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Documentation Index

Fetch the complete documentation index at: https://docs.less.tech/llms.txt

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Use the Cumulative tool to create new columns with a cumulative sum, cumulative average, cumulative minimum, cumulative maximum cumulative product and cumulative count. It can be helpful to analyse revenue growth over time, customer retention, cumulative ROI, etc. Typically users add a Sort tool before using the Cumulative tool to make sure their data is in the right order.
If you’re looking for more advanced ways to do row-wise calculations check out the Cumulative, Running Interval or Loop tool

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Configuration

1

Select Column

Select column you want to calculate cumulative for.
2

Operation

Select the operation you want to perform. You can choose between the following operations:
  • Cumulative Sum (running sum of the values of the column)
  • Cumulative Average (running average of the values of the column)
  • Cumulative Minimum (lowest running minimum value of the column)
  • Cumulative Maximum (highest running maximum value of the column)
  • Cumulative Product (running multiplication of the values of the column)
  • Cumulative Count (running count of the values of the column)
3

Reverse

You can choose to reverse the cumulative calculation whereby you start from the bottom of the dataset and work your way up (instead of default - from the top).
4

Group By (optional)

This is an optional input. You can choose to group the cumulative calculation by a column. For instance, if you want to calculate the cumulative sum of the values of the column by a month column.
You can add any many cumulative calculations as you want by clicking the ”+ Add column” button.

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Example: Cumulative sum within a group

In this example we want to sum our simple_int column within each group of the bool_col column.
Notice how the cumulative_sum_simple_int column resets when the bool_col column changes and starts from 1 (the value of the simple_int column) again on row 5. That’s because the bool_col column is true from row 5 onwards and because we grouped by the that column.