Explore interoperability with parallel and lazy computation workflows

Here is the reviewer’s comment:

> The text mentions pandas, geopandas and xarray - but no mention of dask and the other tools aimed at making these tools suitable for parallel computation (e.g. dask). I assume this is possible but would be useful to add detail.

As far as I know, Dask and Polars are two widely used tools in the Python data ecosystem, but PyGMT compatibility with them has never been formally tested.

PyGMT may already interoperate with Dask-backed DataFrames and DataArrays through its existing support for pandas and xarray objects, but Polars is less likely to work because it uses a different API and data model.

We need to test the current level of interoperability, especially for Dask-backed workflows. Since I‘m not very familiar with these tools, it would be great if @weiji14 could provide some guidance.

Es werden keine Dateien oder Tests genannt. Beginne damit, die bestehende Unterstützung von PyGMT für pandas- und xarray-Objekte mit Dask-basierten Workflows zu testen, und vergleiche anschließend Polars. Als abgeschlossen gilt die Aufgabe, wenn der aktuelle Stand der Interoperabilität und alle Einschränkungen dokumentiert sind.

Vom Indexierungsmodell aus dem Issue-Text verfasst.