issue_comments: 523898016

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https://github.com/pydata/xarray/issues/1097#issuecomment-523898016 https://api.github.com/repos/pydata/xarray/issues/1097 523898016 MDEyOklzc3VlQ29tbWVudDUyMzg5ODAxNg== 5635139 2019-08-22T13:07:44Z 2019-08-22T13:07:44Z MEMBER

The biggest problem is with all the Dataset methods and accessors that return a DataArray, and vice versa. Anybody who wants to create a coupled pair of Dataset and DataArray subclasses will need to hunt down all methods and accessors that return the other class in the pair and override them.

This is correct - functions which convert between DataArray & Dataset wouldn't retain type. That said, this can still be helpful without the ability to change type.

There's a bigger piece of work which would solve this too, at the cost of abstraction: have class attributes which define _array_type=DataArray on Dataset and similar for DataArray. pandas used this

May I ask what are the practical use cases for subclassing? In several years worth of day-to-day use of xarray I always found that encapsulation felt much more natural.

Right, good question and we should catch ourselves from adding every abstraction. I have one specific use-case we've already found helpful: we have an object that is mostly a Dataset, with the addition of some behaviors and constructors - for example from_[dataset_x_and_y] or assert_invariants. Alternatives: - A class which had the dataset as an attribute - but this means 90%+ of object use is x.data, and the object can't be passed into methods expecting a dataset. - Prior to using xarray, we frequently used this pattern to aggregate lots of pandas dataframes (and I've seen this in the wild too) - A normal dataset with some associated functions in a module - but makes discovering the functions harder - The current state - an object inherited from Dataset with methods

We don't use accessors because the behaviors are specific to a class, rather than every xarray object.

 
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