Get count of the value repeated in the last 24 hours in pyspark dataframe

Please help me with this pyspark code. I need to count the number of times an ip appeared in the last 24 hours excluding that instance. The first time an ip appears in the data the count_last24hrs column should return value as 0. From next time onwards, the code should count the number of times the same ip has appeared in the last 24 hours from that timestamp excluding that instance.

I was trying to use window function but was not getting the desired result.

count_last24hrs is the column name in which the result should appear.

Using this data frame as df

column names as (datetime, ip, count_last24hrs)

(10/05/2022 10:14:00 AM, 1.1.1.1, 0)

(10/05/2022 10:16:00 AM, 1.1.1.1, 1)

(10/05/2022 10:18:00 AM, 2.2.2.2, 0)

(10/05/2022 10:21:00 AM, 1.1.1.1, 2)

snapshot of the data using

Code that I was trying

#function to calculate the number of seconds from the number of days

days = lambda i: i * 86400

#create window by casting timestamp to long (number of seconds)

w = (Window.orderBy(F.col("datetime").cast('long')).rangeBetween(-days(1), 0))

#use collect_set and size functions to perform countDistinct over a window

df_new= df.withColumn('count_last24hrs', F.size(F.collect_set("ip”).over(w)))

result = (df
.withColumn('ip_count', F.expr("count(ip_address) over (partition by ip_address order by datetimecol range between interval 24 hours preceding and current row)"))
.withColumn('ip_count',when(f.col('ip')==0,0).otherwise(f.col('ip')-1) ).
select('datetimecol', 'ip_address','ip_count')

The first withColumn statement selects data in last 24 hours and partition the data by "ip_address" ordered by time and finds cumumulative sum

The Second withColumn makes the count decrement by 1. So that the first count is 0 instead of 1.

Result: