本文实例讲述了python使用chardet判断字符串编码的方法。分享给大家供大家参考。具体分析如下:
最近利用python抓取一些网上的数据,遇到了编码的问题。非常头痛,总结一下用到的解决方案。
linux中vim下查看文件编码的命令 set fileencoding
python中一个强力的编码检测包 chardet ,使用方法非常简单。linux下利用pip install chardet实现简单安装
import chardet f = open('file','r') fencoding=chardet.detect(f.read()) print fencoding
fencoding输出格式 {'confidence': 0.96630842899499614, 'encoding': 'GB2312'} ,只能判断是否为某种编码的概率。比较准确的结果了。输入参数为str类型。
了解python中str的编码后可以利用decode和encode来实现编码的转换。
一般流程是str利用decode方法根据str的编码将其解码为unicode字符串类型,然后利用encode根据特定的编码将unicode字符串类型转换为特定的编码。python中str和unicode属于两种不同的类型,如下。
一般情况下window默认编码gbk,linux默认编码utf8
python编程中 系统编码,python编码,文件编码 的概念。
系统编码:默认写源码的编辑器的编码方式。它代表源码文件内的所有内容都是根据词方式编码成二进制码流。存入到磁盘中的。linux下通过locale命令查看。
python编码:指python内设置的解码方式。如果不设定的话,python默认的是ascii解码方式。如果python源代码文件中不出现中文的话,这个地方怎么设定应该不会问题。
设定方法:在源码文件开头(一定是第一行):#-*-coding:UTF-8-*-,源码文件的设置解码方式是UTF-8 或者
import sys reload(sys) sys.setdefaultencoding('UTF-8')
文件编码:文本的编码方式,linux下vim利用set fileencoding查看。
一般情况下输出乱码的原因就是 没有按照系统解码的方式进行编码。
比如print s, s类型为str,linux系统下系统默认编码为utf8编码,s在输出前就应该编码为utf8。如果s为gbk编码就应该这样输出。print s.decode('gbk').encode('utf8')才能输出中文。
window下面情况相同,window默认编码为gbk编码,所以s输出前必须编码为gbk。
python处理中一般处理unicode类型。这样输出前直接编码即可。
希望本文所述对大家的Python程序设计有所帮助。

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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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