从Spark 1.5开始，您可以使用许多日期处理功能：

• 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|

  +----+-----+—+

您可以将simple

map

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())

结果是：

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

顺便说一句：

datetime.datetime