zhengruifeng commented on code in PR #42151:
URL: https://github.com/apache/spark/pull/42151#discussion_r1274258295
##########
python/pyspark/sql/dataframe.py:
##########
@@ -3729,63 +3729,90 @@ def observe(
)
def union(self, other: "DataFrame") -> "DataFrame":
- """Return a new :class:`DataFrame` containing union of rows in this
and another
+ """Return a new :class:`DataFrame` containing the union of rows in
this and another
:class:`DataFrame`.
.. versionadded:: 2.0.0
-
- .. versionchanged:: 3.4.0
- Supports Spark Connect.
+ .. versionchanged:: 3.4.0 Supports Spark Connect.
Parameters
----------
other : :class:`DataFrame`
- Another :class:`DataFrame` that needs to be unioned
+ Another :class:`DataFrame` that needs to be unioned.
Returns
-------
:class:`DataFrame`
+ A new :class:`DataFrame` containing the combined rows with
corresponding columns.
See Also
--------
DataFrame.unionAll
Notes
-----
- This is equivalent to `UNION ALL` in SQL. To do a SQL-style set union
- (that does deduplication of elements), use this function followed by
:func:`distinct`.
-
- Also as standard in SQL, this function resolves columns by position
(not by name).
+ - This method performs a SQL-style set union of the rows from both
`DataFrame` objects,
+ with no automatic deduplication of elements.
+ - Use the `distinct()` method to perform deduplication of rows.
+ - The method resolves columns by position (not by name), following the
standard behavior
+ in SQL.
+ - Alias: The `union` method was previously named `unionAll` in
versions before 2.0.0.
Examples
--------
- >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
- >>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
- >>> df1.union(df2).show()
- +----+----+----+
- |col0|col1|col2|
- +----+----+----+
- | 1| 2| 3|
- | 4| 5| 6|
- +----+----+----+
- >>> df1.union(df1).show()
- +----+----+----+
- |col0|col1|col2|
- +----+----+----+
- | 1| 2| 3|
- | 1| 2| 3|
- +----+----+----+
+ Example 1: Combining two DataFrames with the same schema
+ >>> df1 = spark.createDataFrame([(1, 'A'), (2, 'B')], ['id', 'value'])
+ >>> df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
+ >>> df3 = df1.union(df2)
+ >>> df3.show()
+ +---+-----+
+ | id|value|
+ +---+-----+
+ | 1| A|
+ | 2| B|
+ | 3| C|
+ | 4| D|
+ +---+-----+
+
+ Example 2: Combining two DataFrames with different schemas
Review Comment:
this example is pretty awesome, since I don't know `union`'s type coercion
before
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