This repository contains the lecture notebooks and workbook used for columnar data analysis, presented at CoDaS-HEP at 13:30pm on July 20, 2023 by Jim Pivarski and Ioana Ifrim.

Workbook Live WebAssymbly (Pyodide + JupyterLite) instance: https://ioanaif.github.io/columnar-data-analysis-codas-hep-2023/lab/index.html

Abstract:

Data analysis languages, such as Numpy, MATLAB, R, IDL, and ADL, are typically interactive with an array-at-a-time interface. Instead of performing an entire analysis in a single loop, each step in the calculation is a separate pass, letting the user inspect distributions each step of the way.

Unfortunately, these languages are limited to primitive data types: mostly numbers and booleans. Variable-length and nested data structures, such as different numbers of particles per event, don't fit this model. Fortunately, the model can be extended.

This tutorial will introduce awkward-array, the concepts of columnar data structures, and how to use them in data analysis, such as computing combinatorics (quantities depending on combinations of particles) without any for loops.

You don't need to install anything on your computer to participate; You can run the workbook notebook in your browser with JupyterLite:

https://ioanaif.github.io/columnar-data-analysis-codas-hep-2023/lab/index.html

If you want to install and run the notebooks on your computer (including the lecture notebooks), you can do so by following these steps:

First clone this repository to your local machine via:

git clone https://github.com/ioanaif/columnar-data-analysis-codas-hep-2023.git

If you do not already have the conda package manager installed, please follow the instructions here.

Navigate to the columnar-data-analysis-codas-hep-2023 directory and create a new conda environment with the required packages via:

cd columnar-data-analysis-codas-hep-2023
conda env create --file environment.yml

This will create a new conda environment named "columnar-data-analysis-codas-hep-2023".

Next, activate the environment:

conda activate columnar-data-analysis-codas-hep-2023

Finally, launch JupyterLab with:

jupyter lab