Change column type from string to date in Pyspark

Question

I'm trying to change my column type from string to date. I have consulted answers from:

1. How to change the column type from String to Date in DataFrames?
2. Why I get null results from date_format() PySpark function?

When I tried to apply answers from link 1, I got null result instead, so I referred to answer from link 2 but I don't understand this part:

output_format = ...  # Some SimpleDateFormat string

Answer 1

from pyspark.sql.functions import col, unix_timestamp, to_date

#sample data
df = sc.parallelize([['12-21-2006'],
                     ['05-30-2007'],
                     ['01-01-1984'],
                     ['12-24-2017']]).toDF(["date_in_strFormat"])
df.printSchema()

df = df.withColumn('date_in_dateFormat', 
                   to_date(unix_timestamp(col('date_in_strFormat'), 'MM-dd-yyyy').cast("timestamp")))
df.show()
df.printSchema()

Output is:

root
 |-- date_in_strFormat: string (nullable = true)

+-----------------+------------------+
|date_in_strFormat|date_in_dateFormat|
+-----------------+------------------+
|       12-21-2006|        2006-12-21|
|       05-30-2007|        2007-05-30|
|       01-01-1984|        1984-01-01|
|       12-24-2017|        2017-12-24|
+-----------------+------------------+

root
 |-- date_in_strFormat: string (nullable = true)
 |-- date_in_dateFormat: date (nullable = true)

Answer 2

simple way:

from pyspark.sql.types import *
df_1 = df.withColumn("col_with_date_format",
df["col_with_date_format"].cast(DateType()))

Answer 3

Here is a more easy way by using default to_date function:

from pyspark.sql import functions as F
df= df.withColumn('col_with_date_format',F.to_date(df.col_with_str_format))