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Power Pivot Principles: Introducing the SUMMARIZE Function

22 October 2019

Welcome back to the Power Pivot Principles blog. This week, we are going to look at the SUMMARIZE function.

The SUMMARIZE function returns a summary table for the requested totals over a set of groups. For example, we can use this function to summarise the sales for a specific year and month and return a summary table.

Consider the data table shown below (not displayed in full):

This data table contains the sales amount for a specific date.  We want to summarise the total sales based on a specific year and month by using SUMMARIZE function.

The SUMMARIZE function uses the following syntax to operate:

SUMMARIZE(<table>, <groupBy_columnName>, [<groupBy_columnName>]…[<name>, <expression>]…)

  • The <table> parameter can be table reference or any DAX expression that returns a table of data
  • The <groupBy_columnName> parameter is optional.  It indicates the qualified name of an existing column to be used to create summary groups
  • The <name> parameter is the name given to a total or summarised column, enclosed in double quotes
  • The <expression> parameter is any DAX expression that returns a single scalar value, where the expression is to be evaluated multiple times (for each row / context).

In this case, we create a measure, tableSummary, as shown below:

We use a nested function in this case. The SUMMARIZE function returns a table, which evaluates the sum of sales based on the attribute of Year and Month. Since it returns a table, we need to use another function to turn it into a scalar value. In this case, we use COUNTROWS to obtain the number of rows for the table calculated.

In order to obtain the result table, we import the table from existing connections, as shown below:

Then, we use the feature of Edit DAX in table attribute to input the formula as shown below:

We use expression Evaluate to obtain the table result from the expression below and the result table would be:

The result shows that Monthly Sales are calculated base on the attributes Year and Month.

Stay tuned for our next post on Power Pivot in the Blog section. In the meantime, please remember we have training in Power Pivot which you can find out more about here. If you wish to catch up on past articles in the meantime, you can find all of our past Power Pivot blogs here.