Inserting records in a spark dataframe

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|
+---------+----------+