Best of Python — April 2025

  1. 1
    Video
    Avatar of asaprogrammerAs a Programmer·1y

    Python Full Course for Beginners (2025) - Build 35 Projects

    Learn Python from scratch with a 12-hour course designed for complete beginners. This course features a Python crash course covering syntax and fundamentals, followed by 35 hands-on projects that gradually increase in difficulty. Projects range from creating a chatbot and a drawing app to developing a real-time chat application and deploying a full stack web application. Extra resources, including source code, are provided for free.

  2. 2
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    How to Build a Team of AI Agents for Your Website for Free Using Agno and Groq

    Learn how to integrate a team of AI agents into your website for free using Agno and Groq. The tutorial walks through setting up a portfolio website that includes AI agents to handle customer inquiries, create content, analyze user behavior, and provide personalized experiences. This setup can automate part of your business, improving efficiency and engagement without requiring extensive technical expertise or a large budget.

  3. 3
    Video
    Avatar of TechWithTimTech With Tim·1y

    How I'd Learn ML/AI FAST If I Had to Start Over

    Advocates a strategic approach to learning AI and ML swiftly in the rapidly evolving landscape of 2025. Emphasizes the importance of critical thinking and practical coding skills, particularly in Python, for effective AI/ML projects. Encourages data literacy as foundational and promotes hands-on experience with AI models, APIs, and machine learning techniques before transitioning into advanced concepts like LLMs and AI agents.

  4. 4
    Article
    Avatar of lpythonLearn Python·1y

    Python Crash Course Playlist

    A collection of resources for beginners to learn Python, including video playlists, roadmaps, and beginner projects. Encourages hands-on practice by coding projects.

  5. 5
    Article
    Avatar of attiAT&T Israel·1y

    Python Now Supports True Parallelism. Is It Really That Good?

    Python 3.13 introduces the ability to disable the Global Interpreter Lock (GIL), allowing true parallelism within a single Python process. Benchmarks demonstrate significant performance improvements with this feature, although it adds some overhead to single-threaded processes. The free-threaded mode remains experimental and may require updates to C extensions and careful maintenance of thread-safety.

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    Video
    Avatar of TechWithTimTech With Tim·1y

    How To Automate Your Finances with Python - Full Tutorial (Pandas, Streamlit, Plotly & More)

    The post provides a step-by-step tutorial for building a personal finance automation tool using Python with libraries such as Pandas, Streamlit, and Plotly. The tool can upload bank statements in CSV format, categorize transactions, and summarize expenses using visualizations. It also explains how to convert bank statements into a format conducive to analysis and the importance of utilizing structured, project-based learning resources like Data Camp for Python and finance fundamentals.

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    Article
    Avatar of planetpythonPlanet Python·1y

    Top Python Code Quality Tools

    Improving Python code quality is crucial for developers seeking clean, efficient, and reliable code. This guide explores a variety of tools, including linters like Pylint and Flake8, type checkers like mypy, and formatters like Black, to enhance code readability and correctness. Security scanners such as Bandit and Safety ensure code safety, while tools like Coverage.py help maintain test coverage. Implementing these tools can enhance productivity and reduce errors in Python projects.

  8. 8
    Article
    Avatar of habrhabr·1y

    Google ADK: Easiest Way to Build an AI Agent

    This tutorial introduces Google's Agent Development Kit (ADK) for building AI agents using Python. It explains key concepts like AI, workflows, and AI agents. The ADK provides tools and libraries to simplify creating an AI agent by focusing on logic rather than technical details. The guide includes step-by-step instructions for setting up an environment, using ADK to construct a multi-step content assistant AI agent, and demonstrates its utility through examples.

  9. 9
    Article
    Avatar of neo4jneo4j·1y

    LLM Knowledge Graph Builder Back-End Architecture and API Overview

    Explores the back-end architecture and API design of the Neo4j LLM Knowledge Graph Builder. Utilizes Python and FastAPI to create a scalable system that processes various document sources, integrates LangChain for generative AI, and employs vector embeddings for semantic analysis. The resulting knowledge graphs, stored in Neo4j, enhance information retrieval and content summarization. This modular approach supports AI-driven conversational interfaces and advanced data interactions.

  10. 10
    Article
    Avatar of hnHacker News·1y

    HandsOnLLM/Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book

    The Hands-On Large Language Models repository provides code examples from the book by Jay Alammar and Maarten Grootendorst. The book, known for its visual educational approach with almost 300 custom-made figures, covers practical tools and concepts needed to use Large Language Models. The authors recommend using Google Colab for running examples, but any cloud provider should work. Additional visual guides related to LLMs are also available. The book is a valuable resource for understanding and working with state-of-the-art language models.

  11. 11
    Article
    Avatar of mlmMachine Learning Mastery·1y

    10 Python One-Liners for Machine Learning Modeling

    Python's capability for concise one-liners can streamline the creation and evaluation of machine learning models. This guide covers ten useful one-liners, including loading data with Pandas, removing missing values, encoding categorical data, dataset splitting, model initialization and training, and cross-validation. These compact codes simplify processes such as feature scaling and pipeline building, essential for effective model development and deployment.

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    Video
    Avatar of indentlyIndently·1y

    This will finally change in Python 3.14

    PEP 765 will be introduced in Python 3.14, disallowing return, break, and continue statements from exiting a finally block, thereby preventing potential bugs. The finally block ensures execution regardless of any raised exceptions. This change aims to enhance code reliability and prevent misused statements that could lead to bugs.

  13. 13
    Article
    Avatar of ramp_engRamp Engineering·1y

    Rate limiting with Redis

    Rate limiting is crucial in managing API usage and prevents overloading systems. Different rate limiting algorithms such as fixed-window, sliding window, leaky bucket, and GCRA offer various benefits and pitfalls. Ramp chose the GCRA algorithm for its efficiency and ease of implementation with Redis, tailoring it to handle multiple use cases. They continue to expand its application and monitor its performance.

  14. 14
    Article
    Avatar of planetpythonPlanet Python·1y

    Stop Writing `__init__` Methods

    The post describes the shift away from using custom `__init__` methods in Python classes, recommending the use of dataclasses, classmethod factories, and precise typing to enforce object validity and simplify construction. These practices improve code maintainability, flexibility, and future-proofing against new requirements. The author suggests that leveraging dataclasses handles attribute assignment, while classmethod factories and typing.NewType ensure valid, easy-to-use objects without resorting to custom `__init__` methods.

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    Video
    Avatar of TechWithTimTech With Tim·1y

    Build Anything With a CUSTOM MCP Server - Python Tutorial

    Learn to build a custom MCP server in Python and connect it to an AI agent. The tutorial walks through the process step-by-step, from installing necessary packages like UV to setting up the MCP Python SDK. The guide provides clear instructions on making tools and resources accessible to AI agents, including writing, reading, and summarizing notes with code examples. Additionally covers debugging, integrating with Claude desktop, and highlights the versatility of MCP servers.

  16. 16
    Video
    Avatar of youtubeYouTube·1y

    FastAPI Python Tutorial: Build an Analytics API from Scratch

    Learn how to build an analytics API service using FastAPI, Python, and TimescaleDB. The tutorial covers creating a microservice to ingest and store time series data, deploying the application using Docker, and ensuring it runs in a containerized cloud environment with Railway. The step-by-step guide helps in setting up the development environment, writing code for the API, working with Postgres optimized for time series, and finally deploying the setup for production.

  17. 17
    Article
    Avatar of last9Last9·1y

    FastAPI Python for Infra and Ops, Made Simple

    FastAPI is a high-performance, async-ready Python framework ideal for infrastructure and operations automation. It offers significant performance benefits, built-in validation, automatic documentation, and seamless integration with tools like Docker and Kubernetes. FastAPI simplifies building reliable APIs, handling concurrent requests efficiently, and providing endpoints for monitoring and automation tasks.

  18. 18
    Article
    Avatar of hnHacker News·1y

    Python's new t-strings

    Template strings, or t-strings, will be introduced in Python 3.14 to enhance safe and flexible string processing. Unlike f-strings, t-strings evaluate to a new type, Template, which must be processed before use, allowing developers to safely escape dynamic content. This new feature is expected to reduce risks like SQL injection and cross-site scripting and bring more flexibility to string handling in Python.

  19. 19
    Article
    Avatar of communityCommunity Picks·1y

    PydanticAI

    PydanticAI is a Python agent framework designed to facilitate the development of production-grade applications with Generative AI. Built by the Pydantic team, it supports multiple models such as OpenAI, Anthropic, and Cohere. The framework integrates seamlessly with Pydantic Logfire for real-time debugging and performance monitoring. It offers type-safe and structured responses, dependency injection, and streamed outputs to ensure efficient and accurate AI operations. PydanticAI also supports advanced features like graph definitions with typing hints for complex applications.

  20. 20
    Video
    Avatar of TechWithTimTech With Tim·1y

    How to Build a Discord Bot With Python - Full Tutorial 2025+

    Learn how to build a custom Discord bot using Python in about 100 lines of code. This guide walks you through setting up the bot, connecting to Discord, obtaining tokens, and implementing various functionalities like responding to events, handling commands, assigning roles, and creating polls. The tutorial uses `discord.py` and covers both basic and some advanced features with code examples.

  21. 21
    Article
    Avatar of medium_jsMedium·1y

    Why Write 10 Lines of Code, When a Function Can Do It in 1

    The post explains the importance and utility of functions in programming, particularly in Python. Functions help keep code organized, reusable, and scalable. It describes different types of functions, such as built-in, user-defined, lambda, and functions with parameters, along with types of arguments. Additionally, it emphasizes writing modular code by breaking down functionality into smaller, manageable blocks, and offers practical examples for implementation.

  22. 22
    Video
    Avatar of youtubeYouTube·1y

    Learn MCP Servers with Python (EASY)

    This post provides a crash course on mCP servers, explaining their theory, how to create and use them with Python, and integrating AI assistants to check for the latest library documentation. It includes guidance on creating, setting up, and debugging mCP servers using Python, and also covers their use in both cloud desktop and cloud code environments.

  23. 23
    Article
    Avatar of planetpythonPlanet Python·1y

    Create your first app with Streamlit

    Learn the basics of Streamlit, an open-source Python library for building web apps for machine learning and data science. The guide covers installation, setting up a simple app, and adding interactive features like titles, headings, paragraphs, buttons, sliders, radio buttons, dropdown menus, and a sidebar. It concludes with creating a basic web app that filters the Iris dataset using sidebar controls.

  24. 24
    Article
    Avatar of planetpythonPlanet Python·1y

    From Backend to Frontend: Connecting FastAPI and Streamlit

    The post explains how to create a frontend using Streamlit for a FastAPI backend, providing an efficient, Python-based UI. It discusses the benefits of using Streamlit, offers guidance on making API calls, and covers setting up environment variables and CORS. The author also describes the deployment process using Streamlit Cloud and highlights the advantages of using Docker for development and deployment.

  25. 25
    Article
    Avatar of devblogsDevBlogs·1y

    Microsoft for Python Developers Blog

    The April 2025 release of the Python and Jupyter extensions for Visual Studio Code introduces enhancements to Copilot for Notebooks, improved support for editable installs, faster diagnostics, and the ability to pass custom Node.js arguments with Pylance. Key updates include editing notebooks seamlessly with Copilot and enhanced workflows using different Copilot modes.

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