Python Weekly (Issue 605 June 22 2023)

Python Weekly - Issue 605

June 22, 2023

Python Weekly

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

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A weekly newsletter featuring the best hand curated news, articles, tutorials, talks, tools and libraries etc for programmers.

Articles, Tutorials and Talks

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 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.

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

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.

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.

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.

Overcoming Common Challenges When Building on the Function Calling Feature of ChatGPT APIs.

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.

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

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.

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.

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.

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.

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.

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 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.

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

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

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

Evaluation and Tracking for LLM Experiments.

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

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

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

A better UI for your package managers.

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

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

New Releases

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.

Upcoming Events and Webinars

There will be following talks

  • Secure MicroPython

  • Using the Terraform Cloud Development Kit with Python

There will be following talks

  • Analysing and sharing genetic data with Python

  • Building Interactive Maps with Dynamic Tiles

There will be following talks

  • Autometrics-py: the story behind the module

  • Techniques for Terrible Leadership

  • HoloViz: Visualization and Interactive Dashboards in Python

There will be following talks

  • Uplift Modelling: An Overview

  • Python Package Management: Future, Present, Past 

There will be a talk, Stock Embeddings - Representation Learning for Financial Securities.

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