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Helper III

## Optimise Query for Calculating Number of Business Days

I have a table "DateTable" which has two columns:

• the first is every date between a start date (1st March 2019) and the current date
• the second is the number of business days that have occurred between that row and the first row (i.e. excluding weekends and holidays), where holidays are stored in a table "bank-holidays-dl".

A snippet of the table:

I set up the DateTable by creating a list of dates between the start and end date using List.Dates, converting it to a table, and then applying the following custom function to get the number of business days counting from the 01/03/2019:

`(StartDate as date, EndDate as date) as number =>let    ListDates = List.Dates(StartDate, Number.From(EndDate - StartDate), #duration(1, 0, 0, 0)),    RemoveWeekends = List.Select(ListDates, each Date.DayOfWeek(_, Day.Monday) < 5),    RemoveHolidays = List.RemoveItems(RemoveWeekends, #"bank-holidays-dl"),    CountDays = List.Count(RemoveHolidays)in    CountDays`

The function works as expected, however refreshing the data takes a long time. I was wondering if there could be any way to optimise this? Thank you!

1 ACCEPTED SOLUTION
Super User

The key to performance is to only calculate this column once instead of once for each row of your date table.

My approach would be to add an index column after filtering out weekends and holidays and then merge this back with the original dates.

``````let
StartDate = #date(2019, 3, 1),
EndDate = Date.From(DateTime.LocalNow()),
ListDates = List.Dates(StartDate, Number.From(EndDate - StartDate), #duration(1, 0, 0, 0)),
ListToTable = Table.FromList(ListDates, Splitter.SplitByNothing(), {"Date"}),
DateTable = Table.TransformColumnTypes(ListToTable, {{"Date", type date}}),
RemoveDays = Table.SelectRows(DateTable, each Date.DayOfWeek([Date], 1) < 5 and
not List.Contains(#"bank-holidays-dl",
AddIndex = Table.AddIndexColumn(RemoveDays, "Index", 1, 1, Int64.Type),
MergeQueries = Table.NestedJoin(DateTable, {"Date"}, AddIndex, {"Date"}, "RemovedDays", JoinKind.LeftOuter),
ExplandIndex = Table.ExpandTableColumn(MergeQueries, "RemovedDays", {"Index"}, {"WorkingDaysSinceStart"}),
SortRows = Table.Sort(ExplandIndex,{{"Date", Order.Ascending}}),
FillDown = Table.FillDown(SortRows, {"WorkingDaysSinceStart"})
in
FillDown``````

3 REPLIES 3
Super User
``````(StartDate as date, EndDate as date) =>

List.Accumulate(
List.Dates(StartDate, Duration.TotalDays(EndDate - StartDate), #duration(1, 0, 0, 0)),
0,
(s,c) => if Date.DayOfWeek(c, Day.Monday) >= 5 or List.Contains(#"bank-holidays-dl", c) then s else s+1
)``````

 Thanks to the great efforts by MS engineers to simplify syntax of DAX! Most beginners are SUCCESSFULLY MISLED to think that they could easily master DAX; but it turns out that the intricacy of the most frequently used RANKX() is beyond their comprehension! DAX is simple, but NOT EASY!
Super User

The key to performance is to only calculate this column once instead of once for each row of your date table.

My approach would be to add an index column after filtering out weekends and holidays and then merge this back with the original dates.

``````let
StartDate = #date(2019, 3, 1),
EndDate = Date.From(DateTime.LocalNow()),
ListDates = List.Dates(StartDate, Number.From(EndDate - StartDate), #duration(1, 0, 0, 0)),
ListToTable = Table.FromList(ListDates, Splitter.SplitByNothing(), {"Date"}),
DateTable = Table.TransformColumnTypes(ListToTable, {{"Date", type date}}),
RemoveDays = Table.SelectRows(DateTable, each Date.DayOfWeek([Date], 1) < 5 and
not List.Contains(#"bank-holidays-dl",
AddIndex = Table.AddIndexColumn(RemoveDays, "Index", 1, 1, Int64.Type),
MergeQueries = Table.NestedJoin(DateTable, {"Date"}, AddIndex, {"Date"}, "RemovedDays", JoinKind.LeftOuter),
ExplandIndex = Table.ExpandTableColumn(MergeQueries, "RemovedDays", {"Index"}, {"WorkingDaysSinceStart"}),
SortRows = Table.Sort(ExplandIndex,{{"Date", Order.Ascending}}),
FillDown = Table.FillDown(SortRows, {"WorkingDaysSinceStart"})
in
FillDown``````

Helper III

This worked perfectly, thank you!

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