I have a udf which returns a list of strings. this should not be too hard. I pass in the datatype when executing the udf since it returns an array of strings: ArrayType(StringType).
Now, somehow this is not working:
the dataframe i'm operating on is df_subsets_concat and looks like this:
df_subsets_concat.show(3,False) +----------------------+ |col1 | +----------------------+ |oculunt | |predistposed | |incredulous | +----------------------+ only showing top 3 rows and the code is
from pyspark.sql.types import ArrayType, FloatType, StringType my_udf = lambda domain: ['s','n'] label_udf = udf(my_udf, ArrayType(StringType)) df_subsets_concat_with_md = df_subsets_concat.withColumn('subset', label_udf(df_subsets_concat.col1)) and the result is
/usr/lib/spark/python/pyspark/sql/types.py in __init__(self, elementType, containsNull) 288 False 289 """ --> 290 assert isinstance(elementType, DataType), "elementType should be DataType" 291 self.elementType = elementType 292 self.containsNull = containsNull AssertionError: elementType should be DataType It is my understanding that this was the correct way to do this. Here are some resources: pySpark Data Frames "assert isinstance(dataType, DataType), "dataType should be DataType" How to return a "Tuple type" in a UDF in PySpark?
But neither of these have helped me resolve why this is not working. i am using pyspark 1.6.1.
How to create a udf in pyspark which returns an array of strings?
1 Answer
You need to initialize a StringType instance:
label_udf = udf(my_udf, ArrayType(StringType())) # ^^ df.withColumn('subset', label_udf(df.col1)).show() +------------+------+ | col1|subset| +------------+------+ | oculunt|[s, n]| |predistposed|[s, n]| | incredulous|[s, n]| +------------+------+ 
