[GitHub] [arrow] westonpace commented on a change in pull request #11844: ARROW-14972: [Python][Doc] Document automatic partitioning discovery

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westonpace commented on a change in pull request #11844:
URL: https://github.com/apache/arrow/pull/11844#discussion_r763312562



##########
File path: docs/source/python/dataset.rst
##########
@@ -340,6 +340,30 @@ when constructing a directory partitioning:
 Directory partitioning also supports providing a full schema rather than inferring
 types from file paths.
 
+Automatic partitioning detection
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If the directory is partitioned using the hive partitioning scheme (see above)
+then pyarrow will be able to automatically recognize the partitioning and include
+the partitioning information as a column in the returned table.  There is no
+need to specify the partitioning unless you need to override the inferred data
+types of the partitioning columns:
+
+.. code-block:: python
+
+    dataset = ds.dataset("hive_partitioned", format="parquet")

Review comment:
       That is confusing.  But you are correct:
   
   ```
   import pyarrow as pa
   import pyarrow.dataset as ds
   import pyarrow.parquet as pq
   
   table = pa.Table.from_pydict({'type': ['a', 'a', 'b', 'b'], 'value': [1, 2, 3, 4]})
   ds.write_dataset(table, '/tmp/my_dataset', format='parquet', partitioning=['type'], partitioning_flavor='hive', existing_data_behavior='overwrite_or_ignore')
   
   print(ds.dataset('/tmp/my_dataset').to_table().column_names)
   print(ds.dataset('/tmp/my_dataset', partitioning='hive').to_table().column_names)
   print(pq.read_table('/tmp/my_dataset').column_names)
   ```
   
   Can we make this more intuitive somehow?  Maybe have `hive` be the default partitioning for a dataset similar to how it is for pq.read_table?




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