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Wes McKinney updated ARROW-1660: -------------------------------- Summary: [Python] pandas field values are messed up across rows (was: pandas field values are messed up across rows) > [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 > Fix For: 0.8.0 > > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029)