


Detailed description of the usage of struct.pack() and struct.unpack() in Python
The struct in python is mainly used to process C structure data. When reading, it is first converted to Python's string type, and then converted to Python's structured type. , such as tuple or something~. Generally, the input channels come from files or network binary streams.
1.struct.pack() and struct.unpack()
In the conversion process, a formatting string(format strings), used to specify the conversion method and format.
Let’s talk about the main methods:
1.1 struct.pack(fmt,v1,v2,...)
Put v1, v2 and other parameters The value is wrapped in one layer, and the wrapping method is specified by fmt. The wrapped parameters must strictly conform to fmt. Finally, a wrapped string is returned.
1.2 struct.unpack(fmt,string)
As the name suggests, unpack. For example, pack is packaged, and then unpacked can be used to unpack. Returns a tuple obtained by unpacking data (string), even if there is only one data, it will be unpacked into a tuple. Among them, len(string) must be equal to calcsize(fmt), which involves a calcsizefunction. struct.calcsize(fmt): This is used to calculate the size of the structure described in the fmt format.
The format string consists of one or more format characters. For the description of these format characters, refer to the Python manual as follows:
2. Code example
import struct # native byteorder buffer = struct.pack("ihb", 1, 2, 3) print repr(buffer) print struct.unpack("ihb", buffer) # data from a sequence, network byteorder data = [1, 2, 3] buffer = struct.pack("!ihb", *data) print repr(buffer) print struct.unpack("!ihb", buffer) Output: '\x01\x00\x00\x00\x02\x00\x03' (1, 2, 3) '\x00\x00\x00\x01\x00\x02\x03' (1, 2, 3)
First, package parameters 1,2,3. Before packaging, 1,2,3 obviously belong to integer in pythondata type , after packing, it becomes a C-structured binary string, and when converted to Python's string type, it is displayed as '\x01\x00\x00\x00\x02\x00\x03'. Since this machine is little-endian ('little- endian', please refer to here for the difference between big-endian and little-endian, so the high bits are placed in the low address segment. i represents the int type in the C struct, so this machine The machine occupies 4 bits, and 1 is represented as 01000000; h represents the short type in the C struct, occupying 2 bits, so it is represented as 0200; similarly, b represents the signed char type in the C struct, occupying 1 bit, so it is represented as 03.
The conversion of other structures is also similar. For some special ones, please refer to the Manual of the official document.
At the beginning of the Format string, there is an optional character to determine big endian and little endian. , the list is as follows:
If not appended, the default is @, that is, using the native character order (big endian or little endian), for the size of the C structure and the memory The alignment method is also consistent with the machine (native). For example, some machines have an integer of 2 bits and some machines have a four-bit integer; some machine memories have four-bit alignment, and some have n-bit alignment ( n is unknown, I don’t know how much).
There is also a standard option, which is described as: If you use standard, there is no memory alignment for any type.
For example, in the second half of the applet just now, the first bit in the format string used is! , which is the standard alignment of big endian mode, so the output is '\x00\x00\x00\x01\x00\x02\x03', in which the high bit itself is placed in the high address bit of the memory.
The above is the detailed content of Detailed description of the usage of struct.pack() and struct.unpack() in Python. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Atom editor mac version download
The most popular open source editor

SublimeText3 Linux new version
SublimeText3 Linux latest version

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

Zend Studio 13.0.1
Powerful PHP integrated development environment

SecLists
SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.