The Group By tool is one of the most used tools in Less. The Group By tool works by sorting data into boxes where each box represents a group. Once everything is in its box, you can count, total, or summarize what’s in each one. It’s a way to organize and analyze similar items together. To exemplify, “group by customer_type average session_length” could be translated to “put my data in the customer_type column groups and provide an average of the session length for those customer types”.

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Configuration

The Group By tool consists of one required input and one optional input.

1

Select Column(s) to Group By

Select the column(s) to group by. You can input as many columns as you need. Adding more columns will make your groups/boxes more unique as the “boxes” become more specific.

This is technically an optional input. You don’t have to greate any groups if you just want to

2

Select Aggregations

Add your aggregation(s) pairs by selecting the column you want to aggregate and how you want to aggregate them.

You can choose from the following types of aggregations:

  • Sum: summarize numeric values
  • Concatenate: get all your string values in a comma-separated cell
  • Mean: find the average of numeric values
  • Count: count the occurances
  • Count Distinct: count the unique occurances
  • Std. Deviation: find the standard deviation
  • First: get the first value in the group
  • Last: get the last value in the group
  • Min: find the smallest numeric value
  • Max: find the largest numeric value

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When To Use

The Group By tool can be used to solve a wide range of different problems:

  • The Group By tool is perfect for any analysis the requires aggregating data
  • Use max or min on a date column to find the latest/earliest date a customer bought something, an order was delivered, or when any other event occured the first or last time
  • Aggregating different datasets into similar boxes can be very smart to transform them into similar boxes before using the Combine tool to merge the two datasets. It’s essentially a method to create identical IDs in datasets that don’t naturally match - for example by aggregating by Month.
  • If you want to use an “IN” function, you can do that by using the concatenate function in the Group By tool along with the Append and Filter with a Contains functions.
  • Whenever you want to count rows within a group of customers, suppliers, months, etc. the Group By tool is excellent

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Examples