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: Computes basic statistics for numeric and string columns.
  
  This include count, mean, stddev, min, and max. If no columns are
  given, this function computes statistics for all numerical or string columns.
  
  :param cols: string or list string.
  :return: a panda dataframe of description.
docstr_tok: ["Computes","basic","statistics","for","numeric","and","string","columns",".","This","include","count",",","mean",",","stddev",",","min",",","and","max",".","If","no","columns","are","given",",","this","function","computes","statistics","for","all","numerical","or","string","columns",".",":","param","cols",":","string","or","list","string",".",":","return",":","a","panda","dataframe","of","description","."]
code: def describe(
      self, cols: Optional[Union[str, List[str]]] = None
  ) -> "PandasDataFrame":
      """
      Computes basic statistics for numeric and string columns.
  
      This include count, mean, stddev, min, and max. If no columns are
      given, this function computes statistics for all numerical or string columns.
  
      :param cols: string or list string.
      :return: a panda dataframe of description.
      """
      if self._get_class_name() != "pandas.core.frame.DataFrame":
          invalidInputError(
              False,
              "Currently only support select() on"
              " SparkXShards of Pandas DataFrame",
          )
  
      columns = [c for c in self.rdd.first().columns]
      cols = cols if cols else columns
  
      if isinstance(cols, str):
          cols = [cols]
      invalidInputError(
          isinstance(cols, list), "cols should be str or list"
      )
  
      for c in cols:
          check_cols_exists(columns, c, "cols")
  
      spark_df = self.to_spark_df()
  
      description = spark_df.describe(*cols).toPandas()
      return description
  
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 42 for calling the SparkXShards obj's underlying member method code with example usage: obj.describe(cols) and returns: description