SUMMARIZECOLUMNS
Returns a summary table over a set of groups.
Syntax
SUMMARIZECOLUMNS( <groupBy_columnName> [, < groupBy_columnName >]…, [<filterTable>]…[, <name>, <expression>]…)
Parameters
| Term | Definition |
|---|---|
| groupBy_columnName | A fully qualified column reference (Table[Column]) to a base table for which the distinct values are included in the returned table. Each groupBy_columnName column is cross-joined (different tables) or auto-existed (same table) with the subsequent specified columns. |
| filterTable | A table expression which is added to the filter context of all columns specified as groupBy_columnName arguments. The values present in the filter table are used to filter before cross-join/auto-exist is performed. |
| name | A string representing the column name to use for the subsequent expression specified. |
| expression | Any DAX expression that returns a single value (not a table). |
Return value
A table which includes combinations of values from the supplied columns based on the grouping specified. Only rows for which at least one of the supplied expressions return a non-blank value are included in the table returned. If all expressions evaluate to BLANK/NULL for a row, that row is not included in the table returned.
Remarks
This function does not guarantee any sort order for the results.
A column cannot be specified more than once in the groupBy_columnName parameter. For example, the following formula is invalid.
SUMMARIZECOLUMNS( Sales[StoreId], Sales[StoreId] )This function is not supported for use in DirectQuery mode when used in calculated columns or row-level security (RLS) rules.
Filter context
Consider the following query:
SUMMARIZECOLUMNS (
'Sales Territory'[Category],
FILTER('Customer', 'Customer' [First Name] = "Alicia")
)
In this query, without a measure the groupBy columns do not contain any columns from the FILTER expression (for example, from Customer table). The filter is not applied to the groupBy columns. The Sales Territory and Customer tables may be indirectly related through the Reseller sales fact table. Since they're not directly related, the filter expression is a no-op and the groupBy columns are not impacted.
However, with this query:
SUMMARIZECOLUMNS (
'Sales Territory'[Category], 'Customer' [Education],
FILTER('Customer', 'Customer'[First Name] = "Alicia")
)
The groupBy columns contain a column which is impacted by the filter and that filter is applied to the groupBy results.
With IGNORE
The IGNORE syntax can be used to modify the behavior of the SUMMARIZECOLUMNS function by omitting specific expressions from the BLANK/NULL evaluation. Rows for which all expressions not using IGNORE return BLANK/NULL will be excluded independent of whether the expressions which do use IGNORE evaluate to BLANK/NULL or not. IGNORE can only be used within a SUMMARIZECOLUMNS expression.
Example
SUMMARIZECOLUMNS(
Sales[CustomerId], "Total Qty",
IGNORE( SUM( Sales[Qty] ) ),
"BlankIfTotalQtyIsNot3", IF( SUM( Sales[Qty] )=3, 3 )
)
This rolls up the Sales[CustomerId] column, creating a subtotal for all customers in the given grouping. Without IGNORE, the result is:
| CustomerId | Total Qty | BlankIfTotalQtyIsNot3 |
|---|---|---|
| A | 5 | |
| B | 3 | 3 |
| C | 3 | 3 |
With IGNORE,
| CustomerId | Total Qty | BlankIfTotalQtyIsNot3 |
|---|---|---|
| B | 3 | 3 |
| C | 3 | 3 |
All expression ignored,
SUMMARIZECOLUMNS(
Sales[CustomerId], "Blank",
IGNORE( BLANK() ), "BlankIfTotalQtyIsNot5",
IGNORE( IF( SUM( Sales[Qty] )=5, 5 ) )
)
Even though both expressions return blank for some rows, they're included since there are no unignored expressions which return blank.
| CustomerId | Blank | BlankIfTotalQtyIsNot5 |
|---|---|---|
| A | 5 | |
| B | ||
| C |
With NONVISUAL
The NONVISUAL function marks a value filter in SUMMARIZECOLUMNS function as not affecting measure values, but only applying to groupBy columns. NONVISUAL can only be used within a SUMMARIZECOLUMNS expression.
Example
DEFINE
MEASURE FactInternetSales[Sales] = SUM(FactInternetSales[Sales Amount])
EVALUATE
SUMMARIZECOLUMNS
(
DimDate[CalendarYear],
NONVISUAL(TREATAS({2007, 2008}, DimDate[CalendarYear])),
"Sales", [Sales],
"Visual Total Sales", CALCULATE([Sales], ALLSELECTED(DimDate[CalendarYear]))
)
ORDER BY [CalendarYear]
Returns the result where [Visual Total Sales] is the total across all years:
| DimDate[CalendarYear] | [Sales] | [Visual Total Sales] |
|---|---|---|
| 2007 | 9,791,060.30 | 29,358,677.22 |
| 2008 | 9,770,899.74 | 29,358,677.22 |
In contrast, the same query without the NONVISUAL function:
DEFINE
MEASURE FactInternetSales[Sales] = SUM(FactInternetSales[Sales Amount])
EVALUATE
SUMMARIZECOLUMNS
(
DimDate[CalendarYear],
TREATAS({2007, 2008}, DimDate[CalendarYear]),
"Sales", [Sales],
"Visual Total Sales", CALCULATE([Sales], ALLSELECTED(DimDate[CalendarYear]))
)
ORDER BY [CalendarYear]
Returns the result where [Visual Total Sales] is the total across the two selected years:
| DimDate[CalendarYear] | [Sales] | [Visual Total Sales] |
|---|---|---|
| 2007 | 9,791,060.30 | 19,561,960.04 |
| 2008 | 9,770,899.74 | 19,561,960.04 |
With ROLLUPADDISSUBTOTAL
The addition of the ROLLUPADDISSUBTOTAL syntax modifies the behavior of the SUMMARIZECOLUMNS function by adding rollup/subtotal rows to the result based on the groupBy_columnName columns. ROLLUPADDISSUBTOTAL can only be used within a SUMMARIZECOLUMNS expression.
Example with single subtotal
DEFINE
VAR vCategoryFilter =
TREATAS({"Accessories", "Clothing"}, Product[Category])
VAR vSubcategoryFilter =
TREATAS({"Bike Racks", "Mountain Bikes"}, Product[Subcategory])
EVALUATE
SUMMARIZECOLUMNS
(
ROLLUPADDISSUBTOTAL
(
Product[Category], "IsCategorySubtotal", vCategoryFilter,
Product[Subcategory], "IsSubcategorySubtotal", vSubcategoryFilter
),
"Total Qty", SUM(Sales[Qty])
)
ORDER BY
[IsCategorySubtotal] DESC, [Category],
[IsSubcategorySubtotal] DESC, [Subcategory]
Returns the following table,
| Category | Subcategory | IsCategorySubtotal | IsSubcategorySubtotal | Total Qty |
|---|---|---|---|---|
| True | True | 60398 | ||
| Accessories | False | True | 36092 | |
| Accessories | Bike Racks | False | False | 328 |
| Bikes | Mountain Bikes | False | False | 4970 |
| Clothing | False | True | 9101 |
Example with multiple subtotals
SUMMARIZECOLUMNS (
Regions[State], ROLLUPADDISSUBTOTAL ( Sales[CustomerId], "IsCustomerSubtotal" ),
ROLLUPADDISSUBTOTAL ( Sales[Date], "IsDateSubtotal"), "Total Qty", SUM( Sales[Qty] )
)
Sales is grouped by state, by customer, by date, with subtotals for 1. Sales by state, by date 2. Sales by State, by Customer 3. Rolled up on both customer and date leading to sales by state.
Returns the following table,
| CustomerID | IsCustomerSubtotal | State | Total Qty | Date | IsDateSubtotal |
|---|---|---|---|---|---|
| A | FALSE | WA | 5 | 7/10/2014 | |
| B | FALSE | WA | 1 | 7/10/2014 | |
| B | FALSE | WA | 2 | 7/11/2014 | |
| C | FALSE | OR | 2 | 7/10/2014 | |
| C | FALSE | OR | 1 | 7/11/2014 | |
| TRUE | WA | 6 | 7/10/2014 | ||
| TRUE | WA | 2 | 7/11/2014 | ||
| TRUE | OR | 2 | 7/10/2014 | ||
| TRUE | OR | 1 | 7/11/2014 | ||
| A | FALSE | WA | 5 | TRUE | |
| B | FALSE | WA | 3 | TRUE | |
| C | FALSE | OR | 3 | TRUE | |
| TRUE | WA | 8 | TRUE | ||
| TRUE | OR | 3 | TRUE |
With ROLLUPGROUP
Like with the SUMMARIZE function, ROLLUPGROUP can be used together with ROLLUPADDISSUBTOTAL to specify which summary groups/granularities (subtotals) to include, reducing the number of subtotal rows returned. ROLLUPGROUP can only be used within a SUMMARIZECOLUMNS or SUMMARIZE expression.
Example with multiple subtotals
SUMMARIZECOLUMNS(
ROLLUPADDISSUBTOTAL( Sales[CustomerId], "IsCustomerSubtotal" ),
ROLLUPADDISSUBTOTAL(ROLLUPGROUP(Regions[City], Regions[State]), "IsCityStateSubtotal"),"Total Qty", SUM( Sales[Qty] )
)
Still grouped by City and State, but rolled together when reporting a subtotal returns the following table,
| State | CustomerId | IsCustomerSubtotal | Total Qty | City | IsCityStateSubtotal |
|---|---|---|---|---|---|
| WA | A | FALSE | 2 | Bellevue | FALSE |
| WA | B | FALSE | 2 | Bellevue | FALSE |
| WA | A | FALSE | 3 | Redmond | FALSE |
| WA | B | FALSE | 1 | Redmond | FALSE |
| OR | C | FALSE | 3 | Portland | FALSE |
| WA | TRUE | 4 | Bellevue | FALSE | |
| WA | TRUE | 4 | Redmond | FALSE | |
| OR | TRUE | 3 | Portland | FALSE | |
| A | FALSE | 5 | FALSE | ||
| B | FALSE | 3 | TRUE | ||
| C | FALSE | 3 | TRUE | ||
| TRUE | 11 | TRUE |
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