You can use Bucketizer feature transfrom from ml library in spark.
values = [("a", 23), ("b", 45), ("c", 10), ("d", 60), ("e", 56), ("f", 2), ("g", 25), ("h", 40), ("j", 33)]
df = spark.createDataFrame(values, ["name", "ages"])
from pyspark.ml.feature import Bucketizer
bucketizer = Bucketizer(splits=[ 0, 6, 18, 60, float('Inf') ],inputCol="ages", outputCol="buckets")
df_buck = bucketizer.setHandleInvalid("keep").transform(df)
df_buck.show()
output
+----+----+-------+
|name|ages|buckets|
+----+----+-------+
| a| 23| 2.0|
| b| 45| 2.0|
| c| 10| 1.0|
| d| 60| 3.0|
| e| 56| 2.0|
| f| 2| 0.0|
| g| 25| 2.0|
| h| 40| 2.0|
| j| 33| 2.0|
+----+----+-------+
If you want names for each bucket you can use udf to create a new column with bucket names
from pyspark.sql.functions import udf
from pyspark.sql.types import *
t = {0.0:"infant", 1.0: "minor", 2.0:"adult", 3.0: "senior"}
udf_foo = udf(lambda x: t[x], StringType())
df_buck.withColumn("age_bucket", udf_foo("buckets")).show()
output
+----+----+-------+----------+
|name|ages|buckets|age_bucket|
+----+----+-------+----------+
| a| 23| 2.0| adult|
| b| 45| 2.0| adult|
| c| 10| 1.0| minor|
| d| 60| 3.0| senior|
| e| 56| 2.0| adult|
| f| 2| 0.0| infant|
| g| 25| 2.0| adult|
| h| 40| 2.0| adult|
| j| 33| 2.0| adult|
+----+----+-------+----------+