Are you just applying a function to every row in the DataFrame? you don't need pandas at all. Just get the RDD of Row from it and map a UDF that makes another Row, and go back to DataFrame. Or make a UDF that operates on all columns and returns a new value. mapPartitions is also available if you want to transform an iterator of Row to another iterator of Row.
On Thu, Mar 7, 2019 at 2:33 PM peng yu <yupb...@gmail.com> wrote: > > it is very similar to SCALAR, but for SCALAR the output can't be struct/row > and the input has to be pd.Series, which doesn't support a row. > > I'm doing tensorflow batch inference in spark, > https://github.com/yupbank/tf-spark-serving/blob/master/tss/serving.py#L108 > > Which i have to do the groupBy in order to use the apply function, i'm > wondering why not just enable apply to df ? > > On Thu, Mar 7, 2019 at 3:15 PM Sean Owen <sro...@gmail.com> wrote: >> >> Are you looking for SCALAR? that lets you map one row to one row, but >> do it more efficiently in batch. What are you trying to do? >> >> On Thu, Mar 7, 2019 at 2:03 PM peng yu <yupb...@gmail.com> wrote: >> > >> > I'm looking for a mapPartition(pandas_udf) for a pyspark.Dataframe. >> > >> > ``` >> > @pandas_udf(df.schema, PandasUDFType.MAP) >> > def do_nothing(pandas_df): >> > return pandas_df >> > >> > >> > new_df = df.mapPartition(do_nothing) >> > ``` >> > pandas_udf only support scala or GROUPED_MAP. Why not support just Map? >> > >> > On Thu, Mar 7, 2019 at 2:57 PM Sean Owen <sro...@gmail.com> wrote: >> >> >> >> Are you looking for @pandas_udf in Python? Or just mapPartition? Those >> >> exist already >> >> >> >> On Thu, Mar 7, 2019, 1:43 PM peng yu <yupb...@gmail.com> wrote: >> >>> >> >>> There is a nice map_partition function in R `dapply`. so that user can >> >>> pass a row to udf. >> >>> >> >>> I'm wondering why we don't have that in python? >> >>> >> >>> I'm trying to have a map_partition function with pandas_udf supported >> >>> >> >>> thanks! --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
