Pyspark DataFrame - using LIKE function based on column name instead of string value

1
votes

I'm trying to use the like function on a Column with another Column. Is it possible to use Column inside the like function?

sample code:

df['col1'].like(concat('%',df2['col2'], '%'))

Error log:

py4j.Py4JException: Method like([class org.apache.spark.sql.Column]) does not exist at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318) at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326) at py4j.Gateway.invoke(Gateway.java:274) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:214) at java.lang.Thread.run(Thread.java:748)

1
apache-sparkpysparkapache-spark-sql

1 Answers

3
votes

You can do it using a SQL expression instead. For some reason the python API doesn't directly support it. For example:

from pyspark.sql.functions import expr

data = [
    ("aaaa", "aa"),
    ("bbbb", "cc")
]

df = sc.parallelize(data).toDF(["value", "pattern"])
df = df.withColumn("match", expr("value like concat('%', pattern, '%')"))
df.show()

Outputs this:

+-----+-------+-----+
|value|pattern|match|
+-----+-------+-----+
| aaaa|     aa| true|
| bbbb|     cc|false|
+-----+-------+-----+