pyspark.sql.functions.lead

pyspark.sql.functions.lead(col: ColumnOrName, offset: int = 1, default: Optional[Any] = None) → pyspark.sql.column.Column

Window function: returns the value that is offset rows after the current row, and default if there is less than offset rows after the current row. For example, an offset of one will return the next row at any given point in the window partition.

This is equivalent to the LEAD function in SQL.

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

colColumn or str

name of column or expression

offsetint, optional default 1

number of row to extend

defaultoptional

default value

Returns

Column

value after current row based on offset.

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("next_value", lead("c2").over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
|  a|  1|         2|
|  a|  2|         3|
|  a|  3|      NULL|
|  b|  2|         8|
|  b|  8|      NULL|
+---+---+----------+
>>> df.withColumn("next_value", lead("c2", 1, 0).over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
|  a|  1|         2|
|  a|  2|         3|
|  a|  3|         0|
|  b|  2|         8|
|  b|  8|         0|
+---+---+----------+
>>> df.withColumn("next_value", lead("c2", 2, -1).over(w)).show()
+---+---+----------+
| c1| c2|next_value|
+---+---+----------+
|  a|  1|         3|
|  a|  2|        -1|
|  a|  3|        -1|
|  b|  2|        -1|
|  b|  8|        -1|
+---+---+----------+

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