10. Brief Tour of the Standard Library¶
10.1. Operating System Interface¶
os module provides dozens of functions for interacting with the
>>> import os >>> os.getcwd() # Return the current working directory 'C:\\Python37' >>> 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
os.open() from shadowing the built-in
open() function which
operates much differently.
help() functions are useful as interactive
aids for working with large modules like
>>> 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'
10.2. File Wildcards¶
glob module provides a function for making file lists from directory
>>> import glob >>> glob.glob('*.py') ['primes.py', 'random.py', 'quote.py']
10.3. 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']
getopt module processes sys.argv using the conventions of the Unix
getopt() function. More powerful and flexible command line processing is
provided by the
10.4. Error Output Redirection and Program Termination¶
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
10.5. String Pattern Matching¶
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'
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
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
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 <https://scipy.org> has many other modules for numerical computations.
10.7. 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('email@example.com', 'firstname.lastname@example.org', ... """To: email@example.com ... From: firstname.lastname@example.org ... ... Beware the Ides of March. ... """) >>> server.quit()
(Note that the second example needs a mailserver running on localhost.)
10.8. Dates and Times¶
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
>>> # 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
10.9. Data Compression¶
Common data archiving and compression formats are directly supported by modules
>>> 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
10.10. 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
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
pstats modules provide tools for identifying time critical sections in
larger blocks of code.
10.11. 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.
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
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
unittest module is not as effortless as the
but it allows a more comprehensive set of tests to be maintained in a separate
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
10.12. Batteries Included¶
Python has a “batteries included” philosophy. This is best seen through the sophisticated and robust capabilities of its larger packages. For example:
xmlrpc.servermodules make implementing remote procedure calls into an almost trivial task. Despite the modules names, no direct knowledge or handling of XML is needed.
poplibwhich 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.
jsonpackage provides robust support for parsing this popular data interchange format. The
csvmodule 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.saxpackages. Together, these modules and packages greatly simplify data interchange between Python applications and other tools.
sqlite3module 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
locale, and the