问题
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?
回答1:
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]|
+------------+------+
来源:https://stackoverflow.com/questions/47682927/how-to-create-a-udf-in-pyspark-which-returns-an-array-of-strings