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Posted to reviews@spark.apache.org by "zhengruifeng (via GitHub)" <gi...@apache.org> on 2023/07/26 00:58:20 UTC

[GitHub] [spark] zhengruifeng commented on a diff in pull request #42151: [WIP][PYTHON][DOCS] Refine the docs for `Union`, `UnionAll` and `unionByName`

zhengruifeng commented on code in PR #42151:
URL: https://github.com/apache/spark/pull/42151#discussion_r1274257724


##########
python/pyspark/sql/dataframe.py:
##########
@@ -3831,42 +3856,57 @@ def unionByName(self, other: "DataFrame", allowMissingColumns: bool = False) ->
         allowMissingColumns : bool, optional, default False
            Specify whether to allow missing columns.
 
-           .. versionadded:: 3.1.0
-
         Returns
         -------
         :class:`DataFrame`
-            Combined DataFrame.
+            A new :class:`DataFrame` containing the combined rows with corresponding
+            columns of the two given DataFrames.
+
+        Raises
+        ------
+        Py4JJavaError : If the column name conflict
 
         Examples
         --------
-        The difference between this function and :func:`union` is that this function
-        resolves columns by name (not by position):
-
+        Example 1: Union of two DataFrames by Name
         >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
         >>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
-        >>> df1.unionByName(df2).show()
+        >>> df3 = df1.unionByName(df2)
+        >>> df3.show()
         +----+----+----+
         |col0|col1|col2|
         +----+----+----+
         |   1|   2|   3|
         |   6|   4|   5|
         +----+----+----+
 
-        When the parameter `allowMissingColumns` is ``True``, the set of column names
-        in this and other :class:`DataFrame` can differ; missing columns will be filled with null.
-        Further, the missing columns of this :class:`DataFrame` will be added at the end
-        in the schema of the union result:
-
+        Example 2: Union of DataFrames with missing columns
         >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
         >>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col3"])
-        >>> df1.unionByName(df2, allowMissingColumns=True).show()
+        >>> df3 = df1.unionByName(df2, allowMissingColumns=True)
+        >>> df3.show()
         +----+----+----+----+
         |col0|col1|col2|col3|
         +----+----+----+----+
-        |   1|   2|   3|NULL|
-        |NULL|   4|   5|   6|
+        |   1|   2|   3|null|
+        |null|   4|   5|   6|

Review Comment:
   no, I just copy-paste the result from LLM.
   And the new example result is wrong.



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