euclidean distance between two dataframes - dataframe

Question

I have two dataframes. For simplicity assume, they each have only one entry
+--------------------+
| entry |
+--------------------+
|[0.34, 0.56, 0.87] |
+--------------------+
+--------------------+
| entry |
+--------------------+
|[0.12, 0.82, 0.98] |
+--------------------+
How can I compute the euclidean distance between the entries of these two dataframes? Right now I have the following code:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
from scipy.spatial import distance
inference = udf(lambda x, y: float(distance.euclidean(x, y)), DoubleType())
inference_result = inference(a, b)
but I get the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/spark/python/pyspark/sql/udf.py", line 197, in wrapper
return self(*args)
File "/usr/lib/spark/python/pyspark/sql/udf.py", line 177, in __call__
return Column(judf.apply(_to_seq(sc, cols, _to_java_column)))
File "/usr/lib/spark/python/pyspark/sql/column.py", line 68, in _to_seq
cols = [converter(c) for c in cols]
File "/usr/lib/spark/python/pyspark/sql/column.py", line 68, in <listcomp>
cols = [converter(c) for c in cols]
File "/usr/lib/spark/python/pyspark/sql/column.py", line 56, in _to_java_column
"function.".format(col, type(col)))
TypeError: Invalid argument, not a string or column: DataFrame[embedding:
array<float>] of type <class 'pyspark.sql.dataframe.DataFrame'>. For column
literals, use 'lit', 'array', 'struct' or 'create_map' function.