Medium·2y12+ Things I Regret Not Knowing Earlier About Python Lists
Learn various useful techniques to handle Python lists effectively. Discover the power of the * operator for combining and unpacking lists, the difference between .sort() and sorted(), and advanced methods like list comprehensions, .insert(), and .extend(). Understand the distinct advantages and limitations of tuples compared to lists.
KDnuggets·2y5 Free Certifications to Land Your First Developer Job
Free certifications can be a valuable asset, especially for those switching to software development from non-tech roles. The post lists five certifications, including freeCodeCamp’s Responsive Web Design and JavaScript Data Structures and Algorithms, and Harvard’s CS50 Introduction to Computer Science, which teach essential skills through hands-on projects and coding challenges. Completing these certifications can help build an impressive project portfolio and prepare for technical interviews.
DEV·2yOptimizing Your Work-from-Home Setup for Career Success
Optimizing a work-from-home setup involves creating a conducive workspace that fosters productivity and creativity. Key practices include establishing a consistent daily routine, stepping outside for breaks, investing in ergonomic furniture, setting clear goals, and continuously learning. Adapting to changes and managing time efficiently are also crucial for success in a new career.
Hacker News·2ydarrenburns/posting: The modern API client that lives in your terminal.
Posting is a terminal-based HTTP client designed for efficient keyboard-centric interactions. It stores requests locally in YAML files, ensuring easy version control. Key features include 'jump mode' navigation, environment/variable management with autocompletion, various themes, and a command palette. Installation requires Python 3.11 or later and can be done using pipx. Users can also import OpenAPI 3.x specifications to create collections. Configuration is flexible, supporting YAML files, environment variables, and .env files.
Lobsters·2yScaling One Million Checkboxes to 650,000,000 checks
One Million Checkboxes (OMCB) is a website that scaled from a few expected users to over 650 million checks within days. The architecture involved using Redis for state management, nginx for static content and reverse proxy, and Flask for API handling. The site faced several challenges including unexpected high traffic, Redis connection issues, and bandwidth costs. Solutions included adding more servers, batching updates, using a connection pool, and eventually rewriting the backend in Go, which significantly improved performance. The project emphasized learning and fun over perfect solutions, leading to valuable scaling insights.
Real Python·2yLogging in Python – Real Python
Logging is a crucial practice for Python developers to record relevant information during code execution. Python's standard library includes a powerful logging module that helps in debugging, analyzing performance, and understanding application flow. This guide covers working with the logging module, setting up basic logging configurations, utilizing log levels, formatting log messages, directing log records through handlers, and defining logging rules with filters.
Tech With Tim·2yMaster Python With This ONE Project!
This post guides you through building a personal finance tracker in Python, covering syntax, advanced features, and popular modules like Pandas and Matplotlib. The project involves tracking and logging transactions, organizing data, generating summaries of income and expenses, and visualizing the data with graphs. It also explains how to use CSV files for data storage and offers a quick demo followed by step-by-step instructions for implementation.
freeCodeCamp·2yHow to Use Python Generators – Explained With Code Examples
Python generators are a powerful tool for lazy iteration through sequences, optimizing memory usage and enhancing performance for large datasets. They produce items one at a time only when needed, making them ideal for handling streams of data, iterative algorithms, and real-time simulations. Generators help in writing cleaner and more readable code by managing iteration state automatically and efficiently.
Community Picks·2yI built an AI Agent to validate my PR without actually doing it myself 🚀⚡
Composio developed an AI agent to automate the review process of pull requests (PRs). The agent uses an event listener to detect PR events, then leverages a large language model (LLM) for the review. It posts the review as a comment on the PR and sends a summary to Slack. Integration and triggers can be managed via Composio's CLI or dashboard. The platform supports various tools and integrations to streamline workflows.
Hacker News·2ykitao/pyxel: A retro game engine for Python
Pyxel is a Python-based retro game engine inspired by PICO-8 and TIC-80. It supports a 16-color palette, simple sound channels, and includes image and sound editors. Pyxel runs on multiple platforms including Windows, Mac, Linux, and Web. It's open source and ideal for creating pixel art games. Installation is straightforward with pip, Homebrew, or manual setup, and examples are provided to help get started.
KDnuggets·2yDuck, Duck, Code: An Introduction to Python’s Duck Typing
Duck typing in Python emphasizes object behavior over class type, making code more flexible and reusable. Unlike statically typed languages, Python determines variable types at runtime. This allows functions to operate on any object with the required methods, contributing to simpler and more adaptable code. However, it may also lead to runtime errors and needs careful documentation and testing. Various examples illustrate the concept and its practical use.
KDnuggets·2y10 GitHub Repositories to Master Data Science
Discover 10 essential GitHub repositories to master data science, offering interactive courses, books, guides, code examples, and free resources based on top university curricula. These repositories cover a wide range of topics, from statistics and Python to machine learning and data visualization techniques. Beginners and experienced practitioners alike can benefit from the comprehensive resources and best practices provided.
Community Picks·2yProgramming Languages that Developers should Explore in 2024-2025: Stay Ahead of the Curve
Developers should explore languages like TypeScript, Golang, Python, and Zig to stay ahead in 2024-2025. TypeScript's static typing and compatibility with frameworks like Angular, React, and Vue.js make it essential for scalable JavaScript applications. Go excels in building high-performance network applications and microservices due to its concurrency features and fast compilation times. Python remains crucial in AI, ML, web development, automation, and scripting. Zig provides C-like performance with modern features and improved safety, ideal for cross-platform applications.
GoPenAI·2yOpenAI Basics in Python
Learn the basics of using the OpenAI API with Python by setting up a virtual environment, creating an OpenAI account, and using Jupyter notebooks. The guide covers tasks such as grammar correction, translating text to emojis, code explanation, and image analysis using the GPT model.
Hacker News·2yModern web applications in pure Python
FastHTML enables the creation of modern web applications using pure Python, leveraging solid web foundations and offering seamless scalability. With the ability to utilize any Python or JS library, FastHTML provides flexibility and speed, making it ideal for various applications from dashboards to scalable SPAs. Deployment options are versatile, supporting any service or server compatible with Python, and users familiar with FastAPI will find transitioning to FastHTML intuitive. Additionally, incorporating HTMX can enhance functionality by filling in web browser gaps with a lightweight JavaScript library.
The Palindrome·2yMachine Learning From Zero
Introducing the 'mlfz' library, an educational project aimed at demystifying machine learning algorithms. Written entirely in Python + NumPy, it breaks down complex concepts into readable code. Now available on PyPI and GitHub, this library serves as both a computational tool and an informative textbook on the internal workings of machine learning. More detailed posts on neural networks are expected in the coming months.
Planet Python·2y[July 2024] Python Monthly Newsletter 💻🐍
This edition of Python Monthly highlights a variety of updates and resources for Python enthusiasts. Key topics include the introduction of free-threaded CPython, modern development practices, and new tools like FastHTML. Additional highlights cover a botched update by Crowdstrike, insights into AI models, and various project ideas for portfolios.
KDnuggets·2yHow to Speed Up Python Pandas by Over 300x
Pandas is a popular open-source data manipulation and analysis library for Python, widely used in various fields. To speed up data analysis by over 300x, vectorization can be applied. This method uses entire arrays of data at once, instead of processing each element individually, thus optimizing memory and CPU resource usage. Compared to looping and the apply method, vectorization is significantly faster. Examples demonstrate how dataset calculations that took 3.66 seconds using loops can be reduced to just 10.4 milliseconds using vectorization.
GoPenAI·2yBuilding an Effective RAG Pipeline: A Guide to Integrating Self-RAG, Corrective RAG, and Adaptive RAG
A comprehensive guide to building an effective Retrieval-Augmented Generation (RAG) pipeline by integrating Self-RAG, Corrective RAG, and Adaptive RAG. This pipeline aims to intelligently handle questions of varying complexity, ensure information accuracy, and generate useful answers. It leverages LangGraph for stateful, multi-agent workflows, and includes methods for routing questions, retrieving documents, evaluating relevance, and grading output quality.
KDnuggets·2yBuilding Data Science Pipelines Using Pandas
Learn to build end-to-end data science pipelines using the Pandas pipe method. This method enhances code readability, enables function chaining, and improves code organization. The tutorial includes transforming code into a pipeline structure that handles data ingestion, cleaning, analysis, and visualization, demonstrating a comparison between pipeline and non-pipeline approaches.
ThePrimeTime·2yUse Java For Everything
The post discusses the author's preference for Java as a universal programming language, arguing that many developers claim to choose the 'right tool for the job' but often default to their most familiar language. The author criticizes the misuse of JavaScript and other languages where they might not be optimal. The benefits of static typing and Java's performance are emphasized, alongside a humorous critique of how developers might overcomplicate solutions with unfamiliar languages. The post concludes with a call for using the best-suited language for tasks but also highlights the efficiency and reliability of Java when properly leveraged.
Hacker News·2yPySkyWiFi: completely free, unbelievably stupid wi-fi on long-haul flights
PySkyWiFi is a tool that exploits a loophole in airline airmiles accounts to access the internet on flights without paying. By using Python and implementing a simplified TCP/IP protocol, the author created a sky proxy on their laptop and a ground daemon on an internet-connected computer. This setup allows full HTTP requests to be sent via the airmiles account, albeit very slowly. The post includes detailed instructions on how the system works, as well as highlights the potential fun and productivity achieved during a long-haul flight.
DevBlogs·2yPython in Visual Studio Code
The August 2024 release of the Python and Jupyter extensions for Visual Studio Code introduces improved Python discovery using python-environment-tools, inline variable values in source code during debugging, and enhancements to the VS Code Native REPL for Python. User feedback is encouraged for ongoing improvement and feature development.
