Converting pattern of date in spark dataframe - scala

Question

I have a column in spark dataframe of String datatype (with date in yyyy-MM-dd pattern)
I want to display the column value in MM/dd/yyyy pattern
My data is
val df = sc.parallelize(Array(
("steak", "1990-01-01", "2000-01-01", 150),
("steak", "2000-01-02", "2001-01-13", 180),
("fish", "1990-01-01", "2001-01-01", 100)
)).toDF("name", "startDate", "endDate", "price")
df.show()
+-----+----------+----------+-----+
| name| startDate| endDate|price|
+-----+----------+----------+-----+
|steak|1990-01-01|2000-01-01| 150|
|steak|2000-01-02|2001-01-13| 180|
| fish|1990-01-01|2001-01-01| 100|
+-----+----------+----------+-----+
root
|-- name: string (nullable = true)
|-- startDate: string (nullable = true)
|-- endDate: string (nullable = true)
|-- price: integer (nullable = false)
I want to show endDate in MM/dd/yyyy pattern. All I am able to do is convert the column to DateType from String
val df2 = df.select($"endDate".cast(DateType).alias("endDate"))
df2.show()
+----------+
| endDate|
+----------+
|2000-01-01|
|2001-01-13|
|2001-01-01|
+----------+
df2.printSchema()
root
|-- endDate: date (nullable = true)
I want to show endDate in MM/dd/yyyy pattern. Only reference I found is this which doesn't solve the problem

Answer 1

You can use date_format function.
import sqlContext.implicits._
import org.apache.spark.sql.functions._
val df = sc.parallelize(Array(
("steak", "1990-01-01", "2000-01-01", 150),
("steak", "2000-01-02", "2001-01-13", 180),
("fish", "1990-01-01", "2001-01-01", 100))).toDF("name", "startDate", "endDate", "price")
df.show()
df.select(date_format(col("endDate"), "MM/dd/yyyy")).show
Output :
+-------------------------------+
|date_format(endDate,MM/dd/yyyy)|
+-------------------------------+
| 01/01/2000|
| 01/13/2001|
| 01/01/2001|
+-------------------------------+

Answer 2

Use pyspark.sql.functions.date_format(date, format):
val df2 = df.select(date_format("endDate", "MM/dd/yyyy").alias("endDate"))

Answer 3

Dataframe/Dataset having a string column with date value in it and we need to change the date format.
For the query asked, date format can be changed as below:
val df1 = df.withColumn("startDate1", date_format(to_date(col("startDate"),"yyyy-MM-dd"),"MM/dd/yyyy" ))
In Spark, the default date format is "yyyy-MM-dd" hence it can be re-written as
val df1 = df.withColumn("startDate1", date_format(col("startDate"),"MM/dd/yyyy" ))
(i) By applying to_date, we are changing the datatype of this column (string) to Date datatype.
Also, we are informing to_date that the format in this string column is yyyy-MM-dd so read the column accordingly.
(ii) Next, we are applying date_format to achieve the date format we require which is MM/dd/yyyy.
When time component is involved, use to_timestamp instead of to_date.
Note that 'MM' represents month and 'mm' represents minutes.