Forward Fill New Row to Account for Missing Dates

Question

I currently have a dataset grouped into hourly increments by a variable \"aggregator\". There are gaps in this hourly data and what i would ideally like to do is forward fil

Answer 1

Here is the solution, to fill the missing hours. using windows, lag and udf. With little modification it can extend to days as well.

from pyspark.sql.window import Window
from pyspark.sql.types import *
from pyspark.sql.functions import *
from dateutil.relativedelta import relativedelta

def missing_hours(t1, t2):
    return [t1 + relativedelta(hours=-x) for x in range(1, t1.hour-t2.hour)]

missing_hours_udf = udf(missing_hours, ArrayType(TimestampType()))

df = spark.read.csv('dates.csv',header=True,inferSchema=True)

window = Window.partitionBy("aggregator").orderBy("timestamp")

df_mising = df.withColumn("prev_timestamp",lag(col("timestamp"),1, None).over(window))\
       .filter(col("prev_timestamp").isNotNull())\
       .withColumn("timestamp", explode(missing_hours_udf(col("timestamp"), col("prev_timestamp"))))\
       .drop("prev_timestamp")

df.union(df_mising).orderBy("aggregator","timestamp").show()

which results

+-------------------+----------+
|          timestamp|aggregator|
+-------------------+----------+
|2018-12-27 09:00:00|         A|
|2018-12-27 10:00:00|         A|
|2018-12-27 11:00:00|         A|
|2018-12-27 12:00:00|         A|
|2018-12-27 13:00:00|         A|
|2018-12-27 09:00:00|         B|
|2018-12-27 10:00:00|         B|
|2018-12-27 11:00:00|         B|
|2018-12-27 12:00:00|         B|
|2018-12-27 13:00:00|         B|
|2018-12-27 14:00:00|         B|
+-------------------+----------+