Don't miss your chance to take the Fabric Data Engineer (DP-700) exam on us!
Learn moreWe've captured the moments from FabCon & SQLCon that everyone is talking about, and we are bringing them to the community, live and on-demand. Starts on April 14th. Register now
Hello! I am trying to either unpivot or pivot my data from the top format into the bottom format in order to calculate percent difference week over week. Thank you!
Sure thing
| Week Of | day_abbrev | Service Level |
| 9/4/2022 | Mon | 0.98 |
| 9/4/2022 | Tues | 0.86 |
| 9/4/2022 | Wed | 0.85 |
| 9/4/2022 | Thurs | 0.84 |
| 9/4/2022 | Fri | 0.77 |
| 9/25/2022 | Mon | 0.51 |
| 9/25/2022 | Tues | 0.94 |
| 9/25/2022 | Wed | 0.91 |
| 9/25/2022 | Thurs | 0.91 |
| 9/25/2022 | Fri | 0.92 |
| 9/18/2022 | Mon | 0.64 |
| 9/18/2022 | Tues | 0.86 |
| 9/18/2022 | Wed | 0.77 |
| 9/18/2022 | Thurs | 0.9 |
| 9/18/2022 | Fri | 0.7 |
Hopefully I've understood what you want, as you do not show the desired results based on the source data.
I assume you want the percent change for each weekdays Service Level.
Read the comments and explore the Applied Steps to understand the algorithm:
Original Data
let
//change next line to reflect your actual data source
Source = Excel.CurrentWorkbook(){[Name="Table9"]}[Content],
//set correct data types
#"Changed Type" = Table.TransformColumnTypes(Source,{
{"Week Of", type date},
{"day_abbrev", type text},
{"Service Level", type number}
}),
//Sort by Week Of, Descending
#"Sorted Rows" = Table.Sort(#"Changed Type",{{"Week Of", Order.Descending}}),
//Group by Weekday
// Generate List of percent changes based on week to week change
// Return Week Of Date and Percent change columns
#"Grouped Rows" = Table.Group(#"Sorted Rows", {"day_abbrev"}, {
{"Change", (t)=>
Table.FromColumns({t[Week Of]} &
{List.Generate(
()=>[d=(t[Service Level]{0} - t[Service Level]{1}) / t[Service Level]{1}, idx=0],
each [idx] < (Table.RowCount(t)),
each [d=try (t[Service Level]{[idx]+1} - t[Service Level]{[idx]+2}) / t[Service Level]{[idx]+2} otherwise null, idx=[idx]+1],
each [d])}),
type table[Column1=date, Column2=Percentage.Type]}
}),
#"Expanded Percent Change" = Table.ExpandTableColumn(#"Grouped Rows", "Change",
{"Column1", "Column2"},{"Week Of", "Percent Change"}),
//Pivot on Week Of, with no aggregaton
#"Pivoted Column" = Table.Pivot(Table.TransformColumnTypes(#"Expanded Percent Change",
{{"Week Of", type text}}, "en-US"),
List.Distinct(Table.TransformColumnTypes(#"Expanded Percent Change",
{{"Week Of", type text}}, "en-US")[#"Week Of"]), "Week Of", "Percent Change"),
//add day number column for sorting
// then sort by day number and remove that column
#"Added Custom" = Table.AddColumn(#"Pivoted Column", "day number",
each List.PositionOf({"Mon","Tues","Wed","Thurs","Fri"},[day_abbrev])),
#"Sorted Rows1" = Table.Sort(#"Added Custom",{{"day number", Order.Ascending}}),
#"Removed Columns" = Table.RemoveColumns(#"Sorted Rows1",{"day number"})
in
#"Removed Columns"Results
It would be helpful if you posted your source data as text which can be copy/pasted, rather than as a screenshot.
If you have recently started exploring Fabric, we'd love to hear how it's going. Your feedback can help with product improvements.
A new Power BI DataViz World Championship is coming this June! Don't miss out on submitting your entry.
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
| User | Count |
|---|---|
| 5 | |
| 4 | |
| 3 | |
| 2 | |
| 2 |
| User | Count |
|---|---|
| 8 | |
| 6 | |
| 6 | |
| 6 | |
| 5 |