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Welcome to issue 605 of Python Weekly. Let's get straight to the links this week.

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Articles, Tutorials and Talks

Designing Pythonic library APIs
The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand.

symbex: search Python code for functions and classes, then pipe them into a LLM
symbex is a new Python CLI tool that allows you to search Python code for functions and classes by name or wildcard, then see just the source code of those matching entities. symbex can then be used to pipe the source code into a large language model (LLM) for further analysis.

egglog in Python
Learn how egglog in Python can enable decentralized collaboration in the Python data science ecosystem and provide a faithful authoring environment for egglog.

Building Search DSLs with Django
The article explains how to build a custom search DSL (Domain Specific Language) for Django projects. The DSL allows users to search for content in a more natural way, using keywords and phrases instead of complex SQL queries.

Pandas for Data Analysis by Example

Learn how to use Pandas and Python for Data Analysis, to Data Cleaning and Data Wrangling. You will learn by creating real life projects interactively to help you take the next step in your Data Science Career.

Compiling typed Python
This post discusses the advantages and challenges of using static typing in Python, emphasizing its potential for improving code quality and catching errors at compile-time. It also explores various tools and libraries available for adding type annotations to Python code and highlights their usage in real-world scenarios.

Navigating Sharp Edges in OpenAI's Function Call Feature
Overcoming Common Challenges When Building on the Function Calling Feature of ChatGPT APIs.

Pro-Tip – pytest fixtures are magic!
Fixtures are building blocks for good tests and can increase development speed. The main issue with writing tests is setting up necessary data before the test, but pytest fixtures make it easier by injecting necessary data into your tests.

Bringing Locality of Behaviour to Django Views and URLs
Introducing django-view-decorator, a Django package which brings Locality of Behaviour to your Views and URLs.

The Annotated S4
This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture.

A theme editor for JupyterLab
This post introduces a new theme editor extension for JupyterLab that allows users to customize the look and feel of the JupyterLab interface. The theme editor extension is a simple interface that allows users to select colors, font family, and font size, and to preview the changes in real time.

Remote Interactive Debugging of Python Applications Running in Kubernetes
In this tutorial we will create a setup for remote debugging of Python applications running in Kubernetes, which will allow you to set breakpoints, step through code, and interactively debug your applications without any change to your code or deployment.

Pandas fillna vs scikit-learn SimpleImputer
Missing data is prevalent in real-world data and can be missing for various reasons. Gladly, both pandas and scikit-learn several imputation tools to deal with it. Pandas offers a basic yet powerful interface for univariate imputations using fillna and more advanced functionality using interpolate. scikit-learn offers both SimpleImputer for univariate imputations and KNNImputer and IterativeImputer for multivariate imputations. In this post, we will focus on fillna and SimpleImputer functionality and compare them.

Playing with Streamlit and LLMs.
This post describes how to use Streamlit to build a simple interface for interacting with large language models (LLMs). It also includes code examples that show how to use Streamlit to display text, images, and tables, and to interact with LLMs through prompts and queries.

Using Contextual Bandit models in large action spaces at Instacart
In this post, we will explore the difficulties associated with using contextual bandit models in large action spaces and propose potential solutions to overcome these challenges. One of these solutions was recently launched into production at Instacart.

Interesting Projects, Tools and Libraries

flowpilot
Flowpilot is an open source driver assistance system built on top of openpilot, that can run on most windows/linux and android powered machines. It performs the functions of Adaptive Cruise Control (ACC), Automated Lane Centering (ALC), Forward Collision Warning (FCW), Lane Departure Warning (LDW) and Driver Monitoring (DM) for a growing variety of supported car makes, models, and model years maintained by the community.

languagemodels
Explore large language models on any computer with 512MB of RAM.

LMFlow
An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Model for All.

localrf
An algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video.

TruLens
Evaluation and Tracking for LLM Experiments.

OpenLLM
An open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.

autolabel
Label, clean and enrich text datasets with Large Language Models (LLMs)

FableForge
Generate a picture book from a single prompt using OpenAI function calling, replicate, and Deep Lake.

WingetUI
A better UI for your package managers.

Rift
An AI-native language server for your personal AI software engineer.

arguably
arguably turns functions into command line interfaces (CLIs). arguably has a tiny API and is extremely easy to integrate.

New Releases

PyPy v7.3.12 release
This release includes a new string-to-int algorithm (also appearing in CPython 3.12) that is faster than the older one; support for symlinks in Windows; and our first Python3.10 version.

Python 3.12.0 beta 3 released

Upcoming Events and Webinars

Python Glasgow Meetup June 2023
There will be following talks

Secure MicroPython
Using the Terraform Cloud Development Kit with Python

Python Adelaide Meetup June 2023
There will be following talks

Analysing and sharing genetic data with Python
Building Interactive Maps with Dynamic Tiles

PyBerlin Meetup June 2023
There will be following talks

Autometrics-py: the story behind the module
Techniques for Terrible Leadership
HoloViz: Visualization and Interactive Dashboards in Python

PyData Zurich Meetup June 2023
There will be following talks

Uplift Modelling: An Overview
Python Package Management: Future, Present, Past

PyData Chicago Meetup June 2023
There will be a talk, Stock Embeddings - Representation Learning for Financial Securities.

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