Inserting records in a spark dataframe - apache-spark

Question

I have a dataframe in pyspark. Here is what it looks like,
+---------+---------+
|timestamp| price |
+---------+---------+
|670098928| 50 |
|670098930| 53 |
|670098934| 55 |
+---------+---------+
I want to fill in the gaps in timestamp with the previous state, so that I can get a perfect set to calculate time weighted averages. Here is what the output should be like -
+---------+---------+
|timestamp| price |
+---------+---------+
|670098928| 50 |
|670098929| 50 |
|670098930| 53 |
|670098931| 53 |
|670098932| 53 |
|670098933| 53 |
|670098934| 55 |
+---------+---------+
Eventually, I want to persist this new dataframe on disk and visualize my analysis.
How do I do this in pyspark? (For simplicity sake, I have just kept 2 columns. My actual dataframe has 89 columns with ~670 million records before filling the gaps.)

Answer 1

You can generate timestamp ranges, flatten them and select rows
import pyspark.sql.functions as func
from pyspark.sql.types import IntegerType, ArrayType
a=sc.parallelize([[670098928, 50],[670098930, 53], [670098934, 55]])\
.toDF(['timestamp','price'])
f=func.udf(lambda x:range(x,x+5),ArrayType(IntegerType()))
a.withColumn('timestamp',f(a.timestamp))\
.withColumn('timestamp',func.explode(func.col('timestamp')))\
.groupBy('timestamp')\
.agg(func.max(func.col('price')))\
.show()
+---------+----------+
|timestamp|max(price)|
+---------+----------+
|670098928| 50|
|670098929| 50|
|670098930| 53|
|670098931| 53|
|670098932| 53|
|670098933| 53|
|670098934| 55|
|670098935| 55|
|670098936| 55|
|670098937| 55|
|670098938| 55|
+---------+----------+