class_docstr: A collection of data which can be pre-processed in parallel on Spark
class_docstr_tok: ["A","collection","of","data","which","can","be","pre-processed","in","parallel","on","Spark"]
docstr: Return a unique list of elements of this SparkXShards.
  This is only applicable for SparkXShards of Pandas Series.
  
  :return: a unique list of elements of this SparkXShards.
docstr_tok: ["Return","a","unique","list","of","elements","of","this","SparkXShards",".","This","is","only","applicable","for","SparkXShards","of","Pandas","Series",".",":","return",":","a","unique","list","of","elements","of","this","SparkXShards","."]
code: def unique(self):
      """
  
      Return a unique list of elements of this SparkXShards.
      This is only applicable for SparkXShards of Pandas Series.
  
      :return: a unique list of elements of this SparkXShards.
      """
      if self._get_class_name() == "pandas.core.series.Series":
          import pandas as pd
  
          rdd = self.rdd.map(lambda s: s.unique())
          import numpy as np
  
          result = rdd.reduce(
              lambda list1, list2: pd.unique(
                  np.concatenate((list1, list2), axis=0)
              )
          )
          return result
      else:
          # we may support numpy or other types later
          raise Exception(
              "Currently only support unique() on XShards of Pandas Series"
          )
  
imports: from py4j.protocol import Py4JError
  from zoo.orca.data.utils import *
  from zoo.orca import OrcaContext
  from zoo.common.nncontext import init_nncontext
  from zoo import ZooContext, get_node_and_core_number
  from zoo.util import nest
label_desc: Use image node_id 12 for calling the SparkXShards obj's underlying member method code with example usage: obj.unique() and returns: result