Identifying values that revert in Spark - pyspark

Question

I have a Spark DataFrame of customers as shown below.
#SparkR code
customers <- data.frame(custID = c("001", "001", "001", "002", "002", "002", "002"),
date = c("2017-02-01", "2017-03-01", "2017-04-01", "2017-01-01", "2017-02-01", "2017-03-01", "2017-04-01"),
value = c('new', 'good', 'good', 'new', 'good', 'new', 'bad'))
customers <- createDataFrame(customers)
display(customers)
custID| date | value
--------------------------
001 | 2017-02-01| new
001 | 2017-03-01| good
001 | 2017-04-01| good
002 | 2017-01-01| new
002 | 2017-02-01| good
002 | 2017-03-01| new
002 | 2017-04-01| bad
In the first month observation for a custID the customer gets a value of 'new'. Thereafter they are classified as 'good' or 'bad'. However, it is possible for a customer to revert from 'good' or 'bad' back to 'new' in the case that they open a second account. When this happens I want to tag the customer with '2' instead of '1', to indicate that they opened a second account, as shown below. How can I do this in Spark? Either SparkR or PySpark commands work.
#What I want to get
custID| date | value | tag
--------------------------------
001 | 2017-02-01| new | 1
001 | 2017-03-01| good | 1
001 | 2017-04-01| good | 1
002 | 2017-01-01| new | 1
002 | 2017-02-01| good | 1
002 | 2017-03-01| new | 2
002 | 2017-04-01| bad | 2

Answer 1

In pyspark:
from pyspark.sql import functions as f
spark = SparkSession.builder.getOrCreate()
# df is equal to your customers dataframe
df = spark.read.load('file:///home/zht/PycharmProjects/test/text_file.txt', format='csv', header=True, sep='|').cache()
df_new = df.filter(df['value'] == 'new').withColumn('tag', f.rank().over(Window.partitionBy('custID').orderBy('date')))
df = df_new.union(df.filter(df['value'] != 'new').withColumn('tag', f.lit(None)))
df = df.withColumn('tag', f.collect_list('tag').over(Window.partitionBy('custID').orderBy('date'))) \
.withColumn('tag', f.UserDefinedFunction(lambda x: x.pop(), IntegerType())('tag'))
df.show()
And output:
+------+----------+-----+---+
|custID| date|value|tag|
+------+----------+-----+---+
| 001|2017-02-01| new| 1|
| 001|2017-03-01| good| 1|
| 001|2017-04-01| good| 1|
| 002|2017-01-01| new| 1|
| 002|2017-02-01| good| 1|
| 002|2017-03-01| new| 2|
| 002|2017-04-01| bad| 2|
+------+----------+-----+---+
By the way, pandas can do that easy.

Answer 2

This can be done using the following piece of code:
Filter out all the records with "new"
df_new<-sql("select * from df where value="new")
createOrReplaceTempView(df_new,"df_new")
df_new<-sql("select *,row_number() over(partiting by custID order by date)
tag from df_new")
createOrReplaceTempView(df_new,"df_new")
df<-sql("select custID,date,value,min(tag) as tag from
(select t1.*,t2.tag from df t1 left outer join df_new t2 on
t1.custID=t2.custID and t1.date>=t2.date) group by 1,2,3")