Like many companies with multi-million-line Python codebases, Carta has struggled to adopt best practices like Black formatting and type annotation. The extra work needed to do the right thing competes with the almost overwhelming need for new development, and unclear code ownership and lack of insight into the size and scope of type problems add to the burden. We’ve greatly mitigated these problems by building an automated refactoring pipeline that applies Black formatting and backfills missing types via incremental Github pull requests. Our refactor applications use LibCST and MonkeyType to modify the Python syntax tree and use GitPython/PyGithub to create and manage pull requests. It divides changes into small, easily reviewed pull requests and assigns appropriate code owners to review them. After creating and merging more than 3,000 pull requests, we have fully converted our large codebase to Black format and have added type annotations to more than 50,000 functions. In this talk, you’ll learn to use LibCST to build automated refactoring tools that fix general Python code quality issues at scale and how to use GitPython/PyGithub to automate the code review process.
Slides: https://www.slideshare.net/jimmy_lai/europython-2022-automated-refactoring-large-python-codebases

Automated refactoring on large Python codebases for adopting Black formatting and type annotation with incremental changes by using open source LibCST, GitPython and PyGithub.

Jimmy Lai

Automated Refactoring Large Python Codebases

Computational methods particularly those involving data analytics are now taking root in various humanities disciplines. However, students and researchers working in these disciplines lack the necessary programming proficiency and coding experience . The need then arises to make Python-based computational methods accessible – we present case-studies of how to do this via various Python modules being taught at College of Business in Technological University Dublin and by means of walkthrough of an interdisciplinary social good project called InEire. It comes down to complementing existing quantitative and qualitative methods with methods based on analysis of various types of data specific to the social science problem being solved. We essentially go through the process of building curiosity-driven exploration in social science students via a theoretically driven research question rather than the Python technique itself, and then focusing on the various steps involved in solving that question; and finally boiling it down to a concrete Python-based data analytics methodology. This project-based teaching methodology helped us develop Python skills in newbies eventually leading to a Python-based data analytic skills in students of disciplines other than Computer Science.

Join us for an interactive step-by-step approach to teaching Python for arts, humanities and social sciences in an attempt to bridge divide between data scientists and policymakers

The various areas within humanities and social sciences such as political science, sociology, psychology, economics etc. have evolved to a point where they have been complementing existing qualitative and quantitative methods with methods rooted in data science. This shift in paradigm is primarily driven by real-world, publicly available data sets that cover a variety of scholarly domains and have the potential to solve fundamental research questions in these intriguing fields. There is however a huge bottleneck to be overcome before realizing the full potential of data science in arts, humanities and social sciences; and that bottleneck relates to a fear of programming in students/researchers within these disciplines. Our talk presents some tips and tricks from course modules being taught in Technological University Dublin; the fundamental idea is to present an overview of data science toolkit and how it relates to problem solving in the real world. 

First we will present ways to make the data science lifecycle being made easy via tools such as Google colab and Jupyter notebooks followed by explaining how showing students the big picture and the workflow lifecycle of a data science technique helps grasps concepts in a very effective manner. We will present examples from exploratory data analysis and classification using data-driven research questions; and look into elegant solutions that can easily be plugged into a social scientist’s skillset. Examples of continuous assessment projects based on different Python libraries are also presented with a view to further establish use of Python as a valuable tool for arts, humanities, and social sciences.

Finally, we will give an overview of an Irish research council funded project emanating from a combination of STEM and HUMANIITIES disciplines that aims to perform an economic assessment of anti-immigrant sentiment in Ireland. Various phases of the project will be explained emphasizing particularly those where Python tools play a major role in interpretion of research outcomes. These research outcomes obtained through smart use of Python data analytic tools play a key role in building relationships between data scientists and policy makers in both government and not-for-profit sector.

Arjumand Younus

Dr. Muhammad Atif Qureshi

Python for Arts, Humanities and Social Sciences

Diving into the CPython source code can feel daunting. Whether you want to start contributing or just want to get a better understanding of Python by exploring its source code, it’s often difficult to know where to start or what you’re missing.

In my talk, I will show you around the CPython source code by implementing a new operator, a pipe operator. While doing so, I will discuss core parts of the internals, such as Python’s grammar, its syntax trees, and the underlying logic that will perform the operation. By the end, you will have a good idea of the moving parts involved in core language features.

I will also take you through the steps necessary to make it all work. I’ll show you how I obtained a copy of the source code, regenerated the parser and token files, and how I compiled my modified version of CPython. I will also write and run tests to help me implement my changes. This should give you a mental framework that helps you while diving into more comprehensive resources, like the excellent [Python Developer’s Guide](https://devguide.python.org/).

My talk is aimed at everyone who wants to explore CPython’s internals. You don’t have to be an expert in Python, although some affinity with Python helps with understanding the internals. I will also use C to implement some of the operator logic, but knowledge of C is by no means required. In short, if you’re interested in diving into the CPython source code, this talk is for you.

Always wanted to dive into Python's source code, but felt overwhelmed? Join @SebastiaanZeeff as he takes you through CPython's Internals by implementing a new operator.

Sebastiaan Zeeff

Demystifying Python’s Internals: Diving into CPython by implementing a pipe operator

Have you heard of the critically acclaimed MMORPG Final Fantasy XIV?

As an active player since 2015, I've used my "problem-solving programmer brain" to analyze my experiences in the world of Eorzea and apply them into important software lessons. From finding solutions to a housing crisis, to tracking cheaters, to networking with the president of Square Enix and applying the principles of (Y)MINASWAN, there's a lot to be learned through triumphs and failures as an MMO gamer. I will also talk about my experiences in the software community as a neurodivergent developer, and how gaming helped me break down barriers.

The journey of a neurodivergent developer who used gaming to help her navigate the software industry

This talk will have special meaning for Final Fantasy fans, but anyone with nerdy non-coding hobbies should be able to enjoy it. The intended outcome is for attendees to see their own hobbies in a new light, where they can find their own abstract lessons.

MMO gaming is becoming a more mainstream hobby, and as a lifelong gamer, I have plenty of experiences and stories to share on how the genre helped my transformation from a shy bundle of nerves to a confident professional. I also have experience applying my problem solving skills from software development into solutions for the community to combat problems that arose within the game.

Valerie Shoskes

How a popular MMORPG made me a better developer

In my talk, we explore our usage data requested according to GDPR and leverage it - together with Spotify’s Web API - to build a personalized playlist recommender system with Python.

In 2018, the General Data Protection Regulation (GDPR) became effective in the EU. It sometimes causes data scientists great headaches. But from a consumer and Pythonista point of view this can also be interesting data for exploration. It is very useful for building personalization technology, in particular recommender systems. And there are almost endless ways to use Python for it.
So, let’s request and use our own data to build a playlist recommender system which infers our music taste from our streaming history and uses it to retrieve songs from our favorites in a new way. We will call it “Your Rediscover Past”, a personalized playlist based on your streaming history and saved songs.

In my talk, we explore our usage data requested according to GDPR and leverage it - together with Spotify’s Web API - to build a personalized playlist recommender system with Python.

Personalized Playlist Recommendations on Spotify are great – some of them let us discover new songs, some others help us to rediscover songs. However, rediscovery seems to be limited on the more recent past, i.e. going only a month backwards. This is a problem if you like to rediscover some of your favorite songs you might have listened to a longer while ago. Sometimes we add them to our "liked songs" where they likely fade away. However, you once explicitly declared those tracks as favorites. So, what is it that we can do about this missing piece in personalized playlist recommendations?

Well, the first thing we do is to request our personal usage data from Spotify according to GDPR. Second, we analyze and enrich it with track audio features offered by Spotify’s rich Web API. We derive the music taste profile of ourselves from 12 months of streaming history and use this taste profile to retrieve favorite songs we haven’t listened to for more than a year. In my talk, I present you the Python package I build for this purpose, possible extensions and enable you to create your own personalized playlist to rediscover your past!

Marcel Kurovski

Build your own Playlist Recommender System with Python using your GDPR Data

Ever wondered how hardware is made, or curious about making your own? 

We share our experiences manufacturing a programmable gamepad for use in IoT/MicroPython workshops. 

We will cover the entire production process, including:

- Designing the PCB (Printed Circuit Board)
- Choosing microcontroller and parts
- Finding, ordering and assembling components
- Pulling together firmware, drivers and software

Mistakes were indeed made along the way. Let's turn them into valuable lessons!

Ever wondered how hardware is made, or curious about making your own? In this talk we go all the way!

Sebastian Roll

From circuit board design to finished product: the hobbyist’s guide to hardware manufacturing

Things rarely go as planned, especially in the world of programming. Errors are the bane of a programmer’s existence. You write an awesome piece of code, are ready to execute it and build a powerful machine learning model, and then poof. Python throws up an unexpected error, ending your hope of quick code execution.

Things rarely go as planned, especially in the world of programming. Errors are the bane of a programmer’s existence.  So, lets look at a graceful way to handle them.

In the process of programming, we are always going to encounter various errors. 
Things rarely go as planned, especially in the world of programming. Errors are unavoidable when writing code, which can be frustrating at times. Every single one of us has faced this issue and emerged from it a better programmer. Dealing with bugs and errors is what builds our confidence in the long run and teaches us valuable lessons along the way.
So, in this talk, we'll discuss different ways of handling errors and making our lives a little better. 
We'll talk about how code written with effective exception handling strategies can help us to catch bugs early in the software developmental cycle.

Riya Bansal

Handling Errors the Graceful Way in Python

Tutorials, blog posts, and product docs help developers learn. From our favorite tutorials to bad product docs we all consume technical writing. But what makes for good technical writing? In this talk I’ll share 10 tips and tricks to improve your technical writing skills to help your readers succeed

Think of that feeling you get when you follow an online tutorial or documentation and the code works on the first run. But what makes one tutorial better than another? In this talk I'll discuss how you can write the documentation that developers love.

Think of that feeling you get when you follow an online tutorial or documentation and the code works on the first run. Now think of all the hours spent wasted following broken, outdated, or incomplete documentation. From our favorite tutorials to bad product docs we all consume technical writing. Tutorials, blog posts, and product docs help developers learn new things, build projects, and debug issues. But what makes one tutorial better than another? In this talk I'll discuss how you can write the documentation that developers love and I’ll share 10 tips and tricks to improve your technical writing.

#### Outline
* Introduction (1 min)
* Why is Technical Writing Important? (4 min)
* My Top 10 Tips to improve your Technical Writing (20 min)
  * \# 10 - Make Your End Goal Clear
  * \# 9 - Don’t Be Overly Verbose
  * \# 8 - Inclusive Language
    * Avoid words like Simple, Easy
  * \# 7 - Avoid Technical Jargon
  * \# 6 - Define ALL Acronyms
  * \# 5 - Avoid Memes/Colloquialisms
  * \# 4 - Use Meaningful Code Samples and Variable Names
  * \# 3 - Don’t Make Your Reader Leave Your Article
  * \# 2 - Make it Easy for the Reader to Find a Single Piece of Information
  * \# 1 - Verify Your Instructions! Test, Test, Test!
  * \# 0 Bonus! - Practice, Practice, Practice
* How You Can Get Started in Technical Writing (3 min)
* Conclusion & Questions (2 min)

Mason Egger

Write Docs Devs Love: Ten Tricks To Level Up Your Tech Writing

Every phase across the end-to-end machine learning lifecycle exposes a plethora of security risks that often go unnoticed by machine learning practitioners. In this talk we uncover the most critical (and common) security risks in the machine learning lifecycle, covering in-depth concepts as well as practical examples of ways in which these can be exploited as well as resolved and mitigated (analogous to the OWASP Top 10 industry standard). 

Throughout the talk we will be using a hands on example, where we will be training, packaging and deploying a model from scratch, outlining key risk areas for each step together with tools and practices that can be used to mitigate these risks. By the end of this talk, machine learning practitioners will have a robust intuition of the importance of security best practices throughout the machine learning lifecycle, together with tools and frameworks that can help mitigate undesirable outcomes due to security flaws.

Secure Python ML: Automated Security Best Practices for ML at EuroPython 2022

#### Abstract

Every phase across the end-to-end machine learning lifecycle exposes a plethora of security risks that often go unnoticed by machine learning practitioners. In this talk we uncover the most critical (and common) security risks in the machine learning lifecycle, covering in-depth concepts as well as practical examples of ways in which these can be exploited as well as resolved and mitigated (analogous to the OWASP Top 10 industry standard). 

Throughout the talk we will be using a hands on example, where we will be training, packaging and deploying a model from scratch, outlining key risk areas for each step together with tools and practices that can be used to mitigate these risks. By the end of this talk, machine learning practitioners will have a robust intuition of the importance of security best practices throughout the machine learning lifecycle, together with tools and frameworks that can help mitigate undesirable outcomes due to security flaws.


#### Overview

The operation and maintenance of large scale production machine learning systems has uncovered new challenges which have required fundamentally different approaches to that of traditional software. The area of security in MLOps has seen a rise in attention as machine learning infrastructure expands to further critical usecases across industry. 

In this talk we introduce the conceptual and practical topics around MLSecOps that data science practitioners will be able to adopt or advocate for. We will also provide an intuition on key security challenges that arise in production machine learning systems as well as best practices and frameworks that can be adopted to help mitigate security risks in ML models, ML pipelines and ML services. 

We will cover a practical example showing how we can secure a machine learning model, and showcasing the security risks and best practices that can be adopted during the feature engineering, model training, model deployment and model monitoring stages of the machine learning lifecycle.


#### Benefits to the ecosystem

This talk will provide practitioners with the intuition and tools to secure production machine learning systems, as well as further the discussion around best practices reinforcing SecOps into MLOps.

It will provide best practices on a critical area of machine learning operations which is of paramount importance in production.

Alejandro Saucedo

Secure Python ML: The Major Security Flaws in the ML Lifecycle (and how to avoid them)

MicroPython - a reimplementation of Python for microcontrollers - is nine years old. How can you find your way in a jungle of tiny chips, circuits, and jumper wires? In this session, we will run through a brief introduction to the world of MicroPython. Beyond the basics, we will explore the projects, tools, and the  community that helped your intrepid speaker to get started as a newcomer.

Run Python everywhere! Exploring the world of MicroPython: microcontrollers, LEGO bricks, and blinky LEDs!

MicroPython is a reimplementation of Python for microcontrollers, originally developed as a result of a Kickstarter campaign. Today, it is an approachable way into programming for many young people, via boards like the Raspberry Pi Pico, the BBC micro:bit and the CodeBug - you can even run it on LEGO bricks! It is increasingly being used in commercial fields as well. MicroPython is helping Python to get into even more places, and making programmers more efficient as it does so.

Andy Piper wanted to learn more, so he spent some time travelling and adventuring on the internet, to discover the community and projects around MicroPython.

The goal of this session is to briefly explain the What, Why and How of MicroPython. There *will* be circuit boards, and discussion of microcontrollers! We will take a look at examples, from established development boards to brand new ones. We will also acknowledge how MicroPython has been built upon, to enable different ways of working (with CircuitPython), and LEGO robots (via PyBricks). 

Finally, and most importantly, you'll get a good sense of the places you can find and learn from the MicroPython community, and how you can get involved and contribute!

Andy Piper

Try Something Different: Explore MicroPython! (a rough guide for newcomers)

Distributing Python GUI applications to end users is a challenge: will they need to install Python? If so, which version? If not, how do they install the application? From a random ZIP file? How native does the process feel? Will their system trust your code? For a fluid experience, it needs to be signed and (on macOS) notarized beforehand.

Welcome to [`pup`](https://pypi.org/project/pup/), the tool that the [Mu Editor](https://codewith.mu/) development team has created to package and distribute it in platform-native formats to Windows and macOS users around the world.

In this session I will show how `pup` can be used to package GUI Applications for distribution: natively on Windows and macOS, and in early stages of development for distribution-agnostic Linux artifacts. In short, if it's `pip`-installable it is `pup`-packageable!

I will then describe the way `pup` works (and how it differs from comparable tools) leading on to a call-for-action moment, where I'll share its current state of development, what's good, what's bad, and where I'd like it to be headed to.

I'll wrap up the talk with a set of future-looking thoughts that `pup` has helped identify not only on the specifics of CPython's distribution, but also on the Python ecosystem as whole.

Packaging Python GUI Apps into native Windows and macOS installers in a single command.

Automated Refactoring Large Python Codebases

Abstract

The speaker