Closest Date looking from One Column to another in PySpark Dataframe - date

Question

I have a pyspark dataframe where price of Commodity is mentioned, but there is no data for when was the Commodity bought, I just have a range of 1 year.
+---------+------------+----------------+----------------+
|Commodity| BuyingPrice|Date_Upper_limit|Date_lower_limit|
+---------+------------+----------------+----------------+
| Apple| 5| 2020-07-04| 2019-07-03|
| Banana| 3| 2020-07-03| 2019-07-02|
| Banana| 4| 2019-10-02| 2018-10-01|
| Apple| 6| 2020-01-20| 2019-01-19|
| Banana| 3.5| 2019-08-17| 2018-08-16|
+---------+------------+----------------+----------------+
I have another pyspark dataframe where I can see the market price and date of all commodities.
+----------+----------+------------+
| Date| Commodity|Market Price|
+----------+----------+------------+
|2020-07-01| Apple| 3|
|2020-07-01| Banana| 3|
|2020-07-02| Apple| 4|
|2020-07-02| Banana| 2.5|
|2020-07-03| Apple| 7|
|2020-07-03| Banana| 4|
+----------+----------+------------+
I want to see the closest date to Upper limit of date when Market Price(MP) of that commodity < or = Buying Price(BP).
Expected Output (for 2 top columns):
+---------+------------+----------------+----------------+--------------------------------+
|Commodity| BuyingPrice|Date_Upper_limit|Date_lower_limit|Closest Date to UL when MP <= BP|
+---------+------------+----------------+----------------+--------------------------------+
| Apple| 5| 2020-07-04| 2019-07-03| 2020-07-02|
| Banana| 3| 2020-07-03| 2019-07-02| 2020-07-02|
+---------+------------+----------------+----------------+--------------------------------+
Even though Apple was much lower on 2020-07-01 ($3), but since 2020-07-02 was the first date going backwards from Upper Limit (UL) of date when MP <= BP. So, I selected 2020-07-02.
How can I see backwards to fill date of probable buying?

Answer 1

Try this with conditional join and window function
from pyspark.sql import functions as F
from pyspark.sql.window import Window
w=Window().partitionBy("Commodity")
df1\ #first dataframe shown being df1 and second being df2
.join(df2.withColumnRenamed("Commodity","Commodity1")\
, F.expr("""`Market Price`<=BuyingPrice and Date<Date_Upper_limit and Commodity==Commodity1"""))\
.drop("Market Price","Commodity1")\
.withColumn("max", F.max("Date").over(w))\
.filter('max==Date').drop("max").withColumnRenamed("Date","Closest Date to UL when MP <= BP")\
.show()
#+---------+-----------+----------------+----------------+--------------------------------+
#|Commodity|BuyingPrice|Date_Upper_limit|Date_lower_limit|Closest Date to UL when MP <= BP|
#+---------+-----------+----------------+----------------+--------------------------------+
#| Banana| 3.0| 2020-07-03| 2019-07-02| 2020-07-02|
#| Apple| 5.0| 2020-07-04| 2019-07-03| 2020-07-02|
#+---------+-----------+----------------+----------------+--------------------------------+