List of videos

Our keynote speaker, Sumana Harihareswara, reflects on her journey in open source, from starting as an open source user, contributor, to starting a career in open source. Did you know that Sumana also does stand up comedy? Come join and meet Sumana at PyCon US! Bio: Sumana Harihareswara (https://harihareswara.net/) is an open source contributor and leader who has managed work on pip, PyPI, GNOME, MediaWiki, HTTPS Everywhere, autoconf, GNU Mailman, and other projects -- and who is working on a book to teach what she's learned along the way. Between 2016 and 2021, Harihareswara led fundraising for and managed the next-generation overhauls of PyPI and of pip's dependency resolver and user experience. Her work has earned her an Open Source Citizen Award and a Google Open Source Peer Bonus. Harihareswara has keynoted LibrePlanet, code4lib, SeaGL, and other conventions, and has previously spoken at PyCon US and PyGotham (https://www.harihareswara.net/talks.html). She also performs stand-up comedy, and volunteers to improve local emergency preparedness and open data. She lives in New York City, runs Changeset Consulting (http://changeset.nyc/), and microblogs in the Fediverse (https://social.coop/@brainwane/). PyCon US is the largest Python community conference in the world. May 15 - 23, 2024 in Pittsburgh, Pennsylvania Register: https://us.pycon.org/2024/registration/register Full schedule: https://us.pycon.org/2024/ Keynote speakers: https://us.pycon.org/2024/about/keynote-speakers/ This video was produced, directed, and edited by Mariatta Wijaya (https://mariatta.ca) #PyConUS #PyCon #ThePSF #Python #Community #Conference

Who will you meet at PyCon US? Wondering what to do at the conference? We got you covered. Community members worldwide will be gathering at PyCon US, the largest Python conference in the world. From hobbyist, content creator, open source maintainer, and community leader, they’re all looking forward to meet and connect with you at PyCon US. In this video, hear what made PyCon US special for them, and learn their pro-tips for making the most at the conference. Have your own memorable experience, or tips and advice about PyCon US? Share with our community by using the #PyConUS hashtag It’s not too late to register and join us at the conference! PyCon US is the largest Python community conference in the world. May 15 - 23, 2024 in Pittsburgh, Pennsylvania Register: https://us.pycon.org/2024/registration/register Full schedule: https://us.pycon.org/2024/ Keynote speakers: https://us.pycon.org/2024/about/keynote-speakers/ #PyConUS #PyCon #ThePSF #Python #Community #Conference Thank you to our community members for helping us with the creation of this video: - Carol Willing - Ned Batchelder - Ngazetungue Muheue - Donghee Na - Kojo Idrissa - Abigail Afi Gbadago - Velda Kiara - Kattni Rembor - Takanori Suzuki - Denny Perez - Maria Jose Molina - Mario Munoz - Tammy Do - Jules/Juliana Barros Lima - Georgi Ker

All modern systems use distributed and asynchronous tasks to make proper use of the available hardware and software resources in a safe and reliable manner. The ability to create and distribute asynchronous tasks drastically impact the performance as well as the capability of a system. The most popular way of creating asynchronous task queues involves using streams or pub/sub infrastructures like Apache Kafka, RabbitMQ, Kinesis etc, more or less, are equally suitable for a variety of scenarios. Then, the question is why we’re talking something else i.e. “Celery” over here? The answer is “Python Programming Language”. If you're creating a system (especially microservices based and/or distributed systems) in python__ and want to create an asynchronous (and distributed) task queues which is pure python, simple, super fast, and diminishes the boundary between distributed systems_, then you should consider using “Celery”___. Using something like “Celery” is not about being better than other solutions out there, it’s all about the ability to make use of the python ecosystem and create things faster without leaving the python programming language for something else. Welcome to this tutorial on Celery, the open source distributed task queues. In this tutorial, we'll learn how to use “Celery” and create an end to end system. We’ll also learn about how we can also visualize the distributed task queues at runtime using “Flower” This tutorial will have classroom exercises, post class homeworks as well as complimentary readings. All the presentation, code, exercises will be shared in advance (~ 2 - 3 days) and the solutions of the exercise will be shared after the tutorial is completed.

Pandas and matplotlib are two staples of the Python ecosystem and fundamental if you're delving into the world of data science. In this tutorial, we'll dispense with the typical and uninteresting data sets or highly theoretical introductions, and instead we'll use data from the Pokémon franchise to provide a fun, hands-on introduction to the basics of data science with pandas and matplotlib. You don't need to be a Pokémon enthusiast or connoisseur to join! All you need to know is that this won't be your typical pandas tutorial.

Did you know that you can use Python to program microcontrollers? MicroPython is an re-implementation of Python targeted to microcontrollers. It's optimized to run on devices that have constrained memory and CPU power . It makes it easier to interact with hardware, compared with low level languages such as C and C++. In this tutorial you'll learn how to use the resources of the BBC micro:bit, an open source embedded device that lets you control electronic components using MicroPython. Some of the components you'll learn to use in this tutorial include: * Push buttons * 5x5 LED matrix * Accelerometer sensor * Radio communication. The goal of the tutorial is to implement a version of the Battleship game that can be controlled using the accelerometer sensor. This tutorial is designed for attendees with a basic understanding of Python. No prior knowledge of electronics is required. The tutorial will cover all necessary concepts from the ground up, making it suitable for those who are new to the world of electronics. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/4/2024-05-15T04%3A19%3A39.960613/slides.pdf

Presented by: Felipe de Morais Amanda Vieira Join us at PyCon US 2024 for an exciting tutorial 🎓, where we'll dive into the world of web APIs using Python's beloved Django framework and the Django Rest Framework. This session is perfect for Python enthusiasts 🐍 eager to expand their web development skills in a practical, hands-on environment. What You Will Learn 🔍 Introduction to API Development with Django: Understand the basics of API construction and the role of Django and Django Rest Framework in modern web development. 📊 Django Models: Explore how to create and utilize Django models, the backbone of data handling in Django applications. 🔗 URL Mapping and Views: Master URL configuration for efficient API endpoint creation and discover how Django views and ViewSets drive your API's functionality. 🔄 Serializers in Action: Learn to implement serializers for converting data formats, a critical step in RESTful API development. 💻 Practical Application: Engage in hands-on coding to apply these concepts in building a functional API from scratch. Why This Tutorial? This tutorial is tailored for those ready to step into API development with Django. We focus not just on theoretical knowledge but also emphasize practical skills. By the end of this session, you'll have a clear understanding of the essential components of Django for API development and hands-on experience in building an API. Are you already navigating the Django landscape and seeking to deepen your expertise? 🌱 This tutorial provides an immersive opportunity to advance your web development skills and explore the intricacies of API development using Django and the Django Rest Framework. 🚀 Join us for an enriching journey as we delve into mastering the art of building powerful APIs with Django!

Abstract People are hard to understand, developers doubly so! In this tutorial, we will explore how communities form in organizations to develop a better solution than "The Org Chart". We will walk through using a few key Python libraries in the space, develop a toolkit for Clustering Attributed Graphs (more on that later) and build out an extensible interactive dashboard application that promises to take your legacy HR reporting structure to the next level. Description In this tutorial, we will develop some fundamental knowledge on Graph Theory and capabilities in using key Python libraries to construct and analyze network graphs, including xarray, networkx, and dash-cytoscape. We will also dig into exploratory data analysis of people data, and use faker to generate realistic people data that matches our statistical understanding, without breaching data privacy. The goal of this talk is to build the tools you need to launch your own interactive dashboard in Python that can help explore communities of people based on shared characteristics (e.g. programming languages, projects worked on, apps used, management structure). The data we will dig into focuses on building a better understanding of developers + users and how they form communities, but this could just as easily be extended to any social network. The work we do here can be easily extended to your communities and use cases -- let's build something together! Getting Started Before the tutorial, head over to https://lucasdurand.xyz/network-graph-tutorial/ for more information on setting up your environment. If you have your own people data, bring that along too and we can give it a try, but if not then we will make some synthetic data as a stand-in.

Full title: Tutorials: Introduction to Ibis: blazing fast analytics with DuckDB, Polars, Snowflake, and more, from the comfort of your Python repl. Presented by: Gil Forsyth Jim Crist-Harif Naty Clementi Phillip Cloud Tabular data is ubiquitous, and pandas has been the de facto tool in Python for analyzing it. However, as data size scales, analysis using pandas may become untenable. Luckily, modern analytical databases (like DuckDB) are able to analyze this same tabular data, but perform orders-of-magnitude faster than pandas, all while using less memory. Many of these systems only provide a SQL interface though; something far different from pandas’ dataframe interface, requiring a rewrite of your analysis code. This is where Ibis comes in. Ibis provides a common dataframe interface to many popular databases and analytics tools (BigQuery, Snowflake, Spark, DuckDB, …). This lets users analyze data using the same consistent API, regardless of which backend they’re using, and without ever having to learn SQL (but you can use SQL if you want to!). No more pains rewriting pandas code to something else when you run into performance issues; write your code once using Ibis and run it on any supported backend. In this tutorial we’ll cover: The basic operations of Ibis (select, filter, group_by, order_by, join, and aggregate), and how these operations may be composed to form more complicated queries. How Ibis may be used on a number of different local and remote backend engines to execute the same queries on different systems. The tradeoffs of different database engines, and recommendations for how to choose the best tool for the job. How Ibis integrates into the larger Python data ecosystem, including tools like Scikit-Learn, Matplotlib, PyArrow, pandas, Altair, and VegaFusion. This is a hands-on tutorial, with numerous examples to get your hands dirty. Participants should ideally have some experience using Python and pandas, but no SQL experience is necessary.

From picking the right plot for the particular type of data, statistic, or result; to pre-processing sophisticated datasets, and even making important decisions about the aesthetic of a figure, visualization is both a science and art that requires both knowledge and practice to master. This tutorial is for python users who are familiar with python and basic plotting, and want to build strong visualization skills that will let them effectively communicate any data, statistic, or result. We will use python libraries such as seaborn, matplotlib, plotly, and sklearn; and discuss topics such as density estimation, dimensionality reduction, interactive plotting, and making suitable choices for communication. Drawing examples from datasets in the scientific, financial, geospatial (mapping), education fields, and more.