10. Brief Tour of the Standard Library/Part I

10.1. Operating System Interface

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


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:


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


10.2. File Wildcards

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


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:


The argparse module provides a more sophisticated mechanism to process command line arguments. The following script extracts one or more filenames and an optional number of lines to be displayed:

When run at the command line with python top.py --lines=5 alpha.txt beta.txt, the script sets args.lines to 5 and args.filenames to ['alpha.txt', 'beta.txt'].

10.4. Error Output Redirection and Program Termination

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


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

10.5. 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:


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


10.6. Mathematics

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


The random module provides tools for making random selections:


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


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:


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

10.8. 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.


10.9. Data Compression

Common data archiving and compression formats are directly supported by modules including: zlibgzipbz2lzmazipfile and tarfile.


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 timeit module quickly demonstrates a modest performance advantage:


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.

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.

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:

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:

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:

  • 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.ElementTreexml.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 gettextlocale, and the codecs package.
Translate »