Agenda

The integration of Artificial Intelligence (AI) with data visualization is transforming the way we interpret and communicate data. This presentation explores the synergy between AI and Python, demonstrating how these technologies can enhance data visualization, uncovering deeper insights and fostering data-driven decision-making. In this session, we will delve into the use of Python's extensive ecosystem, including libraries such as Matplotlib, Seaborn, Plotly, and Bokeh for creating compelling visualizations. We will showcase how AI techniques, such as machine learning & deep learning can be seamlessly integrated with these visualization tools to analyze and present complex datasets in a more insightful manner.

OpenTelemetry Python instrumentations may seem indistinguishable from magic: they can be bootstrapped from your installed dependencies, they are able to patch your code without even noticing and most often they work out of the box automatically! Fortunately there's no magic spell involved and they are mostly the combination of not well known Python features, standing on the shoulders of powerful libraries and work done by the community to improve the reach and the quality of the code. Let's dig a bit into the code to see what's inside the black box.

In today’s world, users are increasingly using AI tools (ChatGPT, Copilot, etc) to solve their coding problems. These AI tools using Large Language Models (LLMs) are quite good at coming up with answers for well-established technologies. However, when it comes to new or ever-changing technologies, the LLMs tend to hallucinate. In such cases, it would benefit to have a custom AI bot trained on the technology documentation. In this talk, I cover how to build a chatbot that can answer questions using the existing documentation and code snippets using the Retrieval Augmented Generation (RAG) technique.

In this talk, we will present what is Retrieval Augmented Generation (RAG) in AI applications. We'll see how to use LangChain, an open-source framework, to build such RAG applications.

Join me for a practical look at cognitive load—what it is, its different types, and how it affects software development. In this session, I'll explore real-life examples where the cognitive load can be high and share some tips on how to manage it effectively. This talk is perfect for developers at any level who want to work smarter and reduce stress.

In this talk "Creating the Next Generation of Billionaires" I will build on the previous parts and present the following: Impact of Introducing Computer Programming in Schools: It has been a few fascinating years since computer programming was introduced to these young people and indeed young people worldwide. We have observed the impact of these curricula on young people and society, the effectiveness of their delivery methods, and the principles guiding their formation. Also, recent ground-breaking innovations such as ChatGPT and AI have served to further propel young people’s interests in these matters. Reassessing the Curriculum: Given these observations and changes, it is an opportune moment to reassess the current curriculum. This includes evaluating the content being taught, identifying gaps, and adjusting the emphasis on certain topics to align with recent advancements and future economic, scientific and societal trends. Proposed Curriculum Modifications: This talk will outline proposed modifications and additions to the curriculum, providing the rationale behind each suggestion. We will advocate for comprehensive instruction in program testing, emphasising that students' creations must be fit for purpose, and these principles should be introduced early on. Additionally, we will discuss the need for proper software infrastructure to support the teaching of computer programming and explore effective methods for educating students about AI. Also, we should recognise that it is of paramount importance that proper investment and monies are allocated for such important activities. In conclusion, we feel we are now in a good position to develop a successful and enjoyable curriculum which serves both young people and society properly and well in the 21st century.

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 for visualizing the distribution.

To illustrate this fact, researchers have generated many datasets that are very different visually, but share the same summary statistics. In this talk, I will discuss Data Morph, an open source package that builds on previous research using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points. I will showcase how it works, discuss the challenges faced during development, and explore the limitations of this approach.

Selecting the optimal text embedding model is often guided by benchmarks such as the Massive Text Embedding Benchmark (MTEB). While choosing the best model from the leaderboard is a common practice, it may not always align perfectly with the unique characteristics of your specific dataset. This approach overlooks a crucial yet frequently underestimated element - the tokenizer.

We will delve deep into the tokenizer’s fundamental role, shedding light on its operations and introducing straightforward techniques to assess whether a particular model is suited to your data based solely on its tokenizer. We will explore the significance of the tokenizer in the fine-tuning process of embedding models and discuss strategic approaches to optimize its effectiveness.

Package management in Python is a delicate topic. Especially for new developers, it's easy to get locked into a suboptimal or inadequate system if relying only on a very basic pip/requirements+txt setup. In this talk, we'll go over pip itself as a package installer, its advantages and shortcomings, the role of virtual environments in project setup and development, the reasons why you should probably use something else to handle your project dependencies, and a couple of good options to do so.

Raw Django doesn't take the first places when comparing the performance of Python web frameworks. However, it can be pretty fast if we identify the bottlenecks and find ways to avoid them. Comparing performance and implementation complexity before and after gives us an understanding of which features should be implemented and what can be skipped.

Unlock the power of well-documented code! Join me for a quick dive into Python documentation essentials—learn about Docstrings, Sphinx, reStructuredText, and embracing a docs-as-code approach with different markup languages. Keep your code usable and maintainable. Let’s document smarter, not harder!

In this talk, we'll discuss the concept of AI Agents in general and what is needed to realize those agents. We will then dive deeper into how they can be applied specifically to coding to make your everyday life easier. And we'll finish off with some examples of the ways you can incorporate such agents into your workflow already right after this talk.

Pattern matching has been with us since Python3.10. Since the introduction of the match-case statement, we've got a powerful and elegant tool to control the flow of the program. This talk is to showcase a real-world scenario of handling different messages coming from a broker, using match-case and Pydantic.

In this talk, we'll delve into the performance comparison of HTTP clients in Python and Rust, profiling HTTP client performance & analyzing time consumption. We'll explore what are the main factors affecting HTTP client performance and cover differences between async and blocking clients. We'll also examine the benefits of multithreading and concurrency. Who will emerge victorious? Python, known for simplicity, or Rust, acclaimed for speed?

Podczas mojej prezentacji omówię kluczowe zasady, które należy stosować, pisząc kod przeznaczony na produkcję. Skupię się na podstawowych aspektach, które są niezbędne dla początkujących programistów, aby ich praca była bezpieczna, łatwa w utrzymaniu, skalowalna i bezproblemowa w procesie wdrażania zmian. Zignorowanie tych zasad może prowadzić do poważnych problemów z bezpieczeństwem aplikacji, trudnościami w jej utrzymaniu, ograniczoną skalowalnością oraz komplikacjami podczas wdrażania zmian. Moim celem jest przekazanie uczestnikom praktycznych wskazówek, które pomogą im unikać typowych błędów i tworzyć wysokiej jakości oprogramowanie gotowe do produkcji.

Join us for a deep dive into the dynamic duo of serverless development: Lambda Powertools and Pydantic. Discover how these powerful tools seamlessly integrate to streamline your serverless workflows, enhance reliability, and boost productivity. From automatic request validation to robust error handling, learn how leveraging Lambda Powertools with Pydantic transforms your development experience. Say goodbye to tedious boilerplate code and hello to efficient, scalable serverless solutions. Plus, as a cherry on top, we'll demonstrate how to harness the benefits of static typing in boto3 for added developer friendliness. Whether you're a seasoned developer or just diving into serverless architecture, this session promises actionable insights and practical tips to supercharge your projects. Don't miss out on unlocking the full potential of Lambda Powertools and Pydantic – your ultimate allies in the world of serverless development.

At Corner Case Technologies, we offer clients a service to migrate from on-premises infrastructure to AWS for various purposes, including high availability, cost optimization, and maintainability. Each migration is unique and necessitates thorough preparation for planning, execution, and subsequent development.

In this talk, I will present a specific use case of a migration that we conducted, with a particular focus on the lessons learned during the planning and execution phases.

Explore the intersection of physics simulators, control theory, machine learning and visualization in Python. This talk dives into the capabilities of libraries like SOFA and MuJoCo, showcasing their applications in robotics and medical surgery simulations, and demonstrating how to model and analyze real-world systems using Python tools. We will see how to build simulation environments, control them with reinforcement learning and control theory, and visualize the results for more interactive scientific explorations with replayable enbironments.

DevEx to podejście zwiększenia produktywności w wytwarzaniu oprogramowania, które nie polega na machaniu Scrumbatem czy szybszym tworzeniu zadań w Jirze, a koncentruje się na tym, czego doświadcza programista czy programistka w codziennej pracy.

DevEx ma trzy filary: stan przepływu (ang. flow), skracanie pętli zwrotnych i ograniczanie przeładowania kognitywnego (ang. cognitive load).

W trakcie prezentacji przyjrzymy się, co możemy wykorzystać w Pythonie żeby pracować wydajniej i pewniej. Nie zabraknie asystentów AI i innych nowoczesnych narzędzi, które można wykorzystać na każdym etapie tworzenia oprogramowania.

Niczym David Attenborough postaram się Was zabrać do regionów Pythona, gdzie rzadko zaglądają zwykli programiści. Przyjrzymy się temu jak obiekty przechowują swoje dane. Poza standardowym dict i vars() spojrzymy na nieco bardziej kolczaste slots. Postaramy się wyśledzić ewolucyjne źródła type hintów. Poza "dunder methods" nie zabraknie również odrobiny filozoficznej zadumy (domknięcia) i czegoś dla miłośników mocnej akcji (podglądanie danych binarnych).

Słowniki są filarami Pythona. Znając wewnętrzną implementację, możemy wyjaśnić silne strony i ograniczenia tej struktury danych. Prezentacja o tym, jak Python wykorzystuje tablice mieszające w słownikach.

Co się dzieje, gdy próbujemy pobrać wartość danego klucza. W jaki sposób Python rozmieszcza poszczególne w klucze w tablicy i co w przypadku gdy następuje kolizja haszy.

Podczas wystąpienia zajrzymy "pod maskę” Pythona i rozważymy, dlaczego słowniki dict w Pythonie działają tak, jak działają.

Podczas tej prelekcji omówimy tworzenie inteligentnych chatbotów z wykorzystaniem RAG (Retrieval-Augmented Generation), od teorii po praktyczne demonstracje działania systemu.

Jupyter Notebooks are widely used by data scientists and machine learning engineers. They're easy to use and seem perfect for research focused tasks. However, their advantages can quite easily become a huge problem as the projects grow and/or reach production deployment phase.

The instruction following and in-context learning capabilities of LLMs make them suitable for tackling many NLP tasks. In this talk, we will introduce Scikit-LLM, a rapidly growing, beginner-friendly library that abstracts the complexity of working with LLMs by providing a scikit-learn compatible API. We will showcase how Scikit-LLM can be utilized for solving text classification and text-to-text tasks, and will delve deeper into various methods to improve the model performance, such as prompting strategies and fine-tuning.

How to boost developers' efficiency in today's era? Supercharge them with advanced AI-based tools, automation, and streamlined workflows. Utilise generative AI for code completion, debugging assistance, and intelligent testing, or implement auto-generated documentation for your projects. These approaches help developers write code faster, minimize errors, and deliver high-quality software more effectively (and enjoyably for the devs themselves) – as we will demonstrate during this session.

The scope of this presentation is to show an overview of current state of python packaging and pinning requirements in Python world. Provide an overview of the differences between pip, uv, conda, mamba, provide brief overview of build backends like setuptools, hatchling and poetry and how it differs from application management using poetry, rye, hatch or pixi.

Python comes with some fantastic language design, a rich eco-system of libraries and packages, and a friendly learning curve. This can fool you into thinking that it's a great language for making applications for users; while it certainly can be, deploying and running a python application to the cloud is not as trivial as you might think. In this talk we'll look at all the hurdles you will run into if you were to deploy python applications at scale. Through these problems you'll get a better understanding why a lot of platforms kind of suck for python, how you can cope with that, and how you can contribute to improving the situation.

This presentation will provide a comprehensive overview of concurrency in Python. We will briefly cover the good practices and common pitfalls, but we'll also:

• take a closer look at the long-standing battle with a feature at the heart of language,
• find ways to escape its limitations,
• explore the concurrency model that landed Apollo 11 on the Moon.

Developer tools power many LLM-based chat and Retrieval Augmented Generation applications today. However, there is a non-trivial knowledge barrier for entrants that could hinder developer experience. Our discussion intends to offer actionable insights into building and maintaining generative AI solutions in a secure and economical way, thereby improving the developer experience in this Generative AI wave.

By relying on the OpenAPI™ industry standard, we want to free users from the intricacies of consuming REST APIs. Discover how this tool simplifies data ingestion and frees users to focus on what truly matters - the actual data. Let's untangle the web of complexities and make data integration an enjoyable journey!

Discover how to combine Blender's 3D animation with Python's graphing libraries for dynamic data storytelling. Learn to create immersive 3D models and enhance them with detailed 2D plots in this talk, unlocking new ways to present data insights in education and professional projects.

We generate a sentiment index based on Google SVI for random keywords from dictionaries regarding business and finance terms and common words, and regress it against 2 contrasting portfolios of randomly chosen S&P500 companies; one with large, old, profitable, and dividend yielding firms, and the other exact opposite, to see resonates to people's searches more. The results are quite interesting.

We'll explore the vast landscape of data sources, ranging from native Python datasets, open datasets, and data APIs to specialized repositories, torrents, and data marketplaces. Additionally, we will discuss various techniques and strategies for obtaining data, such as the European Regional Development Fund, web scraping, and data augmentation.

A deep dive on all the different ways your Python code trusts external organisations, why they do that, and how you can leverage it to make your code more secure.

Czy zdarzyło Ci się, że pisząc kod zastanawiałeś się czy kod jest wystarczająco czysty? Czy miałeś kiedyś przemyślenia, że niektóre zasady z Zen Pythona to przerost formy nad treścią? A może ktoś Ci wmówił, że SOLID już się nie aplikuje?

Przyjdź i przekonaj się, że deskryptory, to wcale nie jest magia. Zobacz, jak przy mądrym użyciu dunder methods osiągnąć kod, który czyta się jak prozę. Sam oceń, czy w PYTHON czysty, znaczy SOLIDNY, a SOLIDNy to taki, który wpisuje się w dna i zen pythona

There is an entire spectrum of tools and processes to achieve application monitoring, observability, and telemetry for all your running apps. Where monitoring is an action you take, observability is an overall function or property of a system. The more you can observe a system, the more you can understand its complex ways. We no longer have to assume that various integrated services are a “black box” that we cannot see into.

In this tutorial, we go through building a simple Python app and introduce “code instrumentation” as part of building observability and understanding the app's performance. We will finally see how this instrumentation is helpful to the developers and how can we visualise the final system using tools like Grafana etc.

The workshop, "Fundamentals of Pandas and Polars in Scientific Applications" is tailored for beginners interested in the intersection of biology and robotics data using Python. This condensed and intensive session will delve into the Pandas and Polars libraries, emphasizing their applications in biological data manipulation and robotic signal processing. Participants will engage with essential data analysis concepts including data wrangling, cleaning, and transformation, alongside fundamental statistical methods. The session will feature hands-on exercises based on real-world datasets from biological research and robotic applications, enhancing the practical understanding of data analysis. By the conclusion of the workshop, attendees will be adept at leveraging Python for analyzing and visualizing complex biological and robotics data, preparing them for further scientific explorations.

Generative AI is a very hot topic nowadays. And rightfully so, as with the recent developments in the area, quite a lot of new business applications have been enabled or made significantly easier in the implementation. AI is currently also getting more accessible than ever, with some models becoming small enough to run on one personal device while remaining quite capable, or with all 3rd party models APIs (such as OpenAI’s) available. And with the latter, while the ML experience/knowledge and hardware still help, the entry barrier is lowered enough to allow pretty much any programmer to start experimenting with these exciting models. Python is an ideal language for that – as not only it is a very good scripting “glue” language, but also most of the AI ecosystem and libraries are built with support for it. During this workshop, we will take advantage of that fact (as well as APIs making these models accessible) to start the adventure with generative AI, and build our own chatbot/assistant with conversational capabilities and the ability to interact with custom data.

Hello!

I'm Michal, and I'm reaching out because you enrolled in the workshop "Harnessing GenAI power with your own custom chatbot" which we will conduct today at PyCon PL together with my colleague Kamil. We wanted to provide some organizational information that would allow you to prepare to prepare beforehand and setup some things in advance in order to streamline things and have more time for the "meaty" parts ;)

The workshop is happening today (on Friday), in 3:05PM - 5:35PM slot (starting right after dinner break). Here you can find the repository with the workshop materials: https://github.com/datarabbit-ai/chatbot-workshop

We have an earnest request for you to follow up the setup steps from the README before the workshop. All of them are rather easy steps, which essentially come down to setting up the virtual environment with all the dependencies – but they may take a while (especially if everyone will start to download it using one WiFI ^^). Other than that you're free to explore the overall content on your own – if you're curious and have time before the workshop – go for it ;)

During the workshop, for simplicity purposes, we will utilize some external 3rd party API – here are some API key that will allow us to utilize it:

• sk-svcacct-99DOuA8Cz0OvzdpsL0Nz24_e52EbID1XN2BHERm6bHPPMXtr_P-rzd_Hm_BD5ZpWmB67UCT3BlbkFJgI29br4vBgI4jewb67msmSIL4-1EEMOZg8Kw4K9WcarYp9FzkyKsOZFBB-EzxUC505wjcA (note: it was created for the purpose of the workshop and is budget-constrained – but don't try to brute forcingly burn all the allocated funds please ^^)

Looking forward to seeing you this afternoon! Michal and Kamil

Modern deep learning frameworks such as PyTorch and TensorFlow are highly complex. As a result, many data practitioners find themselves in a challenging position. Despite having a solid understanding of deep learning theory, as well as practical experience with high-level APIs like torch.nn or tf.keras, they often lack the intuition about the actual implementation of these frameworks.

This workshop is bridging the gap by demonstrating how to construct a basic deep learning framework in less than two hours. We will begin with creating a simple automatic differentiation engine, followed by more advanced elements such as models/modules, and optimizers. By the end of the workshop, participants will have the skills to train a multi-layer neural network using the framework they developed.

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.

Pandas makes it possible to work with tabular data and perform all parts of the analysis from collection and manipulation through aggregation and visualization. While most of this session focuses on pandas, during our discussion of visualization, we will also introduce at a high level Matplotlib (the library that pandas uses for its visualization features, which when used directly makes it possible to create custom layouts, add annotations, etc.) and Seaborn (another plotting library, which features additional plot types and the ability to visualize long-format data).

This workshop is designed for those who are new to MongoDB and are eager to learn how to incorporate it into their Python projects.

In this hands-on workshop, you will learn the basics of MongoDB, a leading NoSQL database, and how to use it with Python. We will start with an introduction to NoSQL databases and where they shine over traditional SQL databases. We will then dive into the specifics of MongoDB, including its architecture, data model, and the philosophy of “documents”.

You will learn how to perform CRUD (Create, Read, Update, Delete) operations. We will also cover how to model data effectively using MongoDB’s flexible, JSON-like documents.

By the end of this workshop, you will have a solid understanding of MongoDB and how to use it in your Python applications. You will be equipped with the knowledge and skills to start using MongoDB for managing data in your own projects.

No prior experience with MongoDB is required, but a basic understanding of Python is necessary to get the most out of this workshop.

Preparation: Participants are required to bring their own laptops with the following software pre-installed: Python 3.10 or later MongoDB Community Server or create account on MongoDB Atlas

Join us in this exciting workshop and start your journey in the world of NoSQL databases with MongoDB!

Exploring a new Python feature called Per-interpreter GIL. It allows to use multiple cores in a single Python (CPython) process.

Podczas warsztatu zapoznamy się z TDD (Test Driven Development) i użyjemy go do stworzenia prostego mikroserwisu HTTP. Rozpoczniemy od pokazania, jak napisać pierwszy test, nawet bez istniejącego kodu.

Podstawowa znajomość FastAPI i pytest pomoże skupić się na samym TDD, ale nie jest niezbędna. Przynieś swój komputer z zainstalowanym IDE, Python 3.10 (lub nowszy), poetry, GIT i pragnienie wiedzy.

Nauczymy się następujących rzeczy:

• podstawowe pojęcia i zalety TDD
• jak wybrać pierwszy test i go zaimplementować bez istnienia innego kodu
• jak używać TDD do stopniowego projektowania mikroserwisu
• podstawy pytest i FastAPI
• jak refaktoryzować kod, aby był czysty i łatwy do utrzymania

Na koniec tego warsztatu będziesz mieć lepsze zrozumienie TDD i jak wdrażać je w codziennym procesie programowania.

sktime is the most widely used scikit-learn compatible framework library for learning with time series. sktime is maintained by a neutral non-profit under permissive license, easily extensible by anyone, and interoperable with the python data science stack.

This workshop gives a hands-on introduction to sktime, for common time series learning tasks such as forecasting, and an overview of different model categories, including classical statistical, ML, deep learning, and foundation models.