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https://issues.apache.org/jira/browse/ARROW-564?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16447730#comment-16447730
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ASF GitHub Bot commented on ARROW-564:
--------------------------------------
pitrou commented on a change in pull request #1931: ARROW-564 [Python] Add
support for return zero copy NumPy arrays
URL: https://github.com/apache/arrow/pull/1931#discussion_r183308153
##########
File path: python/pyarrow/tests/test_array.py
##########
@@ -83,6 +83,33 @@ def test_long_array_format():
assert result == expected
+def test_to_numpy_zero_copy():
+ import gc
+
+ arr = pa.array(range(10))
+
+ for i in range(10):
+ np_arr = arr.to_numpy()
+ assert sys.getrefcount(np_arr) == 2
+ np_arr = None # noqa
+
+ assert sys.getrefcount(arr) == 2
+
+ for i in range(10):
+ arr = pa.array(range(10))
+ np_arr = arr.to_numpy()
+ arr = None
+ gc.collect()
+
+ # Ensure base is still valid
+
+ # Because of py.test's assert inspection magic, if you put getrefcount
+ # on the line being examined, it will be 1 higher than you expect
+ base_refcount = sys.getrefcount(np_arr.base)
+ assert base_refcount == 2
+ np_arr.sum()
Review comment:
You should check the result value.
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> [Python] Add methods to return vanilla NumPy arrays (plus boolean mask array
> if there are nulls)
> ------------------------------------------------------------------------------------------------
>
> Key: ARROW-564
> URL: https://issues.apache.org/jira/browse/ARROW-564
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Reporter: Wes McKinney
> Priority: Major
> Labels: beginner, pull-request-available
> Fix For: 1.0.0
>
>
> At the moment, for {{pyarrow.Array}} instances, we have a method called
> {{to_pandas}}. While this method returns NumPy Arrays, it returns them in the
> form that Pandas would use them in its {{Series}}. The difference here is
> visible for example in the case of integers with null values. For Pandas, we
> convert it into a float array and set all entries to NaN where we have null
> entries in the Arrow array. For vanilla NumPy arrays, we would return a tuple
> of a valid bytemap (not bitmap!) and a values array. The values array in this
> case should simply be a view on the underlying Arrow buffer.
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