For information about the maintainers track see the maintainers page.

This program may be subject to changes.


With the tutorial tickets you can attend any tutorial of any track.

Monday, 2 September

Time Baroja Oteiza Chillida
09:00 Getting Started with JupyterLab
Mike Müller
Hands-on TensorFlow 2.0
Josh Gordon
10:30 Coffee break
11:00 Numpy Tutorial
Valerio Maggio
Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
Paolo Galeone, Michele "Ubik" De Simoni
Reproducible Data Science in Python
Rok Roškar, Chandrasekhar Ramakrishnan
12:30 Lunch
14:00 Introduction to pandas Create CUDA kernels from Python using Numba and CuPy.
Valentin Haenel
Building data pipelines in Python: Airflow vs scripts soup
Dr. Tania Allard
15:30 Coffee break
16:00 A Tour of the Data Visualization Ecosystem of Python
Giovanni De Gasperis
Speed up your python code
Jérémie du Boisberranger
Performing Quantum Measurements in QuTiP
Simon Cross
17:30 End

Tuesday, 3 September

Time Baroja Oteiza Chillida
09:00 Introduction to SciPy
Gert-Ludwig Ingold
Sufficiently Advanced Testing with Hypothesis
Zac Hatfield-Dodds
Introduction to geospatial data analysis with GeoPandas and the PyData stack
Joris Van den Bossche
10:30 Coffee break
11:00 Effectively using matplotlib
Tim Hoffmann
12:30 Lunch
14:00 Introduction to scikit-learn: from model fitting to model interpretation
Olivier Grisel, Guillaume Lemaitre
CFFI, Ctypes, Cython, Cppyy: how to run C code from Python
Matti Picus
Parallelizing Python applications with PyCOMPSs
Javier Conejero
15:30 Coffee break
16:00 Astrophysics kCSD - a Python package for reconstruction of brain activity
Jakub M. Dzik, Marta Kowalska
17:30 End
20:00 Tutorials social event

Main conference

Wednesday, 4 September

Time Mitxelena Baroja Oteiza
10:00 Opening notes
10:15 Keynote
11:00 Coffee break
11:30 Distributed GPU Computing with Dask
Peter Andreas Entschev
Diamond sponsor talk
More info about sponsoring here
Sufficiently Advanced Testing with Hypothesis
Zac Hatfield-Dodds
12:00 Modern Data Science: A new approach to DataFrames and pipelines
Maarten Breddels, Jovan Veljanoski
QuTiP: the quantum toolbox in Python as an ecosystem for quantum physics exploration and quantum information science
Alexander Pitchford, Nathan Shammah
What about tests in Machine Learning projects?
Sarah Diot-Girard, Stephanie Bracaloni
12:30 Lunch
14:00 Open source project updates
14:45 Apache Arrow: a cross-language development platform for in-memory data
Joris Van den Bossche
Constrained Data Synthesis
Nick Radcliffe
Scientific DevOps: Designing Reproducible Data Analysis Pipelines with Containerized Workflow Managers
Nicholas Del Grosso
15:15 Caterva: A Compressed And Multidimensional Container For Big Data
Francesc Alted
ToFu - an open-source python/cython library for synthetic tomography diagnostics on Tokamaks
Didier VEZINET, Laura Mendoza
Dashboarding with Jupyter notebooks, voila and widgets
Maarten Breddels
15:45 Modin: Scaling the Capabilities of the Data Scientist, not the machine
Devin Petersohn
Environmental Research and Citizen Science using fractaL
Saulo Jacques
Make your Python code fly at transonic speeds!
Pierre Augier
16:00 Coffee break
16:30 Best Coding Practices in Jupyterlab
Alexander CS Hendorf
Controlling a confounding effect in predictive analysis.
Darya Chyzhyk
PyFETI - An easy and massively Dual Domain Decomposition Solver for Python
Guilherme Jenovencio
16:45 Lessons learned from comparing Numba-CUDA and C-CUDA
Lena Oden
The Rapid Analytics and Model Prototyping (RAMP) framework: tools for collaborative data science challenges
Joris Van den Bossche, Guillaume Lemaitre
High Voltage Lab Common Code Basis library: a uniform user-friendly object-oriented API for a high voltage engineering research.
Mikołaj Rybiński
17:00 Poster introduction
17:15 Poster session
18:30 End

Thursday, 5 September

Time Mitxelena Baroja Oteiza
9:00 Opening notes
9:15 Keynote
10:00 Coffee break
10:30 Inside NumPy: preparing for the next decade
Matti Picus
Visual Diagnostics at Scale
Dr. Rebecca Bilbro
Get Started with Variational Inference using Python
Suriyadeepan Ramamoorthy
11:00 Introduction to TensorFlow 2.0
Brad Miro
Histogram-based Gradient Boosting in scikit-learn 0.21
Olivier Grisel
Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications
Andrii Gakhov
11:30 The Magic of Neural Embeddings with TensorFlow 2
Oliver Zeigermann
Recent advances in python parallel computing
Pierre Glaser
Driving a 30m Radio Telescope with Python
Francesco Pierfederici
12:00 High quality video experience using deep neural networks
Marco Bertini
A practical guide towards algorithmic bias and explainability in machine learning
Alejandro Saucedo
Matrix calculus with SymPy
Francesco Bonazzi
12:30 Lunch
14:00 Keynote
14:45 PyTorch is not only for deep learning!
Alexey Sizanov
Understanding Numba
Valentin Haenel
VeloxChem: Python meets quantum chemistry and HPC
Olav Vahtras
15:15 Tracking migration flows with geolocated Twitter data
Antònia Tugores
PyPy meets SciPy
Ronan Lamy
emzed: a Python based framework for analysis of mass-spectrometry data
Uwe Schmitt
15:45 Deep Learning for Understanding Human Multi-modal Behavior
Ricardo Manhães Savii
High performance machine learning with dislib
Javier Álvarez
vtext: fast text processing in Python using Rust
Roman Yurchak
16:00 Coffee break
16:30 How to process hyperspectral data from a prototype imager using Python
Matti Eskelinen
Can we make Python fast without sacrificing readability? numba for Astrodynamics
Juan Luis Cano Rodríguez
*pystencils*: Speeding up stencil computations on CPUs and GPUs
Martin Bauer
16:45 Enhancing & re-designing the QGIS user interface – a deep dive
Sebastian Ernst
PSYDAC: a parallel finite element solver with automatic code generation
Yaman Güçlü
TelApy a Python module to compute free surface flows and sediments transport in geosciences
yoann audouin
17:00 Lightning talks
18:15 Closing notes
18:30 End
20:00 Talks social event


Friday, 6 September

More sprints will be added when they are proposed.

If you want to propose a sprint, please send us a message to

Time Chillida Oteiza Axular
10:00 Sprint presentations
  • Improving docs for Blosc2 & Caterva
  • PyPy
Mentored sprint for beginners in open source from underrepresented minorities
12:30 Lunch
14:00 (continue) (continue) (continue)
18:30 End