Xithrius

When someone starts learning about classes in Python, one of the first things they'll come across is "self" in the parameter list of a method. To keep it simple, it's usually explained that Python will automatically pass the current instance as the first argument to the method: "self" will refer to the instance the method was called on. This high-level explanation really helps with keeping the focus on learning the basics of classes, but it also side-steps what is really going on: It makes it sound like process of inserting "self" is something automagical that the language just does for you. In reality, the mechanism behind inserting self isn't magical at all and it's something you can very much play with yourself.

In this intermediate level talk, Sebastiaan Zeeff will take you down into the heart of the Python data model to explain how the mechanism behind inserting "self" works. He will talk about the descriptor protocol and how it allows you to modify how attributes are accessed, assigned, or deleted in Python. He hopes that understanding how descriptors work will demystify "self" in Python.

Other people: Sebastiaan Zeeff Matteo Bertucci

How many seasons does a tropical country like Singapore have? Is global warming real, and is rainfall getting heavier?  To answer these questions, I will show how we could use Requests and Pandas to build a data pipeline that extracts Singapore weather station data for a user-defined time period and explore the weather trends and seasons over the past few years.

Most of the world experience the four seasons, but how many seasons does a tropical country like Singapore have? Is rainfall getting heavier? We can get such insights from publicly-available weather data; however, we need to write scripts to make API requests to retrieve the data.

In this talk, I will be sharing about my approach in building a data pipeline that extracts Singapore weather station data from Data.gov.sg APIs for a user-defined time period using pandas. Based on the extracted data, I will also be using a data-driven approach to gain a better understanding on the weather trends and seasons over the past few years, and explore different approaches in forecasting future weather trends based on these insights.

Other people: Chin Hwee Ong

Pinax is an open-source ecosystem of reusable Django starter projects, apps, and themes for building websites. When developers began building Pinax in 2007, they had fun adding to it, but eventually Pinax had grown to become around 80 projects and apps. Without a strategy in place to make Pinax as easy as possible to maintain, the maintainers began to suffer burnout. I was hired to work on Pinax in the fall of 2017. In my talk, I'll outline the critical problems I've discovered and the solutions I'm implementing to make Pinax healthier and easier to maintain today and in the future. Pinax is a work in progress and I'm interested in sharing what I've learned.

Other people: Katherine Michel

As part of the schul-frei project of Teckids e.V. we curate free software and offer it to educational institutions. Besides the general equipment of schools with free software, an equal involvement of students in the development is for us important. One of the solutions presented by the project schul-frei is AlekSIS, a web-based school information system that is being developed jointly by Teckids e.V. and students of the Katharineum in Lübeck. The django-based platform provides data structures that can be used by separately developed apps. The special feature of this project compared to other information systems is that apps can be developed that cover specific application areas of a school. AlekSIS is a framework for apps that can be developed together with students in computer science classes, for example, because the development of such apps with python is easy to follow. Since AlekSIS is just a part of the free software curation, it can be excellently combined with other services or network accounts that are used in school. In our talk we will show, how this framework concretely looks like and how people can contribute to this project or use this system, for example at schools.

Other people: Dominik George Benedict Suska Tom Teichler Jonathan Weth

Everybody hates mundane tasks, they are boring, repetitive and time-consuming. That’s why I love building bots, they can finish my tasks for me by working 24/7. But to build a bot to interact with the users, you have to write in async. If you are afraid of async, don’t worry! Today I am telling you how I learn using async and how I avoid checking in 500+ people in a conference by building a bot with Discord.py.

In the first part of the talk, there will be a short introduction to Discord. As more and more conference goes online, it’s become a more and more popular tool among the Python community. I will also introduce Discord.py, a python library that offered an async API to let you build a Discord bot. This will give the audiences a background about the following quick demo and explanation of a simple bot that I built.

In the second part of the talk, I will walk through how to build a bot for registering attendees for EuroPython. Following a quick demo, I will also give a walkthrough about some of the async coroutines available in Discord.py: event listener like on_ready and commands. I will also explain how the async code is different from the sync code and the danger of mixing them together.

Hopefully, audiences will be inspired to build their own bot and learn a little bit about writing async code in Python and using Discord.py. This talk is suitable for attendees who got basic knowledge in Python but not necessarily know about Discord or async.

Other people: Cheuk Ho

We made the Web accessible to human. What about making the web (of data) accessible to computers? Publish open data could be struggling. Deposit a CSV file on a web server is not enough. The data model used should be explicitly defined.

The Linked Open Data (https://www.w3.org/standards/semanticweb/data) solves this with: * standardized implementation format (RDF) * standardized data access (Content negociation, SPARQL endpoint) * standardized data indentification (URI, data model as data)

For example all the data from the Bibliothèque National de France (national library of France) are widely accessible by using these standards. This allowed the development of data federation initiatives, such as Europeana (https://www.europeana.eu).

CubicWeb is a semantic web application framework, licensed under the LGPL, that follows Linked Open Data best practices. During this presentation we will discuss about Linked Open Data benefits, and how CubicWeb could be used as a "on shelve" framework to do so.

Other people: Fabien Amarger

Mypy has been around since 2012, and in recent years its gaining wide spread adoption. As the framework continues to evolve and improve, more and more useful features are being added. In this talk I'm presenting some hidden gems in the type system you can use to make your code better and safer!

The Mypy typing system, and the complementary extensions module, includes some powerful but lesser known features such immutable types and exhaustiveness checking. Using these advanced features, developers can declare more accurate types, get better warnings, produce better code and be more productive.

Other people: Haki Benita

In 2020, with funding from Mozilla and CZI, the pip packaging team improved pip for all users. These focused on improving the depedenecy resolution for Python packages, and the user experience for all Python users.

We carried out usability testing, user research and improved error and information messages for pip.

This talk will be about these improvements - we'll explain how the new dependency resolver works, what it can (and can't do!), how we improved the user experience of pip, the challenges we faced, and why improving user experience is important for open source software.

This talk with be given by members of the pip team - Pradyun Gedam, Georgia Bullen, Bernard Tyers, and Nicole Harris

Other people: Bernard Tyers

We will talk about different approaches in teaching Data Science with the Python programming language. As a case study, we will use our own experience in providing Data Science education with Python across different audiences in the Asia Pacific region and share approaches and principles that worked for us. The lecture will serve as an anchor for more conversations and discussions for adapting pedagogy that is most effective for various contexts and settings.

Programming is a fairly new human skill. Unlike natural languages and problem solving that have progressed throughout our evolution, we do not yet have neurological learning pathways dedicated for it. As such, effective pedagogies are still experimental. We will share results of experiments in curriculum and conduct that we did and highlight factors such as project-based learning, purpose-driven education, communal philosophies, applications of Agile principles, relevance to industry, and the unique circumstances of our students.

Other people: Albert Yumol