08-26, 11:00–12:30 (Europe/Berlin), Room 6
Are you starting to use Python for scientific computing? Join this tutorial to know more about NumPy, the building block for nearly all libraries in the scientific ecosystem.
You will learn how to manipulate NumPy arrays, understand how they store data and discover how to get optimal performances. By the end of this tutorial, you will be able to start working with NumPy and know the main pitfalls to avoid.
This is a hands-on workshop, please bring a laptop.
You can find the installation instructions for the tutorial here: https://github.com/SdgJlbl/numpy-introduction-tutorial#installation-instructions.
A back-up online solution will be available if you are not able to install everything locally.
Target audience: beginner in the Python scientific ecosystem, some basic knowledge of Python and its tooling are a plus.
Agenda:
- What is NumPy and when to use it? - 10 min
- Workshop set-up - 5 min
- Creating and manipulating NumPy arrays - 10 min
- Basic indexing - 10 min
- Shape and broadcasting - 20 min
- Filtering and masking - 15 min
- Vectorized operations - 15 min
- Wrap-up and key take-away - 5 min
Learn the basics of NumPy, Python's foundational library for numerical computing
Category [High Performance Computing] –Other
Category [Community, Education, and Outreach] –Other
Category [Machine and Deep Learning] –Supervised Learning
Category [Scientific Applications] –Astronomy
Category [Data Science and Visualization] –Data Analysis and Data Engineering
Expected audience expertise: Domain –none
Expected audience expertise: Python –none
Sarah Diot-Girard has been working on Machine Learning since 2012, and she enjoys using data science tools to find solutions to practical problems. She is particularly interested in issues, both technical and ethical, coming from applying ML into real life. She gave talks at international conferences, about data privacy and algorithmic fairness, and software engineering best practices applied to data science. She is employed by Owkin as a maintainer of the Federated Learning platform Substra since 2023.