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The following commit(s) were added to refs/heads/master by this push:
     new e6bbd3172940 [SPARK-45984][PYTHON[DOCS] Refine docstring of 
`DataFrame.intersectAll`
e6bbd3172940 is described below

commit e6bbd317294032d3352e09c01bf9b640dae46f71
Author: Hyukjin Kwon <gurwls...@apache.org>
AuthorDate: Sat Nov 18 10:14:53 2023 -0800

    [SPARK-45984][PYTHON[DOCS] Refine docstring of `DataFrame.intersectAll`
    
    ### What changes were proposed in this pull request?
    
    This PR proposes to improve the docstring of `DataFrame.intersectAll`.
    
    ### Why are the changes needed?
    
    For end users, and better usability of PySpark.
    
    ### Does this PR introduce _any_ user-facing change?
    
    Yes, it fixes the user facing documentation.
    
    ### How was this patch tested?
    
    Manually tested.
    
    ### Was this patch authored or co-authored using generative AI tooling?
    
    No.
    
    Closes #43883 from HyukjinKwon/SPARK-45984.
    
    Authored-by: Hyukjin Kwon <gurwls...@apache.org>
    Signed-off-by: Dongjoon Hyun <dh...@apple.com>
---
 python/pyspark/sql/dataframe.py | 30 +++++++++++++++++++++++++++++-
 1 file changed, 29 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index f83f75a47896..8d7c7d70a501 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -4791,9 +4791,12 @@ class DataFrame(PandasMapOpsMixin, 
PandasConversionMixin):
 
         Examples
         --------
+        Example 1: Intersecting two DataFrames with the same schema
+
         >>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 
4)], ["C1", "C2"])
         >>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", 
"C2"])
-        >>> df1.intersectAll(df2).sort("C1", "C2").show()
+        >>> result_df = df1.intersectAll(df2).sort("C1", "C2")
+        >>> result_df.show()
         +---+---+
         | C1| C2|
         +---+---+
@@ -4801,6 +4804,31 @@ class DataFrame(PandasMapOpsMixin, 
PandasConversionMixin):
         |  a|  1|
         |  b|  3|
         +---+---+
+
+        Example 2: Intersecting two DataFrames with different schemas
+
+        >>> df1 = spark.createDataFrame([(1, "A"), (2, "B")], ["id", "value"])
+        >>> df2 = spark.createDataFrame([(2, "B"), (3, "C")], ["id", "value"])
+        >>> result_df = df1.intersectAll(df2).sort("id", "value")
+        >>> result_df.show()
+        +---+-----+
+        | id|value|
+        +---+-----+
+        |  2|    B|
+        +---+-----+
+
+        Example 3: Intersecting all rows from two DataFrames with mismatched 
columns
+
+        >>> df1 = spark.createDataFrame([(1, 2), (1, 2), (3, 4)], ["A", "B"])
+        >>> df2 = spark.createDataFrame([(1, 2), (1, 2)], ["C", "D"])
+        >>> result_df = df1.intersectAll(df2).sort("A", "B")
+        >>> result_df.show()
+        +---+---+
+        |  A|  B|
+        +---+---+
+        |  1|  2|
+        |  1|  2|
+        +---+---+
         """
         return DataFrame(self._jdf.intersectAll(other._jdf), self.sparkSession)
 


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