Thanks about you knowlage good for download sample file thanks
@LinethDelaCruz2 жыл бұрын
You're welcome!
@alfredocoellovazquez2 жыл бұрын
thank you for the video, really good!!. just a quetion, how about if the list of values goes beyond 2021 into 2022?
@FRANKWHITE19962 жыл бұрын
thanks for sharing and keep going please. you just go a new subscriber :)
@madhun80923 жыл бұрын
This is great… if I have multiple categories .. I want to get percentage . Total sum/one category total . Is that possible?
@marcocruz84402 жыл бұрын
what if i am working with accumulating/conacting text?
@syedaneesdurez71972 жыл бұрын
Awesome
@kebincui4 ай бұрын
❤👍
@ubaidillahmuhammad203 жыл бұрын
nice. put link workbook download. and discuss left lookup ... .
@LinethDelaCruz3 жыл бұрын
Thanks.. pls check the updated description for the sample file
@krishnamanjunatha61832 жыл бұрын
tried and work well but got error when a month having only one Value like Jan = 5 rows, Feb = 3 rows, Mar = 1 row. Jan and Feb returns result but March returns Error. please provide solution🙂
@LinethDelaCruz2 жыл бұрын
I will look into it and get back to you soon. Thanks for raising this error.
@wayneedmondson10652 жыл бұрын
Hi Lineth. YT keeps deleting my comment, so leaving first post below in Reply. Thanks!!
@wayneedmondson10652 жыл бұрын
Hi Lineth. Cool solution using List.Accumulate. I have one I learned using Table.SelectRows and List.Sum plus some custom functions (see below). It's a little shorter in steps, but you do have to write some of the M code. Fun to solve in different ways. Thanks for sharing! Thumbs up!! let Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content], #"Sorted Rows" = Table.Sort(Source,{{"Date", Order.Ascending}}), BufferedTable = Table.Buffer(#"Sorted Rows"), RTByMonth = Table.AddColumn(BufferedTable, "RT by Month", (OT) => List.Sum(Table.SelectRows(BufferedTable, (IT) => IT[Date]
@LinethDelaCruz2 жыл бұрын
Hi Wayne, wow! thanks for sharing this. I have tried it and yes it's shorter which is great and processing time is fast when it comes to large data.