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https://issues.apache.org/jira/browse/SPARK-14141?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16014944#comment-16014944
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Bryan Cutler commented on SPARK-14141:
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Take a look at SPARK-13534 which will make a Pandas DataFrame with equivalent
types from Spark and uses Apache Arrow instead of Pandas.from_records. Right
now not all data types are supported, but if this is merged then complex types
will be be added also. Maybe this could be closed?
> 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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