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:  With SparkXShards of pandas data frame, the api will drop null values in shards.
    For other type of SparkXShards, it will throw exception
  
  :return: a new SparkXShards without null values
   
docstr_tok: ["With","SparkXShards","of","pandas","data","frame",",","the","api","will","drop","null","values","in","shards",".","For","other","type","of","SparkXShards",",","it","will","throw","exception",":","return",":","a","new","SparkXShards","without","null","values"]
code: def drop_missing_value(self) -> "SparkXShards":
      """
       With SparkXShards of pandas data frame, the api will drop null values in shards.
        For other type of SparkXShards, it will throw exception
  
      :return: a new SparkXShards without null values
      """
      if self._get_class_name() != "pandas.core.frame.DataFrame":
          invalidInputError(
              False,
              "Currently only support assembleFeatureLabelCols() on"
              " XShards of Pandas DataFrame",
          )
  
      null_cnt = self.get_null_sum()
  
      def drop_missing_data(pdf):
          df2 = pdf.drop((null_cnt[null_cnt["total"] > 0]["col"]), 1)
          return df2
  
      # dealing with missing data
      return self.transform_shard(drop_missing_data)
  
imports: from py4j.protocol import Py4JError
  from bigdl.orca.data.utils import *
  from bigdl.orca import OrcaContext
  from bigdl.dllib.utils.common import get_node_and_core_number, get_spark_sql_context, get_spark_context
  from bigdl.dllib.utils import nest
  from bigdl.dllib.utils.log4Error import invalidInputError
  import numpy
  from typing import TYPE_CHECKING, Any
  from typing import Callable, Dict, List, Optional, Tuple, Union
label_desc: Use image node_id 22 for calling the SparkXShards obj's underlying member method code with example usage: obj.drop_missing_value() and returns: self, df2