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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