This tutorial is a gentle introduction to Python for folks who are completely new to it and may not have much experience programming. We’ll work in a Jupyter Notebook, one of the most popular tools in scientific Python. You’ll learn how to write beautiful Python while practicing loops, if’s, functions, and usage of Python’s built-in features in a series of fun, interactive exercises. By the end of the tutorial we think you’ll be ready to write your own basic Python -- but most importantly, we want you to learn the form and vocabulary of Python so that you can understand Python documentation, interpret code written by others, and get the most out of other SciPy tutorials.

This tutorial is an introduction to Bayesian data science through the lens of simulation or hacker statistics. We will become familiar with many common probability distributions through i) matching them to real-world stories & ii) simulating them. We will work with joint/conditional probabilities, Bayes Theorem, prior/posterior distributions and likelihoods while seeing their applications in real-world data analyses. We’ll see the utility of Bayesian inference in parameter estimation and comparing groups and we’ll wrap up with a dive into the wonderful world of probabilistic programming.

Jupyter widgets are powerful tools for building user interfaces with graphical controls such as sliders and text boxes inside a Jupyter notebook. Interactive widgets can also be rendered in Sphinx documentation, nbviewer, and static or interactive web pages. The recent release of version 8.0 of the core widgets package adds several new features. Jupyter widgets are also a framework that makes it easy to build custom GUI controls. Examples of custom widget packages include libraries for interactive 2-D charting, 3-D graphics, mapping, complex controls, and more.

Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will build out into progressively more advanced concepts (path and structure finding, and graph theory's relation to linear algebra), as well as an overview of scalable alternatives to NetworkX.

NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in NumPy, and discuss how to write your own routines. Along the way, we'll learn just enough about matplotlib to display results from our examples.

Python has become the lingua fraca in data science and machine learning. However, the "DataFrame" and "array" data structure used in data science means a different teaching and learning approach is needed from more traditional introductory programming classes. A DataFrame centric teaching approach means more complex topics need to be taught earlier when compared to traditional programming classes, but this complexity is offset by its pragmatism. This is a tutorial for beginners on using the Pandas library in Python for data manipulation. We will focus on tidy data principles and how it fits into the data manipulation framework.

This tutorial will show you how to turn nearly any notebook into a deployable dashboard, how to build visualizations easily even for big, streaming, and multidimensional data, and how to build linked, interactive drill-down exploratory tools without having to run a web-technology software development project.

JAX's magic brings your NumPy game to the next level! Come learn how to write loop-less numerical loops, optimize _any_ function, jit-compile your programs, gain reliability over stochastic numbers - basically equip yourself with a bag of tricks to help you write robust numerical programs.

Fairness in AI systems is an interdisciplinary field of research and practice that aims to understand and address some of the negative impacts of AI systems on society. In this tutorial, we will walk through the process of assessing and mitigating fairness-related harms in the context of the U.S. health care system. This tutorial will consist of a mix of instructional content and hands-on demonstrations using Jupyter notebooks. Participants will use the Fairlearn library to assess an ML model for performance disparities across different racial groups and mitigate those disparities using a variety of algorithmic techniques.

In this tutorial, we will explore some of the most critical Python libraries for scientific computing on images, by walking through fundamental bioimage analysis applications of linear filtering (aka convolutions), segmentation, and object measurement, leveraging the napari viewer for interactive visualisation and processing. We will also demonstrate how to extend these concepts to bigger-than-RAM images using Dask.

The size of datasets in diverse industries keeps increasing by the minute and, luckily, for data professionals using Python as their day-to-day tool, there is Dask, a library for large-scale data analytics and data preprocessing. This tutorial takes advantage of dask and other libraries to teach you diverse techniques for data exploration, hypothesis testing, and dashboard creation using large datasets. In addition, this tutorial uses a top-down approach, meaning, in each of the major sections you will start with the results first and then work your way backwards through the large-scale data analytics process.

Code is now a critical part of almost all research, whether for communication or for data collection and analysis. Unfortunately, producing reliably error-free code is an open problem, and result-altering bugs are regularly found (and usually fixed) in everything from preprints to foundational open source packages.

I believe there is a core, fixable problem: writing tests is tedious, difficult, and only covers edge cases we know to test for. The solution? Use tools that write tests for us!

Crucially, this isn't a pipe-dream: it's a proven technique that the scipy ecosystem has already started to use, and just needs to scale up. Hypothesis - https://hypothesis.readthedocs.io/ - is basically a superhuman experimentalist. You write a test function and describe what inputs it should pass for; and the Hypothesis engine searches for a falsifying example.

This process often tries inputs that I wouldn't think of, such as Numpy arrays with a sizezero dimension or a "signalling" NaN represented by a non-standard bit-pattern; and as a result it regularly uncovers bugs that users *and authors* didn't know were possible.

The tutorial is designed for researchers and software engineers who regularly write code that other scientists rely on. You might be 'the Python person' in your lab; a core developer of one of the core SciPy or PyData libraries, or an enthusiast looking for a valuable way to contribute to that ecosystem.

Attendees are expected to be familiar with Numpy and Pandas, as well as with traditional unit testing (i.e. pytest or unittest), and ideally with writing traditional tests for numerical or data-centric code. You don't need to be an expert in any of these, but the tutorial will have plenty of content to engage those who are!

If this sounds too good to be true, Hypothesis has quickly found bugs in approximately everything it has even been pointed at, including Numpy, Astropy, Xarray, CPython, and of course Hypothesis itself. If you already have a large stack of bug reports, maybe ask that it only be used for new features!