问题描述: 代码编写过程中,需要引入文件,但是引入的文件随着项目的变大会变多,所以编写了一个自动导入的方法,会根据文件名称的特点进行导入。
def auto_import(packagePath: str, fileTag: str, interceptLength: int, level=1): """ 自动导入函数,导入具有某个标识的文件 :param packagePath: 当前包路径 :param level: 软件包层级 :param fileTag:文件名称标识 :param interceptLength:导入属性截取名称 :return: """ # 定义导入属性列表和包 att_list = [] # 获取对应的包层级 rank = level package = '' for i in range(level): # 获取父级包名称拼接包名称 package += packagePath.split('\\')[-rank] + '.' rank -= 1 # 遍历当前包下的所有文件 for fileName in os.listdir(packagePath): # 筛选出nameTag的文件进行导入 if fileTag in fileName: print(f'导入包名称:{package}' + fileName[:-3]) # 动态导入包,并获取包内的具体模块、属性 att_list.append( # 导入包中的某个属性 importlib.import_module( # 拼接模块路径 f'{package}' + fileName[:-3] # 获取模块中的对应属性 ).__dict__[fileName[:-interceptLength]]) # 返回属性列表 return att_list
假如我们创建了多个TableModel文件,需要校验对应的文件是否在数据库中存在,那么我们就可以这么用:
import os import auto_import def auto_check_model(): """ 导入tableModel中的所有文件,验证数据库中表是否存在 :return: 返回验证结果 """ # 获取当前文件夹路径 packagePath = os.path.dirname(os.path.realpath(__file__)) # 获取所有model文件 model_list = auto_import(packagePath=packagePath, fileTag='Model', interceptLength=8, level=2) # 建立数据库连接 connect = DatabaseOperation().connect() # 检查model在数据库中是否存在,不存在则创建 for i in range(len(model_list)): model_list[i].metadata.create_all(connect) print(f"#### {model_list[i].__name__}校验完成! ####")
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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.


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