os.path包
os.path包主要用于处理字符串路径,比如'/home/zikong/doc/file.doc',提取出有用的信息。
import os.path
path = '/home/zikong/doc/file.doc'
print(os.path.basename(path)) # 查询路径中包含的文件名
print(os.path.dirname(path)) # 查询路径中包含的目录
info = os.path.split(path) # 将路径分割成文件名和目录两个部分,放在一个表中返回
path2 = os.path.join('\', 'home', 'zikong', 'doc', 'file.doc') #使用目录名和文件名构成一个路径字符串
p_list = [path, path2]
print(os.path.commonprefix(p_list)) # 查询多个路径的共同部分
os.path.normpath(path) # 去除路径path中的冗余。比如'/home/vamei/../.'被转化为'/home'
#os.path还可以查询文件的相关信息(metadata)。文件的相关信息不存储在文件内部,而是由操作系统
#维护的,关于文件的一些信息(比如文件类型,大小,修改时间)。
import os.path
path = '/home/vamei/doc/file.txt'
print(os.path.exists(path)) # 查询文件是否存在
print(os.path.getsize(path)) # 查询文件大小
print(os.path.getatime(path)) # 查询文件上一次读取的时间
print(os.path.getmtime(path)) # 查询文件上一次修改的时间
print(os.path.isfile(path)) # 路径是否指向常规文件
print(os.path.isdir(path)) # 路径是否指向目录文件
glob包
glob是python自己带的一个文件操作相关模块,内容也不多,用它可以查找符合自己目的的文件,就类似于Windows下的文件搜索,而且也 支持通配符,,?,[]这三个通配符,代表0个或多个字符,?代表一个字符,[]匹配指定范围内的字符,如[0-9]匹配数字。
glob方法: 返回所有匹配的文件路径列表,该方法需要一个参数用来指定匹配的路径字符串(本字符串可以为绝对路径也可以为相对路径),比如:
import glob
glob.glob("/home/zikong/doc/*.doc")
/home/zikong/doc/file1.doc /home/zikong/doc/file2.doc
例子
综合利用两个包写的一个类似于linux下的ls函数:
#coding = utf8
import glob
import os.path
path = '/Users/zikong/Pictures'
def ls(path):
#codinf = utf8
print "--name-- --type-- --size-- --atime-- --mtime-- "
path = path + '/*'
filelist = glob.glob(path)
for filepath in filelist:
out = '%s %s %s %s %s'%(filepath.split('/')[4] ,os.path.isfile(filepath) ,os.path.getsize(filepath) ,os.path.getatime(filepath) ,os.path.getmtime(filepath))
print out
ls(path)
注意
#coding=utf
是为了让python能够显示中文

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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