when (Column)
Evaluate a list of conditions and return one of multiple possible result expressions.
Syntax
Python
when(condition, value)
Parameters
Parameter | Type | Description |
|---|---|---|
| Column | Boolean condition |
| value | Value to return if condition is true |
Returns
Column
Examples
Example 1: Using when() with conditions and values to create a new Column.
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age", "name"])
result = df.select(df.name, sf.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0))
result.show()
Output
# +-----+------------------------------------------------------------+
# | name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
# +-----+------------------------------------------------------------+
# |Alice| -1|
# | Bob| 1|
# +-----+------------------------------------------------------------+
Example 2: Chaining multiple when() conditions.
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([(1, "Alice"), (4, "Bob"), (6, "Charlie")], ["age", "name"])
result = df.select(
df.name,
sf.when(df.age < 3, "Young").when(df.age < 5, "Middle-aged").otherwise("Old")
)
result.show()
Output
# +-------+---------------------------------------------------------------------------+
# | name|CASE WHEN (age < 3) THEN Young WHEN (age < 5) THEN Middle-aged ELSE Old END|
# +-------+---------------------------------------------------------------------------+
# | Alice| Young|
# | Bob| Middle-aged|
# |Charlie| Old|
# +-------+---------------------------------------------------------------------------+
Example 3: Using literal values as conditions.
Python
from pyspark.sql import functions as sf
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age", "name"])
result = df.select(
df.name, sf.when(sf.lit(True), 1).otherwise(
sf.raise_error("unreachable")).alias("when"))
result.show()
Output
# +-----+----+
# | name|when|
# +-----+----+
# |Alice| 1|
# | Bob| 1|
# +-----+----+