dataframe-go VS JSON-to-Go

Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for

For guidance on how to use it: https://github.com/rocketlaunchr/dataframe-go/blob/master/exports/parquet.go

That site likely uses Matt's lib for that functionality: https://github.com/mholt/json-to-go

https://mholt.github.io/json-to-go/ is a nice tool for generating a struct definition to fit some sample JSON.

json-to-go is your friend.

JSON to struct. I use this about 100 times a day, no joke.

https://mholt.github.io/json-to-go/

Do your best to get static types on everything. Play a game with yourself where interface{} is banned and try to do your work. One of the most common sources of such loosely-typed values in JSON, so I'll point out this automatic struct generator for instance. If you have questions about a specific place you don't see how to avoid interface, feel free to ask. Every once in a while I do have to reach for something like this, but it should be rare.

But given we know ChatGPT can't accurately generate structs from even moderately complex json objects why bother doing all of that legwork of verifying and chiding chatgpt, when you can just drop it into a tool like https://mholt.github.io/json-to-go/ and get a correct struct in a fraction of the time?

I use https://marketplace.visualstudio.com/items?itemName=maracko.json-to-go, which is an extension version of https://mholt.github.io/json-to-go/

I think using struct in such cases is almost always the right approach. There are tools which generates go structs from json this.