配置好virtualenv 和virtualenvwrapper后,使用pycharm创建新项目。之后要面临的问题就来了,之前一直使用的是sqlite作为开发数据库进行学习,按照之前看教程的原则,好像就是说开发环境要和生产环境尽量的一致,所以现在想尝试一下使用更有可能在生产环境部署的mysql数据库进行开发。
本觉得是一件应该很轻松的事情,没想到遇到了一些麻烦
根据一通百度,搜出来的方案大概有:
MySQLdb
mysql安装时候自带的connector
pymysql
MySQLdb
是django官方推荐的第一个是django官方推荐的数据库链接库,也自然是我第一个尝试的。可是安装的时候居然找不到适合64位,python2.78的安装文件! 通过一篇文章介绍修改勉强装上了支持2.7的版本,结果使用的时候总是unicode报错,mysql的数据库也按照教程说的设置成了utf8编码,只得作罢
2,自带的connector
又是一个看起来很官方的版本,但是按照官方的安装方法总是提示没有mysql.connector.django这个模块。。。。不明白为什么。再仔细找找发现安装成功的同学之后又碰到了中文unicode报错。。。。。残念
3,pymysql
这是博客上关于python3试用django-mysql的解决方案。一开始因为非官方没有使,倒是意外简单的成功了。。。
在project的inti.py里面添加:
import pymysql
pymysql.install_as_MySQLdb()
settings:
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql', #数据库引擎
'NAME': 'django', #数据库名
'USER': 'user', #用户名
'PASSWORD': 'password!', #密码
'HOST': 'localhost', #数据库主机,默认为localhost
'PORT': '3306', #数据库端口,MySQL默认为3306
'OPTIONS': {
'autocommit': True,
},
}
}

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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