[apache-spark] Convert pyspark string to date format

I have a date pyspark dataframe with a string column in the format of MM-dd-yyyy and I am attempting to convert this into a date column.

I tried:

df.select(to_date(df.STRING_COLUMN).alias('new_date')).show()

and I get a string of nulls. Can anyone help?

Try this:

df = spark.createDataFrame([('2018-07-27 10:30:00',)], ['Date_col'])
df.select(from_unixtime(unix_timestamp(df.Date_col, 'yyyy-MM-dd HH:mm:ss')).alias('dt_col'))
df.show()
+-------------------+  
|           Date_col|  
+-------------------+  
|2018-07-27 10:30:00|  
+-------------------+  

possibly not so many answers so thinking to share my code which can help someone

from pyspark.sql import SparkSession
from pyspark.sql.functions import to_date

spark = SparkSession.builder.appName("Python Spark SQL basic example")\
    .config("spark.some.config.option", "some-value").getOrCreate()


df = spark.createDataFrame([('2019-06-22',)], ['t'])
df1 = df.select(to_date(df.t, 'yyyy-MM-dd').alias('dt'))
print df1
print df1.show()

output

DataFrame[dt: date]
+----------+
|        dt|
+----------+
|2019-06-22|
+----------+

the above code to convert to date if you want to convert datetime then use to_timestamp. let me know if you have any doubt.

from datetime import datetime
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DateType



# Creation of a dummy dataframe:
df1 = sqlContext.createDataFrame([("11/25/1991","11/24/1991","11/30/1991"), 
                            ("11/25/1391","11/24/1992","11/30/1992")], schema=['first', 'second', 'third'])

# Setting an user define function:
# This function converts the string cell into a date:
func =  udf (lambda x: datetime.strptime(x, '%m/%d/%Y'), DateType())

df = df1.withColumn('test', func(col('first')))

df.show()

df.printSchema()

Here is the output:

+----------+----------+----------+----------+
|     first|    second|     third|      test|
+----------+----------+----------+----------+
|11/25/1991|11/24/1991|11/30/1991|1991-01-25|
|11/25/1391|11/24/1992|11/30/1992|1391-01-17|
+----------+----------+----------+----------+

root
 |-- first: string (nullable = true)
 |-- second: string (nullable = true)
 |-- third: string (nullable = true)
 |-- test: date (nullable = true)

The strptime() approach does not work for me. I get another cleaner solution, using cast:

from pyspark.sql.types import DateType
spark_df1 = spark_df.withColumn("record_date",spark_df['order_submitted_date'].cast(DateType()))
#below is the result
spark_df1.select('order_submitted_date','record_date').show(10,False)

+---------------------+-----------+
|order_submitted_date |record_date|
+---------------------+-----------+
|2015-08-19 12:54:16.0|2015-08-19 |
|2016-04-14 13:55:50.0|2016-04-14 |
|2013-10-11 18:23:36.0|2013-10-11 |
|2015-08-19 20:18:55.0|2015-08-19 |
|2015-08-20 12:07:40.0|2015-08-20 |
|2013-10-11 21:24:12.0|2013-10-11 |
|2013-10-11 23:29:28.0|2013-10-11 |
|2015-08-20 16:59:35.0|2015-08-20 |
|2015-08-20 17:32:03.0|2015-08-20 |
|2016-04-13 16:56:21.0|2016-04-13 |

In the accepted answer's update you don't see the example for the to_date function, so another solution using it would be:

from pyspark.sql import functions as F

df = df.withColumn(
            'new_date',
                F.to_date(
                    F.unix_timestamp('STRINGCOLUMN', 'MM-dd-yyyy').cast('timestamp')))