paleolimbot commented on PR #464:
URL: https://github.com/apache/arrow-nanoarrow/pull/464#issuecomment-2117540266

   I hear everybody on the naming thing! "Column" is not great because it 
doesn't have a precedent here (the closest thing would be 
`pyarrow.Table.column()`, which is still giving arrow arrays), and "Builder" is 
already used to describe the conversion of <something> to an array. 
"Concatenate" might imply that we're returning an Array, which we *could* do or 
build using this machinery, but is not really the desired endpoint here.
   
   There is precedent for the term "convert" in the R bindings ( 
https://arrow.apache.org/nanoarrow/latest/r/reference/convert_array_stream.html 
) and Arrow C++ ( 
https://github.com/apache/arrow/blob/2dbc5e26dcbc6826b4eb7a330fa8090836f6b727/cpp/src/arrow/util/converter.h#L40
 ), and so I gave that terminology a try in the last few commits.
   
   The crux of what these helpers are trying to do is to get a stream of arrays 
(possibly of indeterminate length) *out* of Arrow land to be represented by 
*something else*. The default *something else* has to be limited to the Python 
standard library because of the zero dependencyness, which is "pybuffer or 
list". In the R bindings you can do things like:
   
   ```r
   convert_array_stream(stream)  # default conversion
   convert_array_stream(stream, tibble::tibble())  # explicit output prototype
   ```
   
   Here, `visitable.convert()` *could* do the same thing (although won't in 
this PR because it's a can of worms, and maybe not ever if nobody ends up using 
the high-level interface).
   
   ```python
   array.convert() # default conversion
   array.convert(np.int32) # ...would get you an np.array with dtype int32
   ```
   
   I will also mark these as "experimental" such that it's clear we're settling 
on the terminology/behaviour/scope here.


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