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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=yes"
/>
<title>stdlib2</title>
<style type="text/css">
code {
white-space: pre-wrap;
}
span.smallcaps {
font-variant: small-caps;
}
span.underline {
text-decoration: underline;
}
div.column {
display: inline-block;
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width: 50%;
}
</style>
</head>
<body>
<h3 id="navigation">Navigation</h3>
<ul>
<li>
<a href="https://docs.python.org/3/genindex.html" title="General Index"
>index</a
>
</li>
<li>
<a
href="https://docs.python.org/3/py-modindex.html"
title="Python Module Index"
>modules</a
>
|
</li>
<li>
<a href="venv.html" title="12. Virtual Environments and Packages"
>next</a
>
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</li>
<li>
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>previous</a
>
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</li>
<li><img src="../_static/py.png" /></li>
<li><a href="https://www.python.org/">Python</a> »</li>
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</li>
<li><a href="index.html">The Python Tutorial</a> »</li>
<li></li>
</ul>
<p><span id="tut-brieftourtwo"></span></p>
<h1 id="brief-tour-of-the-standard-library-part-ii">
<span class="section-number">11. </span>Brief Tour of the Standard Library
— Part II<a
href="#brief-tour-of-the-standard-library-part-ii"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h1>
<p>
This second tour covers more advanced modules that support professional
programming needs. These modules rarely occur in small scripts.
</p>
<p><span id="tut-output-formatting"></span></p>
<h2 id="output-formatting">
<span class="section-number">11.1. </span>Output Formatting<a
href="#output-formatting"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
The
<a
href="https://docs.python.org/3/library/reprlib.html#module-reprlib"
class="reference internal"
title="reprlib: Alternate repr() implementation with size limits."
><code class="sourceCode python">reprlib</code></a
>
module provides a version of
<a
href="https://docs.python.org/3/library/functions.html#repr"
class="reference internal"
title="repr"
><code class="sourceCode python"
><span class="bu">repr</span>()</code
></a
>
customized for abbreviated displays of large or deeply nested containers:
</p>
<pre><code>>>> import reprlib
>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
"{'a', 'c', 'd', 'e', 'f', 'g', ...}"</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/pprint.html#module-pprint"
class="reference internal"
title="pprint: Data pretty printer."
><code class="sourceCode python">pprint</code></a
>
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:
</p>
<pre><code>>>> import pprint
>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
... 'yellow'], 'blue']]]
...
>>> pprint.pprint(t, width=30)
[[[['black', 'cyan'],
'white',
['green', 'red']],
[['magenta', 'yellow'],
'blue']]]</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/textwrap.html#module-textwrap"
class="reference internal"
title="textwrap: Text wrapping and filling"
><code class="sourceCode python">textwrap</code></a
>
module formats paragraphs of text to fit a given screen width:
</p>
<pre><code>>>> 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.</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/locale.html#module-locale"
class="reference internal"
title="locale: Internationalization services."
><code class="sourceCode python">locale</code></a
>
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:
</p>
<pre><code>>>> 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)
'$1,234,567.80'</code></pre>
<p><span id="tut-templating"></span></p>
<h2 id="templating">
<span class="section-number">11.2. </span>Templating<a
href="#templating"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
The
<a
href="https://docs.python.org/3/library/string.html#module-string"
class="reference internal"
title="string: Common string operations."
><code class="sourceCode python">string</code></a
>
module includes a versatile
<a
href="https://docs.python.org/3/library/string.html#string.Template"
class="reference internal"
title="string.Template"
><code class="sourceCode python">Template</code></a
>
class with a simplified syntax suitable for editing by end-users. This
allows users to customize their applications without having to alter the
application.
</p>
<p>
The format uses placeholder names formed by <code>$</code> 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 <code>$$</code> creates a
single escaped <code>$</code>:
</p>
<pre><code>>>> from string import Template
>>> t = Template('${village}folk send $$10 to $cause.')
>>> t.substitute(village='Nottingham', cause='the ditch fund')
'Nottinghamfolk send $10 to the ditch fund.'</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/string.html#string.Template.substitute"
class="reference internal"
title="string.Template.substitute"
><code class="sourceCode python">substitute()</code></a
>
method raises a
<a
href="https://docs.python.org/3/library/exceptions.html#KeyError"
class="reference internal"
title="KeyError"
><code class="sourceCode python"
><span class="pp">KeyError</span></code
></a
>
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
<a
href="https://docs.python.org/3/library/string.html#string.Template.safe_substitute"
class="reference internal"
title="string.Template.safe_substitute"
><code class="sourceCode python">safe_substitute()</code></a
>
method may be more appropriate — it will leave placeholders unchanged if
data is missing:
</p>
<pre><code>>>> 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.'</code></pre>
<p>
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:
</p>
<pre><code>>>> 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</code></pre>
<p>
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.
</p>
<p><span id="tut-binary-formats"></span></p>
<h2 id="working-with-binary-data-record-layouts">
<span class="section-number">11.3. </span>Working with Binary Data Record
Layouts<a
href="#working-with-binary-data-record-layouts"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
The
<a
href="https://docs.python.org/3/library/struct.html#module-struct"
class="reference internal"
title="struct: Interpret bytes as packed binary data."
><code class="sourceCode python">struct</code></a
>
module provides
<a
href="https://docs.python.org/3/library/struct.html#struct.pack"
class="reference internal"
title="struct.pack"
><code class="sourceCode python">pack()</code></a
>
and
<a
href="https://docs.python.org/3/library/struct.html#struct.unpack"
class="reference internal"
title="struct.unpack"
><code class="sourceCode python">unpack()</code></a
>
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
<a
href="https://docs.python.org/3/library/zipfile.html#module-zipfile"
class="reference internal"
title="zipfile: Read and write ZIP-format archive files."
><code class="sourceCode python">zipfile</code></a
>
module. Pack codes <code>"H"</code> and <code>"I"</code> represent two and
four byte unsigned numbers respectively. The <code>"<"</code> indicates
that they are standard size and in little-endian byte order:
</p>
<pre><code>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</code></pre>
<p><span id="tut-multi-threading"></span></p>
<h2 id="multi-threading">
<span class="section-number">11.4. </span>Multi-threading<a
href="#multi-threading"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
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.
</p>
<p>
The following code shows how the high level
<a
href="https://docs.python.org/3/library/threading.html#module-threading"
class="reference internal"
title="threading: Thread-based parallelism."
><code class="sourceCode python">threading</code></a
>
module can run tasks in background while the main program continues to
run:
</p>
<pre><code>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.')</code></pre>
<p>
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.
</p>
<p>
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
<a
href="https://docs.python.org/3/library/queue.html#module-queue"
class="reference internal"
title="queue: A synchronized queue class."
><code class="sourceCode python">queue</code></a
>
module to feed that thread with requests from other threads. Applications
using
<a
href="https://docs.python.org/3/library/queue.html#queue.Queue"
class="reference internal"
title="queue.Queue"
><code class="sourceCode python">Queue</code></a
>
objects for inter-thread communication and coordination are easier to
design, more readable, and more reliable.
</p>
<p><span id="tut-logging"></span></p>
<h2 id="logging">
<span class="section-number">11.5. </span>Logging<a
href="#logging"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
The
<a
href="https://docs.python.org/3/library/logging.html#module-logging"
class="reference internal"
title="logging: Flexible event logging system for applications."
><code class="sourceCode python">logging</code></a
>
module offers a full featured and flexible logging system. At its
simplest, log messages are sent to a file or to <code>sys.stderr</code>:
</p>
<pre><code>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')</code></pre>
<p>This produces the following output:</p>
<pre><code>WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down</code></pre>
<p>
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:
<code>DEBUG</code>, <code>INFO</code>, <code>WARNING</code>,
<code>ERROR</code>, and <code>CRITICAL</code>.
</p>
<p>
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.
</p>
<p><span id="tut-weak-references"></span></p>
<h2 id="weak-references">
<span class="section-number">11.6. </span>Weak References<a
href="#weak-references"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
Python does automatic memory management (reference counting for most
objects and
<a
href="https://docs.python.org/3/glossary.html#term-garbage-collection"
class="reference internal"
><span class="xref std std-term">garbage collection</span></a
>
to eliminate cycles). The memory is freed shortly after the last reference
to it has been eliminated.
</p>
<p>
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
<a
href="https://docs.python.org/3/library/weakref.html#module-weakref"
class="reference internal"
title="weakref: Support for weak references and weak dictionaries."
><code class="sourceCode python">weakref</code></a
>
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:
</p>
<pre><code>>>> 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:/python39/lib/weakref.py", line 46, in __getitem__
o = self.data[key]()
KeyError: 'primary'</code></pre>
<p><span id="tut-list-tools"></span></p>
<h2 id="tools-for-working-with-lists">
<span class="section-number">11.7. </span>Tools for Working with Lists<a
href="#tools-for-working-with-lists"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
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.
</p>
<p>
The
<a
href="https://docs.python.org/3/library/array.html#module-array"
class="reference internal"
title="array: Space efficient arrays of uniformly typed numeric values."
><code class="sourceCode python">array</code></a
>
module provides an
<a
href="https://docs.python.org/3/library/array.html#array.array"
class="reference internal"
title="array.array"
><code class="sourceCode python">array()</code></a
>
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 <code>"H"</code>) rather than
the usual 16 bytes per entry for regular lists of Python int objects:
</p>
<pre><code>>>> from array import array
>>> a = array('H', [4000, 10, 700, 22222])
>>> sum(a)
26932
>>> a[1:3]
array('H', [10, 700])</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/collections.html#module-collections"
class="reference internal"
title="collections: Container datatypes"
><code class="sourceCode python">collections</code></a
>
module provides a
<a
href="https://docs.python.org/3/library/collections.html#collections.deque"
class="reference internal"
title="collections.deque"
><code class="sourceCode python">deque()</code></a
>
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:
</p>
<pre><code>>>> 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)</code></pre>
<p>
In addition to alternative list implementations, the library also offers
other tools such as the
<a
href="https://docs.python.org/3/library/bisect.html#module-bisect"
class="reference internal"
title="bisect: Array bisection algorithms for binary searching."
><code class="sourceCode python">bisect</code></a
>
module with functions for manipulating sorted lists:
</p>
<pre><code>>>> 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')]</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/heapq.html#module-heapq"
class="reference internal"
title="heapq: Heap queue algorithm (a.k.a. priority queue)."
><code class="sourceCode python">heapq</code></a
>
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:
</p>
<pre><code>>>> 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]</code></pre>
<p><span id="tut-decimal-fp"></span></p>
<h2 id="decimal-floating-point-arithmetic">
<span class="section-number">11.8. </span>Decimal Floating Point
Arithmetic<a
href="#decimal-floating-point-arithmetic"
class="headerlink"
title="Permalink to this headline"
>¶</a
>
</h2>
<p>
The
<a
href="https://docs.python.org/3/library/decimal.html#module-decimal"
class="reference internal"
title="decimal: Implementation of the General Decimal Arithmetic Specification."
><code class="sourceCode python">decimal</code></a
>
module offers a
<a
href="https://docs.python.org/3/library/decimal.html#decimal.Decimal"
class="reference internal"
title="decimal.Decimal"
><code class="sourceCode python">Decimal</code></a
>
datatype for decimal floating point arithmetic. Compared to the built-in
<a
href="https://docs.python.org/3/library/functions.html#float"
class="reference internal"
title="float"
><code class="sourceCode python"><span class="bu">float</span></code></a
>
implementation of binary floating point, the class is especially helpful
for
</p>
<ul>
<li>
<p>
financial applications and other uses which require exact decimal
representation,
</p>
</li>
<li><p>control over precision,</p></li>
<li>
<p>control over rounding to meet legal or regulatory requirements,</p>
</li>
<li><p>tracking of significant decimal places, or</p></li>
<li>
<p>
applications where the user expects the results to match calculations
done by hand.
</p>
</li>
</ul>
<p>
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:
</p>
<pre><code>>>> from decimal import *
>>> round(Decimal('0.70') * Decimal('1.05'), 2)
Decimal('0.74')
>>> round(.70 * 1.05, 2)
0.73</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/decimal.html#decimal.Decimal"
class="reference internal"
title="decimal.Decimal"
><code class="sourceCode python">Decimal</code></a
>
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.
</p>
<p>
Exact representation enables the
<a
href="https://docs.python.org/3/library/decimal.html#decimal.Decimal"
class="reference internal"
title="decimal.Decimal"
><code class="sourceCode python">Decimal</code></a
>
class to perform modulo calculations and equality tests that are
unsuitable for binary floating point:
</p>
<pre><code>>>> 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</code></pre>
<p>
The
<a
href="https://docs.python.org/3/library/decimal.html#module-decimal"
class="reference internal"
title="decimal: Implementation of the General Decimal Arithmetic Specification."
><code class="sourceCode python">decimal</code></a
>
module provides arithmetic with as much precision as needed:
</p>
<pre><code>>>> getcontext().prec = 36
>>> Decimal(1) / Decimal(7)
Decimal('0.142857142857142857142857142857142857')</code></pre>
<h3 id="table-of-contents">
<a href="https://docs.python.org/3/contents.html">Table of Contents</a>
</h3>
<ul>
<li>
<a href="#" class="reference internal"
>11. Brief Tour of the Standard Library — Part II</a
>
<ul>
<li>
<a href="#output-formatting" class="reference internal"
>11.1. Output Formatting</a
>
</li>
<li>
<a href="#templating" class="reference internal"
>11.2. Templating</a
>
</li>
<li>
<a
href="#working-with-binary-data-record-layouts"
class="reference internal"
>11.3. Working with Binary Data Record Layouts</a
>
</li>
<li>
<a href="#multi-threading" class="reference internal"
>11.4. Multi-threading</a
>
</li>
<li>
<a href="#logging" class="reference internal">11.5. Logging</a>
</li>
<li>
<a href="#weak-references" class="reference internal"
>11.6. Weak References</a
>
</li>
<li>
<a href="#tools-for-working-with-lists" class="reference internal"
>11.7. Tools for Working with Lists</a
>
</li>
<li>
<a
href="#decimal-floating-point-arithmetic"
class="reference internal"
>11.8. Decimal Floating Point Arithmetic</a
>
</li>
</ul>
</li>
</ul>
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<p>
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><span class="section-number">10. </span>Brief Tour of the Standard
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>
</p>
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