List of videos

Learn how Architects leverage Python in building projects to enable more design possibilities than ever before. Python is one of the fastest growing scripting languages in the Building design and construction field increasingly being used by professional in the industry. This talk will lead you through how Architects design, plan, edit and execute scripts with Python using different editing tools. Learn about how designers tackle the challenge of putting a building together with the aid of code including; using Python script to edit geometry, create algorithmic design for buildings, sort data lists, write content to software and much more. With Python you can unleash the potential in your projects so come and see what’s possible.

In this talk, we'll define exactly what functional programming is and how it helps us. We'll explore the main concepts from functional programming and see how we can apply them to our Python code going over some concrete examples.

This talk will go into the latest efforts to speed up the Python language, and in particular how some things will be sped up much more than others. You may have heard best practices for Python performance before, but there are some new guidelines now, some old ones are no longer as important, and some are no longer true at all. Come to hear how the Python language is being optimized, and what you can do to best take advantage of these optimizations.

For many of us, writing code isn't our job - but we do it anyways. We're not software engineers, and balancing the two isn't easy, but we make do. Because with just a few lines of Python, we can automate the boring, tedious work and enable ourselves to tackle the really hard problems. This is especially true in threat intelligence, where analysts help defenders make informed decisions to protect themselves and their businesses against the security incidents happening every single day. How do major hacks happen, who's responsible, and why? Come and learn about the world of threat intelligence, why we ask these questions, how we answer them, and - most importantly - the Python tools we've built along the way. See how we approach development on a team without any developers, balance process with productivity and enable success at scale. This one is for all the scripts out there helping us do our jobs, and for all the part-time developers who write them. Enjoy!

“Works on my machine”: The cry of developers who can’t reproduce a bug because their development environment is incompatible with their deployment environment. It’s common because setting up clean environments is slow, tedious, and error-prone. Meanwhile, debugging errors introduced by incorrect environments is slow, tedious, and error-prone. Each step in your CI workflow theoretically only has inputs or outputs, but in reality, files can be left along the way by running tests or compiling extensions. These are side-effects, not inputs for subsequent steps in your workflow, let alone deployment, but if included they can affect correctness. You can solve this using “hermetic environments”: running every step of your workflow inside a fresh environment, so steps run truly independently of one another. You can do this manually with Docker, but it’s difficult: you have to understand which inputs are necessary for a step, which newly generated files are meaningful outputs, and what should be discarded. Pantsbuild uses hermetic builds automatically: it understands the inputs each step needs, what outputs it produces, and stores inputs and outputs inside a content-addressable database so it can rapidly build sandboxed environments for subsequent steps of your workflow. The result is a build process where every step is run in isolation, with only the inputs each process truly needs, and only true outputs made available to each subsequent step. Pants’ workflows are fast but verifiably correct — running against incorrect inputs is not a possible failure case. In this talk, we’ll explore how Pantsbuild enables truly hermetic builds. We’ll look at other approaches to sandboxing and how they compare to Pants’ approach, and how you can benefit from adding hermetic builds to your project. You’ll walk away being confident that “works on my machine” means “works everywhere”.

In this talk, I make the case that the developer community has an opportunity to profoundly improve data privacy by shifting privacy upstream into the SDLC, where it belongs. I will share resources and lessons learned from my team's development of open-source, Python-based devtools for data privacy. Analogous to physical infrastructure, our digital infrastructure needs to be designed with trustworthiness at the forefront. As developers, we have often been left out of important design decisions about how technical systems actually process personal data. Typically, privacy risk is addressed reactively, and developers have to manually fulfill users' privacy requests across disparate data infrastructure. This reactive, burdensome approach to privacy pits trustworthiness against innovation. To build trustworthy systems at scale, we need devtools for proactive privacy, and the tools must fit within existing developer workflows. I will walk through the existing points of friction for developers today, the power of privacy embedded into the SDLC, and the tight bond between open-source and privacy. My team and I have learned that we can improve privacy at scale when the tools for privacy fit into developers' existing workflows and the infrastructure they use every day, including Snowflake warehouses, mongoDB databases, Redis session stores, and more. I will demonstrate what proactive privacy can look like for developers and data engineers: automatic flags for privacy risk in the CI pipeline, and streamlined privacy request fulfillment by traversing distributed data systems for custom data operations—such as deleting personal data while upholding referential integrity across databases. Open-source and privacy go hand-in-hand in offering developers and end-users digital infrastructure that they can trust. To tackle a problem as complex as modern privacy, the solution requires all of us to build shared, transparent, and community-informed privacy standards for technology worldwide. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/59/2022-05-01T18%3A53%3A23.697439/Fides-PyCon2022.pdf

In the last couple of years, most people have been moved to a full working from home work-style, which made us realize benefits we were not aware of, but sadly some little inconveniences as well, like health related issues. In this talk, we will explore how to build a functional system to track the air quality, collect our own data using different sensors and implement a predictive approach to avoid future health problems. We are going to dive into the different setups to interact with air quality sensors using Python on microcontrollers and embedded systems, collecting your own data to evaluate different factors like humidity, temperature, CO2, particles, but that’s not all, also we will go into the implementation of a predictive machine learning (ML) model to predict Indoor CO2 levels and alerting us based on predictions before critical levels. The main idea of this talk is to show with a practical example how Python is a great option to build an indoor air quality monitoring complemented with a predictive ML model for Indoor CO2, while having fun building and monitoring their home. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/70/2022-04-29T13%3A37%3A41.013632/pyconusv0-770998_3.pdf

Most of us have heard the saying, "A picture is worth a thousand words," but a movie builds context and a story, especially when conveying data! Data animations allow us to share more information and are far more engaging than static plots. In this talk, I will discuss the importance of animation in analysis and show how to create data animations using play-by-play RFID data from the 2018 NFL season. Within data science, we often use graphical representations of data to convey our analysis engagingly and succinctly. However, a static image does not always do justice to our findings and sometimes can miss important concepts entirely. When we introduce animation, we can show how location, statistics, etc., can change over time. Using this NFL play-by-play data, I will show how to take a static data plot and transform it into an animation using the matplotlib module. By the end of this talk, you will know what data animation is, how it works for matplotlib using FuncAnimation(), how to animate plots successfully using defined functions in conjunction with your iterative function, and how animation can improve your analysis. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/25/2022-04-29T02%3A51%3A33.350370/PyCon_3.pdf

Attributes in Python, which we use dozens of times each day, seem boring, obvious, and not worthy of attention. But it turns out that they're key to the Python language: Every time you say a.b in Python, that little dot is hiding a lot of work, from searching across multiple objects to silently rewriting things. And it turns out that what happens with attributes, while not always obvious to developers, determines a great deal of behavior in the Python language. In this talk, I'll discuss what attributes are (and aren't), what Python does when you use a dot (.) in your code, and how you can take advantage of it. We'll talk about attribute lookup, about inheritance, and about methods vs. functions. We'll also look into properties, and how they allow us to have attributes that look like data but behave like setters and getters. Finally, we'll look at the descriptor protocol, which makes so much of Python's functionality possible, including the automatic insertion of "self" as the first argument in method calls. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/22/2022-05-01T04%3A08%3A16.996945/Presentation__Python_attributes.key.pdf