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Posted to issues@spark.apache.org by "holdenk (JIRA)" <ji...@apache.org> on 2016/10/16 13:07:20 UTC
[jira] [Commented] (SPARK-14141) Let user specify datatypes of
pandas dataframe in toPandas()
[ https://issues.apache.org/jira/browse/SPARK-14141?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15579949#comment-15579949 ]
holdenk commented on SPARK-14141:
---------------------------------
Ah sorry for the delay, so doing the cache + count together is done since if you just do a cache it won't actually do any caching until an action is performed on the rdd / dataframe and the count is used to force evaluation of the entire dataframe.
> 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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