"Easy Python": lies, damned lies, and metaclasses
Grigory Petrov, Maxim Danilov
Best Practice, Coding / Code-Review, Development Methods

top-10 Python complexities and how they are required to fight the "software complexity problem" in big projects

5 Steps to Speed Up Your Data-Analysis on a Single Core
Jonathan Striebel
Data Engineering, Performance

Your data analysis pipeline works. Nice. Could it be faster? Probably. Do you need to parallelize? Not yet. Discover optimization steps that boost the performance of your data analysis pipeline on a single core, reducing time & costs.

5 Things You Want to Know About AI Adoption in the Enterprise
Alexander CS Hendorf
Architecture, Best Practice, Business & Start-Ups, Corporate, Diversity & Inclusion

All one needs is strategy, skill and resources to make digitalization and AI happen. So why is everything taking so long? 5 Things You Want to Know About AI Adoption in the Enterprise.

`python-m5p` - M5 Prime regression trees in python, compliant with scikit-learn
Sylvain Marié
Algorithms, Predictive Modelling, Science

`python-m5p` is an implementation of the M5P algorithm compliant with scikit-learn.

A data scientist's guide to code reviews
Alexandra Wörner
Coding / Code-Review

Code reviews apply to all data science work - you sometimes just need to tweak them a bit. Let me show you when and how as well as what makes a fruitful code review.

Advanced Django ORM
Bas Steins
Databases, Django

Leverage the potential of Django ORM to write complex queries, optimize performance and have fun with constraints

Battle of Pipelines - who will win python orchestration in 2022?
Jannis Grönberg
Architecture, Data Engineering, DevOps

You struggle choosing the right #orchestration tool in #Python ? Join this #PyCon talk about when it's best to use #Kubeflow, #Airflow or #Prefect and learn how to automate your #data #pipelines and #ML workflows. #DataScience #dataengineering #DevOps #MLOps

Biases in Language Models
Diversity & Inclusion, Ethics (Privacy, Fairness,… ), Natural Language Processing

Study of gender biases in popular language models and debiasing model techniques

Building a Sign-to-Speech prototype with TensorFlow, Pytorch and DeepStack: How it happened & What I learned
Steven Kolawole
Computer Vision, Neural Networks / Deep Learning

Building an E2E working prototype that detects sign language meanings in images/videos and generate equivalent voice of words communicated by the sign language, in real-time, won't be completed in a day's work. Here I'd explain how it happened and what I learned in the process.

Building an ORM from scratch
Jonathan Oberländer, Patrick Schemitz
Art, Databases

From an empty Python file to a fully-featured ORM in 45 minutes

But this is an OAuth, is it not?
Sara Jakša
APIs, Backend

OAuth simplified and secured third-party integrations for the end user. But for the developer of the integration, it can still present some friction. This talk talks about examples of real-life problems that were encountered by implementing multiple OAuth integrations.

Can you Read This? (Or: how I Improved Text Readability on the Web for the Visually Impaired)
Asya Frumkin
Algorithms, Computer Vision, Neural Networks / Deep Learning

I will explain my approach of detecting texts on top of an image background that are unreadable to people with visual impairment. I will explain the challenges I. encountered when using different OCR architectures for this task and talk about the solution I came up with.

Challenge Accepted - How to Escape the Quicksand While Engineering a Computer Vision Application
Bettina Heinlein
Computer Vision

Leveraging problem-solving strategies for challenges in building Computer Vision applications and beyond, illustrated with a recent Computer Vision project.

Come as you are: Transitioning from Science to Data Science
Dr. Hannah Bohle
Career & Freelancing

Come as you are: Transitioning from Science to Data Science. How to find your first job in industry after leaving academia.

conda-forge: supporting the growth of the volunteer-driven, community-based packaging project
Wolf Vollprecht, Jannis Leidel, Jaime Rodríguez-Guerra
Community, Packaging, Python - PyPy, Cython, Anaconda

How does the conda-forge packaging community work, what is its relationship to conda and PyPI and how can everyone package software with it?

Creating 3D Maps using Python
Martin Christen
GIS / Geo-Analytics

Create 3DMaps anywhere on the planet using Python and OpenData

Data Apis: Standardization of N-dimensional arrays and dataframes
Stephannie Jimenez Gacha

Introduction to the consortium of Data APIs, where we will be presenting our motivation, objectives and progress of the standardization process after one year of activity.

deepdoctection - An open source package for document intelligence
Janis Meyer
Computer Vision, Natural Language Processing

deepdoctection is a Python package that enables document analysis pipelines to be built using deep learning models.

Demystifying Python's Internals: Diving into CPython by implementing a pipe operator
Sebastiaan Zeeff
Python - CPython new features, Python fundamentals

Do you want to dive into the CPython Source Code but feel a bit overwhelmed? Watch Sebastiaan Zeeff demystify CPython's Internals by taking you through the implementation of a new operator.

Detecting drift: how to evaluate and explore data drift in machine learning systems
Emeli Dral
Best Practice, Data Visualization, Statistics

When ML model is in production, you might encounter data and prediction drift. How exactly to detect and evaluate it? I'll share in this talk.

Do I need to be Dr. Frankenstein to create real-ish synthetic data?
Data Engineering, Ethics (Privacy, Fairness,… ), Governance

Synthetic data not only address the privacy needs but also offer workaround for unprecedented situations. This talk introduces their different types, the options for their generation, and how you don't need to be a mad scientist to make realistic synthetic data

Do we really need Data Scientists?
Dr. Setareh Sadjadi
Career & Freelancing, Community

Is Data Science really cooling down? Do we need Data Scientists? What for?

Easy and flexible imaging with the Core Imaging Library
Vaggelis Papoutsellis, Dr. Jakob Sauer Jørgensen
Algorithms, Big Data, Math

Core Imaging Library is an open-source, object-oriented Python library for inverse problems in imaging developed by the UK academic network CCPi.

Efficient data labelling with weak supervision
Maria Mestre
Data Engineering, Data Visualization, Natural Language Processing

Data labelling should not be a waterfall task. Label your data significantly faster with weak supervision (

Fast native data structures: C/C++ from Python
Stefan Behnel
Big Data, Parallel Programming / Async, Python - PyPy, Cython, Anaconda

Need fast data access in Python? Use native data structures with Cython!

Financial Portfolio Management with Deep Reinforcement Learning
Neural Networks / Deep Learning, Simulation, Time Series


Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, Reproducibility

Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit

Forget Mono vs. Multi-Repo - Building Centralized Git Workflows with Python
David Melamed
Cloud, Coding / Code-Review, DevOps, Security

No need to reinvent the CI/CD wheel for every service - learn how to build centralized git workflows for all your repos in Python.

Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.
Dom Weldon
Art, Social Sciences, Theory

Forget ‘web 3.0’, let's talk about ‘web 0.0’. A brief history of the Internet, and the World Wide Web.

Fundamentals of relational databases
Katharina Rasch

Somewhat comfortable with using SQL to access data, but curious to know what happens behind the scenes when you send off your query?

Grokking LIME: How can we explain why an image classifier "knows" what’s in a photo without looking inside the model?
Kilian Kluge
Computer Vision, Neural Networks / Deep Learning, Transparency / Interpretability

How can LIME explain machine-learning models without peeking inside? Let's find out!

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.
Florian Wilhelm
Math, Predictive Modelling, Statistics

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.

How to build a Python-based Research Cloud Platform from scratch
Andre Fröhlich
Architecture, Business & Start-Ups, Use Case

This talk will present the journey of a quantitative asset manager from an outdated (non-Python) onPrem research setup to a modern Python-centric cloud research platform. We will examine the requirements and challenges associated with the project and present how we navigated find

How to deal with toxic people
Gina Häußge
Best Practice, Community

As an open source maintainer, sooner or later you'll encounter ungrateful, entitled or outright toxic people who can be a real drain on your motivation and general mental health. Here are some coping strategies that work for me!

How to Find Your Way Through a Million Lines of Code
Jürgen Gmach
Best Practice

Scared of a new project? @jugmac00 will show you "How to Find Your Way Through a Million Lines of Code"

How to Trust Your Deep Learning Code
Tilman Krokotsch
Best Practice, Neural Networks / Deep Learning

Write unit tests and learn to trust your Deep Learning code again.

Impact of Cultivating a Diverse and Inclusive Workplace
Riya Bansal
Community, Diversity & Inclusion

Let’s face it. The positive impact of diversity and inclusion is no longer debatable.

Introducing the Dask Active Memory Manager
Guido Imperiale
Algorithms, Architecture, Backend, Cloud, Data Engineering, Distributed Computing, Parallel Programming / Async

The Active Memory Manager is a new experimental feature of Dask which aims to reduce the memory footprint of the cluster, prevent hard to debug out-of-memory issues, and make worker retirement more robust.

Introduction to OPC-UA and industrial IoT: Liberate machines from the proprietary clutches of Big Hardware with the power of opcua-asyncio
Joey Faulkner
Backend, Hardware, Networks

Software around industrial hardware is still highly proprietary, which leads to bad UX and inefficient use of hardware. OPC-UA represents an earnest new start at the world of IIoT, and using opcua-asyncio, we can create this revolution in python.

Introduction to Uplift Modeling
Dr. Juan Orduz
Algorithms, Predictive Modelling, Statistics

In this talk we introduce uplift modelling, a method to estimate conditional average treatment effects (CATE) using machine learning estimators.

It is all about files and HTTP
Efe Öge
APIs, Architecture, Backend, Cloud, DevOps, Django

Managing files won't be easier but more obvious after this talk.

jsonargparse - Say goodbye to configuration hassles
Marianne Stecklina
Best Practice

A proper CLI would be nice, but you're way too lazy to write it? Join this talk to learn about the open-source library jsonargparse!

JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python
Jeremy Tuloup
Jupyter, Reproducibility, Use Case

JupyterLite is a Jupyter distribution that runs entirely in the web browser, backed by in-browser language kernels such as the WebAssembly powered Pyodide kernel. JupyterLite enables data science and interactive computing with the PyData scientific stack, directly in the browser.

Machine Learning Testing Ecosystem of Python
Yunus Emrah Bulut
Computer Vision, Ethics (Privacy, Fairness,… ), Governance, Natural Language Processing, Neural Networks / Deep Learning, Security

Machine learning testing becomes an indispensable part of the MLOps and Python offers great ecosystem for this purpose.

Make the most of Django
Paolo Melchiorre
Best Practice, Community, Django

🐍 "Make the most of Django" 👉 Taking full advantage of #OpenSource software means getting involved in its #community and #contributing to its development. We'll see how this is profoundly true in the #Django case as well. #pyconde #talk #python

Making Machine Learning Applications Fast and Simple with ONNX
Jan-Benedikt Jagusch, Christian Bourjau
Data Engineering, DevOps, Packaging

In this session, you will learn how to use ONNX for your machine learning model deployments, which can reduce your single-row inference time by up to 99% while also drastically simplifying your model management.

Navigating the limitations of Python’s concurrency model in web services
Tarek Mehrez
APIs, Architecture, Parallel Programming / Async

Ever wondered when you should favor an async web framework? How do they compare to your good old python services when scaling is in question? Then this is the talk for you

On Blocks, Copies and Views: updating pandas' internals
Joris Van den Bossche
APIs, Data Structures

As a pandas user, did you ever run into the SettingWithCopyWarning? Quite likely, and this is one of the more confusing aspects of pandas. But it doesn’t have to be this way! Check my proposal to simplify this aspect of pandas

Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?
Sebastian Cattes
Natural Language Processing, Neural Networks / Deep Learning, Statistics

Can AI understand what drives user engagement? Join our talk "Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?" to find out what NLP can bring to the editorial table.

Predictive Maintenance and Anomaly Detection for Wind Energy
Tobias Hoinka
Predictive Modelling, Statistics, Time Series

This talk will describe predictive modeling applications in wind turbine maintenance, the challenges of anomaly detection and ways to move to more automatic diagnoses by modeling past documented defects.

Processing Open Street Map Data with Python and PostgreSQL
Travis Hathaway
Data Engineering, Databases, GIS / Geo-Analytics

Open Street Map is a large, community supported data set covering the entire world. Learn how to process this data with Python and PostgreSQL as I walk you through creating projects of your own. Along the way, we learn how OSM data is structured, and how you can use it yourself.

Python 3.10: Welcome to pattern matching!
Laysa Uchoa
Best Practice, Coding / Code-Review, Python fundamentals

Python 3.10: let us learn about Pattern Matching. In this presentation, you will be surprised how simple, yet powerful, Pattern Matching really is. This talk and you, it is a match! 🔥

Quitting pip: How we use git submodules to manage internal dependencies that require fast iteration
Philipp Stephan
Best Practice, Development Methods, DevOps, Packaging

After a review of the current state of Python dependency management, we’d like to present a versatile method of using git submodules to handle internal dependencies in a dockerized microservice architecture, where common libraries have to be iterated quickly.

Rewriting your R analysis code in Python
Helena Schmidt
Best Practice, Development Methods, R

R and Python are two of the most powerful tools for any kind of data analysis. But both programming languages have their strengths and weaknesses. This leads to the question: When and how to rewrite your R analysis code in Python?

Sankey Plots with Python
Daniel Ringler
Data Visualization, Jupyter, Python fundamentals

Sankey Plots in Python? Get an introduction on how and when to use them.

Secure ML: Automated Security Best Practices in Machine Learning
Alejandro Saucedo
Best Practice, Data Engineering, Security

As data science capabilities scale, the core concept of security becomes growingly critical - in this talk we provide an overview of challenges, solutions and best practices to introduce security into the ML lifecycle.

Securing Django Applications
Gajendra Deshpande
Best Practice, Django, Security

In this talk, we will focus on two aspects. First, performing penetration testing on Django web applications to identify vulnerabilities and scanning for OWASP Top 10 risks. Second, strategies and configuration settings for making the source code and Django applications secure.

Slack bots 101: An introduction into slack bot-based workflow automation
Jordi Smit
APIs, DevOps, Use Case

Most developers work with Slack every day, yet very few of them know about the awesome things you can do when you build your own slack bot. During this talk, we will teach you to build and deploy your first slack bot.

Squirrel - Efficient Data Loading for Large-Scale Deep Learning
Dr. Thomas Wollmann
Distributed Computing, Neural Networks / Deep Learning, Parallel Programming / Async

Learn why we built and open sourced a data infrastructure library for deep learning.

Stupid Things I've Done With Python
Mark Smith
Best Practice, Coding / Code-Review, Python fundamentals

On every computer I've had for the past 20 years, I've created a folder called "stupid python tricks". It's where I put code that should never see the light of day. Code I'm going to teach you.

The Magic of Python Objects
Coen de Groot
Python fundamentals

Discover the Magic of Python Objects and the 125+ methods that keep them running

The Myth of Neutrality: How AI is widening social divides
Stefanie Stoppel
Ethics (Privacy, Fairness,… ), Neural Networks / Deep Learning

AI is not neutral and its creation often perpetuates harmful biases. My talk highlights how difficult it is to build "fair and responsible" AI, but also why it's worth to try & prevent these algorithms from cementing existing injustices.

The secret sauce of data science management
Shir Meir Lador
Best Practice, Big Data, Career & Freelancing, Corporate

In this talk, we will discuss lessons learned on how to build a DS team that prospers while addressing the unique challenges of leading a DS team.

The state of DevOps for Python projects
Tobias Heintz
Data Engineering, Development Methods, DevOps

How alcemy uses DevOps techniques to streamline and accelerate our daily development. Let's look at a number of real-world examples and best practices taken straight from the pipelines we use to release code several times a day.

There Are Python 2 Relics in Your Code
Miroslav Šedivý
Coding / Code-Review, Python - CPython new features, Python fundamentals

Should we return to Python 2 or should we get rid of all Python 2 relics from our code?

Transformer based clustering: Identifying product clusters for E-commerce
Sebastian Wanner, Christopher Lennan
Natural Language Processing, Neural Networks / Deep Learning, Use Case

Transformer based clustering with Sentence-Transformers and Facebook Faiss for an E-commerce use case where we clustered offers to automatically generate new products.

Trojan Source Malware - Can we trust open-source anymore?
Cheuk Ting Ho
Community, Governance, Python fundamentals, Security, Transparency / Interpretability

Trojan Source Malware has been tested on Python and it works. Shall the Python and open-source communities be concerned?

Unclear Code Hurts
Dario Cannone
Best Practice, Coding / Code-Review

Code may work or not, but it will always tell a story. Computers will not complain about how you write it (except correct syntax), but human readers will. This talk is about writing clear code and caring for the human beings that will read it. Yourself included.

Upgrade your Documentation to the Next Level
Shivam Singhal
Community, Development Methods

Learn how to write great documentation to nurture community of your open source project

Web based live visualisation of sensor data
Jannis Lübbe
APIs, Data Visualization, Use Case

Streaming sensor data to multiple end devices using FastAPI and websockets.

What are data unit tests and why we need them
Theodore Meynard
Best Practice, Data Engineering

This talk will introduce the concept of data unit tests and why they are important in the workflow of data scientists when building data products.

What I learned from monitoring more than 30 Machine Learning Use Cases
Lina Weichbrodt
Best Practice, Backend, DevOps

How to implement #MachineLearning #monitoring for the impatient. Lessons I learned from running more than 30 models in production. And good news, you can use your existing monitoring and dashboard stack like #Prometheus and #Grafana

XAI meets Natural Language Processing
Larissa Haas
Data Visualization, Ethics (Privacy, Fairness,… ), Transparency / Interpretability

XAI meets NLP - approaches, workarounds and lessons learned while making an NLP project explainable

You shall not share!
Gönül Aycı
Ethics (Privacy, Fairness,… ), Natural Language Processing

Are you ready to have an agent to help to preserve your privacy in online social networks? "You shall not share!" will be presented by @gonul_ayci ⚡️

Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos
Data Visualization, Predictive Modelling

Your data, your insights: 3 examples to illustrate how we can apply common data science libraries together with data shared via mobile apps or collected manually to build little data visualization projects that provide unique, contextual and intmiate insights.