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PySpark DataFrame | intersectAll method

Machine Learning
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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark DataFrame's intersectAll(~) method returns a new PySpark DataFrame with rows that also exist in the other PySpark DataFrame. Unlike intersect(~), the intersectAll(~) method preserves duplicates.

NOTE

The intersectAll(~) method is identical to to the INTERSECT ALL statement in SQL.

Parameters

1. other | PySpark DataFrame

The other PySpark DataFrame.

Return Value

A new PySpark DataFrame.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([("Alex", 20), ("Alex", 20), ("Bob", 30), ("Cathy", 40)], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+

Suppose the other PySpark DataFrame is:

df_other = spark.createDataFrame([("Alex", 20), ("Alex", 20), ("David", 80), ("Eric", 80)], ["name", "age"])
df_other.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Alex| 20|
|David| 80|
| Eric| 80|
+-----+---+

Here, note the following:

  • the only matching row is Alex's row

  • Alex's row appears twice in both df and df_other

Getting rows that also exist in other PySpark DataFrame while preserving duplicates

To get rows that also exist in other PySpark DataFrame while preserving duplicates:

df_res = df.intersectAll(df_other)
df_res.show()
+----+---+
|name|age|
+----+---+
|Alex| 20|
|Alex| 20|
+----+---+

Note the following:

  • Alex's row is duplicated because Alex's row appears twice in df and df_other each.

  • if Alex's row only appeared once in one DataFrame but appeared multiple times in another, Alex's row will only be included once in the resulting DataFrame.

  • if you want to include duplicating rows only once, then use the intersect(~) method instead.

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Published by Isshin Inada
Edited by 0 others
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