I have a Spark Dataframe in that consists of a series of dates:
from pyspark.sql import SQLContext
from pyspark.sql import Row
from pyspark.sql.types import
Use DoubleType instead of IntegerType
from pyspark.sql import SQLContext, Row
sqlContext = SQLContext(sc)
from pyspark.sql.types import StringType, IntegerType, StructType, StructField
from pyspark.sql.functions import udf
# Build sample data
rdd = sc.parallelize([('X01','2014-02-13T12:36:14.899','2014-02-13T12:31:56.876'),
('X02','2014-02-13T12:35:37.405','2014-02-13T12:32:13.321'),
('X03','2014-02-13T12:36:03.825','2014-02-13T12:32:15.229'),
('XO4','2014-02-13T12:37:05.460','2014-02-13T12:32:36.881'),
('XO5','2014-02-13T12:36:52.721','2014-02-13T12:33:30.323')])
schema = StructType([StructField('ID', StringType(), True),
StructField('EndDateTime', StringType(), True),
StructField('StartDateTime', StringType(), True)])
df = sqlContext.createDataFrame(rdd, schema)
# define timedelta function (obtain duration in seconds)
def time_delta(y,x):
from datetime import datetime
end = datetime.strptime(y, '%Y-%m-%dT%H:%M:%S.%f')
start = datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%f')
delta = (end-start).total_seconds()
return delta
# register as a UDF
f = udf(time_delta, DoubleType())
# Apply function
df2 = df.withColumn('Duration', f(df.EndDateTime, df.StartDateTime))