I want to know if there is a better way of transforming a date column into a datetime column + 1 hour than the method I am currently using.
Here is my dataframe:
df = sc.parallelize([ ['2019-08-29'], ['2019-08-30'], ['2019-09-1'], ['2019-09-2'], ['2019-09-4'], ['2019-09-10'] ]).toDF(['DATE']).withColumn('DATE',col('DATE').cast('date'))
My code:
df1 = df.withColumn( 'DATETIME', ((col('DATE').cast('timestamp').cast('long')+3600)).cast('timestamp') )
Which gives the output:
+----------+-------------------+ | DATE| DATETIME| +----------+-------------------+ |2019-08-29|2019-08-29 01:00:00| |2019-08-30|2019-08-30 01:00:00| |2019-09-01|2019-09-01 01:00:00| |2019-09-02|2019-09-02 01:00:00| |2019-09-04|2019-09-04 01:00:00| |2019-09-10|2019-09-10 01:00:00| +----------+-------------------+
Does anyone know a more efficient way of doing this. Casting to a timestamp twice seems a bit clumsy.
Many thanks.
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Answer
you can use something like this:
from pyspark.sql.functions import expr df1 = df.withColumn('DATETIME', col('DATE').cast('timestamp')+ expr('INTERVAL 1 HOURS'))
then you can read more about syntax for intervals, for example, in following blog post from Databricks.