Shulou(Shulou.com)06/02 Report--

In this issue, the editor will bring you about the application fields of Python. The article is rich in content and analyzes and narrates it from a professional point of view. I hope you can get something after reading this article.

System programming

Python's built-in interface to operating system services makes it an ideal tool for writing portable management tools and components (sometimes referred to as Shell tools) that maintain the operating system. Python programs can search files and directory trees, run other programs, use processes or threads for parallel processing, and so on.

Python's standard library binds POSIX and other general operating system (OS) tools: environment variables, files, sockets, pipes, processes, multithreading, regular expression pattern matching, command line arguments, standard stream interfaces, Shell command launchers, file name extensions, and so on. In addition, many Python system tools are designed with portability in mind. For example, a script that copies a directory tree can run on almost all Python platforms without any modification.

User graphics interface

Python's simplicity and fast development cycle are very suitable for developing GUI programs. Python has built-in TKinter's standard object-oriented interface Tk GUI API, which enables Python programs to generate portable local look and feel GUI. Python/ Tkinter GUI can run on Microsoft Windows, X Windows (UNIX and Linux) and Mac OS (both Classic and OS X) without making any changes. PMW, a free extension package, adds some advanced parts to the Tkinter toolkit. In addition, the C++-based toolkit wxPython GUI API can use Python to build portable GUI.

Some advanced toolkits such as PythonCard and Dabo are built on API, which is the foundation of wxPython and Tkinter. With the appropriate libraries, you can use other GUI toolkits, such as Qt, GTK, MFC, and Swing. For applications running in browsers or driven by the need for some simple interfaces, Jython (Java version of Python, which we will introduce in Chapter 2) and Python server-side CGI scripts provide some other user interface options.

Internet script

Python provides standard Internet modules that enable Python to play a broad role in a variety of network tasks, both on the server side and on the client side. The script can communicate through sockets; parse information from the form of the CGI script sent to the server; get the web page through URL; parse HTML and XML files from the acquired web page; communicate through XML-RPC, SOAP and Telnet. Python's library makes it all quite simple.

Not only that, many third-party tools for Internet programming using Python are also available on the network. For example, HTMLGen can generate HTML files from the description of the Python class, and the mod_python package can make Python programs running on Apache servers more efficient and support server-side templates such as Python Server Page, as well as server-side Applet running on the client. In addition, many Web development kits have emerged in Python, such as Django, TurboGears, Pylons, Zope, and WebWare, which enable Python to quickly build fully functional and high-quality websites.

Component integration

In the introduction of Python as a control language, the role of its component integration was involved. Python can be extended through the CumberCure + system, and can nest the features of the CumberCure + system, making it a flexible glue language to scripting the behavior of other systems and components. For example, by integrating a C library into Python, you can test with Python and call other components in the library; embed Python into the product to customize the product individually without the need to recompile the entire product or distribute the source code.

For use in scripts, code generation tools such as SWIG and SIP allow this to be done automatically when the Python connection compiles the component. Larger frameworks, such as COM supported by Python's Microsoft Windows, Jython based on Java implementation, IronPython based on .NET implementation, and various CORBA toolkits, provide a variety of different scripting components. For example, in Windows, Python scripts can use the framework to script Microsoft Word and Excel files.

Database programming

For traditional database requirements, Python provides interfaces to all major relational database systems, such as Sybase, Oracle, Informix, ODBC, MySQL, PostgreSQL, SQLite. Python defines a portable database API that accesses the SQL database system through Python scripts. This API is unified for all kinds of underlying applications. For example, because the vendor's interface is implemented as a portable API, a script written to a free software MySQL system can work on other systems (for example, Oracle) without much change-you just need to replace the underlying vendor interface.

The pickle module of the Python standard provides a simple object persistence system: it allows programs to easily save and restore entire Python objects to files and file-class objects. On the network, you can also find a third-party system called ZODB, which provides a complete object-oriented database system for Python scripts, and the system SQLObject can map the relational database to the class module of Python. And, since Python version 2.5, SQLite has become part of the standard library that comes with Python.

Rapid prototyping

For Python programs, components written in Python or C all look the same. Because of this, we can use Python to prototype the system at the beginning, and then port the components to a compiled language such as C or C++. Unlike other prototyping tools, Python does not need to be rewritten when the prototype is determined. The parts of the system that do not need to be as efficient as C++ can remain unchanged, making it easier to maintain and use.

Numerical calculation and Scientific Computing programming

The NumPy numerical programming extensions we mentioned earlier include many advanced tools, such as matrix objects, interfaces to standard math libraries, and so on. By integrating Python with general code for numerical calculations written in compiled languages for speed reasons, NumPy turns Python into a rigorous and easy-to-use numerical tool that usually replaces existing code in compiled languages such as FORTRAN or C++. Other numerical computing tools provide support for animation, 3D visualization, parallel processing and other functions for Python.

Games, graphics, artificial intelligence, XML, robots, etc.

Python has many applications, far more than those mentioned in this book. For example, you can use the pygame system to program graphics and games using Python; use PIL and other tools for image processing; use PyRo toolkits for robot control programming; use xml libraries, xmlrpclib modules, and other third-party extensions for XML parsing; use neural network simulators and professional system shell for AI programming; use NLTK packages for natural language analysis; and even use PySol programs for chess entertainment.

These are the application areas of Python shared by the editor. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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