List of videos

Los Diarios Oficiales son las principales formas de comunicación entre la ciudadanía y el poder ejecutivo de una ciudad. En Brasil, por ley, todos los actos oficiales del gobierno deben publicarse en los Diarios. Sin embargo, no existe un estándar sobre como deben estar disponibles estas publicaciones. Entonces tenemos un escenario donde las 5570 ciudades brasileñas publican cada uno a su manera, generalmente utilizando formatos cerrados como PDF que dificultan la consulta y análisis de datos de forma automatizada. El proyecto Querido Diario tiene como objetivo hacer más accesibles estos Diarios, facilitando la búsqueda y consulta de su contenido a través de una página de búsqueda, una API abierta y en el futuro con herramientas de análisis de contenido. En esta charla se presentará todo el proceso, desde la extracción de datos de las páginas de los municipios (mediante data scraping usando el framework Scrapy), el almacenamiento y procesamiento de archivos PDF para permitir la búsqueda en su contenido (usando OCR), a la API y la página de búsqueda, donde cualquier persona tiene acceso centralizado a los Diarios de todos los municipios.

Álgebra de mapas es un lenguaje de expresiones aritméticas que utilizan relaciones (operadores y funciones) y variables que representan datos y valores espaciales para realizar análisis geográficos mediante el modulo ArcPy. El álgebra de mapas básicamente implica hacer matemáticas con mapas. La idea de utilizar datos geográficos existentes para generar nuevos o simplemente extraer de ellos resultados cuantitativos, es una práctica común desde el mismo momento en que aparece la cartografía moderna. En esta charla tendremos una introducción al modulo de ArcPy, su configuración en ArcGis, pasando luego por una mirada holística de todo el modulo desde sus operadores, operaciones, y funciones algebraicas, la creación de expresiones complejas para el procesamiento de datos geoespaciales en determinada temporalidad, la preparación de las capas y concluiremos con la generación de mapas usando Python. Si te gustan la geografía, la matemática y Python, esta charla es para ti.

NOTE: video begins at ~3:45 This talk covers how Kubernetes docs were able to scale from zero to eleven localizations within six months in 2018. It covers what docs maintainers learned, mistakes to avoid, and how you can start localizing your own open source project. Great documentation drives developer adoption...but documentation is only great if it's accessible. One piece of accessibility is localization: the ability for developers to access information in their native or primary language. This talk covers the specifics of scalable localization that other projects can adopt, based on the Kubernetes documentation model: tooling, workflows, standards for minimum viable documentation, and community conduct. This talk also covers some avoidable mistakes to save your maintainers time and stress, as well as the ongoing greater-than-additive benefits that localization can bring. This talk concludes with specific recommendations for other projects to start their own localizations.

Building a web application with Python is super easy. With just a few lines of code, you can get a simple, working app running directly on your computer's browser. Awesome! But then what? This tutorial focuses on that awkward transition from beginner to intermediate—when you want a project to be less of a sketchpad and more of an actual, useful tool. We will learn tactics on how to find and use resources when devising a plan for your web application, as well as hands-on learning for tackling common (and necessary) aspects of building your app, such as configuration, app structure, and database modeling. For the training, you will be following along as we build the foundation of a fully-functional web application, and will leave with the ability to further refine it for real-world scenarios.

Have you ever wondered about how data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, visualizing, and using complex networks to solve problems.

Wasm is a binary code format specification first released in 2017. This technology can be implemented in web browsers or standalone applications in a secure, open, portable, and efficient fashion. More precisely, Wasm is an intermediate language for a stack-based virtual machine that uses a just-in-time (JIT) compiler to produce native machine code. Although Wasm was primarily designed as a compilation target for languages such as C/C++ or Rust, it can be integrated with Python in interesting ways. And that’s what we’ll be focusing on during this tutorial. Some experience with JavaScript and web development might come in handy but is not strictly required. At the end, we’ll show how to develop a tiny compiler that has Wasm as it’s compilation target. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/53/2022-05-05T23%3A36%3A12.746488/webassembly_tutorial.pdf

Many Python developers now use type annotations to catch and fix bugs early in the coding process. This tutorial will introduce you to type annotations in Python. We’ll cover basic ideas about how types work in a dynamic language like Python, and where explicit annotations can provide value. We’ll then explore features of the type system in more depth, and demonstrate how they can be used to precisely yet flexibly express a huge range of programming patterns. Throughout the tutorial, you will have the chance to get your hands dirty by learning how to add types to small code snippets as well as to an example GitHub project, and run a type checker to see errors as you code. You’ll get to practice and play around with each concept as we discuss it, and walk away with concrete experience adding types to and catching bugs in real code. A laptop with Python installed is required along with internet access.

IF IT ISN'T DOCUMENTED, IT DOESN'T EXIST. Documentation can make or break a project. Getting it right takes effort, but that effort doesn't have to be painful. In this tutorial, we will take a multi-stage approach to documentation, starting with the fundamentals, adding complexity and style, then finishing with automated publishing to the web. We will practice a maintainer-friendly workflow that smooths out some of the rough edges of creating documentation. It is never too early or too late to pick up good documentation techniques and tools. As such, this tutorial will have elements that are relevant to brand new Pythonistas (What does a good docstring look like? What is a type hint?) as well as long-time practitioners (How can I make my docs easier to maintain? Where can I host docs? How can I test examples in my docstrings?). We will cover code comments, docstrings, and type annotations as ways to add documentation within your code. Next, we will add a user interface and documentation prose layer with JupyterBook, Jupyter Notebooks, and Markdown. After that, we will use Sphinx to build API documentation. Finally, we will automate the build and publish steps with GitHub Actions and GitHub Pages.

FOR WHOM IS YOUR WORKSHOP Data scientists, engineers and researchers who have no prior experience in knowledge graph data modelling. In this workshop, we will start from the fundamentals - learning how to think in terms of triples to describe relations of different data objects. If your work involves data analysis, data management, data collaboration or anything data-related, this is a workshop for you to have a brand new insight into how data should be represented and stored. SHORT FORMAT OF YOUR WORKSHOP Overview-10 min, Lecture - 60 mins, Breaks- 20 minutes, Hands-on training - 80 mins, Closing - 10 mins WHAT ATTENDEES WILL LEARN By the end of the workshop, you will be able to think like a knowledge graph expert and construct a proper schema to store your data in a knowledge graph format. You will acquire the skills that you need to build knowledge graphs in TerminusDB - an open-source graph database that enables revisional control and collaborations. COURSE BENEFITS You will have learnt a new skill set that may assist you in your project in data science or research. You will have a new tool that you can better model your data and collaborate with others. Also, you gain all the prerequisites to use WOQL - a query language for knowledge graphs and the TerminusDB Python client to manage, manipulate and visualize data in your knowledge graph. Slides: https://pycon-assets.s3.amazonaws.com/2022/media/presentation_slides/50/2022-04-26T08%3A51%3A50.741435/Data_Modelling_Workshop.pdf