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Use when() and otherwise() with PySpark DataFrame

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In Spark SQL, CASE WHEN clause can be used to evaluate a list of conditions and to return one of the multiple results for each column. The same can be implemented directly using pyspark.sql.functions.when and pyspark.sql.Column.otherwise functions. If otherwise is not used together with when, None will be returned for unmatched conditions.

Output:

+---+------+
| id|id_new|
+---+------+
|  1|     1|
|  2|   200|
|  3|  3000|
|  4|   400|
|  5|     5|
|  6|   600|
|  7|     7|
|  8|   800|
|  9|  9000|
+---+------+

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from pyspark.sql import SparkSession
from pyspark.sql.functions import when

appName = "PySpark when and otherwise Example"
master = "local"

# Create Spark session
spark = SparkSession.builder \
    .appName(appName) \
    .master(master) \
    .getOrCreate()

spark.sparkContext.setLogLevel("WARN")

df = spark.range(1, 10)
df = df.withColumn('id_new', when(df.id % 2 == 0, df.id *
                                  100).when(df.id % 3 == 0, df.id*1000).otherwise(df.id))
df.show()

Use when() and otherwise() with PySpark DataFrame

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