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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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org