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https://issues.apache.org/jira/browse/ARROW-1660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16219432#comment-16219432
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MIkhail Osckin commented on ARROW-1660:
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I definitely tested it with the latest pyarrow version at the moment. I had the
same intuition that this issue might be related to splicing, because my initial
dataset was ordered by id field and top of the dataset (after to_pandas) was
something like this 10012, 10015, 10034, and the row with id like 10018 had
values from 100034 and only part of them at least in one column (and if i
remember well 10018 was the exact third id by ascendence.
> [Python] pandas field values are messed up across rows
> ------------------------------------------------------
>
> Key: ARROW-1660
> URL: https://issues.apache.org/jira/browse/ARROW-1660
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.7.1
> Environment: 4.4.0-72-generic #93-Ubuntu SMP x86_64, python3
> Reporter: MIkhail Osckin
> Assignee: Wes McKinney
>
> I have the following scala case class to store sparse matrix data to read it
> later using python
> {code:java}
> case class CooVector(
> id: Int,
> row_ids: Seq[Int],
> rowsIdx: Seq[Int],
> colIdx: Seq[Int],
> data: Seq[Double])
> {code}
> I save the dataset of this type to multiple parquet files using spark and
> then read it using pyarrow.parquet and convert the result to pandas dataset.
> The problem i have is that some values end up in wrong rows, for example,
> row_ids might end up in wrong cooVector row. I have no idea what the reason
> is but might be it is related to the fact that the fields are of variable
> sizes. And everything is correct if i read it using spark. Also i checked
> to_pydict method and the result is correct, so seems like the problem
> somewhere in to_pandas method.
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