cast a date to integer pyspark - dataframe

Question

Is is possible to convert a date column to an integer column in a pyspark dataframe? I tried 2 different ways but every attempt returns a column with nulls. What am I missing?
from pyspark.sql.types import *
# DUMMY DATA
simpleData = [("James",34,"2006-01-01","true","M",3000.60),
("Michael",33,"1980-01-10","true","F",3300.80),
("Robert",37,"1992-07-01","false","M",5000.50)
]
columns = ["firstname","age","jobStartDate","isGraduated","gender","salary"]
df = spark.createDataFrame(data = simpleData, schema = columns)
df=df.withColumn("jobStartDate", df['jobStartDate'].cast(DateType()))
# ATTEMPT 1 with cast()
df=df.withColumn("jobStartDateAsInteger1", df['jobStartDate'].cast(IntegerType()))
# ATTEMPT 2 with selectExpr()
df=df.selectExpr("*","CAST(jobStartDate as int) as jobStartDateAsInteger2")
df.show()

Answer 1

You can try casting it to a UNIX timestamp using F.unix_timestamp():
from pyspark.sql.types import *
import pyspark.sql.functions as F
# DUMMY DATA
simpleData = [("James",34,"2006-01-01","true","M",3000.60),
("Michael",33,"1980-01-10","true","F",3300.80),
("Robert",37,"1992-07-01","false","M",5000.50)
]
columns = ["firstname","age","jobStartDate","isGraduated","gender","salary"]
df = spark.createDataFrame(data = simpleData, schema = columns)
df=df.withColumn("jobStartDate", df['jobStartDate'].cast(DateType()))
df=df.withColumn("jobStartDateAsInteger1", F.unix_timestamp(df['jobStartDate']))
df.show()
+---------+---+------------+-----------+------+------+----------------------+
|firstname|age|jobStartDate|isGraduated|gender|salary|jobStartDateAsInteger1|
+---------+---+------------+-----------+------+------+----------------------+
| James| 34| 2006-01-01| true| M|3000.6| 1136073600|
| Michael| 33| 1980-01-10| true| F|3300.8| 316310400|
| Robert| 37| 1992-07-01| false| M|5000.5| 709948800|
+---------+---+------------+-----------+------+------+----------------------+