This week's newsletter will be out tomorrow. Interesting stuff by Tim Kamanin, Iván Palomares Carrascosa, @rgoswami, Eric Chung ( @towardsdatascience ), @koaning & Johnny covered
https://newsletter.piptrends.com/p/how-can-you-structure-your-python
Many people write Python scripts to automate tasks, but without proper structure, these scripts can quickly become hard to maintain. If you're planning to write a script, check out this Real Python article by @rgoswami Your future self (and your teammates) will thank you.
It’s a good idea to run slow tests last; this way, you catch failed tests early and avoid putting unnecessary stress continuously on resources like databases and external services. In this article, Tim Kamanin shared how to achieve this by creating a custom marker and a pytest plugin tailored for Django application tests.
https://timonweb.com/django/optimizing-test-execution-running-live_server-tests-last-with-pytest/
Tomorrow newsletter will be out. Interesting stuff by Wyatt Ferguson, @driscollis Rahul Jha, Kenneth Burchfiel & Cheng/Racey Chan covered
https://newsletter.piptrends.com/p/build-a-wikipedia-search-engine-working
Did you know Python’s standard library includes configparser, which lets you work with Windows-style INI files? If not, check out this article by @driscollis where he walks through creating, editing and reading INI files with clear examples.
Introduction to GIS Programming: A Practical Python Guide to Open Source Geospatial Tools https://leanpub.com/gispro by Qiusheng Wu is the featured book on the Leanpub homepage! https://leanpub.com #Python #pythonprogramming #ebooks #datascience #newreleases
Unlock the power of geospatial data with Python!
Find it on Leanpub!
New 
Release! Python Data Science Cookbook: Practical solutions across fast data cleaning, processing, and machine learning workflows with pandas, NumPy, and scikit-learn #books #ebooks #programming #python #pythonprogramming #machinelearning #softwareengineering
Find it on Leanpub!
Tomorrow newsletter will be out. Interesting stuff by Gwyneth Peña-Siguenza, Erez Shinan, Corey Schafer, @tonybaloney & Yang Zhou covered
https://newsletter.piptrends.com/p/how-reladiff-works-t-strings-gil
Pinpointing differences between two tables is very important for tasks like validating data migrations or spotting corruption. But when those tables live in different databases, it becomes tricky due to issues like network costs and different SQL dialects. In this article, Erez Shinnan shared how Reladiff tackles these challenges and its development journey.
New Release! Python Data Science Cookbook: Practical solutions across fast data cleaning, processing, and machine learning workflows with pandas, NumPy, and scikit-learn #books #ebooks #programming #python #pythonprogramming #machinelearning #softwareengineering
Find it on Leanpub!
Gwyneth Peña-Siguenza created a 5-part video series on building scalable Python APIs with FastAPI and Azure Cosmos DB. She covered key concepts like Pydantic models, FastAPI's dependency injection, async calls using azure.cosmos.aio, batch operations and centralised exception handling.
Tomorrow newsletter will be out. Interesting stuff by @marcogorelli Fredrik Sjöstrand @huggingface Ian Eyre(@realpython) @treyhunner covered
https://newsletter.piptrends.com/p/tiny-agents-text-editor-in-7-minutes
While indexes are useful, relying on them too much can be like Maslow's hammer. @treyhunner has shown some fantastic alternative methods for common tasks without constantly needing to use indexes.
The @huggingface team has created tiny-agents, a new feature that lets their huggingface_hub software act as a Model Context Protocol (MCP) Client. In their recent article, they explained how to set up these tiny agents to give new abilities to your LLMs to interact with the world and perform complex tasks.
Something exciting is coming…
Applications for the Django Girls Workshop open on Monday 16th of June!
Whether you’ve never written a line of code or are just starting out, this is your sign to learn, connect and try something new 
Ready to dive in? Learn more here: https://djangogirls.org/en/pyconuk/
Pandas used to be the go-to tool for working with data. But now, there are many other excellent options available, like Polars and PySpark. If different teams in your company use different data tools, Narwhals can help. @marcogorelli showed this with a helpful example in his article.
https://codecut.ai/unified-dataframe-functions-pandas-polars-pyspark/
This week's newsletter will be out tomorrow. Interesting stuff by Racey Chan, Vitalii Honchar, Amanda Adoyi, @itamarst & Edward Li covered
https://newsletter.piptrends.com/p/rag-pyrefly-vs-ty-nested-loops-and
Last month, two new Rust-based Python type checkers, pyrefly and ty were released. Both of them are in the alpha stage. While they share some similarities, they differ significantly in design and features. In this article, Edward Li dove deep into both tools, highlighted their differences and what makes each one unique.
Nested loops are often discouraged, but they make perfect sense in scenarios like handling multidimensional data or generating patterns. In this @realpython article, Amanda Adoyi explained what nested loops are, shared practical examples, highlighted common pitfalls and offered tips on how to optimise them.
In this article, Vitalii Honchar explained how to build AI-powered apps that can chat with uploaded PDF files. He showed how to implement Retrieval Augmented Generation (RAG) using FastAPI for the API and LangChain to interact with OpenAI.
