Communities

Writing
Writing
Codidact Meta
Codidact Meta
The Great Outdoors
The Great Outdoors
Photography & Video
Photography & Video
Scientific Speculation
Scientific Speculation
Cooking
Cooking
Electrical Engineering
Electrical Engineering
Judaism
Judaism
Languages & Linguistics
Languages & Linguistics
Software Development
Software Development
Mathematics
Mathematics
Christianity
Christianity
Code Golf
Code Golf
Music
Music
Physics
Physics
Linux Systems
Linux Systems
Power Users
Power Users
Tabletop RPGs
Tabletop RPGs
Community Proposals
Community Proposals
tag:snake search within a tag
answers:0 unanswered questions
user:xxxx search by author id
score:0.5 posts with 0.5+ score
"snake oil" exact phrase
votes:4 posts with 4+ votes
created:<1w created < 1 week ago
post_type:xxxx type of post
Search help
Notifications
Mark all as read See all your notifications »
Q&A

Welcome to Software Development on Codidact!

Will you help us build our independent community of developers helping developers? We're small and trying to grow. We welcome questions about all aspects of software development, from design to code to QA and more. Got questions? Got answers? Got code you'd like someone to review? Please join us.

Comments on How to get conditional running cumulative sum based on current row and previous rows?

Post

How to get conditional running cumulative sum based on current row and previous rows?

+0
−1

How do I perform a running cumulative sum that is based on a condition involving the current row and previous rows?

Given the following table:

acc | value | threshold
3   | 1     | 1
1   | 2     | 2
2   | 3     | 2

I would like to find the cumulative sum of acc if value >= threshold, for all values from the start to the current row. The expected output should be 3, 1, 3.

That is, the equivalent python code might look like:

for i in len(df):
    for j in range(i):
        if df[j].value >= df[i].threshold:
            df[i].cumsum += df[j].value

I tried using a windowed sum:

import pyspark.sql.functions as F
from pyspark.sql.window import Window

df = spark.createDataFrame([(3, 1, 1), (1, 2, 2), (2, 3, 2)], ["acc", "value", "threshold"])
window = Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
display(df.withColumn("output", F.sum(F.when(F.col("value") >= F.col("threshold"), F.col("acc"))).over(window)))

But this gave 3, 4, 6, because it was comparing against the same threshold on each row.

History
Why does this post require attention from curators or moderators?
You might want to add some details to your flag.
Why should this post be closed?

3 comment threads

Discussed on meta (1 comment)
Should be closed (2 comments)
Two questions in one (6 comments)
Should be closed

Skipping 3 deleted comments.

congusbongus‭ wrote 4 months ago · edited 4 months ago

Alas in the other comment thread, it shows that this question is prone to being misinterpreted, so please edit if this question can be improved. I have attempted to make this question clear by showing a concrete example, expected result, and an example of an incorrect attempt. If there is some way to make the wording clearer please suggest how. Is the example unclear?

Alexei‭ wrote 4 months ago · edited 4 months ago

This question is now being discussed on Meta. I have also removed the non-constructive comments in this thread.