[GitHub] [arrow] lidavidm commented on a change in pull request #10548: ARROW-13034: [Python][Docs] Update the cloud examples on the Parquet doc page

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lidavidm commented on a change in pull request #10548:
URL: https://github.com/apache/arrow/pull/10548#discussion_r654478016



##########
File path: docs/source/python/parquet.rst
##########
@@ -545,46 +546,38 @@ This can be disabled by specifying ``use_threads=False``.
    The number of threads to use concurrently is automatically inferred by Arrow
    and can be inspected using the :func:`~pyarrow.cpu_count()` function.
 
+Reading from cloud storage
+--------------------------
 
-Reading a Parquet File from Azure Blob storage
-----------------------------------------------
+In addition to local files, pyarrow supports other filesystems, such as cloud
+filesystems, through the ``filesystem`` keyword:
 
-The code below shows how to use Azure's storage sdk along with pyarrow to read
-a parquet file into a Pandas dataframe.
-This is suitable for executing inside a Jupyter notebook running on a Python 3
-kernel.
+.. code-block:: python
+
+    from pyarrow import fs
 
-Dependencies:
+    s3  = fs.S3FileSystem(region="us-east-2")
+    table = pq.read_table("path/to/file/or/directory/", filesystem=s3)
 
-* python 3.6.2
-* azure-storage 0.36.0
-* pyarrow 0.8.0
+Currently, :class:`HDFS <pyarrow.fs.HadoopFileSystem>` and
+:class:`Amazon S3-compatible storage <pyarrow.fs.S3FileSystem>` are
+supported. See the :ref:`filesystem` docs for more details. For those
+built-in filesystems, the filesystem can also be inferred from the file path,
+if specified as a URI:
 
 .. code-block:: python
 
-   import pyarrow.parquet as pq
-   from io import BytesIO
-   from azure.storage.blob import BlockBlobService
-
-   account_name = '...'
-   account_key = '...'
-   container_name = '...'
-   parquet_file = 'mysample.parquet'
-
-   byte_stream = io.BytesIO()
-   block_blob_service = BlockBlobService(account_name=account_name, account_key=account_key)
-   try:
-      block_blob_service.get_blob_to_stream(container_name=container_name, blob_name=parquet_file, stream=byte_stream)
-      df = pq.read_table(source=byte_stream).to_pandas()
-      # Do work on df ...
-   finally:
-      # Add finally block to ensure closure of the stream
-      byte_stream.close()
-
-Notes:
-
-* The ``account_key`` can be found under ``Settings -> Access keys`` in the
-  Microsoft Azure portal for a given container
-* The code above works for a container with private access, Lease State =
-  Available, Lease Status = Unlocked
-* The parquet file was Blob Type = Block blob
+    table = pq.read_table("s3://path/to/file/or/directory/")

Review comment:
       nit: maybe `s3://bucket/object/key/prefix` or something would be a little more accurate for S3?




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