Spark DataFrame TimestampType – how to get Year, Month, Day values from field?

Since Spark 1.5 you can use a number of date processing functions:

• pyspark.sql.functions.year
• pyspark.sql.functions.month
• pyspark.sql.functions.dayofmonth
• pyspark.sql.functions.dayofweek
• pyspark.sql.functions.dayofyear
• pyspark.sql.functions.weekofyear

import datetime
from pyspark.sql.functions import year, month, dayofmonth

elevDF = sc.parallelize([
    (datetime.datetime(1984, 1, 1, 0, 0), 1, 638.55),
    (datetime.datetime(1984, 1, 1, 0, 0), 2, 638.55),
    (datetime.datetime(1984, 1, 1, 0, 0), 3, 638.55),
    (datetime.datetime(1984, 1, 1, 0, 0), 4, 638.55),
    (datetime.datetime(1984, 1, 1, 0, 0), 5, 638.55)
]).toDF(["date", "hour", "value"])

elevDF.select(
    year("date").alias('year'), 
    month("date").alias('month'), 
    dayofmonth("date").alias('day')
).show()
# +----+-----+---+
# |year|month|day|
# +----+-----+---+
# |1984|    1|  1|
# |1984|    1|  1|
# |1984|    1|  1|
# |1984|    1|  1|
# |1984|    1|  1|
# +----+-----+---+

You can use simple map as with any other RDD:

elevDF = sqlContext.createDataFrame(sc.parallelize([
    Row(date=datetime.datetime(1984, 1, 1, 0, 0), hour=1, value=638.55),
    Row(date=datetime.datetime(1984, 1, 1, 0, 0), hour=2, value=638.55),
    Row(date=datetime.datetime(1984, 1, 1, 0, 0), hour=3, value=638.55),
    Row(date=datetime.datetime(1984, 1, 1, 0, 0), hour=4, value=638.55),
    Row(date=datetime.datetime(1984, 1, 1, 0, 0), hour=5, value=638.55)]))

(elevDF
    .map(lambda (date, hour, value): (date.year, date.month, date.day))
    .collect())

and the result is:

[(1984, 1, 1), (1984, 1, 1), (1984, 1, 1), (1984, 1, 1), (1984, 1, 1)]

By the way, datetime.datetime stores the hour anyway so keeping it separately seems to be a waste of memory.