Diving into python

We will use the python programming language throughout this project. Python is not only widely used in data analysis and science but also features a rich ecosystem of packages which provide industry-standard tools.

Introduction to python

A very good start into learning python is this course:

• https://swcarpentry.github.io/python-novice-inflammation

Complementary to it, you can have a look into these notebooks if you want to brush up specific aspects, such as the numpy or matplotlib packages:

• https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-1-Introduction-to-Python-Programming.ipynb

• https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-2-Numpy.ipynb

• https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb

• https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python

As a reference, you can refer to the freely available book "Think python":

• https://greenteapress.com/thinkpython/thinkpython.pdf

Specific packages for High Energy Physics analysis:

Opening ROOT files

GitHub - scikit-hep/uproot5: ROOT I/O in pure Python and NumPy.GitHub

Creating histograms in python

GitHub - scikit-hep/hist: Histogramming for analysis powered by boost-histogramGitHub

Creating and manipulating four-vectors

GitHub - scikit-hep/vector: Vector classes and utilitiesGitHub

Specialised plotting routines and custom plot styles

GitHub - scikit-hep/mplhep: Extended histogram plotting on top of matplotlib and HEP collaboration compatible stylingGitHub

Specific packages for data analysis:

Handling of columnar datasets

pandas - Python Data Analysis Librarypandas.pydata.org

Data visualisation

seaborn: statistical data visualization — seaborn 0.13.2 documentationseaborn.pydata.org

Scikit-learn

scikit-learn: machine learning in Python — scikit-learn 1.8.0 documentationscikit-learn.org

PyTorch

PyTorch FoundationPyTorch