
Brief Tour of the Standard Library
**********************************


Operating System Interface
==========================

The "os" module provides dozens of functions for interacting with the
operating system:

   >>> import os
   >>> os.getcwd()      # Return the current working directory
   'C:\\Python34'
   >>> os.chdir('/server/accesslogs')   # Change current working directory
   >>> os.system('mkdir today')   # Run the command mkdir in the system shell
   0

Be sure to use the "import os" style instead of "from os import *".
This will keep "os.open()" from shadowing the built-in "open()"
function which operates much differently.

The built-in "dir()" and "help()" functions are useful as interactive
aids for working with large modules like "os":

   >>> import os
   >>> dir(os)
   <returns a list of all module functions>
   >>> help(os)
   <returns an extensive manual page created from the module's docstrings>

For daily file and directory management tasks, the "shutil" module
provides a higher level interface that is easier to use:

   >>> import shutil
   >>> shutil.copyfile('data.db', 'archive.db')
   'archive.db'
   >>> shutil.move('/build/executables', 'installdir')
   'installdir'


File Wildcards
==============

The "glob" module provides a function for making file lists from
directory wildcard searches:

   >>> import glob
   >>> glob.glob('*.py')
   ['primes.py', 'random.py', 'quote.py']


Command Line Arguments
======================

Common utility scripts often need to process command line arguments.
These arguments are stored in the "sys" module's *argv* attribute as a
list.  For instance the following output results from running "python
demo.py one two three" at the command line:

   >>> import sys
   >>> print(sys.argv)
   ['demo.py', 'one', 'two', 'three']

The "getopt" module processes *sys.argv* using the conventions of the
Unix "getopt()" function.  More powerful and flexible command line
processing is provided by the "argparse" module.


Error Output Redirection and Program Termination
================================================

The "sys" module also has attributes for *stdin*, *stdout*, and
*stderr*. The latter is useful for emitting warnings and error
messages to make them visible even when *stdout* has been redirected:

   >>> sys.stderr.write('Warning, log file not found starting a new one\n')
   Warning, log file not found starting a new one

The most direct way to terminate a script is to use "sys.exit()".


String Pattern Matching
=======================

The "re" module provides regular expression tools for advanced string
processing. For complex matching and manipulation, regular expressions
offer succinct, optimized solutions:

   >>> import re
   >>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
   ['foot', 'fell', 'fastest']
   >>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
   'cat in the hat'

When only simple capabilities are needed, string methods are preferred
because they are easier to read and debug:

   >>> 'tea for too'.replace('too', 'two')
   'tea for two'


Mathematics
===========

The "math" module gives access to the underlying C library functions
for floating point math:

   >>> import math
   >>> math.cos(math.pi / 4)
   0.70710678118654757
   >>> math.log(1024, 2)
   10.0

The "random" module provides tools for making random selections:

   >>> import random
   >>> random.choice(['apple', 'pear', 'banana'])
   'apple'
   >>> random.sample(range(100), 10)   # sampling without replacement
   [30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
   >>> random.random()    # random float
   0.17970987693706186
   >>> random.randrange(6)    # random integer chosen from range(6)
   4

The "statistics" module calculates basic statistical properties (the
mean, median, variance, etc.) of numeric data:

   >>> import statistics
   >>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
   >>> statistics.mean(data)
   1.6071428571428572
   >>> statistics.median(data)
   1.25
   >>> statistics.variance(data)
   1.3720238095238095

The SciPy project <http://scipy.org> has many other modules for
numerical computations.


Internet Access
===============

There are a number of modules for accessing the internet and
processing internet protocols. Two of the simplest are
"urllib.request" for retrieving data from URLs and "smtplib" for
sending mail:

   >>> from urllib.request import urlopen
   >>> with urlopen('http://tycho.usno.navy.mil/cgi-bin/timer.pl') as response:
   ...     for line in response:
   ...         line = line.decode('utf-8')  # Decoding the binary data to text.
   ...         if 'EST' in line or 'EDT' in line:  # look for Eastern Time
   ...             print(line)

   <BR>Nov. 25, 09:43:32 PM EST

   >>> import smtplib
   >>> server = smtplib.SMTP('localhost')
   >>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
   ... """To: jcaesar@example.org
   ... From: soothsayer@example.org
   ...
   ... Beware the Ides of March.
   ... """)
   >>> server.quit()

(Note that the second example needs a mailserver running on
localhost.)


Dates and Times
===============

The "datetime" module supplies classes for manipulating dates and
times in both simple and complex ways. While date and time arithmetic
is supported, the focus of the implementation is on efficient member
extraction for output formatting and manipulation.  The module also
supports objects that are timezone aware.

   >>> # dates are easily constructed and formatted
   >>> from datetime import date
   >>> now = date.today()
   >>> now
   datetime.date(2003, 12, 2)
   >>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
   '12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'

   >>> # dates support calendar arithmetic
   >>> birthday = date(1964, 7, 31)
   >>> age = now - birthday
   >>> age.days
   14368


Data Compression
================

Common data archiving and compression formats are directly supported
by modules including: "zlib", "gzip", "bz2", "lzma", "zipfile" and
"tarfile".

   >>> import zlib
   >>> s = b'witch which has which witches wrist watch'
   >>> len(s)
   41
   >>> t = zlib.compress(s)
   >>> len(t)
   37
   >>> zlib.decompress(t)
   b'witch which has which witches wrist watch'
   >>> zlib.crc32(s)
   226805979


Performance Measurement
=======================

Some Python users develop a deep interest in knowing the relative
performance of different approaches to the same problem. Python
provides a measurement tool that answers those questions immediately.

For example, it may be tempting to use the tuple packing and unpacking
feature instead of the traditional approach to swapping arguments. The
"timeit" module quickly demonstrates a modest performance advantage:

   >>> from timeit import Timer
   >>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
   0.57535828626024577
   >>> Timer('a,b = b,a', 'a=1; b=2').timeit()
   0.54962537085770791

In contrast to "timeit"'s fine level of granularity, the "profile" and
"pstats" modules provide tools for identifying time critical sections
in larger blocks of code.


Quality Control
===============

One approach for developing high quality software is to write tests
for each function as it is developed and to run those tests frequently
during the development process.

The "doctest" module provides a tool for scanning a module and
validating tests embedded in a program's docstrings.  Test
construction is as simple as cutting-and-pasting a typical call along
with its results into the docstring. This improves the documentation
by providing the user with an example and it allows the doctest module
to make sure the code remains true to the documentation:

   def average(values):
       """Computes the arithmetic mean of a list of numbers.

       >>> print(average([20, 30, 70]))
       40.0
       """
       return sum(values) / len(values)

   import doctest
   doctest.testmod()   # automatically validate the embedded tests

The "unittest" module is not as effortless as the "doctest" module,
but it allows a more comprehensive set of tests to be maintained in a
separate file:

   import unittest

   class TestStatisticalFunctions(unittest.TestCase):

       def test_average(self):
           self.assertEqual(average([20, 30, 70]), 40.0)
           self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
           with self.assertRaises(ZeroDivisionError):
               average([])
           with self.assertRaises(TypeError):
               average(20, 30, 70)

   unittest.main() # Calling from the command line invokes all tests


Batteries Included
==================

Python has a "batteries included" philosophy.  This is best seen
through the sophisticated and robust capabilities of its larger
packages. For example:

* The "xmlrpc.client" and "xmlrpc.server" modules make implementing
  remote procedure calls into an almost trivial task.  Despite the
  modules names, no direct knowledge or handling of XML is needed.

* The "email" package is a library for managing email messages,
  including MIME and other RFC 2822-based message documents. Unlike
  "smtplib" and "poplib" which actually send and receive messages, the
  email package has a complete toolset for building or decoding
  complex message structures (including attachments) and for
  implementing internet encoding and header protocols.

* The "json" package provides robust support for parsing this
  popular data interchange format.  The "csv" module supports direct
  reading and writing of files in Comma-Separated Value format,
  commonly supported by databases and spreadsheets.  XML processing is
  supported by the "xml.etree.ElementTree", "xml.dom" and "xml.sax"
  packages. Together, these modules and packages greatly simplify data
  interchange between Python applications and other tools.

* The "sqlite3" module is a wrapper for the SQLite database library,
  providing a persistent database that can be updated and accessed
  using slightly nonstandard SQL syntax.

* Internationalization is supported by a number of modules including
  "gettext", "locale", and the "codecs" package.
