Inspired by xarray, Scipp enriches raw NumPy-like multi-dimensional data arrays by adding named dimensions and associated coordinates. For an even more intuitive and less error-prone user experience, Scipp adds physical units to arrays and their coordinates. There are multiple ways of working with units in the Scientific Python world, and there are even new initiatives like the Units/Quantity API and in this talk we will look at Scipp (which wraps around llnl-units).

But units are just one part of working with scientific data. Scipp also has a powerful non-destructive binning method to sort record-based "tabular"/"event" data into arrays of bins which could be useful if you are dealing with lots of data which needs to analyzed quickly. Scipp can also natively propagate uncertainties through your computations. Stop by this talk if you would like to see how Scipp can power scientific data analysis.