How do I use multiple conditions with pyspark.sql.functions.when()?
Question:
I have a dataframe with a few columns. Now I want to derive a new column from 2 other columns:
from pyspark.sql import functions as F
new_df = df.withColumn("new_col", F.when(df["col-1"] > 0.0 & df["col-2"] > 0.0, 1).otherwise(0))
With this I only get an exception:
py4j.Py4JException: Method and([class java.lang.Double]) does not exist
It works with just one condition like this:
new_df = df.withColumn("new_col", F.when(df["col-1"] > 0.0, 1).otherwise(0))
Does anyone know to use multiple conditions?
I’m using Spark 1.4.
Answers:
Use parentheses to enforce the desired operator precedence:
F.when( (df["col-1"]>0.0) & (df["col-2"]>0.0), 1).otherwise(0)
you can also use
from pyspark.sql.functions import col
F.when(col("col-1")>0.0) & (col("col-2")>0.0), 1).otherwise(0)
when in pyspark multiple conditions can be built using &(for and) and | (for or), it 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()
when in spark scala can be used with && and || operator to build multiple conditions
//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|
+---+----+---+----------+
I have a dataframe with a few columns. Now I want to derive a new column from 2 other columns:
from pyspark.sql import functions as F
new_df = df.withColumn("new_col", F.when(df["col-1"] > 0.0 & df["col-2"] > 0.0, 1).otherwise(0))
With this I only get an exception:
py4j.Py4JException: Method and([class java.lang.Double]) does not exist
It works with just one condition like this:
new_df = df.withColumn("new_col", F.when(df["col-1"] > 0.0, 1).otherwise(0))
Does anyone know to use multiple conditions?
I’m using Spark 1.4.
Use parentheses to enforce the desired operator precedence:
F.when( (df["col-1"]>0.0) & (df["col-2"]>0.0), 1).otherwise(0)
you can also use
from pyspark.sql.functions import col
F.when(col("col-1")>0.0) & (col("col-2")>0.0), 1).otherwise(0)
when in pyspark multiple conditions can be built using &(for and) and | (for or), it 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()
when in spark scala can be used with && and || operator to build multiple conditions
//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|
+---+----+---+----------+