Cheuk Ting Ho

When handling a large amount of data, memory profiling the data science workflow becomes more important. It gives you insight into which process consumes lots of memory. In this talk, we will introduce Mamray, a Python memory profiling tool and its new Jupyter plugin.

In this talk, we will be exploring what memory profiling is, and how it can help with data science work. We will start the talk with a basic explanation of how Python arrange memories for various objects. This lays the foundation explanation of why we need a special tool to memory profile Python programs. Then we will be going through a data science use case where we memory profiles some part of the process with the Memray Jupyter plug-in. This would be a use case that a data science practitioner or learner would be familiar with and they can see how memory profiling could be useful. We will then explain how to interpret the frame diagram in Memray, a commonly used diagram in memory profiling to understand how much memory a process and its sub-process uses. This is something that for a new user, it could be hard to understand and not know what to look into. From this example, audiences can see what they can learn about from the frame diagram. ## Goal This talk is for data scientists, learners or anyone who is interested in memory profiling their Python program. Although the talk will be using a data science use case as an example, the explanation and the tool can be expanded to be used in any Python program. However, for data science practitioners and learners who have been using Python to process data, this may be a step forward for them to improve their data workflow and prevent memory leaks from their programs. ## Outline - Introduction (5 mins) - Why we need a special tool for memory profiling (5 mins) - How to use Memray in Jupyter notebook (5 mins) - Demonstration for using Memray in data science work (5 mins) - How to interpret a frame diagram (5 mins) - Conclusion (5 mins)

After learning doing in Python, we started multiple Python or Data Science projects. Dependency management becomes a skill that we need to avoid requirement conflicts amount projects. In this talk, we will learn how dependencies management tools work and how to choose the right one to use.

In this talk, we will use venv, the environment managing tool that come with CPython, and conda, the tool that is popular among data science partitioners to decipher how environment and dependency management works. First, we will go through some basic knowledge of how Python "sees" your package and know where to import them when needed. Then, we will make the audience aware that, to avoid the requirement conflict amount different projects, we need a new set of environments for each project. Then, we will see how tools like venv and conda can create a new set of environments. We will cover both the practical aspect - how to use the tools and which one to use, and the theoretical aspect - the inner working of the tools and why they are different. This will make the audience get familiar with some tools that are at their disposal and inspire them to start using them for their work. This talk is for beginner Pythonistas or data scientists who started to use Python professionally and those who are curious to know how the environment and dependency management tools work. ## Outline - Introduction (5 mins) - How Python sees your packages (5 mins) - Why we need separate environments (1 mins) - How venv create new environment (2 mins) - How conda create new environment (2 mins) - Comparing venv and conda - which one to use (5 mins) - Conclusion (5 mins)

Many Python conferences have "sprints" included in their conference schedule. In this talk, we will explain what is it for the newcomers, why is it important to the open-source community, and as an organizer and maintainer how can you help this "sprints" movement.

We all know the famous quote in the Python community "I came for the language, but I stayed for the community" by Brett Cannon. But I would like to borrow it and say "I came for the conference, but I stayed for the sprint". As one of the organizers of Mentored Sprint, maintainer of an open-source project and long term sprint participant, I would like to explain to the people who are new to the community, what is a "sprint"? Also, I would like to discuss the goal of sprints from different points of view. First of all, I would like to introduce the concept of sprints. In software development, we have sprints to achieve some goals, whether it's fixing a bug or adding a new feature. But what about an open-source sprint? Are we going to achieve "something" in the sprint? What is this "something"? Then I would like to talk about what is the "benefit" of participating in a sprint as a maintainer? Will you get useful help in the sprint? Is it worth the time? What is the expectation of bringing your project into the sprint? In the end, I would like to add my opinion on why sprints, especially mentored sprint is so important to the open-source community and makes the community that we all would like to "stay for".

Ok, I lied, I still write tests. But instead of the example-based tests that we normally write, have you heard of property-based testing? By using Hypothesis, instead of thinking about what data I should test it for, it will generate test data, including boundary cases, for you.

In this talk, we will explore what is property-based testing and why it can do a lot of heavy lifting in writing tests for us. As a contributor, I will introduce Hypothesis, a Python library that can help perform property-based tests with ease. At the start of the talk, we will understand the power of property-based tests, what is it, how is it different from what we “normally do” - testing by example, and why is it useful in testing our code. This will be followed by demonstrations using Hypothesis. With a few examples, we will have a glimpse of how to create strategies - recipes for describing the test data you want to generate. After that, we will also explore the Ghostwriters in Hypothesis which will actually write the test for you. This talk is for Pythonistas who are new to property-based testing and found thinking of what parameters to use for testing a difficult task. This talk may provide them with a new approach to writing tests, which will be more efficient for some cases.

Since the announcement of PyScript, it has gained lots of attention and imagination about how we can run applications of Python in the browser. Out of everything that I have come across, most of the use cases are data visualisation. Let's see how we can up our data viz game with PyScript.

At the beginning of this talk, we will go over what PyScript is and how to use it: no need to install it, just put a CDN URL in an HTML file. This part will cover some basic knowledge about it, why is it unique and why it can revolutionize data visualisation, for those who have not heard much about PyScript. After that, we will go over 3 examples: one interactive data plot, one interactive map visualization and one integration with D3. Besides showing the result, we will also have a deeper look at the code, the key to making similar visualization so you can learn a few tricks to make your own PyScript data visualization as well. This talk is for anyone who needs a simple and no fuzz way to present data findings and visualization. As PyScript runs only on the browser, it will be an optimal way to share data findings through visualization.

Live Stream: https://youtu.be/oIWBW2usic8 The best way to learn Python - for the absolute beginners and improvers by Cheuk Ting Ho, Developer Relations Lead at TerminusDB, Python Software Foundation Fellow, organizer of EuroPython

In the 2021 StackOverflow survey, Python remains the most wanted programming language (5 years in a row). As a Pythonista, I have been asked many times about what is the best way to learn Python. There are many resources, especially online, free or paid, that is available. From working as a data scientist to become an open-source developer, I would like to sum up my Python learning experience to make recommendations to different groups of people. Whether you are an absolute beginner with no programming experience, a beginner with other programming language experience or a beginner in Python who wants to get better and know more about the language, you will get an idea of how to use Python at a whole new level.