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 <div class="section" id="brief-tour-of-the-standard-library-part-ii">
<h1>11. Brief Tour of the Standard Library &#8211; Part II<a class="headerlink" href="#brief-tour-of-the-standard-library-part-ii" title="Permalink to this headline">¶</a></h1>
This second tour covers more advanced modules that support professional
programming needs. These modules rarely occur in small scripts.
<div class="section" id="output-formatting">
<h2>11.1. Output Formatting<a class="headerlink" href="#output-formatting" title="Permalink to this headline">¶</a></h2>
The <a class="reference internal" href="../library/repr.html#module-repr" title="repr: Alternate repr() implementation with size limits."><tt class="xref py py-mod docutils literal">repr</tt></a> module provides a version of <a class="reference internal" href="../library/repr.html#module-repr" title="repr: Alternate repr() implementation with size limits."><tt class="xref py py-func docutils literal">repr()</tt></a> customized for
abbreviated displays of large or deeply nested containers:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import repr
&gt;&gt;&gt; repr.repr(set(&#39;supercalifragilisticexpialidocious&#39;))
&quot;set([&#39;a&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;, &#39;g&#39;, ...])&quot;
</pre></div>
</div>
The <a class="reference internal" href="../library/pprint.html#module-pprint" title="pprint: Data pretty printer."><tt class="xref py py-mod docutils literal">pprint</tt></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 &#8220;pretty printer&#8221; adds line breaks
and indentation to more clearly reveal data structure:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import pprint
&gt;&gt;&gt; t = [[[[&#39;black&#39;, &#39;cyan&#39;], &#39;white&#39;, [&#39;green&#39;, &#39;red&#39;]], [[&#39;magenta&#39;,
... &#39;yellow&#39;], &#39;blue&#39;]]]
...
&gt;&gt;&gt; pprint.pprint(t, width=30)
[[[[&#39;black&#39;, &#39;cyan&#39;],
 &#39;white&#39;,
 [&#39;green&#39;, &#39;red&#39;]],
 [[&#39;magenta&#39;, &#39;yellow&#39;],
 &#39;blue&#39;]]]
</pre></div>
</div>
The <a class="reference internal" href="../library/textwrap.html#module-textwrap" title="textwrap: Text wrapping and filling"><tt class="xref py py-mod docutils literal">textwrap</tt></a> module formats paragraphs of text to fit a given screen
width:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import textwrap
&gt;&gt;&gt; doc = &quot;&quot;&quot;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.&quot;&quot;&quot;
...
&gt;&gt;&gt; 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.
</pre></div>
</div>
The <a class="reference internal" href="../library/locale.html#module-locale" title="locale: Internationalization services."><tt class="xref py py-mod docutils literal">locale</tt></a> module accesses a database of culture specific data formats.
The grouping attribute of locale&#8217;s format function provides a direct way of
formatting numbers with group separators:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import locale
&gt;&gt;&gt; locale.setlocale(locale.LC_ALL, &#39;English_United States.1252&#39;)
&#39;English_United States.1252&#39;
&gt;&gt;&gt; conv = locale.localeconv() # get a mapping of conventions
&gt;&gt;&gt; x = 1234567.8
&gt;&gt;&gt; locale.format(&quot;%d&quot;, x, grouping=True)
&#39;1,234,567&#39;
&gt;&gt;&gt; locale.format_string(&quot;%s%.*f&quot;, (conv[&#39;currency_symbol&#39;],
... conv[&#39;frac_digits&#39;], x), grouping=True)
&#39;$1,234,567.80&#39;
</pre></div>
</div>
</div>
<div class="section" id="templating">
<h2>11.2. Templating<a class="headerlink" href="#templating" title="Permalink to this headline">¶</a></h2>
The <a class="reference internal" href="../library/string.html#module-string" title="string: Common string operations."><tt class="xref py py-mod docutils literal">string</tt></a> module includes a versatile <tt class="xref py py-class docutils literal">Template</tt> class with a
simplified syntax suitable for editing by end-users. This allows users to
customize their applications without having to alter the application.
The format uses placeholder names formed by <tt class="docutils literal">$</tt> 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 <tt class="docutils literal">$$</tt> creates a single escaped <tt class="docutils literal">$</tt>:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; from string import Template
&gt;&gt;&gt; t = Template(&#39;${village}folk send $$10 to $cause.&#39;)
&gt;&gt;&gt; t.substitute(village=&#39;Nottingham&#39;, cause=&#39;the ditch fund&#39;)
&#39;Nottinghamfolk send $10 to the ditch fund.&#39;
</pre></div>
</div>
The <tt class="xref py py-meth docutils literal">substitute()</tt> method raises a <a class="reference internal" href="../library/exceptions.html#exceptions.KeyError" title="exceptions.KeyError"><tt class="xref py py-exc docutils literal">KeyError</tt></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
<tt class="xref py py-meth docutils literal">safe_substitute()</tt> method may be more appropriate &#8212; it will leave
placeholders unchanged if data is missing:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; t = Template(&#39;Return the $item to $owner.&#39;)
&gt;&gt;&gt; d = dict(item=&#39;unladen swallow&#39;)
&gt;&gt;&gt; t.substitute(d)
Traceback (most recent call last):
 ...
KeyError: &#39;owner&#39;
&gt;&gt;&gt; t.safe_substitute(d)
&#39;Return the unladen swallow to $owner.&#39;
</pre></div>
</div>
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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import time, os.path
&gt;&gt;&gt; photofiles = [&#39;img_1074.jpg&#39;, &#39;img_1076.jpg&#39;, &#39;img_1077.jpg&#39;]
&gt;&gt;&gt; class BatchRename(Template):
... delimiter = &#39;%&#39;
&gt;&gt;&gt; fmt = raw_input(&#39;Enter rename style (%d-date %n-seqnum %f-format): &#39;)
Enter rename style (%d-date %n-seqnum %f-format): Ashley_%n%f

&gt;&gt;&gt; t = BatchRename(fmt)
&gt;&gt;&gt; date = time.strftime(&#39;%d%b%y&#39;)
&gt;&gt;&gt; for i, filename in enumerate(photofiles):
... base, ext = os.path.splitext(filename)
... newname = t.substitute(d=date, n=i, f=ext)
... print &#39;{0} --&gt; {1}&#39;.format(filename, newname)

img_1074.jpg --&gt; Ashley_0.jpg
img_1076.jpg --&gt; Ashley_1.jpg
img_1077.jpg --&gt; Ashley_2.jpg
</pre></div>
</div>
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.
</div>
<div class="section" id="working-with-binary-data-record-layouts">
<h2>11.3. Working with Binary Data Record Layouts<a class="headerlink" href="#working-with-binary-data-record-layouts" title="Permalink to this headline">¶</a></h2>
The <a class="reference internal" href="../library/struct.html#module-struct" title="struct: Interpret strings as packed binary data."><tt class="xref py py-mod docutils literal">struct</tt></a> module provides <tt class="xref py py-func docutils literal">pack()</tt> and <tt class="xref py py-func docutils literal">unpack()</tt> 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 class="reference internal" href="../library/zipfile.html#module-zipfile" title="zipfile: Read and write ZIP-format archive files."><tt class="xref py py-mod docutils literal">zipfile</tt></a> module. Pack codes <tt class="docutils literal">&quot;H&quot;</tt> and <tt class="docutils literal">&quot;I&quot;</tt> represent two and four
byte unsigned numbers respectively. The <tt class="docutils literal">&quot;&lt;&quot;</tt> indicates that they are
standard size and in little-endian byte order:
<div class="highlight-python"><div class="highlight"><pre>import struct

data = open(&#39;myfile.zip&#39;, &#39;rb&#39;).read()
start = 0
for i in range(3): # show the first 3 file headers
 start += 14
 fields = struct.unpack(&#39;&lt;IIIHH&#39;, 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
</pre></div>
</div>
</div>
<div class="section" id="multi-threading">
<h2>11.4. Multi-threading<a class="headerlink" href="#multi-threading" title="Permalink to this headline">¶</a></h2>
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 <a class="reference internal" href="../library/threading.html#module-threading" title="threading: Higher-level threading interface."><tt class="xref py py-mod docutils literal">threading</tt></a> module can run
tasks in background while the main program continues to run:
<div class="highlight-python"><div class="highlight"><pre>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, &#39;w&#39;, zipfile.ZIP_DEFLATED)
 f.write(self.infile)
 f.close()
 print &#39;Finished background zip of: &#39;, self.infile

background = AsyncZip(&#39;mydata.txt&#39;, &#39;myarchive.zip&#39;)
background.start()
print &#39;The main program continues to run in foreground.&#39;

background.join() # Wait for the background task to finish
print &#39;Main program waited until background was done.&#39;
</pre></div>
</div>
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
<a class="reference internal" href="../library/queue.html#module-Queue" title="Queue: A synchronized queue class."><tt class="xref py py-mod docutils literal">Queue</tt></a> module to feed that thread with requests from other threads.
Applications using <a class="reference internal" href="../library/queue.html#Queue.Queue" title="Queue.Queue"><tt class="xref py py-class docutils literal">Queue.Queue</tt></a> objects for inter-thread communication
and coordination are easier to design, more readable, and more reliable.
</div>
<div class="section" id="logging">
<h2>11.5. Logging<a class="headerlink" href="#logging" title="Permalink to this headline">¶</a></h2>
The <a class="reference internal" href="../library/logging.html#module-logging" title="logging: Flexible event logging system for applications."><tt class="xref py py-mod docutils literal">logging</tt></a> module offers a full featured and flexible logging system.
At its simplest, log messages are sent to a file or to <tt class="docutils literal">sys.stderr</tt>:
<div class="highlight-python"><div class="highlight"><pre>import logging
logging.debug(&#39;Debugging information&#39;)
logging.info(&#39;Informational message&#39;)
logging.warning(&#39;Warning:config file %s not found&#39;, &#39;server.conf&#39;)
logging.error(&#39;Error occurred&#39;)
logging.critical(&#39;Critical error -- shutting down&#39;)
</pre></div>
</div>
This produces the following output:
<div class="highlight-none"><div class="highlight"><pre>WARNING:root:Warning:config file server.conf not found
ERROR:root:Error occurred
CRITICAL:root:Critical error -- shutting down
</pre></div>
</div>
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: <tt class="xref py py-const docutils literal">DEBUG</tt>, <tt class="xref py py-const docutils literal">INFO</tt>,
<tt class="xref py py-const docutils literal">WARNING</tt>, <tt class="xref py py-const docutils literal">ERROR</tt>, and <tt class="xref py py-const docutils literal">CRITICAL</tt>.
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.
</div>
<div class="section" id="weak-references">
<h2>11.6. Weak References<a class="headerlink" href="#weak-references" title="Permalink to this headline">¶</a></h2>
Python does automatic memory management (reference counting for most objects and
<a class="reference internal" href="../glossary.html#term-garbage-collection">garbage collection</a> 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 <a class="reference internal" href="../library/weakref.html#module-weakref" title="weakref: Support for weak references and weak dictionaries."><tt class="xref py py-mod docutils literal">weakref</tt></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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import weakref, gc
&gt;&gt;&gt; class A:
... def __init__(self, value):
... self.value = value
... def __repr__(self):
... return str(self.value)
...
&gt;&gt;&gt; a = A(10) # create a reference
&gt;&gt;&gt; d = weakref.WeakValueDictionary()
&gt;&gt;&gt; d[&#39;primary&#39;] = a # does not create a reference
&gt;&gt;&gt; d[&#39;primary&#39;] # fetch the object if it is still alive
10
&gt;&gt;&gt; del a # remove the one reference
&gt;&gt;&gt; gc.collect() # run garbage collection right away
0
&gt;&gt;&gt; d[&#39;primary&#39;] # entry was automatically removed
Traceback (most recent call last):
 File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;
 d[&#39;primary&#39;] # entry was automatically removed
 File &quot;C:/python26/lib/weakref.py&quot;, line 46, in __getitem__
 o = self.data[key]()
KeyError: &#39;primary&#39;
</pre></div>
</div>
</div>
<div class="section" id="tools-for-working-with-lists">
<h2>11.7. Tools for Working with Lists<a class="headerlink" href="#tools-for-working-with-lists" title="Permalink to this headline">¶</a></h2>
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 <a class="reference internal" href="../library/array.html#module-array" title="array: Space efficient arrays of uniformly typed numeric values."><tt class="xref py py-mod docutils literal">array</tt></a> module provides an <a class="reference internal" href="../library/array.html#module-array" title="array: Space efficient arrays of uniformly typed numeric values."><tt class="xref py py-class docutils literal">array()</tt></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 <tt class="docutils literal">&quot;H&quot;</tt>) rather than the usual 16 bytes per entry for regular lists of
Python int objects:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; from array import array
&gt;&gt;&gt; a = array(&#39;H&#39;, [4000, 10, 700, 22222])
&gt;&gt;&gt; sum(a)
26932
&gt;&gt;&gt; a[1:3]
array(&#39;H&#39;, [10, 700])
</pre></div>
</div>
The <a class="reference internal" href="../library/collections.html#module-collections" title="collections: High-performance datatypes"><tt class="xref py py-mod docutils literal">collections</tt></a> module provides a <tt class="xref py py-class docutils literal">deque()</tt> 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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; from collections import deque
&gt;&gt;&gt; d = deque([&quot;task1&quot;, &quot;task2&quot;, &quot;task3&quot;])
&gt;&gt;&gt; d.append(&quot;task4&quot;)
&gt;&gt;&gt; print &quot;Handling&quot;, d.popleft()
Handling task1
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>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)
</pre></div>
</div>
In addition to alternative list implementations, the library also offers other
tools such as the <a class="reference internal" href="../library/bisect.html#module-bisect" title="bisect: Array bisection algorithms for binary searching."><tt class="xref py py-mod docutils literal">bisect</tt></a> module with functions for manipulating sorted
lists:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; import bisect
&gt;&gt;&gt; scores = [(100, &#39;perl&#39;), (200, &#39;tcl&#39;), (400, &#39;lua&#39;), (500, &#39;python&#39;)]
&gt;&gt;&gt; bisect.insort(scores, (300, &#39;ruby&#39;))
&gt;&gt;&gt; scores
[(100, &#39;perl&#39;), (200, &#39;tcl&#39;), (300, &#39;ruby&#39;), (400, &#39;lua&#39;), (500, &#39;python&#39;)]
</pre></div>
</div>
The <a class="reference internal" href="../library/heapq.html#module-heapq" title="heapq: Heap queue algorithm (a.k.a. priority queue)."><tt class="xref py py-mod docutils literal">heapq</tt></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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; from heapq import heapify, heappop, heappush
&gt;&gt;&gt; data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
&gt;&gt;&gt; heapify(data) # rearrange the list into heap order
&gt;&gt;&gt; heappush(data, -5) # add a new entry
&gt;&gt;&gt; [heappop(data) for i in range(3)] # fetch the three smallest entries
[-5, 0, 1]
</pre></div>
</div>
</div>
<div class="section" id="decimal-floating-point-arithmetic">
<h2>11.8. Decimal Floating Point Arithmetic<a class="headerlink" href="#decimal-floating-point-arithmetic" title="Permalink to this headline">¶</a></h2>
The <a class="reference internal" href="../library/decimal.html#module-decimal" title="decimal: Implementation of the General Decimal Arithmetic Specification."><tt class="xref py py-mod docutils literal">decimal</tt></a> module offers a <tt class="xref py py-class docutils literal">Decimal</tt> datatype for decimal
floating point arithmetic. Compared to the built-in <a class="reference internal" href="../library/functions.html#float" title="float"><tt class="xref py py-class docutils literal">float</tt></a>
implementation of binary floating point, the class is especially helpful for
<ul class="simple">
<li>financial applications and other uses which require exact decimal
representation,</li>
<li>control over precision,</li>
<li>control over rounding to meet legal or regulatory requirements,</li>
<li>tracking of significant decimal places, or</li>
<li>applications where the user expects the results to match calculations done by
hand.</li>
</ul>
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:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; from decimal import *
&gt;&gt;&gt; x = Decimal(&#39;0.70&#39;) * Decimal(&#39;1.05&#39;)
&gt;&gt;&gt; x
Decimal(&#39;0.7350&#39;)
&gt;&gt;&gt; x.quantize(Decimal(&#39;0.01&#39;)) # round to nearest cent
Decimal(&#39;0.74&#39;)
&gt;&gt;&gt; round(.70 * 1.05, 2) # same calculation with floats
0.73
</pre></div>
</div>
The <tt class="xref py py-class docutils literal">Decimal</tt> 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 <tt class="xref py py-class docutils literal">Decimal</tt> class to perform modulo
calculations and equality tests that are unsuitable for binary floating point:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; Decimal(&#39;1.00&#39;) % Decimal(&#39;.10&#39;)
Decimal(&#39;0.00&#39;)
&gt;&gt;&gt; 1.00 % 0.10
0.09999999999999995

&gt;&gt;&gt; sum([Decimal(&#39;0.1&#39;)]*10) == Decimal(&#39;1.0&#39;)
True
&gt;&gt;&gt; sum([0.1]*10) == 1.0
False
</pre></div>
</div>
The <a class="reference internal" href="../library/decimal.html#module-decimal" title="decimal: Implementation of the General Decimal Arithmetic Specification."><tt class="xref py py-mod docutils literal">decimal</tt></a> module provides arithmetic with as much precision as needed:
<div class="highlight-python"><div class="highlight"><pre>&gt;&gt;&gt; getcontext().prec = 36
&gt;&gt;&gt; Decimal(1) / Decimal(7)
Decimal(&#39;0.142857142857142857142857142857142857&#39;)
</pre></div>
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 <ul>
<li><a class="reference internal" href="#">11. Brief Tour of the Standard Library &#8211; Part II</a><ul>
<li><a class="reference internal" href="#output-formatting">11.1. Output Formatting</a></li>
<li><a class="reference internal" href="#templating">11.2. Templating</a></li>
<li><a class="reference internal" href="#working-with-binary-data-record-layouts">11.3. Working with Binary Data Record Layouts</a></li>
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<li><a class="reference internal" href="#tools-for-working-with-lists">11.7. Tools for Working with Lists</a></li>
<li><a class="reference internal" href="#decimal-floating-point-arithmetic">11.8. Decimal Floating Point Arithmetic</a></li>
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