# -*- coding: utf-8 -*-
"""
author SparkByExamples.com
"""

from pyspark.sql import SparkSession
from pyspark.sql.types import StructType,StructField, StringType, IntegerType

spark = SparkSession.builder.appName('SparkByExamples.com').getOrCreate()
data = [ ("36636","Finance",3000,"USA"), 
    ("40288","Finance",5000,"IND"), 
    ("42114","Sales",3900,"USA"), 
    ("39192","Marketing",2500,"CAN"), 
    ("34534","Sales",6500,"USA") ]
schema = StructType([
     StructField('id', StringType(), True),
     StructField('dept', StringType(), True),
     StructField('salary', IntegerType(), True),
     StructField('location', StringType(), True)
     ])

df = spark.createDataFrame(data=data,schema=schema)
df.printSchema()
df.show(truncate=False)

#Convert scolumns to Map
from pyspark.sql.functions import col,lit,create_map
df = df.withColumn("propertiesMap",create_map(
        lit("salary"),col("salary"),
        lit("location"),col("location")
        )).drop("salary","location")
df.printSchema()
df.show(truncate=False)