I have a dataframe in Spark in which one of the columns contains an array.Now,I have written a separate UDF which converts the array to another array with distinct values in it only. See example below:
Ex: [24,23,27,23] should get converted to [24, 23, 27] Code:
def uniq_array(col_array):
x = np.unique(col_array)
return x
uniq_array_udf = udf(uniq_array,ArrayType(IntegerType()))
Df3 = Df2.withColumn("age_array_unique",uniq_array_udf(Df2.age_array))
In the above code, Df2.age_array
is the array on which I am applying the UDF to get a different column "age_array_unique"
which should contain only unique values in the array.
However, as soon as I run the command Df3.show()
, I get the error:
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.core.multiarray._reconstruct)
Can anyone please let me know why this is happening?
Thanks!
The source of the problem is that object returned from the UDF doesn't conform to the declared type. np.unique
not only returns numpy.ndarray
but also converts numerics to the corresponding NumPy
types which are not compatible with DataFrame
API. You can try something like this:
udf(lambda x: list(set(x)), ArrayType(IntegerType()))
or this (to keep order)
udf(lambda xs: list(OrderedDict((x, None) for x in xs)),
ArrayType(IntegerType()))
instead.
If you really want np.unique
you have to convert the output:
udf(lambda x: np.unique(x).tolist(), ArrayType(IntegerType()))