pyspark.sql.functions.nth_value¶

pyspark.sql.functions.nth_value(col: ColumnOrName, offset: int, ignoreNulls: Optional[bool] = False) → pyspark.sql.column.Column[source]¶

Window function: returns the value that is the offsetth row of the window frame (counting from 1), and null if the size of window frame is less than offset rows.

It will return the offsetth non-null value it sees when ignoreNulls is set to true. If all values are null, then null is returned.

This is equivalent to the nth_value function in SQL.

New in version 3.1.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colColumn or str: name of column or expression
offsetint: number of row to use as the value
ignoreNullsbool, optional: indicates the Nth value should skip null in the determination of which row to use

Returns

Column: value of nth row.

Examples

>>> from pyspark.sql import Window
>>> df = spark.createDataFrame([("a", 1),
...                             ("a", 2),
...                             ("a", 3),
...                             ("b", 8),
...                             ("b", 2)], ["c1", "c2"])
>>> df.show()
+---+---+
| c1| c2|
+---+---+
|  a|  1|
|  a|  2|
|  a|  3|
|  b|  8|
|  b|  2|
+---+---+
>>> w = Window.partitionBy("c1").orderBy("c2")
>>> df.withColumn("nth_value", nth_value("c2", 1).over(w)).show()
+---+---+---------+
| c1| c2|nth_value|
+---+---+---------+
|  a|  1|        1|
|  a|  2|        1|
|  a|  3|        1|
|  b|  2|        2|
|  b|  8|        2|
+---+---+---------+
>>> df.withColumn("nth_value", nth_value("c2", 2).over(w)).show()
+---+---+---------+
| c1| c2|nth_value|
+---+---+---------+
|  a|  1|     null|
|  a|  2|        2|
|  a|  3|        2|
|  b|  2|     null|
|  b|  8|        8|
+---+---+---------+

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