We could also provide a JSONEncoder/JSONDecoder pair, too, but as I mention in one of the Github issues you link to, there are a number of different expectations that people could have for what the JSON representation of an array is. Some will want to use the JData standard. Others might just want the arrays to be represented as lists of lists of plain-old JSON numbers in order to talk with software in other languages that have no particular standard for array data.

hi Robert

I agree with you that different users have different expectations, but if you want, this can be accommodated by defining slightly different (builtin) subclasses of JSONEncoder functions and tell users what to expect when using those with cls=.... , or use one JSONEncoder and different parameters.

other projects like pandas.DataFrame handle this via a format parameter ("orient") to the to_json() function

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html

from my limited experience, as long as the "TypeError" from json keeps popping up, requests like this ( #12481, #16432, #18994, pallets/flask#4012, openmm/openmm#3202, zarr-developers/zarr-python#354) will unlikely to cease (and maintainers will have to keep on closing with "wontfix") - after all, no matter how different the format expectations a user may have, seeing some sort of default behavior is still a lot more satisfying than seeing an error.

It seems to me that the jdata package is the right place for implementing the JData standard. I'm happy for our documentation to point to it in all the places that we talk about serialization of arrays. If the json module did have some way for us to specify a default representation for our objects, then that would be a different matter. But for the present circumstances, I'm not seeing a substantial benefit to moving this code inside of numpy. Outside of numpy, you can evolve the JData standard at its own pace.

--

Robert Kern

I appreciate that you are willing to add this to the documentation, that is totally fine - I will just leave the links/resources here in case solving this issue becomes a priority in the future.

Qianqian

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