dict.get_deep()

[Kirill Ratkin]

Hi Marco.

Recently I met same issue. A service I intergated with was documented 
badly and sent ... unpredictable jsons.

And pattern matching helped me in first solution. (later I switched to 
Pydantic models)

For your example I'd make match rule for key path you need. For example:


data = {"users": [{"address": {"street": "Baker"}}]}

match data:
     case {"users": [{"address": {"street": street}}]}:
         print(f"street: {street}")

     case _:
         print("unsupported message structure")


Structural matching gives you warranty you process exactly message you 
expect and explicitly discards messages with another structure.

But type is still issue. I don't know how to say 'street' must be 'str' 
not 'int'. That's why I switched to Pydantic.


02.04.2022 23:44, Marco Sulla пишет:
> A proposal. Very often dict are used as a deeply nested carrier of
> data, usually decoded from JSON. Sometimes I needed to get some of
> this data, something like this:
>
> data["users"][0]["address"]["street"]
>
> What about something like this instead?
>
> data.get_deep("users", 0, "address", "street")
>
> and also, instead of this
>
> try:
>      result = data["users"][0]["address"]["street"]
> except KeyError, IndexError:
>      result = "second star"
>
> write this:
>
> data.get_deep("users", 0, "address", "street", default="second star")
>
> ?