Python学习第一篇。把之前学习的Python基础知识总结一下。
一、认识Python
首先我们得清楚这个:Python这个名字是从Monty Python借鉴过来的,而不是源于大家所知道的大蟒蛇的意思。我们为什么要学习Python呢?就我而言,我知道豆瓣在使用、重视Python,加上我想学习网页爬虫技术,所以,我要学习Python编程。另外在国外,Yahoo和Google都在使用Python。那么,Python就很值得我们认真学习。
二、Hello,World!
首先我们需要安装Python,大家可以直接访问http://www.python.org/download ,下载Python的最新版本。然后根据其安装向导的几个步骤直接安装即可,比较简单。
安装完成之后,进入cmd(Windows),输入Python,按下回车键,此时dos窗口进入交互式Python解释器,接着我们就可以看见期望已久的HelloWorld了。
不大放心,还是为大家简单介绍一下Python环境搭建。
关于Python的编译器很多,在这里只介绍两种,仅供参考:
1)、去官网下载Python2.7的编译器IDLE(安装简单,使用方便):https://www.python.org/
安装完成后,在开始菜单可以看到:
单击IDLE就可以打开编辑器编写小程序了。
2)、另外一个Python的编译器Pycharm(界面布局和VC6.0很相似,启动时比较慢)去网站下载:http://www.jetbrains.com/pycharm/download/ 选择适合自己的机器下载;
三、基础知识
我们首先了解一下Python中数字、表达式和语句以及简单的用户输入,这里我们可以和C/C++基础编程联系起来有异曲同工之妙,下面我直接用截图展示出来Python的简单运用。
这时需要注意了Python和C++的不同之处了,Python中提供了一个用于实现整除的操作运算符—双斜线
3.1注释:井号(#)在Python中作为注释的标识。
3.2字符串:用print打印字符串语句的时候,用单引号或者双引号均可,但是如果字符串之间有单引号的时候,我们用两种输出方法:用双引号输出或者对字符串的引号进行转义。相应的,如果字符串之间有双引号的时候,我们可以用单引号或者对字符串的引号进行转义。转义的方法是在字符串中引号的前面加上反斜线。
在我们输出字符串的时候,可以使用‘+'来拼接两个输出的字符串,虽然用的不是很多,但是非常有用。
另外,repr函数会创建一个字符串,它以合法的表达式的形式来表示值,repr(x)的功能也可以用`x`(注意是反引号)实现。
如果我们需要输入很长的字符串的话,就需要在输入的时候进行换行处理,然后再接着输入,此时我们就需要用三个引号来代替普通的单引号。
3.3 input和raw_input函数的区别:两者都是实现用户输入的函数,但是,如果我们输入的字符串里有引号的话,此时input就体现不出了输入原有内容了,用raw_input会保存引号。
Python是一种解释型的面向对象的高级程序设计语言,相对于C++和Java而言要简单易懂易学一些。在JavaWeb学习之余,努力学习Python,然后就有机会学习高大上的爬虫了。
希望大家会喜欢这篇文章。

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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