docstr: Read dataset including pandas metadata, if any. Other arguments passed
  through to :func:`read`, see docstring for further details.
  
  Parameters
  ----------
  **kwargs : optional
      Additional options for :func:`read`
  
  Examples
  --------
  Generate an example parquet file:
  
  >>> import pyarrow as pa
  >>> import pandas as pd
  >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021],
  ...                    'n_legs': [2, 2, 4, 4, 5, 100],
  ...                    'animal': ["Flamingo", "Parrot", "Dog", "Horse",
  ...                    "Brittle stars", "Centipede"]})
  >>> table = pa.Table.from_pandas(df)
  >>> import pyarrow.parquet as pq
  >>> pq.write_table(table, 'table_V2.parquet')
  >>> dataset = pq.ParquetDataset('table_V2.parquet')
  
  Read the dataset with pandas metadata:
  
  >>> dataset.read_pandas(columns=["n_legs"])
  pyarrow.Table
  n_legs: int64
  ----
  n_legs: [[2,2,4,4,5,100]]
  
  >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata
  {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...}
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code: def read_pandas(self, **kwargs):
      """
      Read dataset including pandas metadata, if any. Other arguments passed
      through to :func:`read`, see docstring for further details.
  
      Parameters
      ----------
      **kwargs : optional
          Additional options for :func:`read`
  
      Examples
      --------
      Generate an example parquet file:
  
      >>> import pyarrow as pa
      >>> import pandas as pd
      >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021],
      ...                    'n_legs': [2, 2, 4, 4, 5, 100],
      ...                    'animal': ["Flamingo", "Parrot", "Dog", "Horse",
      ...                    "Brittle stars", "Centipede"]})
      >>> table = pa.Table.from_pandas(df)
      >>> import pyarrow.parquet as pq
      >>> pq.write_table(table, 'table_V2.parquet')
      >>> dataset = pq.ParquetDataset('table_V2.parquet')
  
      Read the dataset with pandas metadata:
  
      >>> dataset.read_pandas(columns=["n_legs"])
      pyarrow.Table
      n_legs: int64
      ----
      n_legs: [[2,2,4,4,5,100]]
  
      >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata
      {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...}
      """
      return self.read(use_pandas_metadata=True, **kwargs)
  
imports: from collections import defaultdict
  from contextlib import nullcontext
  from functools import reduce
  import inspect
  import json
  import os
  import re
  import operator
  import warnings
  import pyarrow
  from pyarrow._parquet import ParquetReader, Statistics, FileMetaData, RowGroupMetaData, ColumnChunkMetaData, ParquetSchema, ColumnSchema, ParquetLogicalType, FileEncryptionProperties, FileDecryptionProperties, SortingColumn
  from pyarrow.fs import LocalFileSystem, FileSystem, FileType, _resolve_filesystem_and_path, _ensure_filesystem
  from pyarrow import filesystem
  from pyarrow.util import guid, _is_path_like, _stringify_path, _deprecate_api
label_desc: Use image node_id 7 for calling the ParquetDataset obj's underlying member method code with example usage: obj.read_pandas() and returns: self