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https://issues.apache.org/jira/browse/ARROW-4497?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16762523#comment-16762523
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Micah Kornfield edited comment on ARROW-4497 at 2/7/19 9:49 AM:
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[~pitrou] This is consistent with how java handles things
([https://docs.oracle.com/javase/7/docs/api/java/lang/Double.html#equals(java.lang.Object]))
so I'm ok with the approach. It seems pandas takes another approach (but I
could be mis-using the groupby operator:
{{>>> df = pd.DataFrame(}}{{{'a': [-0.0, 0.0, float('nan')]}}}{{)}}
{{ >>> df}}
{{ a}}
{{ 0 -0.0}}
{{ 1 0.0}}
{{ 2 NaN}}
{{ >>> a=df.groupby(df.a)}}
{{ >>> a.count(}}
{{ ... )}}
{{ Empty DataFrame}}
{{ Columns: []}}
{{ Index: [-0.0]}}
was (Author: emkornfi...@gmail.com):
[~pitrou] This is consistent with how java handles things
(https://docs.oracle.com/javase/7/docs/api/java/lang/Double.html#equals(java.lang.Object))
so I'm ok with the approach. It seems pandas takes another approach (but I
could be mis-using the groupby operator:
{{>>> df = pd.DataFrame({'a': [-0.0, 0.0, float('nan')]})
>>> df
a
0 -0.0
1 0.0
2 NaN
>>> a=df.groupby(df.a)
>>> a.count(
... )
Empty DataFrame
Columns: []
Index: [-0.0]}}
> [C++] Determine how we want to handle hashing of floating point edge cases
> --------------------------------------------------------------------------
>
> Key: ARROW-4497
> URL: https://issues.apache.org/jira/browse/ARROW-4497
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++
> Reporter: Micah Kornfield
> Priority: Major
> Labels: analytics
> Fix For: 0.14.0
>
>
> We should document expected behavior or implement improvements to hashing
> floating point code:
> 1. -0.0 and 0.0 (should these be collapsed to 0.0)
> 2. NaN (Should we reduce to a single canonical version).
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