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https://issues.apache.org/jira/browse/ARROW-2295?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16394199#comment-16394199
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Antoine Pitrou commented on ARROW-2295:
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{quote}Also, `pyarrow.lib.Array.to_pandas()` returns a `numpy.ndarray`, which 
imho is very confusing{quote}
Agreed, it also surprises me often.

{quote}either a ordered dict of `numpy.ndarray` or a structured `numpy.ndarray` 
depending on a flag, for example{quote}

Converting to a struct array sounds like the reciprocal of ARROW-1886. That 
doesn't have to be part of a Numpy conversion function, though.

{quote} ListArray is internally represented as two arrays: offsets and 
contents, and there are applications where we'd want to get a zero-copy view of 
these arrays{quote}

You can use {{Array.buffers()}} to get zero-copy views of those buffers and 
call {{np.frombuffer}} on each of them.


> Add to_numpy functions
> ----------------------
>
>                 Key: ARROW-2295
>                 URL: https://issues.apache.org/jira/browse/ARROW-2295
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Lawrence Chan
>            Priority: Minor
>
> There are `to_pandas()` functions, but no `to_numpy()` functions. I'd like to 
> propose that we include both.
> Also, `pyarrow.lib.Array.to_pandas()` returns a `numpy.ndarray`, which imho 
> is very confusing :). I think it would be more intuitive for the 
> `to_pandas()` functions to return `pandas.Series` and `pandas.DataFrame` 
> objects, and the `to_numpy()` functions to return `numpy.ndarray` and either 
> a ordered dict of `numpy.ndarray` or a structured `numpy.ndarray` depending 
> on a flag, for example. The `to_pandas()` function is of course welcome to 
> use the `to_numpy()` func to avoid the additional index and whatnot of the 
> `pandas.Series`.
>  



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