Tengo un Spark Dataframe que consta de una serie de fechas:

from pyspark.sql import SQLContext
from pyspark.sql import Row
from pyspark.sql.types import *
sqlContext = SQLContext(sc)
import pandas as pd

rdd = sc.parallelizesc.parallelize([('X01','2014-02-13T12:36:14.899','2014-02-13T12:31:56.876','sip:4534454450'),
                                    ('X02','2014-02-13T12:35:37.405','2014-02-13T12:32:13.321','sip:6413445440'),
                                    ('X03','2014-02-13T12:36:03.825','2014-02-13T12:32:15.229','sip:4534437492'),
                                    ('XO4','2014-02-13T12:37:05.460','2014-02-13T12:32:36.881','sip:6474454453'),
                                    ('XO5','2014-02-13T12:36:52.721','2014-02-13T12:33:30.323','sip:8874458555')])
schema = StructType([StructField('ID', StringType(), True),
                     StructField('EndDateTime', StringType(), True),
                     StructField('StartDateTime', StringType(), True)])
df = sqlContext.createDataFrame(rdd, schema)

Lo que quiero hacer es encontrarduration restandoEndDateTime yStartDateTime. Pensé que intentaría hacer esto usando una función:

# Function to calculate time delta
def time_delta(y,x): 
    end = pd.to_datetime(y)
    start = pd.to_datetime(x)
    delta = (end-start)
    return delta

# create new RDD and add new column 'Duration' by applying time_delta function
df2 = df.withColumn('Duration', time_delta(df.EndDateTime, df.StartDateTime)) 

Sin embargo, esto solo me da:

>>> df2.show()
ID  EndDateTime          StartDateTime        ANI            Duration
X01 2014-02-13T12:36:... 2014-02-13T12:31:... sip:4534454450 null    
X02 2014-02-13T12:35:... 2014-02-13T12:32:... sip:6413445440 null    
X03 2014-02-13T12:36:... 2014-02-13T12:32:... sip:4534437492 null    
XO4 2014-02-13T12:37:... 2014-02-13T12:32:... sip:6474454453 null    
XO5 2014-02-13T12:36:... 2014-02-13T12:33:... sip:8874458555 null  

No estoy seguro de si mi enfoque es correcto o no. Si no, con mucho gusto aceptaría otra forma sugerida de lograr esto.