这几天有这样一个需求,要将用户登陆系统的信息统计出来,做成一个报表。当用户登陆成功的时候,服务器会往日志文件里写一条像下面这种格式的记录:”日期时间@用户名@IP“,这样的日志文件第天生成一个。所以,我们只要编历这些日志文件,将所有的登陆信息提取出来,并重新组织数据格式就可以了。用python写一个分析工具非常简单,你会说,用glob获取所有的日志文件,然后对每个日志文件都open(logfile),再一行一行的读取;或者用os.walk,也很简单。其实,标准库提供了另一个辅助模块,我们可以非常方便的完成这个工作,那就是fileinput。下面我们就通过fileinput来编历所有的D盘下的文本文件,将每一行的长度打印出来:
import fileinput from glob import glob for line in fileinput.input(glob(r'd:/*.txt')): print fileinput.lineno(), u'文件:', fileinput.filename(), / u'行号:', fileinput.filelineno(), u'长度:', len(line.strip('/n')) fileinput.close()
代码非常简单明了。input()接受要编历的所有文件路径的列表,通过filename()返回当前正在读取的文件的文件名,filelineno()返回当前读取的行的行号,而lineno()返回当前已经读取的行的数量(或者序号)。其实,模块内部通过FileInput类来实现文件的编历读取,input()在内部创建了该类的一个对象,当处理完数据行之后,通过fileinput.close()来关闭这个内部对象。

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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