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https://issues.apache.org/jira/browse/SPARK-21199?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Franklyn Dsouza updated SPARK-21199:
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Description:
There are cases where nulls end up in vector columns in dataframes. Currently
there is no way to fill in these nulls because its not possible to create a
literal vector column expression using lit().
Also the entire pyspark ml api will fail when they encounter nulls so this
makes it hard to work with the data.
I think that either vector support should be added to the imputer or vectors
should be supported in column expressions so they can be used in a coalesce.
[~mlnick]
was:
There are cases where nulls end up in vector columns in dataframes. Currently
there is no way to fill in these nulls because its not possible to create a
literal vector column expression using lit().
Also the entire pyspark ml api will fail when they encounter nulls so this
makes it hard to work with the data.
I think that either vector support should be added to the imputer or vectors
should be supported in column expressions so they can be used in a coalesce.
@mlnick
> Its not possible to impute Vector types
> ---------------------------------------
>
> Key: SPARK-21199
> URL: https://issues.apache.org/jira/browse/SPARK-21199
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.0.0, 2.1.1
> Reporter: Franklyn Dsouza
>
> There are cases where nulls end up in vector columns in dataframes. Currently
> there is no way to fill in these nulls because its not possible to create a
> literal vector column expression using lit().
> Also the entire pyspark ml api will fail when they encounter nulls so this
> makes it hard to work with the data.
> I think that either vector support should be added to the imputer or vectors
> should be supported in column expressions so they can be used in a coalesce.
> [~mlnick]
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