There are a few efficient ways to implement this. Let's start with required imports:
from pyspark.sql.functions import col, expr, when
You can use Hive IF function inside expr:
new_column_1 = expr(
"""IF(fruit1 IS NULL OR fruit2 IS NULL, 3, IF(fruit1 = fruit2, 1, 0))"""
)
or when + otherwise:
new_column_2 = when(
col("fruit1").isNull() | col("fruit2").isNull(), 3
).when(col("fruit1") == col("fruit2"), 1).otherwise(0)
Finally you could use following trick:
from pyspark.sql.functions import coalesce, lit
new_column_3 = coalesce((col("fruit1") == col("fruit2")).cast("int"), lit(3))
With example data:
df = sc.parallelize([
("orange", "apple"), ("kiwi", None), (None, "banana"),
("mango", "mango"), (None, None)
]).toDF(["fruit1", "fruit2"])
you can use this as follows:
(df
.withColumn("new_column_1", new_column_1)
.withColumn("new_column_2", new_column_2)
.withColumn("new_column_3", new_column_3))
and the result is:
+------+------+------------+------------+------------+
|fruit1|fruit2|new_column_1|new_column_2|new_column_3|
+------+------+------------+------------+------------+
|orange| apple| 0| 0| 0|
| kiwi| null| 3| 3| 3|
| null|banana| 3| 3| 3|
| mango| mango| 1| 1| 1|
| null| null| 3| 3| 3|
+------+------+------------+------------+------------+