A place to come and talk about the geometry of the universe.

Sagittarius A* and where is the Sun?

gamma ray bursts

gravitational waves.

What will James Webb see?

How to test different models?

Python, matplotlib, scipy, astropy

units and constants.   Hubble and c

But maybe Hubble's not constant?

From Einstein and Fred Hoyle to the James Webb telescope and beyond.

John Gill

The Geometry of the Universe

If you try to use Pylint or Flake8 on a legacy project, the results are usually truly overwhelming. There might be thousands of warnings, hundreds of errors and maybe even no unit tests. 
The usual emotional response to this is distress, exasperation... even despair. And then the question comes: *Where do I start?*

During this talk we will see why it’s better to set old code aside and focus first on the new code you’re writing. We’ll show some possible approaches and tools that can help you keep the focus and deliver new code with a high level of quality.

If you try to use Pylint or Flake8 on a legacy project, the results are usually truly overwhelming. How to deal with all these thousands of warnings and errors? Leave the past behind, focus on new code!

As developers we often have to deal with legacy projects and, at the same time, we want to keep the quality and security of our deliverables under control.

As soon as we start running some linter (like Pylint or Flake8) on such a legacy project, there is a huge number of violations. To handle those issues, we might want to start by only looking at the changed files in a pull request instead of the entire project, for example by using _git diff_
```
pylint `git diff --name-only --diff-filter=d`
```

During this talk I’d like to push this concept a bit further and outline an approach and philosophy that can be helpful in dealing with code quality : *Clean as you code*.
### What is *Clean as you code*?
* Not only about violations: It can be extended to code coverage and all code metrics in general.
* The quality you want to measure should be based only on recent changes.

### Why *Clean as you code* matters?

* It helps your team stay focused on delivering new features
* It helps you deal with technical debt incrementally: Sometimes you might need to modify old code, and, at that point, you might be able to fix existing violations

### How to apply *Clean as you code*?
* Shaping a *quality gate* in order to define code quality standards for the software delivered by your team today
* Using appropriate tools (like SonarQube)

Andrea Guarino

Managing the code quality of your project. Leave the past behind: Focus on new code.

Pandas is the first Python library that I learned to use. It is used by data scientists to manage, transform and inspect data. As more and more open-source tools appear, it seems the spotlight has shifted and I would love to shine some light on this tool that all should know.

Pandas is the first Python library that I learned to use. It is used by data scientists to manage, transform and inspect data. I would love to shine some light on this tool that all should know.

In this talk, Cheuk will reapproach Pandas as someone who is a fluent user of the library. Cheuk will review why Pandas is useful to work with data and what advantage it has over the alternatives methods. As Pandas has many functionalities, Cheuk will dissect the discussion on the potential usage of Pandas in day-to-day data science workflow: data inspection, data cleaning, feature engineering etc.

After discussing the advantage of using Pandas, Cheuk will discuss the flip side. What makes Pandas a difficult tool to use at first. Here Cheuk will share how she used it efficiently with new users and some fundamental concepts about Pandas.

This talk is for Pandas users (new and old) or potential users. Those who are familiar with Pandas may get a refreshing idea from a new angle about the tool. And for the new users, it will be a good startup guide to make the journey of using it a bit easier.

Cheuk Ting Ho

I have to Confess, I still Love Pandas

Protocols have been around since Python 3.8. So what are they, and how can they help you write better code? And how are they different from Abstract Base Classes? In this talk I will introduce you to both concepts (ABCs and Protocols), and show you by example how they can make your life easier, and your code cleaner.

Protocols in Python: Why You Need Them - Protocols have been around since Python 3.8. So what are they, and how can they help you write better code? And how are they different from Abstract Base Classes?

Rogier van der Geer

Protocols in Python: Why You Need Them

Building interactive Machine Learning demos is now easier than ever. With Open Source libraries such as Gradio and Streamlit, you can use Python to craft demos, and use Spaces to share them with the rest of the ML ecosystem as well as non-ML people.  Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting in them. All of this leverages free, open-source tools that anyone can use.

Learn how to create viral Machine Learning demos with Python

Omar Sanseviero

How to craft awesome Machine Learning demos with Python

New to python 3.11, Exception Groups help you raise and handle errors more robustly than ever before - you will delve deep into the current gaps in python's exception handling mechanisms, and get to know Error Groups, and a new python keyword except*, that can be used to overcome those issues and to write cleaner code.

Improve your error handling with Error Groups, a new feature introduced in Python 3.11

Or Chen

Raise better errors with Exception Groups

Come meet the folks who make the Python programming language!

A panel discussion of core Python developers will take place on Wednesday at 2pm. Hear what's on their mind, what they're working on and what the future holds for Python.

With Pablo Galindo Salgado, Steve Dower, Batuhan Taskaya, Ken Jin, Irit Katriel and Dr.Mark "HotPy" Shannon. Chaired by the esteemed Łukasz "Any color you like so long as it's black" Langa.

Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel Panel

The Geometry of the Universe

Abstract

The speaker