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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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