| Python 3.6.5 Documentation > Brief Tour of the Standard Library — Part II
Brief Tour of the Standard Library — Part II
********************************************
This second tour covers more advanced modules that support
professional programming needs. These modules rarely occur in small
scripts.
Output Formatting
=================
The "reprlib" module provides a version of "repr()" customized for
abbreviated displays of large or deeply nested containers:
>>> import reprlib
>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
"{'a', 'c', 'd', 'e', 'f', 'g', ...}"
The "pprint" module offers more sophisticated control over printing
both built-in and user defined objects in a way that is readable by
the interpreter. When the result is longer than one line, the “pretty
printer” adds line breaks and indentation to more clearly reveal data
structure:
>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]
The "textwrap" module formats paragraphs of text to fit a given screen
width:
>>> import textwrap
>>> doc = """The wrap() method is just like fill() except that it returns
... a list of strings instead of one big string with newlines to separate
... the wrapped lines."""
...
>>> print(textwrap.fill(doc, width=40))
The wrap() method is just like fill()
except that it returns a list of strings
instead of one big string with newlines
to separate the wrapped lines.
The "locale" module accesses a database of culture specific data
formats. The grouping attribute of locale’s format function provides a
direct way of formatting numbers with group separators:
>>> import locale
>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
'English_United States.1252'
>>> conv = locale.localeconv() # get a mapping of conventions
>>> x = 1234567.8
>>> locale.format("%d", x, grouping=True)
'1,234,567'
>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
... conv['frac_digits'], x), grouping=True)
'$1,234,567.80'
Templating
==========
The "string" module includes a versatile "Template" class with a
simplified syntax suitable for editing by end-users. This allows
users to customize their applications without having to alter the
application.
The format uses placeholder names formed by "$" with valid Python
identifiers (alphanumeric characters and underscores). Surrounding
the placeholder with braces allows it to be followed by more
alphanumeric letters with no intervening spaces. Writing "$$" creates
a single escaped "$":
>>> from string import Template
>>> t = Template('${village}folk send $$10 to $cause.')
>>> t.substitute(village='Nottingham', cause='the ditch fund')
'Nottinghamfolk send $10 to the ditch fund.'
The "substitute()" method raises a "KeyError" when a placeholder is
not supplied in a dictionary or a keyword argument. For mail-merge
style applications, user supplied data may be incomplete and the
"safe_substitute()" method may be more appropriate — it will leave
placeholders unchanged if data is missing:
>>> t = Template('Return the $item to $owner.')
>>> d = dict(item='unladen swallow')
>>> t.substitute(d)
Traceback (most recent call last):
...
KeyError: 'owner'
>>> t.safe_substitute(d)
'Return the unladen swallow to $owner.'
Template subclasses can specify a custom delimiter. For example, a
batch renaming utility for a photo browser may elect to use percent
signs for placeholders such as the current date, image sequence
number, or file format:
>>> import time, os.path
>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
>>> class BatchRename(Template):
... delimiter = '%'
>>> fmt = input('Enter rename style (%d-date %n-seqnum %f-format): ')
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f
>>> t = BatchRename(fmt)
>>> date = time.strftime('%d%b%y')
>>> for i, filename in enumerate(photofiles):
... base, ext = os.path.splitext(filename)
... newname = t.substitute(d=date, n=i, f=ext)
... print('{0} --> {1}'.format(filename, newname))
img_1074.jpg --> Ashley_0.jpg
img_1076.jpg --> Ashley_1.jpg
img_1077.jpg --> Ashley_2.jpg
Another application for templating is separating program logic from
the details of multiple output formats. This makes it possible to
substitute custom templates for XML files, plain text reports, and
HTML web reports.
Working with Binary Data Record Layouts
=======================================
The "struct" module provides "pack()" and "unpack()" functions for
working with variable length binary record formats. The following
example shows how to loop through header information in a ZIP file
without using the "zipfile" module. Pack codes ""H"" and ""I""
represent two and four byte unsigned numbers respectively. The ""<""
indicates that they are standard size and in little-endian byte order:
import struct
with open('myfile.zip', 'rb') as f:
data = f.read()
start = 0
for i in range(3): # show the first 3 file headers
start += 14
fields = struct.unpack('<IIIHH', data[start:start+16])
crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
start += 16
filename = data[start:start+filenamesize]
start += filenamesize
extra = data[start:start+extra_size]
print(filename, hex(crc32), comp_size, uncomp_size)
start += extra_size + comp_size # skip to the next header
Multi-threading
===============
Threading is a technique for decoupling tasks which are not
sequentially dependent. Threads can be used to improve the
responsiveness of applications that accept user input while other
tasks run in the background. A related use case is running I/O in
parallel with computations in another thread.
The following code shows how the high level "threading" module can run
tasks in background while the main program continues to run:
import threading, zipfile
class AsyncZip(threading.Thread):
def __init__(self, infile, outfile):
threading.Thread.__init__(self)
self.infile = infile
self.outfile = outfile
def run(self):
f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
f.write(self.infile)
f.close()
print('Finished background zip of:', self.infile)
background = AsyncZip('mydata.txt', 'myarchive.zip')
background.start()
print('The main program continues to run in foreground.')
background.join() # Wait for the background task to finish
print('Main program waited until background was done.')
The principal challenge of multi-threaded applications is coordinating
threads that share data or other resources. To that end, the
threading module provides a number of synchronization primitives
including locks, events, condition variables, and semaphores.
While those tools are powerful, minor design errors can result in
problems that are difficult to reproduce. So, the preferred approach
to task coordination is to concentrate all access to a resource in a
single thread and then use the "queue" module to feed that thread with
requests from other threads. Applications using "Queue" objects for
inter-thread communication and coordination are easier to design, more
readable, and more reliable.
Logging
=======
The "logging" module offers a full featured and flexible logging
system. At its simplest, log messages are sent to a file or to
"sys.stderr":
import logging
logging.debug('Debugging information')
logging.info('Informational message')
logging.warning('Warning:config file %s not found', 'server.conf')
logging.error('Error occurred')
logging.critical('Critical error -- shutting down')
This produces the following output:
WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
By default, informational and debugging messages are suppressed and
the output is sent to standard error. Other output options include
routing messages through email, datagrams, sockets, or to an HTTP
Server. New filters can select different routing based on message
priority: "DEBUG", "INFO", "WARNING", "ERROR", and "CRITICAL".
The logging system can be configured directly from Python or can be
loaded from a user editable configuration file for customized logging
without altering the application.
Weak References
===============
Python does automatic memory management (reference counting for most
objects and *garbage collection* to eliminate cycles). The memory is
freed shortly after the last reference to it has been eliminated.
This approach works fine for most applications but occasionally there
is a need to track objects only as long as they are being used by
something else. Unfortunately, just tracking them creates a reference
that makes them permanent. The "weakref" module provides tools for
tracking objects without creating a reference. When the object is no
longer needed, it is automatically removed from a weakref table and a
callback is triggered for weakref objects. Typical applications
include caching objects that are expensive to create:
>>> import weakref, gc
>>> class A:
... def __init__(self, value):
... self.value = value
... def __repr__(self):
... return str(self.value)
...
>>> a = A(10) # create a reference
>>> d = weakref.WeakValueDictionary()
>>> d['primary'] = a # does not create a reference
>>> d['primary'] # fetch the object if it is still alive
10
>>> del a # remove the one reference
>>> gc.collect() # run garbage collection right away
0
>>> d['primary'] # entry was automatically removed
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
d['primary'] # entry was automatically removed
File "C:/python36/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'
Tools for Working with Lists
============================
Many data structure needs can be met with the built-in list type.
However, sometimes there is a need for alternative implementations
with different performance trade-offs.
The "array" module provides an "array()" object that is like a list
that stores only homogeneous data and stores it more compactly. The
following example shows an array of numbers stored as two byte
unsigned binary numbers (typecode ""H"") rather than the usual 16
bytes per entry for regular lists of Python int objects:
>>> from array import array
>>> a = array('H', [4000, 10, 700, 22222])
>>> sum(a)
26932
>>> a[1:3]
array('H', [10, 700])
The "collections" module provides a "deque()" object that is like a
list with faster appends and pops from the left side but slower
lookups in the middle. These objects are well suited for implementing
queues and breadth first tree searches:
>>> from collections import deque
>>> d = deque(["task1", "task2", "task3"])
>>> d.append("task4")
>>> print("Handling", d.popleft())
Handling task1
unsearched = deque([starting_node])
def breadth_first_search(unsearched):
node = unsearched.popleft()
for m in gen_moves(node):
if is_goal(m):
return m
unsearched.append(m)
In addition to alternative list implementations, the library also
offers other tools such as the "bisect" module with functions for
manipulating sorted lists:
>>> import bisect
>>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
>>> bisect.insort(scores, (300, 'ruby'))
>>> scores
[(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
The "heapq" module provides functions for implementing heaps based on
regular lists. The lowest valued entry is always kept at position
zero. This is useful for applications which repeatedly access the
smallest element but do not want to run a full list sort:
>>> from heapq import heapify, heappop, heappush
>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> heapify(data) # rearrange the list into heap order
>>> heappush(data, -5) # add a new entry
>>> [heappop(data) for i in range(3)] # fetch the three smallest entries
[-5, 0, 1]
Decimal Floating Point Arithmetic
=================================
The "decimal" module offers a "Decimal" datatype for decimal floating
point arithmetic. Compared to the built-in "float" implementation of
binary floating point, the class is especially helpful for
* financial applications and other uses which require exact decimal
representation,
* control over precision,
* control over rounding to meet legal or regulatory requirements,
* tracking of significant decimal places, or
* applications where the user expects the results to match
calculations done by hand.
For example, calculating a 5% tax on a 70 cent phone charge gives
different results in decimal floating point and binary floating point.
The difference becomes significant if the results are rounded to the
nearest cent:
>>> from decimal import *
>>> round(Decimal('0.70') * Decimal('1.05'), 2)
Decimal('0.74')
>>> round(.70 * 1.05, 2)
0.73
The "Decimal" result keeps a trailing zero, automatically inferring
four place significance from multiplicands with two place
significance. Decimal reproduces mathematics as done by hand and
avoids issues that can arise when binary floating point cannot exactly
represent decimal quantities.
Exact representation enables the "Decimal" class to perform modulo
calculations and equality tests that are unsuitable for binary
floating point:
>>> Decimal('1.00') % Decimal('.10')
Decimal('0.00')
>>> 1.00 % 0.10
0.09999999999999995
>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
True
>>> sum([0.1]*10) == 1.0
False
The "decimal" module provides arithmetic with as much precision as
needed:
>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857')
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