


Detailed explanation of examples of python operations on Mysql database
import MySQLdb#引入mysql模块 class ManagerDB:#创建一个类 def __init__(self): self.db=None self.cursor=None self.connit() def connit(self):#链接数据库 self.db=MySQLdb.connect(host='127.0.0.1',user='root',passwd='123456',db='exam_python') #host主机名 #user用户名 #passwd用户名密码 #db数据库 self.cursor=self.db.cursor() def start(self):#开始 while True: self.menu()#引入菜单栏 xz=input('请输入要选择的编号:') if xz==1: self.student = self.addStudent() if xz==2: self.showStudent() if xz==3: self.delStudent() if xz==4: print '再见' self.db.close() self.cursor.close() break def addStudent(self):#添加 sname=raw_input('请输入要添加学生的姓名') ssex=raw_input('请输入要添加学生的性别') sage=raw_input('请输入要添加学生的年龄') try: sq1="insert into student(name,sex,age)values('%s','%s','%s')"%(sname,ssex,sage) for i in range(10): self.cursor.execute(sq1) self.db.commit() print '成功添加10条信息' except: print '添加失败' self.db.rollback() def showStudent(self):#查看 self.cursor.execute('select * from student') print 'id 姓名 性别 年龄' for i in self.cursor: print i[0],i[1],i[2],i[3] def delStudent(self):#删除 try: self.cursor.execute('delete from student where id=5') self.db.commit() print '成功删除id为5的信息' except: print '删除失败' self.db.rollback() def menu(self): print ''' ---------------------------- 1 添加信息 2 显示数据 3 删除数据 4 退出系统 ---------------------------- ''' if __name__ == '__main__': s=ManagerDB()#实例化对象 s.start()
The above is the detailed content of Detailed explanation of examples of python operations on Mysql database. For more information, please follow other related articles on 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.


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

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

SublimeText3 Linux new version
SublimeText3 Linux latest version

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

Dreamweaver CS6
Visual web development tools