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Hi,
I have 1500 unique IDs, everyday many are seen and some multiple times a day.
I would like to do a cumulative count and know how many days does it takes to see them all 1500, once it reaches that amount, the cumulative sum needs to reset for another count until the next time the goal is met.
I've done a running total after a grouped count by date, but my issue aside from the needed reset is that the duplicates needs to be removed based on the time period needed to attain the goal. If i'm grouping my IDs by date then i'm losing the ability to sort out duplicates...
So i'm guessing I need to first find how much time is needed, then remove the duplicates from a custom column spanning over its own run tower the goal, and then, calculate the running...
Here's an ugly paint mockup of what i'd like my final result to look like (I realize I need my date format to be date.time otherwise i'll never get a correct goal and it'll be tricky)
I've found this solution here from Lind25 but the syntax is wrong, maybe from a previous PQ version:
let
Source = Excel.CurrentWorkbook(){[Name="Table1"]}[Content],
AddedIndex = Table.AddIndexColumn(Source, "Index", 1, 1),
#"Added Custom" = Table.AddColumn(AddedIndex, "Running total reset", each List.Accumulate(List.FirstN(AddedIndex[Difference],[Index]),0,(state,current)=>if state+current > 60 then 0 else state+current))
in
#"Added Custom"
Any takers ? I'm completely lost on that one...
Thanks
Solved! Go to Solution.
I've simulated a scenario with only 10 IDs using some of the data you provided, see if I'm on the right track? If I'm right, I'll think of the code to implement the end result.
I did a test performing the core operation in Python and it seems to finish in less than 20 secs for the data you posted. See it in the attached file. You'll have to update the path to the excel file you shared. Here is the M code for the main query:
Note it should be cleaned up a bit, as it is doing a lot of stuff that might not be necessary
let
Source = Excel.Workbook(File.Contents("d:\Downloads\Sample PQ Help.xlsx"), null, true),
Maintenance_Itinérante___CSV_Table = Source{[Item="Maintenance_Itinérante___CSV",Kind="Table"]}[Data],
#"Changed Type" = Table.TransformColumnTypes(Maintenance_Itinérante___CSV_Table,{{"Date&Time Seen", type datetime}, {"ID", Int64.Type}}),
#"Removed Duplicates" = Table.Distinct(#"Changed Type", {"Date&Time Seen", "ID"}),
#"Sorted Rows" = Table.Sort(#"Removed Duplicates",{{"Date&Time Seen", Order.Ascending}}),
#"Added Custom" = Table.AddColumn(#"Sorted Rows", "Date", each Date.From([#"Date&Time Seen"])),
#"Removed Columns" = Table.RemoveColumns(#"Added Custom",{"Date&Time Seen"}),
#"Reordered Columns" = Table.ReorderColumns(#"Removed Columns",{"Date", "ID"}),
#"Removed Duplicates1" = Table.Distinct(#"Reordered Columns", {"Date", "ID"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Removed Duplicates1",{{"Date", Int64.Type}}),
#"Run Python script" = Python.Execute("# 'dataset' holds the input data for this script#(lf)groupeddataset = dataset.groupby(['Date'])['ID'].apply(lambda x: list(x)).to_frame().reset_index()#(lf)#test.groupby(['Pos'])['Pos2'].apply(lambda x: list(x)).to_frame().reset_index()#(lf)a = list(groupeddataset['ID']) #(lf)acc=list(initial['ID']); res=[]#(lf)for i in range(len(a)):#(lf) acc=set(acc)-set(a[i])#(lf) #acc=set(acc)-set([a[i]])#(lf) if acc == set(): #(lf) acc=initial#(lf) res=res+[i]#(lf)#(lf)output=pandas.DataFrame(res,columns=['Positions'])",[dataset=#"Changed Type1", initial=Table.SelectRows(All_IDsT,each [ID]<> 15133)]),
groupeddataset = #"Run Python script"{[Name="groupeddataset"]}[Value],
groupeddataset2 = Table.TransformColumnTypes(groupeddataset,{{"Date", Int64.Type}}),
#"Changed Type3" = Table.TransformColumnTypes(groupeddataset2,{{"Date", type date}}),
#"Sorted Rows1" = Table.Sort(#"Changed Type3",{{"Date", Order.Ascending}}),
CompletionPositionsT = #"Run Python script"{[Name="output"]}[Value],
CompletionPositionsT2 = Table.TransformColumnTypes(CompletionPositionsT,{{"Positions", Int64.Type}}),
result = List.Select(groupeddataset2[Date], each List.Contains(CompletionPositionsT2[Positions],_ - List.Min(groupeddataset2[Date]))),
#"Converted to Table" = Table.FromList(result, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
#"Changed Type2" = Table.TransformColumnTypes(#"Converted to Table",{{"Column1", type date}}),
#"Renamed Columns" = Table.RenameColumns(#"Changed Type2",{{"Column1", "Completion dates"}})
in
#"Renamed Columns"
The main step is #"Run Python script", with the following Python code:
groupeddataset = dataset.groupby(['Date'])['ID'].apply(lambda x: list(x)).to_frame().reset_index()
a = list(groupeddataset['ID'])
acc=list(initial['ID']); res=[]
for i in range(len(a)):
acc=set(acc)-set(a[i])
if acc == set():
acc=initial
res=res+[i]
output=pandas.DataFrame(res,columns=['Positions'])
It groups the IDs by Dates (day level) and then operates on that to extract a list of the positions where each section with all IDs seen ends. Note I have filtered out ID 15133 from the list of IDs so that there is at least one section that has all IDs
Please mark the question solved when done and consider giving kudos if posts are helpful.
Contact me privately for support with any larger-scale BI needs, tutoring, etc.
Cheers
As much a I love M, I will put another plug in for the DAX approach for this one. I don't know if your IDs have category columns associated with them and you will want to also have slicers (which would require a DAX approach). Attached is a pbix with your example data. It uses the below DAX expression to generate the shown table (I also added a small table with the "Cycle" values of 1,2,3). You could do them separate but it is calculation intensive (so since I already had the virtual table in the measure, I generated both outputs and concatenated them together).
Time and Span for Completion =
VAR thiscycle =
SELECTEDVALUE ( Cycle[Completion Cycle] )
VAR ids =
ALLSELECTED ( Seen[ID] )
VAR idcount =
COUNTROWS ( ids )
VAR summarylastcycle =
ADDCOLUMNS (
VALUES ( Seen[Date&Time Seen] ),
"IDsSoFar",
VAR thistime = Seen[Date&Time Seen]
RETURN
COUNTROWS (
FILTER (
ids,
CALCULATE (
COUNT ( Seen[ID] ),
Seen[Date&Time Seen] <= thistime
) >= thiscycle - 1
)
)
)
VAR completiontimelastcycle =
IF (
thiscycle = 1,
MIN ( Seen[Date&Time Seen] ),
MINX (
FILTER (
summarylastcycle,
[IDsSoFar] >= idcount
),
Seen[Date&Time Seen]
)
)
VAR summarythiscycle =
ADDCOLUMNS (
FILTER (
VALUES ( Seen[Date&Time Seen] ),
Seen[Date&Time Seen] >= completiontimelastcycle
),
"IDsSoFar",
VAR thistime = Seen[Date&Time Seen]
RETURN
COUNTROWS (
FILTER (
ids,
CALCULATE (
COUNT ( Seen[ID] ),
Seen[Date&Time Seen] <= thistime
) >= thiscycle
)
)
)
VAR completiontimethiscycle =
MINX (
FILTER (
summarythiscycle,
[IDsSoFar] >= idcount
),
Seen[Date&Time Seen]
)
VAR span =
DATEDIFF (
completiontimelastcycle,
completiontimethiscycle,
DAY
)
VAR range = completiontimelastcycle & " - " & completiontimethiscycle
RETURN
span & " days" & "; " & range
Regards,
Pat
To learn more about Power BI, follow me on Twitter or subscribe on YouTube.
the code that implements the algorithm I described in the previous message
let
completion = (tab)=>
let
grpID=Table.Group(tab, {"ID"}, {"grp", each _}),
nids=Table.RowCount(grpID),
currLastDate=List.Min(List.Last(grpID[grp])[Date]),
rest= Table.SelectRows(tab, each _[Date] > currLastDate ),
result= if Table.RowCount(Table.Distinct(rest,"ID")) < nids then {currLastDate} else {currLastDate} & @ completion(rest)
in
result
in
completion
This function receives as input a table with Date and ID columns and provides a list of dates where the saturation cycle of all the distinct ids of the table is completed.
It seems very fast, respect previou solution based on list.generate and list.difference and so on ...
to obtain a more pleasant output, I grafted a copy of the vector of ids in the original table in three different random points of the [ID] column 😊
Hi, @Kiwizqt
In the two worksheets you provided, the number of non-duplicate IDs in the seen worksheet is 1698, but the number of IDs in the Total ID worksheet is 1374.
Can you re-provide a correct form file?
Hi @ziying35 sorry for the inconvenience, I cleared wrong datas by applying an inner merge. The 324 ID excess came from wrong inputs from users and I thought it didn't matter for the solution as I would've done that merge before any solution given here, or that I could manually edit the m-code.
Bellow the finite data:
https://drive.google.com/file/d/1gWHYl3FxX3t2Dt6jQ8MCBWV1WF4d80V0/view?usp=sharing
I provide a solution, but the solution deals with a file where all IDs must be valid, and my solution shows 10 non-duplicate IDs.
The result of the code run is shown below
// output
let
Source = Table.FromRecords(Json.Document(Binary.Decompress(Binary.FromText("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", BinaryEncoding.Base64),Compression.Deflate))),
chtype = Table.TransformColumnTypes(Source,{{"Date&Time Seen", type datetime}}),
n = Table.RowCount(chtype),
rows = List.Buffer(Table.ToRows(chtype)),
gen = List.Generate(
()=>{[], null, 0, 0, 1},//{dictionary, counter, id_counter, index, cycle_index}
each (_{1}? ??0)<n,
each let
fx1 = (i)=>if Record.HasFields(_{0}, Text.From(rows{i}?{1}?)) then _{2}
else _{2}+1,
fx2 = (initial_value, variable)=>
if id_ctr=10 then
if fx1(ci+1)=10 and Date.From(rows{ci}?{0}?)=Date.From(rows{ci+1}?{0}?) then variable
else initial_value
else variable,
ci = _{1}?+1 ??0,
dic =if Record.HasFields(_{0}, Text.From(rows{ci}?{1}?)) then _{0}
else _{0}&Record.AddField([], Text.From(rows{ci}?{1}?), id_ctr),
id_ctr = fx1(ci),
idx = Record.Field(dic, rows{ci}?{1}?),
dic_var = fx2([], dic),
id_ctr_var = fx2(0, id_ctr),
cycle_index = if _{3} = 10 then if idx = 10 then _{4} else _{4}+1 else _{4}
in { dic_var, ci, id_ctr_var, idx, cycle_index },
each {rows{_{1}}{0}, rows{_{1}}{1}, Text.Format("#{1}-#{0}", {_{3}, _{4}}) }
),
result = Table.FromRows(List.Skip(gen), type table [#"Date&Time Seen"= datetime, ID = any, Index = any])
in
result
@ziying35Thank you, this is almost perfect and exactly what I need. However, do you know if there'd be a way to easily distinguish the completed cycles from one another ? Or even to modify the indexes to link them to their dates such as 1a-10a, 1b-10b etc in chronological orders of occurrences ? Or return them in another table ?
@AlB@Anonymous I've yet to open that pandora box but I for sure will one day, i'm currently in the process of learning me some Python. I haven't forgone both of your solutions and will learn from it, i'll try to implemant them and see what it is that you've designed, I've gotta learn myself some List.Accumulate, those seem very useful but quite dense to learn.
I probably see what you mean.You want to put another special number on the result of each loop to distinguish the results of different loops
@ziying35That would be amazing yes, so that I could sort out by min & max later on and we'd be done here!
Hi @Kiwizqt
You could try to implement this strategy:
1) Groups the Seen table (which is sorted on the date.time column) by ID.
2) Then you have the first block / completion of the Seen table by selecting all the rows preceding the first element of the last group of the previous step;
The new Seen table becomes the old one minus this first group.
Apply the same sequence to this one and so on ...
PS
This way to proceed reduce the size of the problem (and of the time !?!?) from the size of Seen table (100k) to the size of Tot ids (1k)
here the first two steps of the task ...
per scaricarli, fai click sul seguente link e segui le istruzioni.
I think with some work it is possible to automate this sequence of operations and have power query do everything ...
I did a test performing the core operation in Python and it seems to finish in less than 20 secs for the data you posted. See it in the attached file. You'll have to update the path to the excel file you shared. Here is the M code for the main query:
Note it should be cleaned up a bit, as it is doing a lot of stuff that might not be necessary
let
Source = Excel.Workbook(File.Contents("d:\Downloads\Sample PQ Help.xlsx"), null, true),
Maintenance_Itinérante___CSV_Table = Source{[Item="Maintenance_Itinérante___CSV",Kind="Table"]}[Data],
#"Changed Type" = Table.TransformColumnTypes(Maintenance_Itinérante___CSV_Table,{{"Date&Time Seen", type datetime}, {"ID", Int64.Type}}),
#"Removed Duplicates" = Table.Distinct(#"Changed Type", {"Date&Time Seen", "ID"}),
#"Sorted Rows" = Table.Sort(#"Removed Duplicates",{{"Date&Time Seen", Order.Ascending}}),
#"Added Custom" = Table.AddColumn(#"Sorted Rows", "Date", each Date.From([#"Date&Time Seen"])),
#"Removed Columns" = Table.RemoveColumns(#"Added Custom",{"Date&Time Seen"}),
#"Reordered Columns" = Table.ReorderColumns(#"Removed Columns",{"Date", "ID"}),
#"Removed Duplicates1" = Table.Distinct(#"Reordered Columns", {"Date", "ID"}),
#"Changed Type1" = Table.TransformColumnTypes(#"Removed Duplicates1",{{"Date", Int64.Type}}),
#"Run Python script" = Python.Execute("# 'dataset' holds the input data for this script#(lf)groupeddataset = dataset.groupby(['Date'])['ID'].apply(lambda x: list(x)).to_frame().reset_index()#(lf)#test.groupby(['Pos'])['Pos2'].apply(lambda x: list(x)).to_frame().reset_index()#(lf)a = list(groupeddataset['ID']) #(lf)acc=list(initial['ID']); res=[]#(lf)for i in range(len(a)):#(lf) acc=set(acc)-set(a[i])#(lf) #acc=set(acc)-set([a[i]])#(lf) if acc == set(): #(lf) acc=initial#(lf) res=res+[i]#(lf)#(lf)output=pandas.DataFrame(res,columns=['Positions'])",[dataset=#"Changed Type1", initial=Table.SelectRows(All_IDsT,each [ID]<> 15133)]),
groupeddataset = #"Run Python script"{[Name="groupeddataset"]}[Value],
groupeddataset2 = Table.TransformColumnTypes(groupeddataset,{{"Date", Int64.Type}}),
#"Changed Type3" = Table.TransformColumnTypes(groupeddataset2,{{"Date", type date}}),
#"Sorted Rows1" = Table.Sort(#"Changed Type3",{{"Date", Order.Ascending}}),
CompletionPositionsT = #"Run Python script"{[Name="output"]}[Value],
CompletionPositionsT2 = Table.TransformColumnTypes(CompletionPositionsT,{{"Positions", Int64.Type}}),
result = List.Select(groupeddataset2[Date], each List.Contains(CompletionPositionsT2[Positions],_ - List.Min(groupeddataset2[Date]))),
#"Converted to Table" = Table.FromList(result, Splitter.SplitByNothing(), null, null, ExtraValues.Error),
#"Changed Type2" = Table.TransformColumnTypes(#"Converted to Table",{{"Column1", type date}}),
#"Renamed Columns" = Table.RenameColumns(#"Changed Type2",{{"Column1", "Completion dates"}})
in
#"Renamed Columns"
The main step is #"Run Python script", with the following Python code:
groupeddataset = dataset.groupby(['Date'])['ID'].apply(lambda x: list(x)).to_frame().reset_index()
a = list(groupeddataset['ID'])
acc=list(initial['ID']); res=[]
for i in range(len(a)):
acc=set(acc)-set(a[i])
if acc == set():
acc=initial
res=res+[i]
output=pandas.DataFrame(res,columns=['Positions'])
It groups the IDs by Dates (day level) and then operates on that to extract a list of the positions where each section with all IDs seen ends. Note I have filtered out ID 15133 from the list of IDs so that there is at least one section that has all IDs
Please mark the question solved when done and consider giving kudos if posts are helpful.
Contact me privately for support with any larger-scale BI needs, tutoring, etc.
Cheers
As much a I love M, I will put another plug in for the DAX approach for this one. I don't know if your IDs have category columns associated with them and you will want to also have slicers (which would require a DAX approach). Attached is a pbix with your example data. It uses the below DAX expression to generate the shown table (I also added a small table with the "Cycle" values of 1,2,3). You could do them separate but it is calculation intensive (so since I already had the virtual table in the measure, I generated both outputs and concatenated them together).
Time and Span for Completion =
VAR thiscycle =
SELECTEDVALUE ( Cycle[Completion Cycle] )
VAR ids =
ALLSELECTED ( Seen[ID] )
VAR idcount =
COUNTROWS ( ids )
VAR summarylastcycle =
ADDCOLUMNS (
VALUES ( Seen[Date&Time Seen] ),
"IDsSoFar",
VAR thistime = Seen[Date&Time Seen]
RETURN
COUNTROWS (
FILTER (
ids,
CALCULATE (
COUNT ( Seen[ID] ),
Seen[Date&Time Seen] <= thistime
) >= thiscycle - 1
)
)
)
VAR completiontimelastcycle =
IF (
thiscycle = 1,
MIN ( Seen[Date&Time Seen] ),
MINX (
FILTER (
summarylastcycle,
[IDsSoFar] >= idcount
),
Seen[Date&Time Seen]
)
)
VAR summarythiscycle =
ADDCOLUMNS (
FILTER (
VALUES ( Seen[Date&Time Seen] ),
Seen[Date&Time Seen] >= completiontimelastcycle
),
"IDsSoFar",
VAR thistime = Seen[Date&Time Seen]
RETURN
COUNTROWS (
FILTER (
ids,
CALCULATE (
COUNT ( Seen[ID] ),
Seen[Date&Time Seen] <= thistime
) >= thiscycle
)
)
)
VAR completiontimethiscycle =
MINX (
FILTER (
summarythiscycle,
[IDsSoFar] >= idcount
),
Seen[Date&Time Seen]
)
VAR span =
DATEDIFF (
completiontimelastcycle,
completiontimethiscycle,
DAY
)
VAR range = completiontimelastcycle & " - " & completiontimethiscycle
RETURN
span & " days" & "; " & range
Regards,
Pat
To learn more about Power BI, follow me on Twitter or subscribe on YouTube.
@AlB @mahoneypat @Anonymous @ziying35 All of you are wizards and honestly I kind of feel embarassed to have put you all to this much work, especially with how poorly it was initialy phrased.
This last solution from Pat gives me all that I want and then some, i'll apologize once again because I only have so many thanks and being new to this forum I hope that I can all verify those solutions simulatenously as they all gave a different take to my problem.
Again, thank you and I will be sure to learn to my best of my abilities all of the different method you've shown me so far. I'll do better on the problem description next time I have an issue and lurk around here in the meantime to learn new things.
Cheers
I'm sorry, there's a judgement logic in my code that I haven't written yet, I just can't figure it out, I'll post it when the code is complete.
@Anonymous , @mahoneypat , @Kiwizqt
If we remove ID 15133 (which is what @mahoneypat is effectively doing by considering ALLSELECTED ( Seen[ID] )),
I only see one period with all the IDs in the data provided: the one finishing on the 25th of August. From the 25th of August to the 3rd of September not all IDs come up. Neither do they if we extend the period up to the latest date on the data. I actually consider the second period would start after the 25th of August rather than on that very same day but that's just a matter of fine tuning.
@mahoneypat , I don't think your code complies with the requirements (at least how I've interpreted them). For the Nth period, we need to know the completion date of the (N-1)th, and for the completion of the (N-1)th we need the (N-2)th, etc. That means for the Nth period we need first the completion dates of all the previous ones. It is this recursive aspect that makes it very difficult to implement in DAX, as it would imply a circular reference. I haven't been able to figure it out in DAX. I would certainly be interested in seeing a solution if you manage to get it. I have a feeling it cannot be done (unless you write a separate fragment of code for each period, which would not very flexible or elegant). That's why I resorted to M, and ultimately to Python given how slow the M solution was. A loop is all you need.
But getting back to why I think @mahoneypat's code doesn't provide the expected result: your
VAR summarylastcycle
looks at how many times each ID has appeared from the "beginning of time" up to the current date and gives the green light if each ID has been seen at least (thiscycle - 1) times. But what we need to do is to look at how many IDs have been seen in the previous completion period at least once, irrespective of the cycle. These are two different things.
It can always be, of course, that I've misunderstood the code. I'd be happy to hear back if it were so.
PD: I fully agree with the numbers provided by @Anonymous regarding the number of IDs seen per period. Additionally, the number of IDs from the 25th of August up to the latest date is the same as up to the 19th of September. The number of IDs in @mahoneypat's second period is 1202
Please mark the question solved when done and consider giving kudos if posts are helpful.
Contact me privately for support with any larger-scale BI needs, tutoring, etc.
Cheers
@AlB @Kiwizqt Although I agree DAX is not the language for recursive calculation, the approach I've suggested is not recursive. The calculation of N-1 does not require knowing N-2. For example, the completion times for cycles 1, 2, and 3 is the minimum time where the count of all items is >=1, >=2, and >=3, respectively. To get the span, one only needs the current and previous calculations (not all the values). While it may need some fine tuning, I believe the approach is sound.
Also, I used ALLSELECTED() in case the ID table had other columns to be used as slicers, so that the calculation would be dynamic.
Regards,
Pat
To learn more about Power BI, follow me on Twitter or subscribe on YouTube.
@mahoneypat wrote:
...the completion times for cycles 1, 2, and 3 is the minimum time where the count of all items is >=1, >=2, and >=3, respectively.
@mahoneypat Not really. In the example below, your reasoning (if I understand it correctly) would yield the highlighted completion times, while there is only one actual completion time ending on date 6.
Date | ID | |
1 | 100 | |
2 | 100 | |
3 | 100 | |
4 | 200 | |
5 | 200 | |
6 | 300 | <--1st period |
7 | 300 | <--2nd period |
8 | 200 | |
9 | 300 | <--3rd period |
Please mark the question solved when done and consider giving kudos if posts are helpful.
Contact me privately for support with any larger-scale BI needs, tutoring, etc.
Cheers
@AlB You are right. I will think more on a potential non-recursive DAX approach, but it may not be possible.
Regards,
Pat
To learn more about Power BI, follow me on Twitter or subscribe on YouTube.
@Anonymous
PS: I have a question. Why is there no ID 15133 in the sheet named Seen in the file I downloaded, but there are multiple ones in the one you downloaded?
Hi @ziying35 ,
I'm not sure I understand your question. But, in the hypothesis that you have not completely read my previous message, I propose again here the part related to the presence of the ID 15133, in the hope that this can be an answer to your question.
<<
As you can verify the table Seen used contains 3 times the ID 15133 which is not rpesent at all in the original table.
Those are the three positions where I grafted the entire vector of ids (Total ID) to get at least three full "completion" and be able to test the function to see if, as well as pretty, it really worked ...
>>
I take this opportunity to make a request.
Since now I don't have time to look at the code and analyze it, you could explain in detail the situation you were referring to with the following sentence:
"If the last ID happens to occur consecutively at the end of the loop, your code has a problem! For example, in the following case, which I've marked yellow." ?
@Anonymous
I see, because I'm using a machine translator, and there's a misspelling of a word, so I don't understand the translation.
In my simulation table, I think the result should look like this:
but your code looks like this:
my simulation table:
= Table.FromRecords(Json.Document(Binary.Decompress(Binary.FromText("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", BinaryEncoding.Base64),Compression.Deflate)))
Hi @ziying35 ,
if the data of dates are those (and there isn't typo in teh picture you provided) they seems to me both correct 😁, then ther isn't contraddiction.
The logic that the function implements is that for which the start time of following completion period is greater (or not less ) than time of previous completion period and this is what the request seems to me to be.
is that so?
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