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From "Luke Miner (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-14141) Let user specify datatypes of pandas dataframe in toPandas()
Date Mon, 28 Mar 2016 19:11:25 GMT

    [ https://issues.apache.org/jira/browse/SPARK-14141?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15214718#comment-15214718
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Luke Miner commented on SPARK-14141:
------------------------------------

Good to know. Would rdd.toLocalIterator() be a scalable way to do this?

> Let user specify datatypes of pandas dataframe in toPandas()
> ------------------------------------------------------------
>
>                 Key: SPARK-14141
>                 URL: https://issues.apache.org/jira/browse/SPARK-14141
>             Project: Spark
>          Issue Type: New Feature
>          Components: Input/Output, PySpark, SQL
>            Reporter: Luke Miner
>            Priority: Minor
>
> Would be nice to specify the dtypes of the pandas dataframe during the toPandas() call.
Something like:
> bq. pdf = df.toPandas(dtypes={'a': 'float64', 'b': 'datetime64', 'c': 'bool', 'd': 'category'})
> Since dtypes like `category` are more memory efficient, you could potentially load many
more rows into a pandas dataframe with this option without running out of memory.



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