Stefanie Molin

Are you curious how linters and code formatters work? Would you like to build your own code analyzer? In this talk, I will walk you through the process of doing so using a data structure called an abstract syntax tree, which represents your code's structure and allows you to access its components.

You’ve likely used a tool like black, flake8, or ruff to lint or format your code, or a tool like sphinx to document it, but you probably do not know how they accomplish their tasks. These tools and many more use Abstract Syntax Trees (ASTs) to analyze and extract information from Python code.

Statistics do not come intuitively to humans; they always try to find simple ways to describe complex things. Given a complex dataset, they may feel tempted to use simple summary statistics like the mean, median, or standard deviation to describe it. However, these numbers are not a replacement...

Maintaining code quality can be challenging, no matter the size of your project or number of contributors. Different team members may have different opinions on code styling and preferences for code structure, while solo contributors might find themselves spending a considerable amount of time making sure the code conforms to accepted conventions.

Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make this process easier.

Relying solely on simple summary statistics like the mean, median, or standard deviation is not enough to describe complex data. Come and see why this is the case and learn what it takes to translate research into an open-source library.

Statistics do not come intuitively to humans; they always try to find simple ways to describe complex things. Given a complex dataset, they may feel tempted to use simple summary statistics like the mean, median, or standard deviation to describe it. However, these numbers are not a replacement...

The open source community is all about giving back and learning from one another. No matter how small, every contribution is valuable. And everyone can contribute something with a little bit of help. The hardest part is finding something to work on that fits your interests and skills. In this...

The open source community is all about giving back and learning from one another. No matter how small, every contribution is valuable. And everyone can contribute something with a little bit of help. The hardest part is finding something to work on that fits your interests and skills. In this...

Data visualization is essential for anyone working with data, but sometimes it can be difficult to create impactful visualizations in Python. In this workshop, we will move beyond the plotting basics and explore how to make compelling static, animated, and interactive visualizations.

Relying solely on simple summary statistics like the mean, median, or standard deviation is not enough to describe complex data. Come and see why this is the case and learn what it takes to translate research into an open-source library.

This talk is for anyone that has wanted to contribute to open source, but didn't know where to start or thought they wouldn't be able to do it with their skill set or time. Come learn how to get started!

Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to manipulate and visualize it. This session will teach you to effectively use pandas to make this process easier.

Data visualization is essential for anyone working with data, but sometimes it can be difficult to create impactful visualizations in Python. In this workshop, we will move beyond the plotting basics and explore how to make compelling static, animated, and interactive visualizations.

Data often doesn’t come in the best format for analysis, and understanding it enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas to make this process easier.

Data visualization is essential for anyone working with data, but sometimes it can be difficult to create impactful visualizations in Python. In this workshop, we will move beyond the plotting basics and explore how to make compelling static, animated, and interactive visualizations.