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I have a table in Power query, which besides other fields has the following key fields:
CMT | Year | Week | Customer | Transaction | Value
AB587 | 2019 | 12 | Tom | Purchase | 200
AB587 | 2019 | 12 | Tom | Sale | 15
AB587 | 2019 | 13 | Tom | Purchase | 60
AB587 | 2019 | 13 | Tom | Sale | 100
AB587 |2019 | 12 | Tom | Stock | 1600
AB587 | 2019 | 14 | Tom | Purchase | 50
AB587 | 2019 | 14 | Tom | Sale | 450
This is a table with about 300,000 rows with all the CMT and a couple of year's worth of transactions for all customers.
This is what it looks like right now:
CMT and Transaction as rows, and Weeks as columns, with Value as values, and customer as report filter.
the pivot table obviously shows what's in the raw data. What I want to do is to have the pivot table calculate the Stock for Weeks 13, 14 and so on. In the above example, I would expect the Stock in Week 13 to have 1600-100+60=1560, and Week 14 Stock to have 1560-450+50=1160, and so on.
Basically the pivot table should be projecting the stock in hand. I also want the pivot table to be able to do that when the CMT is removed from the rows and replaced by Customer or any other such combination. One more thing is that if the user brings in "months" instead of Weeks, the Stock should show the value of the last week of each month (the raw data has a month next to week in each row).
Solved! Go to Solution.
If you're worried about performance, you can use this trick to substantially improve speed for a case like this: https://www.thebiccountant.com/2017/05/29/performance-tip-partition-tables-crossjoins-possible-power...
Your code would look like so:
let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WcnQytTBX0lEyMjC0BFKGRkAiJD8XSAaUFiVnJBangiUNlGJ18CkOTswBKTQ0xarOGJuhZtjNNMYwk7DlJfnJ2SAhMxxKTbDZb0pILdR+EySFFkSEFCHV8KDCrhBHWBFSjAgsgvYjhRZ2tTiCi5BiRHgBVcYCAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [CMT = _t, Year = _t, Week = _t, Customer = _t, Transaction = _t, Value = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"CMT", type text}, {"Year", Int64.Type}, {"Week", Int64.Type}, {"Customer", type text}, {"Transaction", type text}, {"Value", Int64.Type}}), #"Pivoted Column" = Table.Pivot(#"Changed Type", List.Distinct(#"Changed Type"[Transaction]), "Transaction", "Value"), #"Added Custom1" = Table.AddColumn(#"Pivoted Column", "YearWeek", each [Year] * 100 + [Week], Int64.Type), #"Grouped Rows" = Table.Group(#"Added Custom1", {"CMT"}, {{"All", (Partition) => Table.AddColumn(Partition, "Custom", each List.Sum( Table.AddColumn( Table.SelectRows( Partition, let _earWeek = [YearWeek] in each [YearWeek] <= _earWeek ), "var", each [Sale]- [Purchase] )[var] )) }}), #"Expanded All" = Table.ExpandTableColumn(#"Grouped Rows", "All", {"Year", "Week", "Customer", "Purchase", "Sale", "Stock", "YearWeek", "Custom"}, {"Year", "Week", "Customer", "Purchase", "Sale", "Stock", "YearWeek", "Custom"}), #"Replaced Value" = Table.ReplaceValue(#"Expanded All", each [Stock], each [Stock] + [Custom],Replacer.ReplaceValue,{"Stock"}), #"Filled Down" = Table.FillDown(#"Replaced Value",{"Stock"}), #"Changed Type1" = Table.TransformColumnTypes(#"Filled Down",{{"Stock", Int64.Type}}), #"Replaced Value1" = Table.ReplaceValue(#"Changed Type1",each [Stock], each [Stock] - [Custom],Replacer.ReplaceValue,{"Stock"}), #"Changed Type2" = Table.TransformColumnTypes(#"Replaced Value1",{{"Stock", Int64.Type}}), #"Removed Other Columns" = Table.SelectColumns(#"Changed Type2",{"CMT", "Year", "Week", "Customer", "Purchase", "Sale", "Stock"}) in #"Removed Other Columns"
You'll find some more performance tricks here: https://www.thebiccountant.com/speedperformance-aspects/
Imke Feldmann (The BIccountant)
If you liked my solution, please give it a thumbs up. And if I did answer your question, please mark this post as a solution. Thanks!
How to integrate M-code into your solution -- How to get your questions answered quickly -- How to provide sample data -- Check out more PBI- learning resources here -- Performance Tipps for M-queries
Hi @nexami ,
if you want to learn how to integrate M code into your own solution, this video might help: https://community.powerbi.com/t5/Webinars-and-Video-Gallery/Power-BI-Forum-Help-How-to-integrate-M-c...
Imke Feldmann (The BIccountant)
If you liked my solution, please give it a thumbs up. And if I did answer your question, please mark this post as a solution. Thanks!
How to integrate M-code into your solution -- How to get your questions answered quickly -- How to provide sample data -- Check out more PBI- learning resources here -- Performance Tipps for M-queries
hello @Mariusz glad to hear from you, well the stock updates regularly having the following formulation:
Stock = Opening Stock + Purchase - Sale
Please check attached output example
each SKU unique ID will have its own Stock value.
Hi @nexami
Sorry, will it appear only one in a dataset for every sku and the rest need to be calculated?
Hi @Mariusz look at the provided screenshot, stock is calculated on weekly basis.
also not 12/ 13 / 14 / 15 are week numbers (just to avoid confusion).
Hi @nexami
Please see the below output based on the original data sample provided.
Please see the below M expression I used to Pivot the data on transaction column and later creatred a running total for Purchase - sales to be deducted of the stock value for every line.
let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WcnQytTBX0lEyMjC0BFKGRkAiJD8XSAaUFiVnJBangiUNlGJ18CkOTswBKTQ0xarOGJuhZtjNNMYwk7DlJfnJ2SAhMxxKTbDZb0pILdR+E5DCWAA=", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [CMT = _t, Year = _t, Week = _t, Customer = _t, Transaction = _t, Value = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"CMT", type text}, {"Year", Int64.Type}, {"Week", Int64.Type}, {"Customer", type text}, {"Transaction", type text}, {"Value", Int64.Type}}), #"Pivoted Column" = Table.Pivot(#"Changed Type", List.Distinct(#"Changed Type"[Transaction]), "Transaction", "Value"), #"Added Custom1" = Table.AddColumn(#"Pivoted Column", "YearWeek", each [Year] * 100 + [Week], Int64.Type), #"Added Custom2" = Table.AddColumn(#"Added Custom1", "Custom", each List.Sum( Table.AddColumn( Table.SelectRows( #"Added Custom1", let _cmt = [CMT], _earWeek = [YearWeek] in each [CMT] = _cmt and [YearWeek] <= _earWeek ), "var", each [Sale]- [Purchase] )[var] )), #"Replaced Value" = Table.ReplaceValue(#"Added Custom2", each [Stock], each [Stock] + [Custom],Replacer.ReplaceValue,{"Stock"}), #"Filled Down" = Table.FillDown(#"Replaced Value",{"Stock"}), #"Changed Type1" = Table.TransformColumnTypes(#"Filled Down",{{"Stock", Int64.Type}}), #"Replaced Value1" = Table.ReplaceValue(#"Changed Type1",each [Stock], each [Stock] - [Custom],Replacer.ReplaceValue,{"Stock"}), #"Changed Type2" = Table.TransformColumnTypes(#"Replaced Value1",{{"Stock", Int64.Type}}), #"Removed Other Columns" = Table.SelectColumns(#"Changed Type2",{"CMT", "Year", "Week", "Customer", "Purchase", "Sale", "Stock"}) in #"Removed Other Columns"
Sales and purchase will be normal sum aggregation where Last Stock is as below.
Last Stock = VAR _maxMonth = MAX( test[Week] ) VAR _maxYear = MAX( test[Year] ) RETURN CALCULATE( SUM( test[Stock] ), test[Week] = _maxMonth && test[Year] = _maxYear )
Due to complicated M expression and the size of your data this will perform rather slow.
If you're worried about performance, you can use this trick to substantially improve speed for a case like this: https://www.thebiccountant.com/2017/05/29/performance-tip-partition-tables-crossjoins-possible-power...
Your code would look like so:
let Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("i45WcnQytTBX0lEyMjC0BFKGRkAiJD8XSAaUFiVnJBangiUNlGJ18CkOTswBKTQ0xarOGJuhZtjNNMYwk7DlJfnJ2SAhMxxKTbDZb0pILdR+EySFFkSEFCHV8KDCrhBHWBFSjAgsgvYjhRZ2tTiCi5BiRHgBVcYCAA==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type text) meta [Serialized.Text = true]) in type table [CMT = _t, Year = _t, Week = _t, Customer = _t, Transaction = _t, Value = _t]), #"Changed Type" = Table.TransformColumnTypes(Source,{{"CMT", type text}, {"Year", Int64.Type}, {"Week", Int64.Type}, {"Customer", type text}, {"Transaction", type text}, {"Value", Int64.Type}}), #"Pivoted Column" = Table.Pivot(#"Changed Type", List.Distinct(#"Changed Type"[Transaction]), "Transaction", "Value"), #"Added Custom1" = Table.AddColumn(#"Pivoted Column", "YearWeek", each [Year] * 100 + [Week], Int64.Type), #"Grouped Rows" = Table.Group(#"Added Custom1", {"CMT"}, {{"All", (Partition) => Table.AddColumn(Partition, "Custom", each List.Sum( Table.AddColumn( Table.SelectRows( Partition, let _earWeek = [YearWeek] in each [YearWeek] <= _earWeek ), "var", each [Sale]- [Purchase] )[var] )) }}), #"Expanded All" = Table.ExpandTableColumn(#"Grouped Rows", "All", {"Year", "Week", "Customer", "Purchase", "Sale", "Stock", "YearWeek", "Custom"}, {"Year", "Week", "Customer", "Purchase", "Sale", "Stock", "YearWeek", "Custom"}), #"Replaced Value" = Table.ReplaceValue(#"Expanded All", each [Stock], each [Stock] + [Custom],Replacer.ReplaceValue,{"Stock"}), #"Filled Down" = Table.FillDown(#"Replaced Value",{"Stock"}), #"Changed Type1" = Table.TransformColumnTypes(#"Filled Down",{{"Stock", Int64.Type}}), #"Replaced Value1" = Table.ReplaceValue(#"Changed Type1",each [Stock], each [Stock] - [Custom],Replacer.ReplaceValue,{"Stock"}), #"Changed Type2" = Table.TransformColumnTypes(#"Replaced Value1",{{"Stock", Int64.Type}}), #"Removed Other Columns" = Table.SelectColumns(#"Changed Type2",{"CMT", "Year", "Week", "Customer", "Purchase", "Sale", "Stock"}) in #"Removed Other Columns"
You'll find some more performance tricks here: https://www.thebiccountant.com/speedperformance-aspects/
Imke Feldmann (The BIccountant)
If you liked my solution, please give it a thumbs up. And if I did answer your question, please mark this post as a solution. Thanks!
How to integrate M-code into your solution -- How to get your questions answered quickly -- How to provide sample data -- Check out more PBI- learning resources here -- Performance Tipps for M-queries
@Mariusz can you please send me the excel file as attachment having the code written in it as i tried to put up the codes under query but i think its not what it was suppose to be.
Hi @nexami ,
if you want to learn how to integrate M code into your own solution, this video might help: https://community.powerbi.com/t5/Webinars-and-Video-Gallery/Power-BI-Forum-Help-How-to-integrate-M-c...
Imke Feldmann (The BIccountant)
If you liked my solution, please give it a thumbs up. And if I did answer your question, please mark this post as a solution. Thanks!
How to integrate M-code into your solution -- How to get your questions answered quickly -- How to provide sample data -- Check out more PBI- learning resources here -- Performance Tipps for M-queries
Hi @nexami
Please see the below link.
https://drive.google.com/file/d/11VxbtLymDOKLOeEyfdBYfXhlgVWAxLIJ/view?usp=sharing
Hi @nexami
Are you referring to M code or DAX code?
Where do you need apply the filters is it in query editor or data model?
Please can you give an example of the code not working and show what outcome tha you expect.
M code.
see int he screenshots above, where i've increased the purchases / sales in each row, but after refreshing the query page, nothing happening.
Hi @nexami
Can you copy from excel and paste in here, so its easier to work with data?
Many Thanks
Mariusz
SKU Transaction Week13 Week14 Week15
AB504 Purchase 200 60 50
AB504 Sales 15 100 450
AB504 Stock 1600 ???? ????