A naukri.com initiative
Python
Dev
1M
352
Image Credit: Dev
Peewee ORM: The Most Elegant Python ORM Ever
- Peewee is a lightweight and efficient ORM in Python, suitable for platforms like Leapcell, known for its simplicity and ease of use for database operations.
- Creating records in Peewee is straightforward using the User.create() method, allowing the insertion of new records with matching keyword arguments.
- Batch insertion in Peewee for bulk data migration is more efficient than iterative methods, utilizing the insert_many() function.
- Updating records with Peewee involves single-instance updates using save() or bulk operations with Model.update().where().
- Deleting records in Peewee can be done individually with delete_instance() or conditionally with Model.delete().where().
- Peewee provides various methods for querying records, including single record queries with get() and get_by_id(), multiple record selection with select(), filtering, and sorting.
- Additional features in Peewee include pagination, counting records, aggregation functions, window functions, reusable window definitions, frame types control, and data retrieval as tuples or dictionaries.
- Peewee vs SQLAlchemy comparison shows Peewee's lower learning curve, better performance in complex operations, and suitability for lightweight applications like those on Leapcell.
- Peewee offers a streamlined approach to database operations, making it ideal for projects prioritizing efficiency, ease of use, and rapid development, especially in platforms like Leapcell.
- The tutorial explores Peewee's capabilities in CRUD operations, aggregations, advanced querying, and contrasts it with SQLAlchemy's complexities and performance considerations.
- Peewee's lightweight design, performance advantages, flexibility in database support, intuitive API, and suitability for cloud-based services like Leapcell make it a preferred ORM for agile and scalable projects.
Read Full Article
21 Likes
RealPython
1M
316
Image Credit: RealPython
Python's T-Strings Coming Soon and Other Python News for May 2025
- Python will soon have template strings, or t-strings, included in the next release, providing a new mechanism for defining string templates.
- PEP 750 introduces template strings as structured objects, allowing separate definition of string structure from data used to populate it.
- Python 3.14 development continues with a new alpha release (3.14.0a7) focusing on fixes and tweaks as it moves towards stabilization for the first beta.
- Various PEPs accepted or proposed for Python 3.14 indicate developments in debugging, dependency management, and type checking.
Read Full Article
19 Likes
Dev
1M
109
Image Credit: Dev
Why Python’s zip() Is More Powerful Than You Think
- Python's zip() function is a powerful tool that pairs elements from multiple iterables together.
- It allows for easy pairing of lists, dealing gracefully with uneven lengths by dropping extras.
- Zip() can also be used to unzip previously zipped lists and create tools for various tasks like transposers and CSV readers.
- Its simplicity and elegance make it a valuable asset in Python for tasks like dictionary creation and parallel iteration.
Read Full Article
6 Likes
Dev
1M
203
Image Credit: Dev
⚡ Lightning-Fast REST APIs: Python & FastAPI
- FastAPI has gained popularity as a modern, async-first Python web framework for building high-performance, scalable APIs with minimal setup.
- Key features of FastAPI include native support for async/await, auto-generation of documentation and input validation using Python type hints, and built-in Swagger and Redoc interfaces.
- To build a simple API using FastAPI, developers can follow a step-by-step process, including installing FastAPI and Uvicorn, setting up project structure, and writing API code in main.py.
- Running the API server with Uvicorn allows access to API documentation at http://127.0.0.1:8000/docs and connecting to the frontend using basic fetch() calls in JavaScript for interaction with the backend.
- FastAPI also offers built-in testing and documentation tools such as Swagger UI and Redoc UI, simplifying API testing and visualization.
- For frontend integration, adding CORS support by installing fastapi[all] and configuring the main.py file can be crucial, especially when serving the frontend from a different port.
- Advanced ideas to explore with FastAPI include JWT Authentication, SQLModel or SQLAlchemy ORM, Async Database like Tortoise ORM, Background tasks with Celery or FastAPI’s BackgroundTasks, and WebSockets for live updates.
- FastAPI's speed, syntax, and automatic documentation generation contribute to reducing boilerplate code and enhancing productivity, making it a valuable tool for modern web development projects like SPAs or mobile apps.
- To scale content creation efficiently, pre-packaged article kits are available, enabling users to publish ready-to-use articles for blogs, newsletters, social media, or email sequences.
- FastAPI's combination of async capabilities, clear syntax, and auto-generated docs streamlines the development of powerful APIs for various web applications, ensuring efficient backend processes.
Read Full Article
12 Likes
Discover more
Dev
1M
416
Image Credit: Dev
How to Detect Your Own Reactions in Telethon with Python?
- When working with the Telethon library in Python, detecting your own reactions to messages can be challenging.
- The UpdateMessageReactions only triggers when someone else reacts to a message, not recognizing your own reactions.
- To detect your reactions effectively, you can combine Telethon capabilities with direct updates to the message entity you reacted to.
- By setting up your environment, creating the client, and monitoring events, you can reliably identify your reactions in Telethon.
Read Full Article
25 Likes
Hackernoon
1M
99
Image Credit: Hackernoon
DIY Chatbots Like It’s 2021: A Python Throwback Tutorial
- The article is a throwback tutorial on how to build chatbots using Python without the need for APIs or large language models.
- It highlights the use of the 'chatterbot' library to train simple chatbots based on conversation datasets.
- The tutorial starts by setting up the environment and importing necessary libraries like 'chatterbot', 'pyjokes', and 'pandas.'
- One key advantage of using 'chatterbot' is the ability to create a dataset from scratch for customized chatbot responses.
- A specific dataset named '3K Conversations Dataset for ChatBot' from Kaggle is recommended for this tutorial.
- The process involves creating a chatbot instance, loading the dataset, and training the chatbot using ListTrainer.
- Additional code snippets demonstrate customizing chatbot responses based on user inputs, like generating jokes.
- The chat loop in the code allows users to interact with the chatbot, with predefined responses and conditions.
- The tutorial emphasizes that the basic chatbot created is limited in capabilities and lacks advanced features like context understanding or memory retention.
- Overall, the tutorial provides a nostalgic look at building chatbots from scratch and is more of an educational overview than a deep dive into ML or AI modeling.
Read Full Article
5 Likes
Dev
1M
438
Image Credit: Dev
How to Document Constants in Python Effectively?
- Constants in Python, like PATH_DATASETS = 'data/datasets/' and WIDTH = 416, aid in code organization and readability.
- Documenting functions that use constants raises the question of whether to refer to constants directly or use their values for clarity.
- Best practices suggest keeping constant references in documentation to maintain a link between the constant and its value.
- Balancing readability and maintainability is crucial, and clarifying constant usage in both docstrings and constants file can enhance code understanding.
Read Full Article
26 Likes
PlanetPython
1M
348
Image Credit: PlanetPython
Python GUIs: What does @Slot() do? ��� Is the Slot decorator even necessary?
- The @Slot decorator in PySide6 is used to mark a function or method as a slot to which a Qt signal can be connected.
- While not mandatory, using @Slot can reduce memory usage and make slots slightly faster.
- The decorator is necessary when working with threads to avoid segmentation faults.
- Connecting a signal to a decorated Python method reduces memory usage and is marginally faster.
- PyQt6 allows any Python callable as a slot, but @Slot can provide benefits in performance and memory usage.
- The Slot decorator is essential for QRunnable run methods to ensure thread safety.
- Decorating slots can enhance performance in applications with numerous signal connections.
- Speed tests show minimal difference in signal emission speed between decorated and non-decorated slots.
- Decorating slots can lead to a slight improvement in connection speed and memory usage.
- Using Slot for signals that transmit multiple data types is a deprecated approach in Qt6.
Read Full Article
20 Likes
Medium
1M
244
Image Credit: Medium
Implementing the Quantum Excess Evaluation Algorithm in Python
- The Quantum Excess Evaluation Algorithm involves calculating excess payouts in insurance scenarios.
- The algorithm models reinsurance payments based on loss and retention limit parameters.
- It uses lognormal distribution assumptions for losses and insurer payments with a set retention limit.
- The implementation in Python involves utilizing quantum computing concepts and circuits.
- Quantum Monte Carlo simulations are used to compute expected values and simulate excess payouts.
- Comparing Quantum Monte Carlo to Classical Monte Carlo shows faster convergence due to reduced error growth.
- The Python implementation includes setting up qubit states, quantum circuit operations, and measurement simulations.
- The use of Quantum Subtractor and controlled rotations is vital in computing expected reinsurance payments.
- Qiskit libraries are employed for quantum operations, with a focus on efficient circuit design.
- The ultimate goal is to showcase the benefits of Quantum Monte Carlo over Classical Monte Carlo for insurance risk assessment.
Read Full Article
14 Likes
Arxiv
1M
416
Image Credit: Arxiv
CoverUp: Effective High Coverage Test Generation for Python
- Testing in software development is crucial, and automating test generation is challenging for achieving high coverage.
- CoverUp is a new method proposed to enhance the creation of high-coverage Python regression tests through a combination of coverage analysis, code context, and iterative feedback.
- The prototype implementation of CoverUp was tested on challenging code from open-source Python projects, outperforming existing tools like CodaMosa and MuTAP in achieving better line and branch coverage.
- CoverUp's success is attributed to the efficient utilization of its components rather than just the LLM algorithm used for test generation.
Read Full Article
25 Likes
Dev
1M
417
Image Credit: Dev
Why Developers Are Choosing Tortoise ORM as Python’s Modern ORM
- Tortoise ORM is an asyncio ORM for Python, inspired by Django ORM and supports traditional tabular as well as relational data efficiently.
- It supports multiple databases including SQLite, PostgreSQL, MySQL/MariaDB, and Microsoft SQL Server.
- For SQLite, MySQL, PostgreSQL, and Microsoft SQL Server, specific connection string formats are provided for configuration.
- Database creation and initialization involve using Tortoise ORM and generating schemas for the database table structure.
- Model definitions in Tortoise ORM require defining classes with specific attributes like fields and Meta options.
- Field types in Tortoise ORM include data fields and relationship fields like ForeignKeyField, OneToOneField, and ManyToManyField.
- Query operations in Tortoise ORM include methods like filter, exclude, all, first, and annotate for data retrieval.
- Specific query examples in Tortoise ORM include simple queries, range queries, and string queries based on defined models.
- Tortoise ORM is well-documented, offering detailed instructions for various operations and queries.
- Leapcell is recommended for deploying Python services, allowing development in multiple languages and flexible, cost-effective deployment options.
Read Full Article
25 Likes
Dev
1M
113
Image Credit: Dev
I Taught Python to Write Fanfiction Using Reddit Comments and It Got Weird Fast
- The article discusses training a Python script to write fanfiction using only Reddit comments, without deep learning.
- The tutorial covers scraping Reddit comments, generating sentences, writing fanfic, saving and sharing, and creating a web interface.
- Steps involve using libraries like praw for scraping, markovify for sentence generation, and tweepy for tweeting fanfics.
- The Python script creates bizarre sentences and auto-generates short fanfics from random Reddit comments.
- The script's output includes nonsensical sentences that serve as fuel for the fanfiction creation.
- The fun project reinforces that Python can be used creatively beyond just scraping and automation tasks.
- The tutorial also mentions using Flask to turn the fanfic bot into a web toy for sharing.
- Challenges like setting rate limits to avoid spamming are highlighted in the article.
- The author shares their learning that text can be more fun when broken, and quirky tools can enhance creativity.
- The article encourages exploring odd Python ideas and provides links to developer resources and trending repositories.
Read Full Article
6 Likes
Dev
1M
403
Image Credit: Dev
I Let AI Rediscover Python’s Forgotten Powers
- The article explores rediscovering the overlooked powers of Python with the help of AI, such as ChatGPT.
- It delves into utilizing Python's standard library effectively, showcasing modules like http.server, asyncio, sched, collections, and dataclasses.
- The author sets out a challenge to build 5 tools using only the standard library, enhancing familiarity with built-in functionalities.
- Examples include creating a local file explorer over HTTP, a task scheduler with email alerts, a script to detect unused files, a tiny JSON database, and monitoring internet connection with sound alerts.
- Key takeaways include leveraging underused modules, exploring powerful file intelligence, and realizing the potential for building tools without external libraries.
- The article emphasizes the importance of exploring the capabilities of Python's standard library before resorting to external packages.
- Using AI like GPT can provide fresh perspectives on Python's existing capabilities and encourage curiosity in exploring lesser-known features.
- Recommended resources for Python developers include Python Developer Resources by 0x3d.site for discovering hidden modules, reading informative articles, tracking development trends, and exploring GitHub repositories.
- The final advice is to use AI as a guide to discover new code and functionalities, rather than limiting its use to writing code that is already familiar.
- Curated learning kits mentioned in the article offer deep dives into computing, tech history, and programming oddities for the curious learner.
- Featured learning kit highlighted in the article is 'The Secret Operating Systems You Were Never Meant to Use,' providing insights into lesser-known operating systems and their intriguing histories.
Read Full Article
24 Likes
Dev
1M
32
Image Credit: Dev
🐍 Python Web Development: A Comprehensive Guide for Modern Developers
- Python has become a powerful force in web development, offering simplicity and a rich ecosystem of resources.
- Key advantages of Python in web development include readability, extensive libraries, community support, and versatility.
- Popular Python web frameworks like Django, Flask, and FastAPI offer different features and use cases for developers.
- Essential libraries like Requests, Beautiful Soup, SQLAlchemy, and tools like Celery enhance Python web development projects.
- The article covers building a simple web application using Flask with basic steps for installation, code creation, and running the application.
- Advanced topics in Python web development include database integration, user authentication, API development, testing, and deployment.
- Resources for continued learning in Python web development include tutorials, courses, and guides for developers at all levels.
- Python's role in web development is emphasized for its simplicity, powerful frameworks, and supportive community for developers.
- The article also features information on ready-to-publish article kits for online monetization and scaling content creation.
- Included in the article are kits on various tech topics, showcasing pre-made articles for quick publishing and monetization efforts.
Read Full Article
1 Like
Dev
1M
267
Image Credit: Dev
🐍 Mastering Python Virtual Environments: A Practical Guide for Developers
- Virtual environments are crucial for Python development to manage dependencies effectively, ensuring project isolation and reproducibility.
- A Python virtual environment includes an interpreter and an isolated site-packages directory for third-party libraries.
- Using virtual environments prevents conflicts between project dependencies and global installations, improving maintainability.
- Creation involves commands like python -m venv env and activation with source env/bin/activate.
- Forgetting to activate the environment before package installations is a common pitfall that compromises isolation.
- Freezing dependencies via pip freeze > requirements.txt is essential to record exact package versions for reproducibility.
- Tools like pipdeptree and pip check help visualize dependency trees and detect broken dependencies for resolution.
- Managing multiple Python versions in virtual environments requires caution, proper installation, and version-specific environments.
- Best practices include creating a new virtual environment for each project, avoiding global package installations, and documenting setups.
- Virtual environments play a crucial role in CI/CD pipelines for clean, reproducible testing environments.
Read Full Article
16 Likes
For uninterrupted reading, download the app