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Welcome to issue 614 of Python Weekly. Let's get straight to the links this week.

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Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization
Microsoft Excel now supports Python, enabling users to leverage the power of Python to analyze and visualize data. This integration opens up a world of possibilities for Excel users, making it easier to automate tasks, perform complex calculations, and create interactive visualizations.

CFP for XtremePython 2023
XtremePython 2023 is an online conference that includes short sessions, with speakers from all over the world. The call for papers is now open.

Articles, Tutorials and Talks

Error Handling: From Charles Babbage to Python 3.11

This talk introduces the new error handling features of Python 3.11, while placing them within their historical perspective.

Fixit 2: Meta’s next-generation auto-fixing linter
Fixit is dead! Long live Fixit 2 – the latest version of our open-source auto-fixing linter. Fixit 2 allows developers to efficiently build custom lint rules and perform auto-fixes for their codebases.

Containerized PDF Summarizer with FastAPI and Hamilton
A quick guide to building a PDF Summarizer (using OpenAI) with Hamilton, FastAPI and Streamlit.

Jupyter Notebooks vs Python Scripts | When to Use Which?

This talk discusses Jupyter Notebooks and make a comparison to traditional caveman methods, like scripts.

State of standardized lock files for Python: August 2023
The article provides an August 2023 update on the status of standardized lock files for Python, discussing ongoing efforts, challenges, and potential solutions to enhance dependency management and reproducibility within Python projects.

My favorite prime number generator
Many years ago I've re-posted a Stack Overflow answer with Python code for a terse prime sieve function that generates a potentially infinite sequence of prime numbers ("potentially" because it will run out of memory eventually). Since then, I've used this code many times - mostly because it's short and clear. In this post I will explain how this code works, where it comes from (I didn't come up with it), and some potential optimizations.

Composition over inheritance: The case for function-based views
The author shares some of his thoughts re. function- vs class-based views in Django.

What learning APL taught me about Python
This post draws parallels between APL and Python, revealing insights gained from learning APL that can improve Python programming, including concise code, functional programming, and enhanced problem-solving approaches.

Structuring your Infrastructure as Code
The author proposes a layered approach to IaC, with each layer representing a different aspect of your infrastructure. This approach makes it easy to isolate changes to different parts of your infrastructure, and to reuse code across different environments.

Development with Large Language Models Tutorial – OpenAI, Langchain, Agents, Chroma

Throughout this course, you will complete hands-on projects will help you learn how to harness LLMs for your own projects. You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.

Exploring LLMs and prompts: A guide to the PromptTools Playground
Learn how to build dynamic, stateful applications that harness multiple LLMs at once.

A Comprehensive Guide to Python Logging with Structlog
Structlog is an open-source logging tool for Python known for its simple API, performance, and quality of life features. This tutorial explores the essential aspects of Structlog.

How to implement CommandBus in Python
CommandBus is a communication mechanism used to decouple the sender of a command from its handler or executor. It is a part of the Command Pattern and is commonly used in applications that follow the CQRS (Command Query Responsibility Segregation) pattern or other similar architectural styles. This article provides a comprehensive guide on implementing the CommandBus in Python

GPU-Accelerated, Deterministic ML Dev Environments with Docker and CUDA
A Better Way to Build Your AI Enhanced Project.

PyCon Australia 2023 Videos

How CPython Implements Reference Counting: Dissecting CPython Internals

How to Use Symbolic Python to Solve and Simulate Dynamical Systems

Interesting Projects, Tools and Libraries

Shell-AI
Shell-AI (shai) is a CLI utility that brings the power of natural language understanding to your command line. Simply input what you want to do in natural language, and shai will suggest single-line commands that achieve your intent.

Pipeless
An open source multimedia framework to build and deploy computer perception apps in minutes without worrying about multimedia pipelines.

OpenAgent
OpenAgent is a library of modular components and an orchestration framework. Inspired by a microservices approach, it gives developers all the components they need to build robust, stable & reliable AI applications and experimental autonomous agents.

VisionScript
A high-level programming language for using computer vision.

facefusion
Next generation face swapper and enhancer.

nl2query
A framework for converting natural language text inputs to corresponding Pandas, MongoDB, Kusto and Neo4j (Cypher) queries.

seamless_communication
SeamlessM4T is designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text.

Upcoming Events and Webinars

PyMunich Meetup August 2023
There will be following talks

Kick-off your models versioning with MLflow
Using Python contextvars to enrich logs with contextual data
Introducing langsearch: a Python package for harnessing the power of Large Language Models on private data

PyData Pittsburgh Meetup August 2023
There will be a talk, When AI Goes Awry: Examples and Mitigation Strategies.

PyData Hamburg Meetup August 2023
There will be following talks

Fun with Graphs - Malte Klemm
Urban Modeling and Digital Twins for Sustainable Urban Planning - Rico Herzog

PyData Montreal Meetup August 2023
There will be following talks

How we used Python to see how many lobbyists attended Liberal fundraisers
Using Video Games for Autonomous driving

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