回复内容:
awesome-python这个awesome系列基本囊括了python的杀手应用了吧
杀手级这个词太暴力,只能根据你不同的需求提不同的杀手了。 fuckit.py https://github.com/ajalt/fuckitpy scapy
排名不分先后:
Django/Flask 快速建站
Scrapy 支持分布式抓取 配合redis有奇效
Numpy/Scipy 各种酷炫的计算
urllib/urllib2 干坏事必备库之一
Selenium 干坏事必备库之二
requests 利用了urllib3的新特性连接池
别私信问我怎么干坏事,哼。
Openstack https://github.com/vinta/awesome-pythonA curated list of awesome Python frameworks, libraries and software 用了这么多年python,我想说已经死去的zope2还是很nb的,真的。其他的功能都有替代品,但是当年的zope2还基本没有替代。不想耽误大家,大家千万不要再去折腾zope,因为他后来放弃了自己的杀手特性,自杀了
好,谢谢 @absente 的评论,刚刚看了下,现在基于Pyramid+ZODB的框架 Substance D Web Application Server 是一个替代。基本上是完全山寨zope2,是对zope2的一个致敬(刚刚看了发起人,就是当年zope那帮老鸟啊!)。当然这个SubstanceD估计难发展到当年zope的高度,因为到现在再建立当年zope辉煌时候的生态太难了。 想来想去就只有OpenStack了,这块目前py应该还是领先世界的,这才称得上超级牛逼。
不过Go所代表的docker甚至Kubernetes也是在迅速赶超的。个人对py的未来表示悲观。 matplotlib + iPython Notebook
功能够用而且免费开源,穷学生的福音 框架都很水,此处略;
论第三方库的话,zope.interface,没有之一;
应用的领域太广,基本没有可比性,可略。

Pythonisbothcompiledandinterpreted.WhenyourunaPythonscript,itisfirstcompiledintobytecode,whichisthenexecutedbythePythonVirtualMachine(PVM).Thishybridapproachallowsforplatform-independentcodebutcanbeslowerthannativemachinecodeexecution.

Python is not strictly line-by-line execution, but is optimized and conditional execution based on the interpreter mechanism. The interpreter converts the code to bytecode, executed by the PVM, and may precompile constant expressions or optimize loops. Understanding these mechanisms helps optimize code and improve efficiency.

There are many methods to connect two lists in Python: 1. Use operators, which are simple but inefficient in large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use the = operator, which is both efficient and readable; 4. Use itertools.chain function, which is memory efficient but requires additional import; 5. Use list parsing, which is elegant but may be too complex. The selection method should be based on the code context and requirements.

There are many ways to merge Python lists: 1. Use operators, which are simple but not memory efficient for large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use itertools.chain, which is suitable for large data sets; 4. Use * operator, merge small to medium-sized lists in one line of code; 5. Use numpy.concatenate, which is suitable for large data sets and scenarios with high performance requirements; 6. Use append method, which is suitable for small lists but is inefficient. When selecting a method, you need to consider the list size and application scenarios.

Compiledlanguagesofferspeedandsecurity,whileinterpretedlanguagesprovideeaseofuseandportability.1)CompiledlanguageslikeC arefasterandsecurebuthavelongerdevelopmentcyclesandplatformdependency.2)InterpretedlanguageslikePythonareeasiertouseandmoreportab

In Python, a for loop is used to traverse iterable objects, and a while loop is used to perform operations repeatedly when the condition is satisfied. 1) For loop example: traverse the list and print the elements. 2) While loop example: guess the number game until you guess it right. Mastering cycle principles and optimization techniques can improve code efficiency and reliability.

To concatenate a list into a string, using the join() method in Python is the best choice. 1) Use the join() method to concatenate the list elements into a string, such as ''.join(my_list). 2) For a list containing numbers, convert map(str, numbers) into a string before concatenating. 3) You can use generator expressions for complex formatting, such as ','.join(f'({fruit})'forfruitinfruits). 4) When processing mixed data types, use map(str, mixed_list) to ensure that all elements can be converted into strings. 5) For large lists, use ''.join(large_li

Pythonusesahybridapproach,combiningcompilationtobytecodeandinterpretation.1)Codeiscompiledtoplatform-independentbytecode.2)BytecodeisinterpretedbythePythonVirtualMachine,enhancingefficiencyandportability.


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