr-xr-xr-x   5 ftp      ftp          4096 May  6 10:43 CentOS
dr-xr-xr-x   3 ftp      ftp            18 Jul 10  2008 Fedora

Other file-like objects such as mmap.mmap and fileinput.input() also grew auto-closing context managers:

with fileinput.input(files=('log1.txt', 'log2.txt')) as f:
    for line in f:
        process(line)

(Contributed by Tarek Ziadé and Giampaolo Rodolà in issue 4972, and by Georg Brandl in issue 8046 and issue 1286.)

The FTP_TLS class now accepts a context parameter, which is a ssl.SSLContext object allowing bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure.

(Contributed by Giampaolo Rodolà; issue 8806.)

popen

The os.popen() and subprocess.Popen() functions now support with statements for auto-closing of the file descriptors.

(Contributed by Antoine Pitrou and Brian Curtin in issue 7461 and issue 10554.)

select

The select module now exposes a new, constant attribute, PIPE_BUF, which gives the minimum number of bytes which are guaranteed not to block when select.select() says a pipe is ready for writing.

>>> import select
>>> select.PIPE_BUF
512

(Available on Unix systems. Patch by Sébastien Sablé in issue 9862)

gzip and zipfile

gzip.GzipFile now implements the io.BufferedIOBase abstract base class (except for truncate()). It also has a peek() method and supports unseekable as well as zero-padded file objects.

The gzip module also gains the compress() and decompress() functions for easier in-memory compression and decompression. Keep in mind that text needs to be encoded as bytes before compressing and decompressing:

>>> s = 'Three shall be the number thou shalt count, '
>>> s += 'and the number of the counting shall be three'
>>> b = s.encode()                        # convert to utf-8
>>> len(b)
89
>>> c = gzip.compress(b)
>>> len(c)
77
>>> gzip.decompress(c).decode()[:42]      # decompress and convert to text
'Three shall be the number thou shalt count,'

(Contributed by Anand B. Pillai in issue 3488; and by Antoine Pitrou, Nir Aides and Brian Curtin in issue 9962, issue 1675951, issue 7471 and issue 2846.)

Also, the zipfile.ZipExtFile class was reworked internally to represent files stored inside an archive. The new implementation is significantly faster and can be wrapped in an io.BufferedReader object for more speedups. It also solves an issue where interleaved calls to read and readline gave the wrong results.

(Patch submitted by Nir Aides in issue 7610.)

tarfile

The TarFile class can now be used as a context manager. In addition, its add() method has a new option, filter, that controls which files are added to the archive and allows the file metadata to be edited.

The new filter option replaces the older, less flexible exclude parameter which is now deprecated. If specified, the optional filter parameter needs to be a keyword argument. The user-supplied filter function accepts a TarInfo object and returns an updated TarInfo object, or if it wants the file to be excluded, the function can return None:

>>> import tarfile, glob

>>> def myfilter(tarinfo):
       if tarinfo.isfile():             # only save real files
            tarinfo.uname = 'monty'     # redact the user name
            return tarinfo

>>> with tarfile.open(name='myarchive.tar.gz', mode='w:gz') as tf:
        for filename in glob.glob('*.txt'):
            tf.add(filename, filter=myfilter)
        tf.list()
-rw-r--r-- monty/501        902 2011-01-26 17:59:11 annotations.txt
-rw-r--r-- monty/501        123 2011-01-26 17:59:11 general_questions.txt
-rw-r--r-- monty/501       3514 2011-01-26 17:59:11 prion.txt
-rw-r--r-- monty/501        124 2011-01-26 17:59:11 py_todo.txt
-rw-r--r-- monty/501       1399 2011-01-26 17:59:11 semaphore_notes.txt

(Proposed by Tarek Ziadé and implemented by Lars Gustäbel in issue 6856.)

hashlib

The hashlib module has two new constant attributes listing the hashing algorithms guaranteed to be present in all implementations and those available on the current implementation:

>>> import hashlib

>>> hashlib.algorithms_guaranteed
{'sha1', 'sha224', 'sha384', 'sha256', 'sha512', 'md5'}

>>> hashlib.algorithms_available
{'md2', 'SHA256', 'SHA512', 'dsaWithSHA', 'mdc2', 'SHA224', 'MD4', 'sha256',
'sha512', 'ripemd160', 'SHA1', 'MDC2', 'SHA', 'SHA384', 'MD2',
'ecdsa-with-SHA1','md4', 'md5', 'sha1', 'DSA-SHA', 'sha224',
'dsaEncryption', 'DSA', 'RIPEMD160', 'sha', 'MD5', 'sha384'}

(Suggested by Carl Chenet in issue 7418.)

ast

The ast module has a wonderful a general-purpose tool for safely evaluating expression strings using the Python literal syntax. The ast.literal_eval() function serves as a secure alternative to the builtin eval() function which is easily abused. Python 3.2 adds bytes and set literals to the list of supported types: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.

>>> from ast import literal_eval

>>> request = "{'req': 3, 'func': 'pow', 'args': (2, 0.5)}"
>>> literal_eval(request)
{'args': (2, 0.5), 'req': 3, 'func': 'pow'}

>>> request = "os.system('do something harmful')"
>>> literal_eval(request)
Traceback (most recent call last):
  ...
ValueError: malformed node or string: <_ast.Call object at 0x101739a10>

(Implemented by Benjamin Peterson and Georg Brandl.)

os

Different operating systems use various encodings for filenames and environment variables. The os module provides two new functions, fsencode() and fsdecode(), for encoding and decoding filenames:

>>> filename = 'Sehenswürdigkeiten'
>>> os.fsencode(filename)
b'Sehensw\xc3\xbcrdigkeiten'

Some operating systems allow direct access to encoded bytes in the environment. If so, the os.supports_bytes_environ constant will be true.

For direct access to encoded environment variables (if available), use the new os.getenvb() function or use os.environb which is a bytes version of os.environ.

(Contributed by Victor Stinner.)

shutil

The shutil.copytree() function has two new options:

• ignore_dangling_symlinks: when symlinks=False so that the function copies a file pointed to by a symlink, not the symlink itself. This option will silence the error raised if the file doesn’t exist.
• copy_function: is a callable that will be used to copy files. shutil.copy2() is used by default.

(Contributed by Tarek Ziadé.)

In addition, the shutil module now supports archiving operations for zipfiles, uncompressed tarfiles, gzipped tarfiles, and bzipped tarfiles. And there are functions for registering additional archiving file formats (such as xz compressed tarfiles or custom formats).

The principal functions are make_archive() and unpack_archive(). By default, both operate on the current directory (which can be set by os.chdir()) and on any sub-directories. The archive filename needs to be specified with a full pathname. The archiving step is non-destructive (the original files are left unchanged).

>>> import shutil, pprint

>>> os.chdir('mydata')                               # change to the source directory
>>> f = shutil.make_archive('/var/backup/mydata',
                            'zip')                   # archive the current directory
>>> f                                                # show the name of archive
'/var/backup/mydata.zip'
>>> os.chdir('tmp')                                  # change to an unpacking
>>> shutil.unpack_archive('/var/backup/mydata.zip')  # recover the data

>>> pprint.pprint(shutil.get_archive_formats())      # display known formats
[('bztar', "bzip2'ed tar-file"),
 ('gztar', "gzip'ed tar-file"),
 ('tar', 'uncompressed tar file'),
 ('zip', 'ZIP file')]

>>> shutil.register_archive_format(                  # register a new archive format
        name = 'xz',
        function = xz.compress,                      # callable archiving function
        extra_args = [('level', 8)],                 # arguments to the function
        description = 'xz compression'
)

(Contributed by Tarek Ziadé.)

sqlite3

The sqlite3 module was updated to pysqlite version 2.6.0. It has two new capabilities.

• The sqlite3.Connection.in_transit attribute is true if there is an active transaction for uncommitted changes.
• The sqlite3.Connection.enable_load_extension() and sqlite3.Connection.load_extension() methods allows you to load SQLite extensions from ”.so” files. One well-known extension is the fulltext-search extension distributed with SQLite.

(Contributed by R. David Murray and Shashwat Anand; issue 8845.)

html

A new html module was introduced with only a single function, escape(), which is used for escaping reserved characters from HTML markup:

>>> import html
>>> html.escape('x > 2 && x < 7')
'x &gt; 2 &amp;&amp; x &lt; 7'

socket

The socket module has two new improvements.

• Socket objects now have a detach() method which puts the socket into closed state without actually closing the underlying file descriptor. The latter can then be reused for other purposes. (Added by Antoine Pitrou; issue 8524.)
• socket.create_connection() now supports the context management protocol to unconditionally consume socket.error exceptions and to close the socket when done. (Contributed by Giampaolo Rodolà; issue 9794.)

ssl

The ssl module added a number of features to satisfy common requirements for secure (encrypted, authenticated) internet connections:

• A new class, SSLContext, serves as a container for persistent SSL data, such as protocol settings, certificates, private keys, and various other options. It includes a wrap_socket() for creating an SSL socket from an SSL context.
• A new function, ssl.match_hostname(), supports server identity verification for higher-level protocols by implementing the rules of HTTPS (from RFC 2818) which are also suitable for other protocols.
• The ssl.wrap_socket() constructor function now takes a ciphers argument. The ciphers string lists the allowed encryption algorithms using the format described in the OpenSSL documentation.
• When linked against recent versions of OpenSSL, the ssl module now supports the Server Name Indication extension to the TLS protocol, allowing multiple “virtual hosts” using different certificates on a single IP port. This extension is only supported in client mode, and is activated by passing the server_hostname argument to ssl.SSLContext.wrap_socket().
• Various options have been added to the ssl module, such as OP_NO_SSLv2 which disables the insecure and obsolete SSLv2 protocol.
• The extension now loads all the OpenSSL ciphers and digest algorithms. If some SSL certificates cannot be verified, they are reported as an “unknown algorithm” error.
• The version of OpenSSL being used is now accessible using the module attributes ssl.OPENSSL_VERSION (a string), ssl.OPENSSL_VERSION_INFO (a 5-tuple), and ssl.OPENSSL_VERSION_NUMBER (an integer).

(Contributed by Antoine Pitrou in issue 8850, issue 1589, issue 8322, issue 5639, issue 4870, issue 8484, and issue 8321.)

nntp

The nntplib module has a revamped implementation with better bytes and text semantics as well as more practical APIs. These improvements break compatibility with the nntplib version in Python 3.1, which was partly dysfunctional in itself.

Support for secure connections through both implicit (using nntplib.NNTP_SSL) and explicit (using nntplib.NNTP.starttls()) TLS has also been added.

(Contributed by Antoine Pitrou in issue 9360 and Andrew Vant in issue 1926.)

certificates

http.client.HTTPSConnection, urllib.request.HTTPSHandler and urllib.request.urlopen() now take optional arguments to allow for server certificate checking against a set of Certificate Authorities, as recommended in public uses of HTTPS.

(Added by Antoine Pitrou, issue 9003.)

imaplib

Support for explicit TLS on standard IMAP4 connections has been added through the new imaplib.IMAP4.starttls method.

(Contributed by Lorenzo M. Catucci and Antoine Pitrou, issue 4471.)

http.client

There were a number of small API improvements in the http.client module. The old-style HTTP 0.9 simple responses are no longer supported and the strict parameter is deprecated in all classes.

The HTTPConnection and HTTPSConnection classes now have a source_address parameter for a (host, port) tuple indicating where the HTTP connection is made from.

Support for certificate checking and HTTPS virtual hosts were added to HTTPSConnection.

The request() method on connection objects allowed an optional body argument so that a file object could be used to supply the content of the request. Conveniently, the body argument now also accepts an iterable object so long as it includes an explicit Content-Length header. This extended interface is much more flexible than before.

To establish an HTTPS connection through a proxy server, there is a new set_tunnel() method that sets the host and port for HTTP Connect tunneling.

To match the behavior of http.server, the HTTP client library now also encodes headers with ISO-8859-1 (Latin-1) encoding. It was already doing that for incoming headers, so now the behavior is consistent for both incoming and outgoing traffic. (See work by Armin Ronacher in issue 10980.)

unittest

The unittest module has a number of improvements supporting test discovery for packages, easier experimentation at the interactive prompt, new testcase methods, improved diagnostic messages for test failures, and better method names.

• The command-line call python -m unittest can now accept file paths instead of module names for running specific tests (issue 10620). The new test discovery can find tests within packages, locating any test importable from the top-level directory. The top-level directory can be specified with the -t option, a pattern for matching files with -p, and a directory to start discovery with -s:

  $ python -m unittest discover -s my_proj_dir -p _test.py
  

  (Contributed by Michael Foord.)

• Experimentation at the interactive prompt is now easier because the unittest.case.TestCase class can now be instantiated without arguments:

  >>> TestCase().assertEqual(pow(2, 3), 8)
  

  (Contributed by Michael Foord.)

• The unittest module has two new methods, assertWarns() and assertWarnsRegex() to verify that a given warning type is triggered by the code under test:

  with self.assertWarns(DeprecationWarning):
      legacy_function('XYZ')
  

  (Contributed by Antoine Pitrou, issue 9754.)

  Another new method, assertCountEqual() is used to compare two iterables to determine if their element counts are equal (whether the same elements are present with the same number of occurrences regardless of order):

  def test_anagram(self):
      self.assertCountEqual('algorithm', 'logarithm')
  

  (Contributed by Raymond Hettinger.)

• A principal feature of the unittest module is an effort to produce meaningful diagnostics when a test fails. When possible, the failure is recorded along with a diff of the output. This is especially helpful for analyzing log files of failed test runs. However, since diffs can sometime be voluminous, there is a new maxDiff attribute that sets maximum length of diffs displayed.

• In addition, the method names in the module have undergone a number of clean-ups.

  For example, assertRegex() is the new name for assertRegexpMatches() which was misnamed because the test uses re.search(), not re.match(). Other methods using regular expressions are now named using short form “Regex” in preference to “Regexp” – this matches the names used in other unittest implementations, matches Python’s old name for the re module, and it has unambiguous camel-casing.

  (Contributed by Raymond Hettinger and implemented by Ezio Melotti.)

• To improve consistency, some long-standing method aliases are being deprecated in favor of the preferred names:

  Old Name	Preferred Name
  assert_()	assertTrue()
  assertEquals()	assertEqual()
  assertNotEquals()	assertNotEqual()
  assertAlmostEquals()	assertAlmostEqual()
  assertNotAlmostEquals()	assertNotAlmostEqual()

  Likewise, the TestCase.fail* methods deprecated in Python 3.1 are expected to be removed in Python 3.3. Also see the Deprecated aliases section in the unittest documentation.

  (Contributed by Ezio Melotti; issue 9424.)

• The assertDictContainsSubset() method was deprecated because it was misimplemented with the arguments in the wrong order. This created hard-to-debug optical illusions where tests like TestCase().assertDictContainsSubset({'a':1, 'b':2}, {'a':1}) would fail.

  (Contributed by Raymond Hettinger.)

random

The integer methods in the random module now do a better job of producing uniform distributions. Previously, they computed selections with int(n*random()) which had a slight bias whenever n was not a power of two. Now, multiple selections are made from a range up to the next power of two and a selection is kept only when it falls within the range 0 <= x < n. The functions and methods affected are randrange(), randint(), choice(), shuffle() and sample().

(Contributed by Raymond Hettinger; issue 9025.)

poplib

POP3_SSL class now accepts a context parameter, which is a ssl.SSLContext object allowing bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure.

(Contributed by Giampaolo Rodolà; issue 8807.)

asyncore

asyncore.dispatcher now provides a handle_accepted() method returning a (sock, addr) pair which is called when a connection has actually been established with a new remote endpoint. This is supposed to be used as a replacement for old handle_accept() and avoids the user to call accept() directly.

(Contributed by Giampaolo Rodolà; issue 6706.)

tempfile

The tempfile module has a new context manager, TemporaryDirectory which provides easy deterministic cleanup of temporary directories:

with tempfile.TemporaryDirectory() as tmpdirname:
    print('created temporary dir:', tmpdirname)

(Contributed by Neil Schemenauer and Nick Coghlan; issue 5178.)

inspect

• The inspect module has a new function getgeneratorstate() to easily identify the current state of a generator-iterator:

  >>> from inspect import getgeneratorstate
  >>> def gen():
          yield 'demo'
  >>> g = gen()
  >>> getgeneratorstate(g)
  'GEN_CREATED'
  >>> next(g)
  'demo'
  >>> getgeneratorstate(g)
  'GEN_SUSPENDED'
  >>> next(g, None)
  >>> getgeneratorstate(g)
  'GEN_CLOSED'
  

  (Contributed by Rodolpho Eckhardt and Nick Coghlan, issue 10220.)

• To support lookups without the possibility of activating a dynamic attribute, the inspect module has a new function, getattr_static(). Unlike hasattr(), this is a true read-only search, guaranteed not to change state while it is searching:

  >>> class A:
          @property
          def f(self):
              print('Running')
              return 10
  
  >>> a = A()
  >>> getattr(a, 'f')
  Running
  10
  >>> inspect.getattr_static(a, 'f')
  <property object at 0x1022bd788>
  

(Contributed by Michael Foord.)

pydoc

The pydoc module now provides a much-improved Web server interface, as well as a new command-line option -b to automatically open a browser window to display that server:

$ pydoc3.2 -b

(Contributed by Ron Adam; issue 2001.)

dis

The dis module gained two new functions for inspecting code, code_info() and show_code(). Both provide detailed code object information for the supplied function, method, source code string or code object. The former returns a string and the latter prints it:

>>> import dis, random
>>> dis.show_code(random.choice)
Name:              choice
Filename:          /Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/random.py
Argument count:    2
Kw-only arguments: 0
Number of locals:  3
Stack size:        11
Flags:             OPTIMIZED, NEWLOCALS, NOFREE
Constants:
   0: 'Choose a random element from a non-empty sequence.'
   1: 'Cannot choose from an empty sequence'
Names:
   0: _randbelow
   1: len
   2: ValueError
   3: IndexError
Variable names:
   0: self
   1: seq
   2: i

In addition, the dis() function now accepts string arguments so that the common idiom dis(compile(s, '', 'eval')) can be shortened to dis(s):

>>> dis('3*x+1 if x%2==1 else x//2')
  1           0 LOAD_NAME                0 (x)
              3 LOAD_CONST               0 (2)
              6 BINARY_MODULO
              7 LOAD_CONST               1 (1)
             10 COMPARE_OP               2 (==)
             13 POP_JUMP_IF_FALSE       28
             16 LOAD_CONST               2 (3)
             19 LOAD_NAME                0 (x)
             22 BINARY_MULTIPLY
             23 LOAD_CONST               1 (1)
             26 BINARY_ADD
             27 RETURN_VALUE
        >>   28 LOAD_NAME                0 (x)
             31 LOAD_CONST               0 (2)
             34 BINARY_FLOOR_DIVIDE
             35 RETURN_VALUE

Taken together, these improvements make it easier to explore how CPython is implemented and to see for yourself what the language syntax does under-the-hood.

(Contributed by Nick Coghlan in issue 9147.)

dbm

All database modules now support the get() and setdefault() methods.

(Suggested by Ray Allen in issue 9523.)

ctypes

A new type, ctypes.c_ssize_t represents the C ssize_t datatype.

site

The site module has three new functions useful for reporting on the details of a given Python installation.

• getsitepackages() lists all global site-packages directories.
• getuserbase() reports on the user’s base directory where data can be stored.
• getusersitepackages() reveals the user-specific site-packages directory path.

>>> import site
>>> site.getsitepackages()
['/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/site-packages',
 '/Library/Frameworks/Python.framework/Versions/3.2/lib/site-python',
 '/Library/Python/3.2/site-packages']
>>> site.getuserbase()
'/Users/raymondhettinger/Library/Python/3.2'
>>> site.getusersitepackages()
'/Users/raymondhettinger/Library/Python/3.2/lib/python/site-packages'

Conveniently, some of site’s functionality is accessible directly from the command-line:

$ python -m site --user-base
/Users/raymondhettinger/.local
$ python -m site --user-site
/Users/raymondhettinger/.local/lib/python3.2/site-packages

(Contributed by Tarek Ziadé in issue 6693.)

sysconfig

The new sysconfig module makes it straightforward to discover installation paths and configuration variables that vary across platforms and installations.

The module offers access simple access functions for platform and version information:

• get_platform() returning values like linux-i586 or macosx-10.6-ppc.
• get_python_version() returns a Python version string such as “3.2”.

It also provides access to the paths and variables corresponding to one of seven named schemes used by distutils. Those include posix_prefix, posix_home, posix_user, nt, nt_user, os2, os2_home:

• get_paths() makes a dictionary containing installation paths for the current installation scheme.
• get_config_vars() returns a dictionary of platform specific variables.

There is also a convenient command-line interface:

C:\Python32>python -m sysconfig
Platform: "win32"
Python version: "3.2"
Current installation scheme: "nt"

Paths:
        data = "C:\Python32"
        include = "C:\Python32\Include"
        platinclude = "C:\Python32\Include"
        platlib = "C:\Python32\Lib\site-packages"
        platstdlib = "C:\Python32\Lib"
        purelib = "C:\Python32\Lib\site-packages"
        scripts = "C:\Python32\Scripts"
        stdlib = "C:\Python32\Lib"

Variables:
        BINDIR = "C:\Python32"
        BINLIBDEST = "C:\Python32\Lib"
        EXE = ".exe"
        INCLUDEPY = "C:\Python32\Include"
        LIBDEST = "C:\Python32\Lib"
        SO = ".pyd"
        VERSION = "32"
        abiflags = ""
        base = "C:\Python32"
        exec_prefix = "C:\Python32"
        platbase = "C:\Python32"
        prefix = "C:\Python32"
        projectbase = "C:\Python32"
        py_version = "3.2"
        py_version_nodot = "32"
        py_version_short = "3.2"
        srcdir = "C:\Python32"
        userbase = "C:\Documents and Settings\Raymond\Application Data\Python"

(Moved out of Distutils by Tarek Ziadé.)

pdb

The pdb debugger module gained a number of usability improvements:

• pdb.py now has a -c option that executes commands as given in a .pdbrc script file.
• A .pdbrc script file can contain continue and next commands that continue debugging.
• The Pdb class constructor now accepts a nosigint argument.
• New commands: l(list), ll(long list) and source for listing source code.
• New commands: display and undisplay for showing or hiding the value of an expression if it has changed.
• New command: interact for starting an interactive interpreter containing the global and local names found in the current scope.
• Breakpoints can be cleared by breakpoint number.

(Contributed by Georg Brandl, Antonio Cuni and Ilya Sandler.)

configparser

The configparser module was modified to improve usability and predictability of the default parser and its supported INI syntax. The old ConfigParser class was removed in favor of SafeConfigParser which has in turn been renamed to ConfigParser. Support for inline comments is now turned off by default and section or option duplicates are not allowed in a single configuration source.

Config parsers gained a new API based on the mapping protocol:

>>> parser = ConfigParser()
>>> parser.read_string("""
[DEFAULT]
location = upper left
visible = yes
editable = no
color = blue

[main]
title = Main Menu
color = green

[options]
title = Options
""")
>>> parser['main']['color']
'green'
>>> parser['main']['editable']
'no'
>>> section = parser['options']
>>> section['title']
'Options'
>>> section['title'] = 'Options (editable: %(editable)s)'
>>> section['title']
'Options (editable: no)'

The new API is implemented on top of the classical API, so custom parser subclasses should be able to use it without modifications.

The INI file structure accepted by config parsers can now be customized. Users can specify alternative option/value delimiters and comment prefixes, change the name of the DEFAULT section or switch the interpolation syntax.

There is support for pluggable interpolation including an additional interpolation handler ExtendedInterpolation:

>>> parser = ConfigParser(interpolation=ExtendedInterpolation())
>>> parser.read_dict({'buildout': {'directory': '/home/ambv/zope9'},
                      'custom': {'prefix': '/usr/local'}})
>>> parser.read_string("""
    [buildout]
    parts =
      zope9
      instance
    find-links =
      ${buildout:directory}/downloads/dist

    [zope9]
    recipe = plone.recipe.zope9install
    location = /opt/zope

    [instance]
    recipe = plone.recipe.zope9instance
    zope9-location = ${zope9:location}
    zope-conf = ${custom:prefix}/etc/zope.conf
    """)
>>> parser['buildout']['find-links']
'\n/home/ambv/zope9/downloads/dist'
>>> parser['instance']['zope-conf']
'/usr/local/etc/zope.conf'
>>> instance = parser['instance']
>>> instance['zope-conf']
'/usr/local/etc/zope.conf'
>>> instance['zope9-location']
'/opt/zope'

A number of smaller features were also introduced, like support for specifying encoding in read operations, specifying fallback values for get-functions, or reading directly from dictionaries and strings.

(All changes contributed by Łukasz Langa.)

urllib.parse

A number of usability improvements were made for the urllib.parse module.

The urlparse() function now supports IPv6 addresses as described in RFC 2732:

>>> import urllib.parse
>>> urllib.parse.urlparse('http://[dead:beef:cafe:5417:affe:8FA3:deaf:feed]/foo/')
ParseResult(scheme='http',
            netloc='[dead:beef:cafe:5417:affe:8FA3:deaf:feed]',
            path='/foo/',
            params='',
            query='',
            fragment='')

The urldefrag() function now returns a named tuple:

>>> r = urllib.parse.urldefrag('http://python.org/about/#target')
>>> r
DefragResult(url='http://python.org/about/', fragment='target')
>>> r[0]
'http://python.org/about/'
>>> r.fragment
'target'

And, the urlencode() function is now much more flexible, accepting either a string or bytes type for the query argument. If it is a string, then the safe, encoding, and error parameters are sent to quote_plus() for encoding:

>>> urllib.parse.urlencode([
         ('type', 'telenovela'),
         ('name', '¿Dónde Está Elisa?')],
         encoding='latin-1')
'type=telenovela&name=%BFD%F3nde+Est%E1+Elisa%3F'

As detailed in Parsing ASCII Encoded Bytes, all the urllib.parse functions now accept ASCII-encoded byte strings as input, so long as they are not mixed with regular strings. If ASCII-encoded byte strings are given as parameters, the return types will also be an ASCII-encoded byte strings:

>>> urllib.parse.urlparse(b'http://www.python.org:80/about/')
ParseResultBytes(scheme=b'http', netloc=b'www.python.org:80',
                 path=b'/about/', params=b'', query=b'', fragment=b'')

(Work by Nick Coghlan, Dan Mahn, and Senthil Kumaran in issue 2987, issue 5468, and issue 9873.)

mailbox

Thanks to a concerted effort by R. David Murray, the mailbox module has been fixed for Python 3.2. The challenge was that mailbox had been originally designed with a text interface, but email messages are best represented with bytes because various parts of a message may have different encodings.

The solution harnessed the email package’s binary support for parsing arbitrary email messages. In addition, the solution required a number of API changes.

As expected, the add() method for mailbox.Mailbox objects now accepts binary input.

StringIO and text file input are deprecated. Also, string input will fail early if non-ASCII characters are used. Previously it would fail when the email was processed in a later step.

There is also support for binary output. The get_file() method now returns a file in the binary mode (where it used to incorrectly set the file to text-mode). There is also a new get_bytes() method that returns a bytes representation of a message corresponding to a given key.

It is still possible to get non-binary output using the old API’s get_string() method, but that approach is not very useful. Instead, it is best to extract messages from a Message object or to load them from binary input.

(Contributed by R. David Murray, with efforts from Steffen Daode Nurpmeso and an initial patch by Victor Stinner in issue 9124.)

turtledemo

The demonstration code for the turtle module was moved from the Demo directory to main library. It includes over a dozen sample scripts with lively displays. Being on sys.path, it can now be run directly from the command-line:

$ python -m turtledemo

(Moved from the Demo directory by Alexander Belopolsky in issue 10199.)