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PySpark SQL Functions | collect_set method

Machine Learning
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PySpark
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PySpark SQL Functions
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark SQL Functions' collect_set(~) method returns a unique set of values in a column. Null values are ignored.

NOTE

Use collect_list(~) instead to obtain a list of values that allows for duplicates.

Parameters

1. col | string or Column object

The column label or a Column object.

Return Value

A PySpark SQL Column object (pyspark.sql.column.Column).

WARNING

Assume that the order of the returned set may be random since the order is affected by shuffle operationslink.

Examples

Consider the following PySpark DataFrame:

data = [("Alex", "A"), ("Alex", "B"), ("Bob", "A"), ("Cathy", "C"), ("Dave", None)]
df = spark.createDataFrame(data, ["name", "group"])
df.show()
+-----+-----+
| name|group|
+-----+-----+
| Alex| A|
| Alex| B|
| Bob| A|
|Cathy| C|
| Dave| null|
+-----+-----+

Getting a set of column values in PySpark

To get the unique set of values in the group column:

import pyspark.sql.functions as F
df.select(F.collect_set("group")).show()
+------------------+
|collect_set(group)|
+------------------+
| [C, B, A]|
+------------------+

Equivalently, you can pass in a Column object to collect_set(~) as well:

import pyspark.sql.functions as F
df.select(F.collect_set(df.group)).show()
+------------------+
|collect_set(group)|
+------------------+
| [C, B, A]|
+------------------+

Notice how the null value does not appear in the resulting set.

Getting the set as a standard list

To get the set as a standard list:

list_rows = df.select(F.collect_set(df.group)).collect()
list_rows[0][0]
['C', 'B', 'A']

Here, the PySpark DataFrame's collect() method returns a list of Row objects. This list is guaranteed to be length one due to the nature of collect_set(~). The Row object contains the list so we need to include another [0].

Getting a set of column values of each group in PySpark

The method collect_set(~) is often used in the context of aggregation. Consider the same PySpark DataFrame as before:

df.show()
+-----+-----+
| name|group|
+-----+-----+
| Alex| A|
| Alex| B|
| Bob| A|
|Cathy| C|
| Dave| null|
+-----+-----+

To flatten the group column into a single set for each name:

import pyspark.sql.functions as F
df.groupby("name").agg(F.collect_set("group")).show()
+-----+------------------+
| name|collect_set(group)|
+-----+------------------+
| Alex| [B, A]|
| Bob| [A]|
|Cathy| [C]|
+-----+------------------+
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Published by Isshin Inada
Edited by 0 others
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