parameters: (
      filepath_or_buffer: FilePathOrBuffer, compression: Optional[str] = "infer"
  )
docstring: Load pickled pandas object (or any object) from file.
  
      .. warning::
  
         Loading pickled data received from untrusted sources can be
         unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
  
      Parameters
      ----------
      filepath_or_buffer : str, path object or file-like object
          File path, URL, or buffer where the pickled object will be loaded from.
  
          .. versionchanged:: 1.0.0
             Accept URL. URL is not limited to S3 and GCS.
  
      compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
          If 'infer' and 'path_or_url' is path-like, then detect compression from
          the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
          compression) If 'infer' and 'path_or_url' is not path-like, then use
          None (= no decompression).
  
      Returns
      -------
      unpickled : same type as object stored in file
  
      See Also
      --------
      DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
      Series.to_pickle : Pickle (serialize) Series object to file.
      read_hdf : Read HDF5 file into a DataFrame.
      read_sql : Read SQL query or database table into a DataFrame.
      read_parquet : Load a parquet object, returning a DataFrame.
  
      Notes
      -----
      read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3.
  
      Examples
      --------
      >>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
      >>> original_df
         foo  bar
      0    0    5
      1    1    6
      2    2    7
      3    3    8
      4    4    9
      >>> pd.to_pickle(original_df, "./dummy.pkl")
  
      >>> unpickled_df = pd.read_pickle("./dummy.pkl")
      >>> unpickled_df
         foo  bar
      0    0    5
      1    1    6
      2    2    7
      3    3    8
      4    4    9
  
      >>> import os
      >>> os.remove("./dummy.pkl")
docstring_summary: Load pickled pandas object (or any object) from file.
docstring_tokens: Load pickled pandas object ( or any object ) from file .
function: def read_pickle(
      filepath_or_buffer: FilePathOrBuffer, compression: Optional[str] = "infer"
  ):
      """
      Load pickled pandas object (or any object) from file.
  
      .. warning::
  
         Loading pickled data received from untrusted sources can be
         unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
  
      Parameters
      ----------
      filepath_or_buffer : str, path object or file-like object
          File path, URL, or buffer where the pickled object will be loaded from.
  
          .. versionchanged:: 1.0.0
             Accept URL. URL is not limited to S3 and GCS.
  
      compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
          If 'infer' and 'path_or_url' is path-like, then detect compression from
          the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
          compression) If 'infer' and 'path_or_url' is not path-like, then use
          None (= no decompression).
  
      Returns
      -------
      unpickled : same type as object stored in file
  
      See Also
      --------
      DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
      Series.to_pickle : Pickle (serialize) Series object to file.
      read_hdf : Read HDF5 file into a DataFrame.
      read_sql : Read SQL query or database table into a DataFrame.
      read_parquet : Load a parquet object, returning a DataFrame.
  
      Notes
      -----
      read_pickle is only guaranteed to be backwards compatible to pandas 0.20.3.
  
      Examples
      --------
      >>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
      >>> original_df
         foo  bar
      0    0    5
      1    1    6
      2    2    7
      3    3    8
      4    4    9
      >>> pd.to_pickle(original_df, "./dummy.pkl")
  
      >>> unpickled_df = pd.read_pickle("./dummy.pkl")
      >>> unpickled_df
         foo  bar
      0    0    5
      1    1    6
      2    2    7
      3    3    8
      4    4    9
  
      >>> import os
      >>> os.remove("./dummy.pkl")
      """
      fp_or_buf, _, compression, should_close = get_filepath_or_buffer(
          filepath_or_buffer, compression=compression
      )
      if not isinstance(fp_or_buf, str) and compression == "infer":
          compression = None
      f, fh = get_handle(fp_or_buf, "rb", compression=compression, is_text=False)
  
      # 1) try standard library Pickle
      # 2) try pickle_compat (older pandas version) to handle subclass changes
      # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError
  
      try:
          excs_to_catch = (AttributeError, ImportError, ModuleNotFoundError)
          try:
              with warnings.catch_warnings(record=True):
                  # We want to silence any warnings about, e.g. moved modules.
                  warnings.simplefilter("ignore", Warning)
                  return pickle.load(f)
          except excs_to_catch:
              # e.g.
              #  "No module named 'pandas.core.sparse.series'"
              #  "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib"
              return pc.load(f, encoding=None)
      except UnicodeDecodeError:
          # e.g. can occur for files written in py27; see GH#28645 and GH#31988
          return pc.load(f, encoding="latin-1")
      finally:
          f.close()
          for _f in fh:
              _f.close()
          if should_close:
              try:
                  fp_or_buf.close()
              except ValueError:
                  pass
function_tokens: def read_pickle ( filepath_or_buffer : FilePathOrBuffer , compression : Optional [ str ] = "infer" ) : fp_or_buf , _ , compression , should_close = get_filepath_or_buffer ( filepath_or_buffer , compression = compression ) if not isinstance ( fp_or_buf , str ) and compression == "infer" : compression = None f , fh = get_handle ( fp_or_buf , "rb" , compression = compression , is_text = False ) # 1) try standard library Pickle # 2) try pickle_compat (older pandas version) to handle subclass changes # 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError try : excs_to_catch = ( AttributeError , ImportError , ModuleNotFoundError ) try : with warnings . catch_warnings ( record = True ) : # We want to silence any warnings about, e.g. moved modules. warnings . simplefilter ( "ignore" , Warning ) return pickle . load ( f ) except excs_to_catch : # e.g. #  "No module named 'pandas.core.sparse.series'" #  "Can't get attribute '__nat_unpickle' on <module 'pandas._libs.tslib" return pc . load ( f , encoding = None ) except UnicodeDecodeError : # e.g. can occur for files written in py27; see GH#28645 and GH#31988 return pc . load ( f , encoding = "latin-1" ) finally : f . close ( ) for _f in fh : _f . close ( ) if should_close : try : fp_or_buf . close ( ) except ValueError : pass