#docstrings

[tecnologiaylogistica]

Docstrings en Pythom | ¿Cómo aplicar docstrings | Curso Python Completo

[optimisticduckbird]

Today I learned that if you write something between two sets of three quotation marks in python, it's like doing a comment, kind of, and it's called a docstring. You use it to document your code. In the reST format, you use colons to describe parameters included in your code. On Stackoverflow.com, someone called daouzli put this example:

"""

This is a reST style.

:param param1: this is a first param

:param param2:

this is a second param

:returns: this is a description of what is returned

:raises keyError: raises an exception

"""

I had to look it up because my module in python coding was dropping it into all the examples without any context or explanation!

I also learned that you use def to declare a user-defined function which in plain talk would be a function that you've made and you use def to give it a name so you can call it up easily. For example, if you knew you might want to print a specific combination of things multiple times, you might make a function for it and call the function instead of writing out the whole combination every time.

[utopicwork]

Finally figured out I needed the napoleon plugin for sphinx because I'm doing numpy style docstrings and the docs look a lot better now. I've also fixed linking issues and written a bit of rst to add links directly to the code in the documentation. I need to finish documenting a number of things but I'm pretty sure I'll finish in the projected timeframe (so 1 more day) but even if I don't it'll probably be the day after

[andmaybegayer]

it's hack week at work so I'm writing a Language Server for one of our build tooling files, and I forgot how much fun programming can be when you have a clear and measurable goal which can be achieved in a relatively predictable timespan.

Problems that arise here are like "Figure out how to send debug messages to the editor" and "Find out why only five matches are appearing" or "decide on how to structure the docstrings for faster access."

My normal day job problems are like "Hey. You know the entire Linux kernel multidisk subsystem. Well the customer says they're getting these three error messages. They cannot under any circumstances test code on their system, which is set up in a massive high availability cluster that you can't replicate, and no one on earth has ever hit this exact problem before. Actually one of those error messages is known to be a lie that just hasn't been fixed in that kernel version. So. Do you know what's up?"

It's not hard, really, the hard stuff is handled by system developers who know their shit, it's just slow and unrewarding and intractable.

[berryberrytaeberry]

Girlies, theylies, and gaylies, if I knew how to deploy anything I'd be unstoppable. Can I code an entire locally hosted API in a day YES I CAN can I make it exist anywhere not on my laptop NO I CANT can I make it scale up to actual users WHAT DO I LOOK LIKE, A WIZARD? OF COURSE I CANT but look woo locally hosted API boop boop I did that and my docstrings are all pretty and my IDE shows no yellow and red lines and my to-dos are to-dones so coquette 🎀

[machine-saint]

thinking about how at old_work, I had a coworker who was an immigrant from Japan. cool guy, good at what he did, and his English was fine

but I will always remember the time I saw a method whose docstring said it would "reflesh" the database

[charyou-tree]

Dwarf code is written by a council of guild masters directly in assembly, and never ever has bugs. There are libraries of software dedicated to the semi-holy style guides, and dwarves can tell which clan wrote a given piece of code by the signature indentation and docstring conventions.

Elf video games: 300 hour jrpgs with legions of characters and several novels worth of text. Labrynthine upgrade trees and customization options. The most insufferably unintuitive UI possible. A single turn based battle can take hours. Every character has an ennui stat.

Dwarf video games: Basebuilding strategy FPSs that has a whole wiki page on the flexile vs tensile strengths of different building materials. Dwarven rhythm games have minigames where you have to manage supply lines. Mortals cannot comprehend dwarven grand strategy games.

Halfling video games: What appears on the surface to be a viscerally calming farming sim is actually an extraordinarily complex social combat game about cutthroat HOA politics.

Goblin video games: Wildly unbalanced collectathon gatchas where half the fun is finding new hilariously broken strategies. Zany uberviolent team shooters about bugs. MOBAs so bad it's almost art.

Orc video games: Addictive in-browser flash games with names like "Beast Crush 4" and "Borag Meat Game." The art is always kinda bad but in a charming way. The music always slaps.

[cromacampusinstitute]

In 2025, Python best practices emphasize type hints, modular code, async programming, and test automation. Use virtual environments, follow PEP 8, and leverage tools like Black, Ruff, and MyPy. Adopt secure coding practices, optimize performance with built-ins, and document code clearly using docstrings and Markdown-friendly formats.

[xaltius]

Using an LLM API As an Intelligent Virtual Assistant for Python Development

The world of Python development is constantly evolving, demanding efficiency, creativity, and a knack for problem-solving. What if you had a tireless, knowledgeable assistant right at your fingertips, ready to answer your questions, generate code snippets, and even help you debug? Enter the era of Large Language Model (LLM) APIs, powerful tools that can transform your development workflow into a more intelligent and productive experience.

Forget endless Google searches and sifting through Stack Overflow threads. Imagine having an AI companion that understands your coding context and provides relevant, insightful assistance in real-time. This isn't science fiction; it's the potential of integrating LLM APIs into your Python development process.

Beyond Autocomplete: What an LLM API Can Do for You:

Think of an LLM API not just as a sophisticated autocomplete, but as a multifaceted intelligent virtual assistant capable of:

Code Generation on Demand: Need a function to perform a specific task? Describe it clearly, and the LLM can generate Python code snippets, saving you valuable time and effort.

Example Prompt: "Write a Python function that takes a list of dictionaries and sorts them by the 'age' key."

Intelligent Code Completion: Going beyond basic syntax suggestions, LLMs can understand the context of your code and suggest relevant variable names, function calls, and even entire code blocks.

Answering Coding Questions Instantly: Stuck on a syntax error or unsure how to implement a particular library? Ask the LLM directly, and it can provide explanations, examples, and even point you to relevant documentation.

Example Prompt: "What is the difference between map() and apply() in pandas?"

Explaining Complex Code: Faced with legacy code or a library you're unfamiliar with? Paste the code snippet, and the LLM can provide a clear and concise explanation of its functionality.

Example Prompt: "Explain what this Python code does: [x**2 for x in range(10) if x % 2 == 0]"

Refactoring and Code Improvement Suggestions: Want to make your code more readable, efficient, or Pythonic? The LLM can analyze your code and suggest improvements.

Example Prompt: "Refactor this Python code to make it more readable: [your code snippet]"

Generating Docstrings and Comments: Good documentation is crucial. The LLM can automatically generate docstrings for your functions and suggest relevant comments to explain complex logic.

Example Prompt: "Write a docstring for this Python function: [your function definition]"

Debugging Assistance: Encountering an error? Paste the traceback, and the LLM can help you understand the error message, identify potential causes, and suggest solutions.

Example Prompt: "I'm getting a TypeError: 'int' object is not iterable. What could be the issue in this code: [your code snippet and traceback]"

Learning New Libraries and Frameworks: Trying to grasp the basics of a new library like Flask or Django? Ask the LLM for explanations, examples, and common use cases.

Example Prompt: "Explain the basic routing mechanism in Flask with a simple example."

Integrating an LLM API into Your Workflow:

Several ways exist to leverage an LLM API for Python development:

Direct API Calls: You can directly interact with the API using Python's requests library or dedicated client libraries provided by the LLM service. This allows for highly customized integrations within your scripts and tools.

IDE Extensions and Plugins: Expect to see more IDE extensions and plugins that seamlessly integrate LLM capabilities directly into your coding environment, providing real-time assistance as you type.

Dedicated Code Editors and Platforms: Some emerging code editors and online platforms are already embedding LLM features as core functionalities.

Command-Line Tools: Command-line interfaces that wrap LLM APIs can provide quick and easy access to AI assistance without leaving your terminal.

The Benefits of Having an AI Assistant:

Increased Productivity: Generate code faster, debug more efficiently, and spend less time on repetitive tasks.

Faster Learning Curve: Quickly grasp new concepts, libraries, and frameworks with instant explanations and examples.

Improved Code Quality: Receive suggestions for better coding practices, refactoring, and documentation.

Reduced Errors: Catch potential bugs and syntax errors earlier in the development process.

Enhanced Creativity: Brainstorm ideas and explore different approaches with an AI partner.

The Future is Intelligent:

While LLM APIs are not a replacement for skilled Python developers, they represent a powerful augmentation of our abilities. By embracing these intelligent virtual assistants, we can streamline our workflows, focus on higher-level problem-solving, and ultimately become more effective and innovative Python developers. The future of coding is collaborative, with humans and AI working together to build amazing things. So, explore the available LLM APIs, experiment with their capabilities, and unlock a new level of intelligence in your Python development journey.

[vastperhaps]

smartfunc: Turn Docstrings into LLM-Functions | Hacker News

[codingprolab]

CSC148 Lab#8

In this lab you will review Test02 questions together with other topics you learned in this course. setup Download circular_linkedlist.py, tree.py, binary_tree.py, csc148_stack, and csc148_queue.py to a subdirectory called lab08. Circularly Linked Lists Example of a circular linked list implement reverse_print1 Read the docstring of reverse_print1(self, current), and implement it using…

[wingedmiraclesublime]

好的，以下是根据您的需求生成的文章：

```

蜘蛛池需要哪些注释？TG@yuantou2048

在进行爬虫开发时，使用“蜘蛛池”是一种常见的策略，它可以帮助我们更高效地管理和运行多个爬虫任务。为了确保代码的可读性和维���性，添加适当的注释是非常重要的。下面是一些在蜘蛛池中常用的注释类型：

1. 模块注释：在每个模块或文件的开头，可以添加模块注释来描述该模块的功能、用途以及依赖关系等信息。例如：

```python

"""

模块名称: spider_pool

功能描述: 用于管理多个爬虫任务的调度和执行

作者: YourName

创建日期: YYYY-MM-DD

"""

```

2. 函数注释：对于每个自定义函数，都应该有详细的注释说明其功能、参数、返回值等信息。

```python

def start_spiders():

"""

启动所有爬虫任务

:return: None

"""

```

3. 变量注释：对关键变量进行解释，特别是那些具有特定含义或者复杂逻辑的变量。

```python

爬虫任务队列

task_queue = Queue()

```

4. 类注释：当定义一个类时，可以在类定义之前添加文档字符串（docstring），以清晰地表达该类的作用及使用方法。

```python

class SpiderManager:

"""

管理爬虫任务的类

"""

5. 方法/函数注释：为每个方法或函数提供简短但明确的描述。

```python

def add_task(self, url):

"""

将新的URL添加到任务队列中

:param url: 需要爬取的网页链接

```

6. 流程注释：在实现具体业务逻辑的地方添加必要的注释，帮助其他开发者理解这段代码的目的和工作原理。

```python

def process_response(self, response):

"""

处理HTTP响应数据

:param response: 请求返回的结果对象

:return: None

"""

```

7. 异常处理注释：当遇到可能引发错误的情况时，应该注明可能出现的异常情况及其处理方式。

```python

try:

...

except Exception as e:

print(f"Error occurred: {e}")

```

8. 算法逻辑注释：如果某个部分涉及到复杂的算法或逻辑判断，则需要详细解释这些步骤背后的思路和设计意图。

```python

def parse_item(self, item):

"""

解析提取出的数据项

:param item: 抓取到的具体内容

```

9. 重要步骤注释：在关键步骤前后加上注释，指出此段代码做了什么以及为什么这么做。

```python

try:

...

finally:

pass

```

10. 调试信息注释：在调试过程中记录下每一步操作的原因和预期结果。

```python

if condition:

do_something()

else:

raise ValueError("Invalid input")

```

11. TODO注释：标记待完成的任务或需要注意的地方。

```python

TODO: 实现具体的解析规则

```

12. 日志记录：利用logging模块记录关键点的日志信息，方便后期维护和排查问题。

```

13. 版本控制相关注释：如果项目中有版本控制机制（如Git），则应在相应位置添加注释来指示下一步该如何做。

```

14. 优化建议：如果某些地方有待改进之处，请务必留下提示以便于后续优化。

```python

logger.info('Processing...')

```

15. 性能优化建议：如果某段代码是为了提高性能而编写，则应给出相应的注释，指明这样做的原因是什么样的场景下会触发这个分支条件。

```

16. 配置文件注释：对于一些配置项做出合理安排，并且保持一致性。

17. 遗留问题注释：如果当前实现存在缺陷或者未来可能需要修改的地方也请做好标注。

```

18. 外部接口调用注释：当调用了第三方库或其他模块的功能时，最好能够指出这部分代码的意义所在。

```

19. 测试用例注释：编写单元测试时，可以考虑加入关于如何改进现有实现的注释，这将有助于团队成员快速定位问题所在区域。

```

20. 数据库操作注释：针对数据库交互的部分给予适当提醒。

```

21. API接口注释：当调用外部服务或API时，记得写清楚调用过程中的注意事项。

```

22. 环境变量注释：如果程序依赖于特定环境变量，则需特别强调它们之间的联系。

```

23. 安全相关注释：任何与安全性有关的操作都需要详细说明其作用范围内的上下文环境要求等信息。

```

24. 遗留代码注释：有时候我们会遇到一些暂时无法解决的问题，此时可以通过这种方式告知他人此处存在的风险点在哪里。

```

25. 资源清理注释：比如关闭连接等操作前后的状态变化情况。

```

26. 遗留代码注释：有时因为历史原因保留下来的冗余代码块，通常包括但不限于以下几点：

```

27. 遗留代码注释：如果有一些未完成的功能点，可以用特殊符号``标识出来。

```

28. 遗留代码注释：如果有些地方采用了临时解决方案，则必须详细记录下来。

```

29. 遗留代码注释：当引入了新特性后，别忘了更新文档以反映最新改动。

```

30. 遗留代码注释：如果遗留了一些没有被充分利用的功能点，也应该给出原因以及预期效果。

```

31. 遗留代码注释：如果遗留了旧版遗留代码，请尽量减少重��劳动并保持良好的编程习惯总是优于直接删除不适用的代码块，而不是简单地删除掉它们。

```

32. 遗留代码注释：如果系统架构发生变化，则需要特别注意的地方。

```

加飞机@yuantou2048

Google外链购买

谷歌留痕

[utopicwork]

Mattermost server will be live tomorrow (🤞) so again, let me know if you'd like to join (also I will need an email to allow you to register), the idea is this will be the main place to discuss PierMesh as a project for people who are contributing to/want to contribute to PierMesh. This is also the channel I'll give access to the code early on though I'll need to do some configuration on the gitea server to get people set up with the private repo when it's up (also I will set up some docstring based auto generated documentation and do some cleanup so access to the code is actually useful)

[generativeinai]

How Generative AI in IT Workspace is Revolutionizing Software Development

Generative AI is transforming various industries, and the IT workspace is no exception. One of its most profound impacts is in software development, where AI-driven tools are reshaping how applications are designed, coded, tested, and maintained. By automating repetitive tasks, enhancing creativity, and reducing human error, Generative AI in IT workspace is revolutionizing the way software developers work.

In this blog, we’ll explore the various ways generative AI is influencing software development, its benefits, challenges, and what the future holds for AI-powered coding.

What is Generative AI in Software Development?

Generative AI refers to artificial intelligence models that can create content, including code, text, images, and even complex algorithms. In the context of software development, generative AI is used to write code, detect errors, generate documentation, optimize software performance, and even suggest new functionalities.

AI-powered coding assistants such as GitHub Copilot, OpenAI Codex, and Google’s Codey are already proving their value by streamlining the software development lifecycle.

How Generative AI is Transforming Software Development

1. Automating Code Generation

One of the most significant ways generative AI is revolutionizing software development is by automating code writing. AI-powered tools can generate code snippets, functions, or even entire programs based on natural language instructions.

Example: A developer can simply type a prompt like "Generate a Python function to sort a list using quicksort", and AI-powered coding assistants can write the function in seconds.

Benefits:

Reduces manual coding effort

Speeds up development

Minimizes syntax and logical errors

2. Enhancing Code Quality and Debugging

Generative AI can analyze existing code to detect bugs, suggest fixes, and optimize performance. AI-powered debugging tools can automatically scan for vulnerabilities, ensuring that software remains secure and efficient.

Example: AI tools like DeepCode and CodeQL can analyze thousands of lines of code and highlight potential security flaws before deployment.

Benefits:

Faster bug detection and resolution

Improved security and reliability

Reduced manual debugging efforts

3. Accelerating Software Testing

Testing is a crucial phase in software development, but it is often time-consuming. Generative AI can automate test case generation, execute test scripts, and even predict potential failures.

Example: AI-powered tools like Testim and Applitools can generate automated test scripts based on user behavior, reducing the need for manual testing.

Benefits:

Reduces testing time

Improves software quality

Ensures better coverage of test scenarios

4. Boosting Developer Productivity

Generative AI allows developers to focus on high-level problem-solving rather than routine coding tasks. By automating repetitive work, developers can concentrate on more creative and strategic aspects of software development.

Example: A full-stack developer can leverage AI to generate frontend UI components, backend logic, and API integrations—saving significant time.

Benefits:

Faster project delivery

Reduced cognitive load for developers

Enhanced collaboration between teams

5. Simplifying Code Documentation and Knowledge Sharing

Writing documentation is a tedious task, but AI can automatically generate comprehensive documentation based on existing codebases. This makes it easier for developers to understand and maintain complex projects.

Example: AI tools like Mintlify and CodiumAI can generate meaningful docstrings, comments, and even full documentation pages based on the code structure.

Benefits:

Saves developers’ time

Improves code maintainability

Facilitates onboarding of new team members

Challenges of Using Generative AI in Software Development

While generative AI offers numerous benefits, it also comes with some challenges:

1. AI-Generated Code May Contain Errors

AI-generated code is not always perfect and may contain logical errors or inefficiencies.

Developers must review and validate AI-generated code to ensure its correctness.

2. Ethical and Security Concerns

AI models may generate biased or insecure code, leading to potential vulnerabilities.

Organizations need to establish AI governance policies to ensure ethical and secure AI usage.

3. Over-Reliance on AI

Developers must be careful not to become too dependent on AI tools.

While AI assists in coding, critical thinking and problem-solving skills remain essential.

The Future of Generative AI in Software Development

The future of Generative AI in IT workspace looks promising. Here are some key trends we can expect:

AI-Driven DevOps: AI will play a bigger role in automating CI/CD pipelines, monitoring software performance, and predicting failures.

AI-Assisted Collaboration: AI-powered chatbots and virtual coding assistants will enhance collaboration among developers by providing real-time coding suggestions.

More Advanced AI Code Review Systems: Future AI tools will not only generate code but also analyze entire projects to suggest architecture improvements.

Hybrid AI-Developer Workflows: AI will act as a co-pilot, working alongside developers rather than replacing them.

Conclusion

Generative AI in IT workspace is revolutionizing software development by automating code generation, improving debugging, accelerating testing, and enhancing productivity. While AI presents exciting opportunities, it also requires responsible usage to avoid security risks and ethical concerns.

As AI technology continues to evolve, software development will become more efficient, innovative, and collaborative. Developers who learn to work alongside AI will have a significant advantage in the future of IT.

[learning-code-ficusoft]

Mastering Python Decorators: A Deep Dive

1. Introduction to Decorators

Python decorators are a powerful way to modify or enhance functions and methods without changing their code. They follow the higher-order function concept, allowing functions to be passed as arguments and returned from other functions.

Basic Example

pythondef decorator_function(original_function): def wrapper_function(): print(f"Executing {original_function.__name__}") return original_function() return wrapper_function@decorator_function def say_hello(): print("Hello, World!")say_hello()

🔹 Output:nginxExecuting say_hello Hello, World!

💡 @decorator_function is syntactic sugar for say_hello = decorator_function(say_hello).

2. Understanding Function Decorators

Decorators with Arguments

pythondef repeat(n): def decorator(func): def wrapper(*args, **kwargs): for _ in range(n): func(*args, **kwargs) return wrapper return decorator@repeat(3) def greet(): print("Hello!")greet()

🔹 Output:Hello! Hello! Hello!

💡 The decorator takes arguments (n), modifying how the function behaves dynamically.

3. Using functools.wraps to Preserve Metadata

Using functools.wraps ensures that the decorated function retains its original metadata (name, docstring).pythonimport functoolsdef decorator_function(original_function): @functools.wraps(original_function) def wrapper_function(*args, **kwargs): print(f"Executing {original_function.__name__}") return original_function(*args, **kwargs) return wrapper_function@decorator_function def example(): """This is an example function.""" print("Example function executed!")print(example.__name__) # Retains 'example' instead of 'wrapper_function' print(example.__doc__) # Displays the function's docstring

4. Chaining Multiple Decorators

You can apply multiple decorators to a function:pythondef uppercase(func): def wrapper(): return func().upper() return wrapperdef exclaim(func): def wrapper(): return func() + "!!!" return wrapper@uppercase @exclaim def greet(): return "hello"print(greet()) # Output: HELLO!!!

💡 Execution order: exclaim → uppercase → greet().

5. Class-Based Decorators

Instead of functions, decorators can also be implemented as classes with __call__.pythonclass Logger: def __init__(self, func): self.func = func def __call__(self, *args, **kwargs): print(f"Logging: {self.func.__name__} called with {args}") return self.func(*args, **kwargs)@Logger def add(a, b): return a + bprint(add(3, 5)) # Output: Logging: add called with (3, 5)

💡 Useful for stateful decorators that maintain persistent data.

6. Real-World Use Cases

a) Logging Function Calls

pythonCopyEditimport timedef log_time(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) end = time.time() print(f"{func.__name__} took {end - start:.2f}s") return result return wrapper@log_time def process_data(): time.sleep(2) print("Data processed!")process_data()

💡 Use Case: Performance monitoring.

b) Access Control (Authentication Decorator)

pythondef requires_auth(role): def decorator(func): def wrapper(*args, **kwargs): user_role = "admin" # Example user role if user_role != role: print("Access denied!") return return func(*args, **kwargs) return wrapper return decorator@requires_auth("admin") def sensitive_function(): print("Sensitive operation performed!")sensitive_function()

💡 Use Case: API authentication.

c) Caching Expensive Function Calls

pythonimport functoolsdef cache(func): cache_data = {} @functools.wraps(func) def wrapper(*args): if args in cache_data: print("Fetching from cache...") return cache_data[args] result = func(*args) cache_data[args] = result return result return wrapper@cache def expensive_calculation(x): print("Performing expensive calculation...") return x * xprint(expensive_calculation(4)) # Performs calculation print(expensive_calculation(4)) # Fetches from cache

💡 Use Case: Optimizing API calls, database queries, or expensive calculations.

Conclusion

Python decorators provide a powerful way to modify functions dynamically while keeping code clean and modular. From simple function wrappers to advanced logging, caching, and authentication, decorators are an essential tool in every Python developer’s toolkit.

WEBSITE: https://www.ficusoft.in/python-training-in-chennai/

[programmingandengineering]

HW5 CS103

Instructions To work on HW5: build a HW5directory under your home directory (cs103sp22) start Jupyter Notebook and from dashboard navigate to hw5 folder create hw5.ipynb in hw5 folder in the notebook, create the myName and myBlazerID functions which returns your name and your blzerid (the same function you had in hw1) Create docstrings for each function (-2 points for each missing…