What Can I Do with Python?

In addition to being a well-designed programming language, Python is useful for ac-
complishing real-world tasks—the sorts of things developers do day in and day out.
It’s commonly used in a variety of domains, as a tool for scripting other components
and implementing standalone programs. In fact, as a general-purpose language,
Python’s roles are virtually unlimited: you can use it for everything from website de-
velopment and gaming to robotics and spacecraft control.
However, the most common Python roles currently seem to fall into a few broad cat-
egories. The next few sections describe some of Python’s most common applications
today, as well as tools used in each domain. We won’t be able to explore the tools

Systems Programming
Python’s built-in interfaces to operating-system services make it ideal for writing
portable, maintainable system-administration tools and utilities (sometimes called shell
tools). Python programs can search files and directory trees, launch other programs, do
parallel processing with processes and threads, and so on.
Python’s standard library comes with POSIX bindings and support for all the usual OS
tools: environment variables, files, sockets, pipes, processes, multiple threads, regular
expression pattern matching, command-line arguments, standard stream interfaces,
shell-command launchers, filename expansion, zip file utilities, XML and JSON pars-
ers, CSV file handlers, and more. In addition, the bulk of Python’s system interfaces
are designed to be portable; for example, a script that copies directory trees typically
runs unchanged on all major Python platforms. The Stackless Python implementation,
and used by EVE Online, also offers advanced solutions to
multiprocessing requirements.

Internet Scripting
Python comes with standard Internet modules that allow Python programs to perform
a wide variety of networking tasks, in client and server modes. Scripts can communicate
over sockets; extract form information sent to server-side CGI scripts; transfer files by
FTP; parse and generate XML and JSON documents; send, receive, compose, and parseemail; fetch web pages by URLs; parse the HTML of fetched web pages; communicate
over XML-RPC, SOAP, and Telnet; and more. Python’s libraries make these tasks re-
markably simple.
In addition, a large collection of third-party tools are available on the Web for doing
Internet programming in Python. For instance, the HTMLGen system generates HTML
files from Python class-based descriptions, the mod_python package runs Python effi-
ciently within the Apache web server and supports server-side templating with its
Python Server Pages, and the Jython system provides for seamless Python/Java inte-
gration and supports coding of server-side applets that run on clients.
In addition, full-blown web development framework packages for Python, such as
Django, TurboGears, web2py, Pylons, Zope, and WebWare, support quick construction
of full-featured and production-quality websites with Python. Many of these include
features such as object-relational mappers, a Model/View/Controller architecture,
server-side scripting and templating, and AJAX support, to provide complete and en-
terprise-level web development solutions.
More recently, Python has expanded into rich Internet applications (RIAs), with tools
such as Silverlight in IronPython, and pyjs (a.k.a. pyjamas) and its Python-to-JavaScript
compiler, AJAX framework, and widget set. Python also has moved into cloud com-
puting, with App Engine, and others descr

Database Programming
For traditional database demands, there are Python interfaces to all commonly used
relational database systems—Sybase, Oracle, Informix, ODBC, MySQL, PostgreSQL,SQLite, and more. The Python world has also defined a portable database API for ac-
cessing SQL database systems from Python scripts, which looks the same on a variety
of underlying database systems. For instance, because the vendor interfaces implement
the portable API, a script written to work with the free MySQL system will work largely
unchanged on other systems (such as Oracle); all you generally have to do is replace
the underlying vendor interface. The in-process SQLite embedded SQL database engine
is a standard part of Python itself since 2.5, supporting both prototyping and basic
program storage needs.
In the non-SQL department, Python’s standard pickle module provides a simple object
persistence system—it allows programs to easily save and restore entire Python objects
to files and file-like objects. On the Web, you’ll also find third-party open source sys-
tems named ZODB and Durus that provide complete object-oriented database systems
for Python scripts; others, such as SQLObject and SQLAlchemy, that implement object
relational mappers (ORMs), which graft Python’s class model onto relational tables;
and PyMongo, an interface to MongoDB, a high-performance, non-SQL, open source
JSON-style document database, which stores data in structures very similar to Python’s
own lists and dictionaries, and whose text may be parsed and created with Python’s
own standard library json module.
Still other systems offer more specialized ways to store data, including the datastore in
Google’s App Engine, which models data with Python classes and provides extensive
scalability, as well as additional emerging cloud storage options such as Azure, Pi-
Cloud, OpenStack, and Stackato.

And More: Gaming, Images, Data Mining, Robots, Excel...
Python is commonly applied in more domains than can be covered here. For example,
you’ll find tools that allow you to use Python to do:
• Game programming and multimedia with pygame, cgkit, pyglet, PySoy,
Panda3D, and others
• Serial port communication on Windows, Linux, and more with the PySerial ex-
tension
• Image processing with PIL and its newer Pillow fork, PyOpenGL, Blender, Maya,
and more
• Robot control programming with the PyRo toolkit
• Natural language analysis with the NLTK package
• Instrumentation on the Raspberry Pi and Arduino boards
• Mobile computing with ports of Python to the Google Android and Apple iOS
platforms
• Excel spreadsheet function and macro programming with the PyXLL or DataNi-
tro add-ins
• Media file content and metadata tag processing with PyMedia, ID3, PIL/Pillow,
and more
• Artificial intelligence with the PyBrain neural net library and the Milk machine
learning toolkit
• Expert system programming with PyCLIPS, Pyke, Pyrolog, and pyDatalog
• Network monitoring with zenoss, written in and customized with Python
• Python-scripted design and modeling with PythonCAD, PythonOCC, FreeCAD,
and others
• Document processing and generation with ReportLab, Sphinx, Cheetah, PyPDF,
and so on
• Data visualization with Mayavi, matplotlib, VTK, VPython, and more
• XML parsing with the xml library package, the xmlrpclib module, and third-party
extensions
• JSON and CSV file processing with the json and csv modules
• Data mining with the Orange framework, the Pattern bundle, Scrapy, and custom
code