This tutorial covers more advanced topics in Python for data analysis. It is aimed at people who have some familiarity with Python and want to expand their knowledge of the language and learn about tools for data analysis.

• Session 1: Introduction to Python classes
• Session 2: Advanced function signature; Exceptions
• Session 3: Data handling & manipulation
• Session 4: Statistics; curve fitting

If the installation fails, you can just run it in the cloud using Binder:

Clone the repository and install the required packages using the following commands (use conda instead of mamba if mamba is not installed):

git clone https://github.com/SyracuseUniversity/ospo-workshop-advpython.git
cd ospo-workshop-advpython

Create a new conda environment (or recreate an existing one) with the required packages:

mamba env create -f environment.yml

or if you don't have mamba installed:

conda env create -f environment.yml

If you already have the environment created, you can update it with:

mamba env update --file environment.yml --prune

Activate the environment by running:

mamba activate advanced-python

Finally, start Jupyter by running:

jupyter notebook

or

jupyter lab

for a slightly different experience

Alternatively, you can also use colaboratory to run the notebooks. Therefore, open the link https://colab.research.google.com/ and select the "GitHub" tab. Enter the URL of this repository (https://github.com/SyracuseUniversity/ospo-workshop-advpython) and select the notebook you want to run.

Parts of this tutorial LHCb analysis essentials tutorials, from the Data Science Handbook and from J.R. Johansson Python lectures