You are viewing a plain text version of this content. The canonical link for it is here.
Posted to jira@arrow.apache.org by "Jongbin Park (Jira)" <ji...@apache.org> on 2021/02/01 02:05:00 UTC
[jira] [Updated] (ARROW-11450) [Python] pyarrow<3 incompatible with
numpy>=1.20.0
[ https://issues.apache.org/jira/browse/ARROW-11450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jongbin Park updated ARROW-11450:
---------------------------------
Description:
pyarrow 1.0 and 2.0 is not compatible with numpy 1.20.0
Running the following command would fail:
{{pa.array(np.arange(10))}}
with error
{{pyarrow.lib.ArrowTypeError: Did not pass numpy.dtype object}}
Numpy release note [[link|#compatibility-notes]]] mentions about the np.dtype related compatibility breaks. Also there is a C API change, which implies numpy dependency constraints should be tighter (whether <=1.20 or >1.20) depending on the compiled numpy version (if pyarrow is depending on it; I'm not aware of its implementation).
was:
pyarrow 1.0 and 2.0 is not compatible with numpy 1.20.0
Running the following command would fail:
{{pa.array(np.arange(10))}}
with error
{{pyarrow.lib.ArrowTypeError: Did not pass numpy.dtype object}}
Numpy release note [[link|[https://numpy.org/doc/1.20/release/1.20.0-notes.html#compatibility-notes]]] mentions about the np.dtype related compatibility breaks. Also there is a C API change, which implies numpy dependency constraints should be tighter (whether <=1.20 or >1.20) depending on the compiled numpy version.
> [Python] pyarrow<3 incompatible with numpy>=1.20.0
> --------------------------------------------------
>
> Key: ARROW-11450
> URL: https://issues.apache.org/jira/browse/ARROW-11450
> Project: Apache Arrow
> Issue Type: Bug
> Environment: python>=3.7
> Debian 5.7 x86_64
> Reporter: Jongbin Park
> Priority: Major
>
> pyarrow 1.0 and 2.0 is not compatible with numpy 1.20.0
> Running the following command would fail:
> {{pa.array(np.arange(10))}}
> with error
> {{pyarrow.lib.ArrowTypeError: Did not pass numpy.dtype object}}
> Numpy release note [[link|#compatibility-notes]]] mentions about the np.dtype related compatibility breaks. Also there is a C API change, which implies numpy dependency constraints should be tighter (whether <=1.20 or >1.20) depending on the compiled numpy version (if pyarrow is depending on it; I'm not aware of its implementation).
--
This message was sent by Atlassian Jira
(v8.3.4#803005)