[jira] [Created] (SPARK-12774) DataFrame.mapPartitions apply function operates on Pandas DataFrame instead of a generator or rows

["Josh (JIRA)" <ji...@apache.org>]

Josh created SPARK-12774:
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             Summary: DataFrame.mapPartitions apply function operates on Pandas DataFrame instead of a generator or rows
                 Key: SPARK-12774
                 URL: https://issues.apache.org/jira/browse/SPARK-12774
             Project: Spark
          Issue Type: Improvement
          Components: PySpark
            Reporter: Josh


Currently DataFrame.mapPatitions is analogous to DataFrame.rdd.mapPatitions in both Spark and pySpark. The function that is applied to each partition _f_ must operate on a list generator. This is however very inefficient in Python. It would be more logical and efficient if the apply function _f_  operated on Pandas DataFrames instead and also returned a DataFrame. This avoids unnecessary iteration in Python which is slow.

Currently:
{code:python}
def apply_function(rows):
    df = pd.DataFrame(list(rows))
    df = df % 100   # Do something on df
    return df.values.tolist()

table = sqlContext.read.parquet("")
table = table.mapPatitions(apply_function)
{code}

New apply function would accept a Pandas DataFrame and return a DataFrame:
{code:python}
def apply_function(df):
    df = df % 100   # Do something on df
    return df
{code}



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