Q: PySpark - Add map function as column 

I have a pyspark DataFrame 
a = [
    ('Bob', 562),
    ('Bob',880),
    ('Bob',380),
    ('Sue',85),
    ('Sue',963)
] 
df = spark.createDataFrame(a, ["Person", "Amount"])

I need to create a column that hashes the Amount and returns the amount. The problem is I can't use a UDF so I have used a mapping function. 
df.rdd.map(lambda x: hash(x["Amount"]))


A: If you can't use udf you can use the map function, but as you've currently written it, there will only be one column. To keep all the columns, do the following:
df = df.rdd\
    .map(lambda x: (x["Person"], x["Amount"], hash(str(x["Amount"]))))\
    .toDF(["Person", "Amount", "Hash"])

df.show()
#+------+------+--------------------+
#|Person|Amount|                Hash|
#+------+------+--------------------+
#|   Bob|   562|-4340709941618811062|
#|   Bob|   880|-7718876479167384701|
#|   Bob|   380|-2088598916611095344|
#|   Sue|    85|    7168043064064671|
#|   Sue|   963|-8844931991662242457|
#+------+------+--------------------+

Note: In this case, hash(x["Amount"]) is not very interesting so I changed it to hash Amount converted to a string.
Essentially you have to map the row to a tuple containing all of the existing columns and add in the new column(s).
If your columns are too many to enumerate, you could also just add a tuple to the existing row.
df = df.rdd\
    .map(lambda x: x + (hash(str(x["Amount"])),))\
    .toDF(df.columns + ["Hash"])\


I should also point out that if hashing the values is your end goal, there is also a pyspark function pyspark.sql.functions.hash that can be used to avoid the serialization to rdd:
import pyspark.sql.functions as f
df.withColumn("Hash", f.hash("Amount")).show()
#+------+------+----------+
#|Person|Amount|      Hash|
#+------+------+----------+
#|   Bob|   562|  51343841|
#|   Bob|   880|1241753636|
#|   Bob|   380| 514174926|
#|   Sue|    85|1944150283|
#|   Sue|   963|1665082423|
#+------+------+----------+

This appears to use a different hashing algorithm than the python builtin.
