List of videos

Has testing got you down? Ever spent a day writing tests, only to discover that you missed a bug because of some edge case you didn’t know about? Does it ever feel like writing tests is just a formality - that you already know your test cases will pass? Property-based testing might be just what you need! After this introduction to property-based testing, you’ll be comfortable with Hypothesis, a friendly but powerful property-based testing library. You’ll also known how to check and enforce robust properties in your code, and will have hands-on experience finding real bugs. Where traditional example-based tests require you to write out each exact scenario to check - for example, assert divide(3, 4) == 0.75 - property-based tests are generalised and assisted. You describe what kinds of inputs are allowed, write a test that should pass for any of them, and Hypothesis does the rest! from hypothesis import given, strategies as st @given(a=st.integers(), b=st.integers()) def test_divide(a, b): result = a / b assert a == b * result There’s the obvious ZeroDivisionError, fixable with b = st.integers().filter(lambda b: b != 0), but there’s another bug lurking. Can you see it? Hypothesis can! Audience: This tutorial is for anybody who regularly writes tests in Python, and would like an easier and more effective way to do so. We assume that you are comfortable with traditional unit tests - reading, running, and writing; as well as familar with ideas like assertions. Most attendees will have heard "given, when, then" and "arrange, act, assert". You may or may not have heard of pre- and post-conditions - we will explain what "property-based" means without reference to Haskell or anything algebraic.

Brand new to programming and want to get some hands-on Python experience? Let's learn some Python together! During this tutorial we will work through a number of programming exercises together. We'll be doing a lot of asking questions, taking guesses, trying things out, and seeking out help from others. In this tutorial we'll cover: - Types of things in Python: strings, numbers, lists - Conditionally executing code - Repeating code with loops - Getting user input This tutorial is intended to ease you into Python. Each exercise section is careful not to assume prior programming knowledge. I expect you to have experience typing on computers and to have rudimentary math skills (just arithmetic). I am not expecting you to have experience with programming. We will define new terms as we use them You'll leave this tutorial, having written a couple small programs Python yourself. Hopefully you'll also leave with a bit of excitement about what Python can do and curiosity to keep diving deeper.

Comprehensions are one of the most important — and misunderstood — parts of Python. In this tutorial, I'll walk you through comprehensions, including how to write them, and why you would want to do so. By the time you finish this tutorial, you'll fully understand list, set and dict comprehensions, as well as nested comprehensions and generator expressions. You'll understand the differences between regular "for" loops and comprehensions, and where to use them.

This workshop will teach you how to create a production ready GraphQL API using Python and Strawberry. We will be using using Django as our framework of choice, but most of the concept will be applicable to other frameworks too. We'll learn how GraphQL works under the hood, and how we can leverage type hints to create end to end type safe GraphQL queries. We'll also learn how to authenticate users when using GraphQL and how to make sure our APIs are performant. If we have enough time we'll take a look at doing realtime APIs using GraphQL subscriptions and how to use GraphQL with frontend frameworks such as React.

SQLite is the world's most widely used database and has been a part of the Python standard library since 2006. It continues to evolve and offer more capabilities every year. This tutorial will transform you into a SQLite power-user. You'll learn to use SQLite with Python, level up your SQL skills and take advantage of libraries such as sqlite-utils and tools such as Datasette to explore and analyze data in all kinds of shapes and sizes. This hands-on tutorial will cover: - The sqlite3 module in the Python standard library - A review of SQL basics, plus advanced SQL features available in SQLite - Using sqlite-utils for advanced manipulation of SQLite databases - Datasette as a tool for exploring, analyzing and publishing data - Applying the Baked Data architectural pattern to build a data application using Datasette and deploy it to the cloud This tutorial is aimed at beginner-to-intermediate Python users with some previous exposure to basic SQL. Attendees will leave this workshop with a powerful new set of tools for productively exploring, analyzing and publishing data.

Want to bring hypermedia into your web design workflow, ditching the complexity of JSON-over-HTTP for a more RESTful approach? Create and design your web application with htmx and spark joy in your design process. Splash in a little Tailwind CSS, too. (Ssshh. You're a Full-Stack Developer now.)

In Machine Learning, features are the inputs for a machine learning model. Feature Engineering is the process of creating features from raw data and selecting the best features for a model. However, it is not just a tool for Data Scientists - Data Analysts and Developers can use it too. In this tutorial, we will create features that can be used for creating Data Visualizations, Rules Based Automations, and Machine Learning Models. Attendees will learn how to explore, create and select features from various data types such as “discrete/categorical” and “continuous” data. Attendees will learn techniques such as One-hot encodings for categories, text vectorization, date manipulation and more. By the end of this tutorial, attendees will understand how to create features for their projects.

While most folks aren't at the scale of cloud giants or black hole research teams that analyze Petabytes of data every day, you can easily fall into a situation where your laptop doesn't have quite enough power to do the analytics you need. "Big data" refers to any data that is too large to handle comfortably with your current tools and infrastructure. As the leading language for data science, Python has many mature options that allow you to work with datasets that are orders of magnitudes larger than what can fit into a typical laptop's memory. In this hands-on tutorial, you will learn the fundamentals of analyzing massive datasets with real-world examples on actual powerful machines on a public cloud – starting from how the data is stored and read, to how it is processed and visualized. You will understand how large-scale analysis differs from local workflows, the unique challenges associated with scale, and some best practices to work productively with your data. By the end, you will be able to answer: What makes some data formats more efficient at scale? Why, how, and when (and when not) to leverage parallel and distributed computation (primarily with Dask) for your work? How to manage cloud storage, resources, and costs effectively? How interactive visualization can make large and complex data more understandable (primarily with hvPlot)? How to comfortably collaborate on data science projects with your entire team? The tutorial focuses on the reasoning, intuition, and best practices around big data workflows, while covering the practical details of Python libraries like Dask and hvPlot that are great at handling large data. It includes plenty of exercises to help you build a foundational understanding within three hours.

In this 3.5 hour tutorial, attendees will learn how to use the streamlit library in Python to create interactive graphical user interfaces (GUIs) for their data science projects. Through a series of hands-on exercises, attendees will gain practical experience using streamlit to build and customize their own interactive GUIs. The tutorial will begin by introducing attendees to the basics of streamlit, including how to install and set up the library, as well as the key concepts and components of streamlit applications. Attendees will then learn how to use streamlit to create simple, yet effective, GUIs for their data science projects, including how to display and interact with data, add text and images, and create custom layouts and widgets. As the tutorial progresses, attendees will have the opportunity to work on more advanced topics, such as using streamlit to create custom interactive plots and charts, and integrating streamlit with other popular libraries such as Pandas and Altair. By the end of the tutorial, attendees will have a solid understanding of how to use streamlit to create effective and engaging interactive GUIs for their own data science projects. The tutorial will be led by an experienced data scientist with a strong background in Python and streamlit, and will include plenty of hands-on exercises to help attendees apply what they learn in a practical setting. Attendees will also have access to detailed tutorial materials and code samples, as well as support from the instructor and other attendees throughout the tutorial.