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: Concatenate all pandas dataframes in SparsXShards into one single pandas dataframe
  
  :param axis, integer, default 0
docstr_tok: ["Concatenate","all","pandas","dataframes","in","SparsXShards","into","one","single","pandas","dataframe",":","param","axis",",","integer",",","default","0"]
code: def concat_to_pdf(self, axis: int = 0) -> "PandasDataFrame":
      """
      Concatenate all pandas dataframes in SparsXShards into one single pandas dataframe
  
      :param axis, integer, default 0
      """
      if self._get_class_name() != "pandas.core.frame.DataFrame":
          invalidInputError(
              False,
              "Currently only support concat_to_pdf() on"
              " XShards of Pandas DataFrame",
          )
  
      dfs = self.rdd.collect()
      import pandas as pd
  
      return pd.concat(dfs, axis=axis)
  
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 44 for calling the SparkXShards obj's underlying member method code with example usage: obj.concat_to_pdf(axis) and returns: pd