Scikit-HEP Tutorial: Glossary

Be familiar with the syntax of Python dicts, NumPy arrays, slicing rules, and bitwise logic operators.

Large-scale computations in Python tend to be performed one array at a time, rather than one scalar operation at a time.

You, as a user, will likely be gluing together many packages in each data analysis.

Uproot TDirectories and TTrees have a dict-like interface.

Uproot reading methods are primarily intended to get data into a more specialized library.

Uproot writing is more limited, but it can write histograms and TTrees.

ROOT files have a structure that enables partial reading. This is essential for large datasets.

Be aware of how much data you’re reading and when.

The Python + Jupyter + Uproot interface provides a gradual path from interactive tinkering to scaled-up workflows.

NumPy (and almost all array libraries) is only for rectilinear collections of numbers: arrays, tables, and tensors.

Awkward Array extends NumPy’s slicing and array-manipulation to jagged arrays and more general data types (such as nested records).

These extensions are useful for physics.

There’s usually more than one way to get what you want.

High-energy physicists approach histogramming in a different way from NumPy, Matplotlib, SciPy, etc.

Scikit-HEP tools make histogramming and fitting Pythonic.

Instead of building vector methods into multiple packages, a standalone package provides just that.

The value of these small packages amplify when used together.

See Coffea for more about scaling up your analysis.

Pythonic high-energy physics is a broad and growing ecosystem.