The example in this article describes the Python character encoding judgment method. Share it with everyone for your reference, the details are as follows:
Method 1:
isinstance(s, str) is used to determine whether it is a general string
isinstance(s, unicode) is used to determine whether it is unicode
or
if type(str).__name__!="unicode": str=unicode(str,"utf-8") else: pass
Method 2 :
Python chardet character encoding judgment
Using chardet can easily implement string/file encoding detection. Especially for Chinese web pages, some pages use GBK/GB2312, and some use UTF8. If you need to crawl some pages, it is important to know the web page encoding. Although HTML pages have charset tags, sometimes they are incorrect. Then chardet can help us a lot.
chardet instance
>>> import urllib >>> rawdata = urllib.urlopen('http://www.google.cn/').read() >>> import chardet >>> chardet.detect(rawdata) {'confidence': 0.98999999999999999, 'encoding': 'GB2312'} >>>chardet可以直接用detect函数来检测所给字符的编码。函数返回值为字典,有2个元数,一个是检测的可信度,另外一个就是检测到的编码。
chardet installation
After downloading chardet, unzip the chardet compressed package, place the chardet folder directly in the application directory, and then use import chardet to start using chardet.
Or use the setup.py installation file to copy chardet to the Python system directory, so that all your python programs only need to import chardet.
python setup.py install reference
chardet official website: http://chardet.feedparser.org/
chardet download page: http://chardet.feedparser.org/download/
For more articles related to Python character encoding judgment methods, please pay attention to the PHP Chinese website!

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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