PySpark: multiple conditions in when clause
You get SyntaxError
error exception because Python has no &&
operator. It has and
and &
where the latter one is the correct choice to create boolean expressions on Column
(|
for a logical disjunction and ~
for logical negation).
Condition you created is also invalid because it doesn't consider operator precedence. &
in Python has a higher precedence than ==
so expression has to be parenthesized.
(col("Age") == "") & (col("Survived") == "0")
## Column<b'((Age = ) AND (Survived = 0))'>
On a side note when
function is equivalent to case
expression not WHEN
clause. Still the same rules apply. Conjunction:
df.where((col("foo") > 0) & (col("bar") < 0))
Disjunction:
df.where((col("foo") > 0) | (col("bar") < 0))
You can of course define conditions separately to avoid brackets:
cond1 = col("Age") == ""
cond2 = col("Survived") == "0"
cond1 & cond2
when in pyspark multiple conditions can be built using &(for and) and | (for or).
Note:In pyspark t is important to enclose every expressions within parenthesis () that combine to form the condition
%pyspark
dataDF = spark.createDataFrame([(66, "a", "4"),
(67, "a", "0"),
(70, "b", "4"),
(71, "d", "4")],
("id", "code", "amt"))
dataDF.withColumn("new_column",
when((col("code") == "a") | (col("code") == "d"), "A")
.when((col("code") == "b") & (col("amt") == "4"), "B")
.otherwise("A1")).show()
In Spark Scala code (&&) or (||) conditions can be used within when function
//scala
val dataDF = Seq(
(66, "a", "4"), (67, "a", "0"), (70, "b", "4"), (71, "d", "4"
)).toDF("id", "code", "amt")
dataDF.withColumn("new_column",
when(col("code") === "a" || col("code") === "d", "A")
.when(col("code") === "b" && col("amt") === "4", "B")
.otherwise("A1")).show()
=======================
Output:
+---+----+---+----------+
| id|code|amt|new_column|
+---+----+---+----------+
| 66| a| 4| A|
| 67| a| 0| A|
| 70| b| 4| B|
| 71| d| 4| A|
+---+----+---+----------+
This code snippet is copied from sparkbyexamples.com