PyCon SE 2018

Now is better than Never: What the Zen of Python can teach us about Data Ethics We Pythonistas welcome newcomers with the wisdom of Tim Peter’s “import this”. Okay, well maybe. The Zen of Python provides us as a community general aphorisms on how to write Python and how to be a good Pythonista by offering loose guidelines that promotes discussion. What lessons, then, can the Zen of Python teach us about Data Ethics? Data ethics is a nebulous concept, a necessity in the era of algorithms and the data economy. Together we’ll review some stories from the headlines about the data economy where there were ethical concerns and apply the Zen of Python. Starting with the impact of social media likes on political campaigns to censorship on social media in the #MeToo movement, we’ll use big challenges to highlight obvious and not so obvious lessons. We’ll wrap this discussion with how Python today is helping challenge data ethics concerns and what you can do to participate in this fight. Ultimately the Zen of Python teaches us that ‘Now is Better than Never’ and we must ask as data practitioners - what principles will we develop and champion to respond to ethical dilemmas?

Data Classes, in Python 3.6 and beyond Python 3.7 is here and the @dataclass-decorator is a major new feature simplifying class-creation. In this talk, we will learn to use the power of data classes to make our codebases cleaner and leaner in a pythonic way. We will also learn how to use the backport in Python 3.6 codebases before upgrading.

Digging MUD in Python Text-based Multi-User Dungeons (MUDs) were the first MMOs. Not only are they still played, they are great for small teams learning Python and game development. This talk introduces the open-source Evennia MUD engine for developing a new MUD in pure Python using Django and Twisted.

Designing an intuitive framework for complex pipelines in PySpark When creating complex and pipelined jobs in PySpark, your code quickly gets unstructured and virtually impossible to read. Especially when working with the dataframe API. As part of a large scale project, we developed a flexible code structure, dependent on the Spark SQL interface, which allowed easy addition of new jobs, increased readability and made collaboration easier. In this presentation we share our key findings in how to structure your project for readability, flexibility and maintainability.

* Anders Hovmöller

* Lorena Mesa

* Helio Loureiro

Gait phase recognition using machine learning algorithm with IMU sensors Gait phase recognition is of great importance to develop accurate timing feedback for exoskeleton control. A powered exoskeleton can provide proper assistance to the subject if the current gait phase can be identified accurately. Machine learning classifiers were built in python to distinguish 4 gait phases in a gait cycle.

A developer amongst (data) journalists I've been working as a developer with data journalists for a year now and I contribute to teaching Python to journalists, building a pipeline of data stories, analysing climate data... In my talk I'll share my experience at jplusplus and discuss technologies and processes transferable between software development and data journalism.

Binding the Sea Python is good but sometimes C is better. ctypes offers a strange but not too difficult bridge.

Build your own event loop Want to handle a lot of traffic with the same hardware? This talk will show you asyncio examples. Together we will build a loop where you’ll be buying the latest accessories for your new horse! We will also explore this in other event loop such as Trio and Curio.

A Crystal Ball To Prioritize Technical Debt Most organizations find it hard to prioritize and repay their technical debt. The main reason is due to the scale of modern systems with million lines of code and multiple development teams; No one has a holistic overview. So what if we could mine the collective intelligence of all contributing programmers and start to make decisions based on data from how the organization actually works with the code? This session introduces one such approach with the potential to change how we view software systems. In this session you'll get an introduction to techniques that help us uncover both problematic code as well as the social dimension of the teams that build your software. The techniques are based on software evolution and findings from various fields within psychology. This combination lets you prioritize the parts of your system that benefit the most from improvements, detect organizational issues and make practical decisions guided by data. Each point is illustrated with a case study from a real-world codebase. This is a new perspective on software development that will change how you work with code.