zhengruifeng commented on code in PR #42151:
URL: https://github.com/apache/spark/pull/42151#discussion_r1275730095
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python/pyspark/sql/dataframe.py:
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@@ -3867,6 +3885,28 @@ def unionByName(self, other: "DataFrame",
allowMissingColumns: bool = False) ->
| 1| 2| 3|NULL|
|NULL| 4| 5| 6|
+----+----+----+----+
+
+ Example 3: Union of two DataFrames with few common columns.
+ >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
+ >>> df2 = spark.createDataFrame([[4, 5, 6, 7]], ["col1", "col2",
"col3","col4"])
+ >>> df1.unionByName(df2, allowMissingColumns=True).show()
+ +----+----+----+----+----+
+ |col0|col1|col2|col3|col4|
+ +----+----+----+----+----+
+ | 1| 2| 3|NULL|NULL|
+ |NULL| 4| 5| 6| 7|
+ +----+----+----+----+----+
+
+ Example 4: Union of two DataFrames with completely different columns.
+ >>> df1 = spark.createDataFrame([[0, 1, 2]], ["col0", "col1", "col2"])
Review Comment:
this example is good
I manually rename the column names from `a/b/c` to `col0/col1/col2` to be
more consistent with other examples
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