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https://issues.apache.org/jira/browse/ARROW-9637?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joris Van den Bossche closed ARROW-9637.
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Resolution: Not A Problem
> [Python] Speed degradation with categoricals
> --------------------------------------------
>
> Key: ARROW-9637
> URL: https://issues.apache.org/jira/browse/ARROW-9637
> Project: Apache Arrow
> Issue Type: Bug
> Affects Versions: 1.0.0
> Reporter: Larry Parker
> Priority: Major
> Attachments: fact1__c.parquet.zip
>
>
> I have noticed some major speed degradation when using categorical data
> types. For example, a Parquet file with 1 million rows that sums 10 float
> columns and groups by two columns (one a date column and one a category
> column). The cardinality of the category seems to have a major effect. When
> grouping on category column of cardinality 10, performance is decent (query
> runs in 150 ms). But with cardinality of 100, the query runs in 10 seconds.
> If I switch over to my Parquet file that does *not* have categorical columns,
> the same query that took 10 seconds with categoricals now runs in 350 ms.
> I would be happy to post the Pandas code that I'm using (including how I'm
> creating the Parquet file), but I first wanted to report this and see if it's
> a known issue.
> Thanks.
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