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Dev
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Real-Time DonationAlerts Notifications with Python, OAuth & WebSockets 🚀
- In this article, the focus is on setting up real-time DonationAlerts notifications using Python, OAuth, and WebSockets to instantly react to donations received.
- By leveraging WebSockets, developers can receive instant notifications without the inefficiency of API polling.
- The process involves registering an application on DonationAlerts, implementing OAuth 2.0 Authorization Code flow with Python, and connecting to the DonationAlerts Centrifugo WebSocket.
- Key steps include handling the OAuth flow securely, storing credentials outside the code, setting up FastAPI endpoints for OAuth, and exchanging codes for tokens.
- Connecting to the WebSocket for real-time events requires obtaining various tokens and following a specific sequence for authentication and subscription.
- The article explains the structure of donation messages and provides Python code snippets to handle, parse, and display donation data received through the WebSocket.
- To display donations in a web interface, developers can establish WebSocket connections between the backend and frontend, utilizing JavaScript to parse and show the donation data.
- This comprehensive guide equips developers with the knowledge to integrate real-time DonationAlerts notifications into their applications efficiently.
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Dev
1M
150
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Inter-Process Communication between NodeJS and Python
- Inter-Process Communication between NodeJS and Python involves sending JSON messages back and forth over stdin/out using NULL as a delimiter.
- Python process is blocking, allowing the NodeJS side to send multiple messages until processed by Python.
- JS side uses 'child_process' and 'path' modules to spawn and communicate with the Python script.
- Python script encodes and decodes messages using JSON format and delimiter to achieve effective communication.
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Dev
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246
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A2A Python Tutorial - Comprehensive Guide
- This Python tutorial covers building an A2A server, including setting up the environment, creating a project, and adding agent skills.
- It demonstrates creating agent skills, setting up a project environment, and adding Google-A2A Python libraries.
- The tutorial covers implementing agent skills in code, creating an Agent Card, and setting up an A2A server with a task manager.
- It explains the process of interacting with the A2A server using Google-A2A's command-line tool and adding agent capabilities for streaming responses.
- The tutorial also guides integrating a local Ollama model for AI functionality, including requirements, implementation steps, and testing.
- Finally, it suggests next steps like connecting AI models with MCP tools and exploring Google's Agent Development Kit.
- The tutorial provides a comprehensive guide for building an A2A server with AI capabilities.
- It involves creating agent skills, setting up server environment, interacting with the server, and integrating AI models.
- The tutorial covers tasks like adding libraries, implementing agent skills in code, and creating agent cards.
- It explains the process of setting up a task manager, handling requests, and streaming responses in an A2A server.
- The tutorial concludes with suggestions for future development like connecting AI models with MCP tools and exploring Agent Development Kit.
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Towards Data Science
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385
Rust for Python Developers: Why You Should Take a Look at the Rust Programming Language
- The programming language Rust is gaining popularity due to its performance and security features, making it an interesting alternative for Python developers, especially in the field of Data Science.
- Rust, developed by Mozilla, focuses on memory management control and an ownership model to prevent errors at compile time, offering advantages in performance over languages like Python.
- Rust is a compiled language that translates code into native machine code before execution, leading to faster execution and early error detection during compilation.
- Rust's memory safety without a garbage collector and ownership model prevent errors like NullPointer exceptions and race conditions, making it ideal for memory-intensive applications.
- Rust's tooling includes 'cargo' for managing code processes, and its growing community and high performance make it a favored language among programmers.
- Rust differs from Python in type specification, speed, memory usage, multithreading, error handling, and syntax, with Python being easier to start with but Rust offering higher performance and control.
- Rust complements Python by providing more control, high performance, and robustness for tasks that require parallelism and performance optimization, making it a valuable addition to a developer's toolset.
- In a comparison of opening a CSV file and counting rows, Python's code is straightforward but may have memory issues with large files, while Rust's code is more robust, efficient, and memory-saving.
- Rust shines in data science for tasks like data preprocessing with Polars for faster processing, compute-intensive tasks with PyO3 for integration with Python, and web backends with actix-web for high-performance APIs.
- Rust is recommended for developers interested in low-level programming, seeking improved performance over Python, working on backend services or CLI tools, understanding memory interactions, and developing robust, maintainable tools.
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Dev
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37
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Automated API Testing with Python: A Pragmatic Guide for Real-World Applications
- API testing is crucial in modern development environments, as it serves as the contract between systems.
- Automated API testing in Python can be achieved using the requests library and Python's built-in unittest framework.
- The project structure includes separate test logic, config, and runners for better organization and potential CI/CD integration.
- Python enables efficient API testing with tools like requests and unittest, offering flexibility for dynamic data and extensions like authentication and CI/CD integration.
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Hackernoon
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13
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SQL Transactions - How to Implement COMMIT, ROLLBACK, and Savepoints With Python
- Tools like DataGrip simplify transaction handling by offering graphical interfaces for commit, rollback, and savepoints.
- In SQL, COMMIT finalizes changes, ROLLBACK reverts transactions, and SAVEPOINT allows partial rollbacks.
- Python libraries like cx_Oracle, mysql-connector-python, psycopg2 enable explicit transaction controls.
- Transactions involve starting, executing SQL statements, committing or rolling back changes, and using savepoints for finer control.
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PlanetPython
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191
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Python Software Foundation: Python Software Foundation Names New Deputy Executive Director
- Python Software Foundation (PSF) has promoted Loren Crary to Deputy Executive Director citing her outstanding contributions and leadership.
- Loren Crary, previously the Director of Resource Development, has been recognized for her continuous growth and dedication to the PSF's mission.
- Her promotion acknowledges the expanding role she plays within the organization, setting the PSF up for continued success and growth.
- Loren Crary will continue to strengthen strategic planning, budgeting processes, support PyCon US, manage sponsorships, and collaborate across the organization to further the PSF's mission.
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Dev
1M
338
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How I Replaced Python with C in My Project
- The author replaced Python with C in a high-performance data processing project due to performance bottlenecks.
- Key reasons for choosing C included predictable performance, direct memory control, and better hardware integration.
- The rewrite process involved creating C extensions for critical functions using Python's C API.
- A hybrid approach resulted in a 5-8x speedup in critical functions while maintaining most of the system in Python.
- Challenges included adapting to manual memory management and error handling in C.
- Lessons learned included not rewriting all code, using hybrid approaches, and prioritizing testing.
- The current architecture involves a Python frontend, a C core engine, and direct hardware communication.
- The migration to C led to significant performance improvements, better hardware integration, and reduced resource requirements.
- While C offered benefits for this project, the tradeoffs in development speed and maintainability should be considered.
- The author suggests considering Rust for future projects needing a balance between safety and performance.
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Medium
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219
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Just Stop Writing Python Functions Like This!!!
- Python functions packed with flags, messy logic, and unclear responsibilities can lead to chaotic code.
- It's important to avoid writing functions that rely heavily on flags for different functionalities.
- Instead of using multiple flags, focus on writing functions that have clear, singular purposes for better code maintainability.
- By following best practices such as using type hints for clarity and ensuring functions perform only one task, you can write cleaner and more readable Python functions.
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Towards Data Science
1M
456
Modern GUI Applications for Computer Vision in Python
- Computer vision engineers often need visual feedback for image processing tasks, and interactive GUI applications can be helpful for this purpose.
- OpenCV provides basic interactive elements for creating GUIs in Python for computer vision projects.
- The article outlines setting up the environment with required packages, building a GUI application using OpenCV and customtkinter for real-time image processing.
- It demonstrates displaying webcam feed, using keyboard inputs for filters, adding captions to images, implementing sliders for filter selection, and applying various image processing filters like grayscale, blur, threshold, edge detection.
- To enhance the GUI appearance and user experience, a modern GUI using customtkinter is introduced.
- The article also discusses multithreading to separate image processing from the UI to prevent blocking the main thread during heavy processing tasks.
- A queue is utilized for synchronization between threads to ensure smooth updating of frames without flickering in the GUI.
- The code examples and steps provided offer a comprehensive guide to building interactive GUI applications for computer vision projects in Python.
- The article concludes by emphasizing the combination of Tkinter and OpenCV for creating modern GUI applications, with a Github repository link for the demo code.
- The interactive GUI applications enable efficient iteration and visualization for computer vision tasks, enhancing the development process and user experience.
- Overall, the article highlights practical techniques and considerations for developing modern GUI applications tailored to computer vision projects.
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Hackernoon
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82
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Compute Global Reactivity Index (GRI) Using Python
- The Global Reactivity Index (GRI) provides information about a molecule's reactivity, derived from DFT-based descriptors like chemical potential, hardness, softness, electronegativity, and electrophilicity.
- GRI parameters are obtained from HOMO and LUMO energies, aiding in predicting organic and organometallic chemical reactivity trends.
- Parameters such as Electronegativity, Chemical potential, Global hardness, Global softness, and Electrophilicity index play crucial roles in reactivity and stability predictions.
- Frontier molecular orbital energies are used to determine these parameters, with calculations typically performed using quantum chemistry software like ORCA, Psi4, Gaussian, etc.
- Python scripts simplify the computation of GRI parameters, automating the process with equations for Ionization potential, Electron affinity, and subsequent derivations.
- Theoretical background and software options for HOMO/LUMO calculations are showcased, with detailed steps on extracting these energies using Python.
- Batch processing capabilities using a CSV file input and a Python script enable efficient computation of GRI descriptors for multiple molecules.
- The GRI calculation script provides outputs such as Ionization Potential, Electronegativity, Global Hardness, Global Softness, and Electrophilicity Index for each molecule.
- Automated orbital energy extraction and GRI calculation techniques enhance the study of reactivity trends and stability in molecular systems.
- The integration of quantum chemistry methods, Python scripting, and batch processing streamlines computational reactivity analysis for various chemistry research applications.
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Dev
1M
146
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How to scrape TikTok using Python
- TikTok users generate valuable data for analysis, and scraping this data can provide insights on trending hashtags, influencer engagement rates, and content creator topics.
- The blog discusses how to scrape TikTok using Crawlee for Python, covering steps like project setup, analyzing TikTok for scraping strategy, configuring Crawlee, extracting TikTok data, creating a TikTok Actor on the Apify platform, and deployment.
- The prerequisites for the project include Python 3.9+, web scraping familiarity, Crawlee for Python v0.6.0+, uv v0.6+, and an Apify account.
- Project setup involves using uv for package management, creating the project with uvx, and navigating to the project folder.
- Analyzing TikTok involves understanding its JavaScript-heavy site, using Playwright for crawling, and inspecting HTML structure and data extraction.
- Configuring Crawlee includes limiting elements, setting scraping intensity, specifying browser type, and handling permissions and timeouts.
- Extracting TikTok data involves navigating, handling infinite scrolling, and extracting video links and information using PlaywrightCrawlingContext.
- Creating a TikTok Actor on the Apify platform requires adapting the project structure, defining metadata, input parameters, and updating the code to accept input.
- Deployment to Apify involves using the CLI to upload the code, configuring runs, testing with input parameters, logging, and viewing results in the dataset.
- The project lays a foundation for effective TikTok crawling, suggests improvements like error handling and CAPTCHA handling, and provides access to the code repository for further reference.
- The blog encourages support for Crawlee for Python by starring the repository or joining the maintainer team, and invites engagement and discussions on Discord with a community of developers.
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RealPython
1M
123
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Modern Web Automation With Python and Selenium
- Selenium is a web automation tool that allows Python to interact with dynamic web pages by driving a real browser.
- Implementing the Page Object Model design pattern helps create clean and scalable automation scripts.
- Selenium enables launching browsers, visiting URLs, and interacting with web elements.
- Headless browsers allow running scripts without displaying a browser window, beneficial for automation and testing.
- Different locators like CSS selectors, XPath, or IDs can be used to target web elements.
- Explicit waits offer a flexible way to handle dynamic content by waiting for specific conditions.
- Page Object Model design pattern separates page structure from business logic.
- Selenium with Python can be used to build a music player that interacts with Bandcamp's Discover page.
- By implementing POM, automation scripts can be kept clean, testable, and maintainable.
- Selenium is useful for automated testing and generic web automation, providing a convenient API to control browsers.
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Dev
1M
269
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Using Python & WebSockets to Generate Live OHLC Forex Data
- This tutorial guides you on building a real-time Forex OHLC data generator using Python and WebSockets.
- Accessing real-time market data is crucial for trading and financial analytics, and WebSockets provide an efficient method for continuous data streaming.
- You'll need a TraderMade API key to stream live Forex data, access a 14-day free trial to get the key from your account dashboard.
- The process involves installing the TraderMade Package, importing required libraries, initializing data structures, defining utility functions, connecting to the WebSocket stream, and running your script to generate OHLC output.
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