pyspark.sql.DataFrame.intersectAll#

DataFrame.intersectAll(other)[source]#

Return a new DataFrame containing rows in both this DataFrame and another DataFrame while preserving duplicates.

This is equivalent to INTERSECT ALL in SQL. As standard in SQL, this function resolves columns by position (not by name).

New in version 2.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

otherDataFrame: Another DataFrame that needs to be combined.

Returns

DataFrame: Combined DataFrame.

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"])
>>> result_df = df1.intersectAll(df2).sort("C1", "C2")
>>> result_df.show()
+---+---+
| C1| C2|
+---+---+
|  a|  1|
|  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|
+---+---+