DOC: Replace @doc decorator with inlined docstrings in pandas/io/parquet.py (#63445)

Author: sdhjebngc

pandas/io/parquet.py

@@ -21,17 +21,13 @@
     AbstractMethodError,
     Pandas4Warning,
 )
-from pandas.util._decorators import (
-    doc,
-    set_module,
-)
+from pandas.util._decorators import set_module
 from pandas.util._validators import check_dtype_backend
 
 from pandas import (
     DataFrame,
     get_option,
 )
-from pandas.core.shared_docs import _shared_docs
 
 from pandas.io._util import arrow_table_to_pandas
 from pandas.io.common import (
@@ -410,7 +406,6 @@ def read(
                 handles.close()
 
 
-@doc(storage_options=_shared_docs["storage_options"])
 def to_parquet(
     df: DataFrame,
     path: FilePath | WriteBuffer[bytes] | None = None,
@@ -430,10 +425,10 @@ def to_parquet(
     df : DataFrame
     path : str, path object, file-like object, or None, default None
         String, path object (implementing ``os.PathLike[str]``), or file-like
-        object implementing a binary ``write()`` function. If None, the result is
-        returned as bytes. If a string, it will be used as Root Directory path
-        when writing a partitioned dataset. The engine fastparquet does not
-        accept file-like objects.
+        object implementing a binary ``write()`` function. If None, the result
+        is returned as bytes. If a string, it will be used as Root Directory
+        path when writing a partitioned dataset. The engine fastparquet does
+        not accept file-like objects.
     engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
         Parquet library to use. If 'auto', then the option
         ``io.parquet.engine`` is used. The default ``io.parquet.engine``
@@ -460,8 +455,15 @@ def to_parquet(
         Column names by which to partition the dataset.
         Columns are partitioned in the order they are given.
         Must be None if path is not a string.
-    {storage_options}
-
+    storage_options : dict, optional
+        Extra options that make sense for a particular storage connection, e.g.
+        host, port, username, password, etc. For HTTP(S) URLs the key-value
+        pairs are forwarded to ``urllib.request.Request`` as header options.
+        For other URLs (e.g. starting with "s3://", and "gcs://") the
+        key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec``
+        and ``urllib`` for more details, and for more examples on storage
+        options refer `here <https://pandas.pydata.org/docs/user_guide/io.html?
+        highlight=storage_options#reading-writing-remote-files>`_.
     filesystem : fsspec or pyarrow filesystem, default None
         Filesystem object to use when reading the parquet file. Only implemented
         for ``engine="pyarrow"``.
@@ -504,7 +506,6 @@ def to_parquet(
 
 
 @set_module("pandas")
-@doc(storage_options=_shared_docs["storage_options"])
 def read_parquet(
     path: FilePath | ReadBuffer[bytes],
     engine: str = "auto",
@@ -547,8 +548,15 @@ def read_parquet(
         if you wish to use its implementation.
     columns : list, default=None
         If not None, only these columns will be read from the file.
-    {storage_options}
-
+    storage_options : dict, optional
+        Extra options that make sense for a particular storage connection, e.g.
+        host, port, username, password, etc. For HTTP(S) URLs the key-value
+        pairs are forwarded to ``urllib.request.Request`` as header options.
+        For other URLs (e.g. starting with "s3://", and "gcs://") the
+        key-value pairs are forwarded to ``fsspec.open``. Please see ``fsspec``
+        and ``urllib`` for more details, and for more examples on storage
+        options refer `here <https://pandas.pydata.org/docs/user_guide/io.html?
+        highlight=storage_options#reading-writing-remote-files>`_.
     dtype_backend : {{'numpy_nullable', 'pyarrow'}}
         Back-end data type applied to the resultant :class:`DataFrame`
         (still experimental). If not specified, the default behavior
@@ -609,7 +617,7 @@ def read_parquet(
 
     Examples
     --------
-    >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})
+    >>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
     >>> original_df
        foo  bar
     0    0    5