xarray VS dask-awkward

PyTorch and JAX are used heavily in climate science on the ML side. For more general analytics, not so much. Many of our users like to use Xarray as a high-level API. There has been some work to integrate Xarray with PyTorch (https://github.com/pydata/xarray/issues/3232) but we're not there yet.

The Python Array API standard should help align these different back-ends: https://data-apis.org/array-api/latest/

Does polars have N-D labelled arrays, and if so can it perform computations on them quickly? I've been thinking of moving from pandas to xarray [0], but might consider poplars too if it has some of that functionality.

[0] https://xarray.dev/

https://xarray.dev

Pydata's xarray

It does seem like it isn't installed. If you take a look at the source, it catches import errors, meaning it won't error out immediately if the package isn't installed.

Hi! I'm the original author of Awkward Array (Jim Pivarski), though there are now many contributors with about five regulars. Two of my colleagues just pointed me here—I'm glad you're interested! I can answer any questions you have about it.

First, sorry about all the TODOs in the documentation: I laid out a table of contents structure as a reminder to myself of what ought to be written, but haven't had a chance to fill in all of the topics. From the front page (https://awkward-array.org/), if you click through to the Python API reference (https://awkward-array.readthedocs.io/), that site is 100% filled in. Like NumPy, the library consists of one basic data type, `ak.Array`, and a suite of functions that act on it, `ak.this` and `ak.that`. All of those functions are individually documented, and many have examples.

The basic idea starts with a data structure like Apache Arrow (https://arrow.apache.org/)—a tree of general, variable-length types, organized in memory as a collection of columnar arrays—but performs operations on the data without ever taking it out of its columnar form. (3.5 minute explanation here: https://youtu.be/2NxWpU7NArk?t=661) Those columnar operations are compiled (in C++); there's a core of structure-manipulation functions suggestively named "cpu-kernels" that will also be implemented in CUDA (some already have, but that's in an experimental stage).

A key aspect of this is that structure can be manipulated just by changing values in some internal arrays and rearranging the single tree organizing those arrays. If, for instance, you want to replace a bunch of objects in variable-length lists with another structure, it never needs to instantiate those objects or lists as explicit types (e.g. `struct` or `std::vector`), and so the functions don't need to be compiled for specific data types. You can define any new data types at runtime and the same compiled functions apply. Therefore, JIT compilation is not necessary.

We do have Numba extensions so that you can iterate over runtime-defined data types in JIT-compiled Numba, but that's a second way to manipulate the same data. By analogy with NumPy, you can compute many things using NumPy's precompiled functions, as long as you express your workflow in NumPy's vectorized way. Numba additionally allows you to express your workflow in imperative loops without losing performance. It's the same way with Awkward Array: unpacking a million record structures or slicing a million variable-length lists in a single function call makes use of some precompiled functions (no JIT), but iterating over them at scale with imperative for loops requires JIT-compilation in Numba.

Just as we work with Numba to provide both of these programming styles—array-oriented and imperative—we'll also be working with JAX to add autodifferentiation (Anish Biswas will be starting on this in January; he's actually continuing work from last spring, but in a different direction). We're also working with Martin Durant and Doug Davis to replace our homegrown lazy arrays with industry-standard Dask, as a new collection type (https://github.com/ContinuumIO/dask-awkward/). A lot of my time, with Ianna Osborne and Ioana Ifrim at my university, is being spent refactoring the internals to make these kinds of integrations easier (https://indico.cern.ch/event/855454/contributions/4605044/). We found that we had implemented too much in C++ and need more, but not all, of the code to be in Python to be able to interact with third-party libraries.

If you have any other questions, I'd be happy to answer them!