Hi I am trying to add a AVERAGE column in a matrix, but when I put my metric added the average per column, but I need a total AVERAGE and total at the end just once
What I have:
What I need:
Group
Maria
Pedro
average
total
First
4
6
5
10
Second
5
10
7.5
15
Regards
Following the example detailed in the sample data table, to get the Total you could add the following measure;
Total By Group = CALCULATE( SUM(AverageExample[Maria]) + SUM(AverageExample[Pedro]))
and to average
Average By Group = [Total By Group] / 2
Based on the first three columns, this will provide
You have to build a DAX table (or Power Query) and a designated measure.
Matrix Table =
UNION(
DATATABLE("Detail", STRING, "Detail Order", INTEGER, "Type", STRING, {{"Average", 1000, "Agregate"}, {"Total", 1001, "Agregate"}}),
SUMMARIZE('Your Names Table', 'Your Names Table'[Name], 'Your Names Table'[Name Order], "Type", "Names")
)
This should give you a table with the list of people and 2 more lines for the agregations.
After that, you create a measure using variables and a switch function.
Matrix Measure =
var ft = FIRSTNONBLANK('Matrix Table'[Type], 0)
var fd = FIRSTNONBLANK('Matrix Table'[Detail], 0)
return SWITCH(TRUE,
ft = "Names", CALCULATE([Total], KEEPFILTERS('Your Names Table'[Name] = fd)),
fd = "Total", [Your Total Measure],
fd = "Average", [Your Averagex Measure]
)
The rest is up to you to fiddle with orders, add any agregate measures and whatnot.
Note that the Matrix Table should have no relation with any table from your model.
You can also hide it and the Matrix measure.
Related
Using Power Query in Excel. I am trying to implement a custom column that would iteratively calculate the row based on the previous row's value of the same column.
I have a 3 column table and the 4th column will be the calculation column that I am failing to implement.
The calculation is very easy to apply in Excel which goes as follows:
Formula in cell D3 --> = =IF(A3=1,C3+6.4,IF(C3+D2>=12.8,12.8,IF(C3+D2<=1.28,1.28,C3+D2)))
The same formula is applied to the whole column by dragging.
The idea behind it:
For each category, I have an index column starting from 1,
If Index = 1, then Calculation is Value + 6.4,
else if Value + Value(previous row Custom cumulative) >= 12.8 then 12.8
else if Value + Value(previous row Custom cumulative) <= 1.28 then 1.28
else Value + Value(previous row Custom cumulative)
So, the calculation is a cumulative sum with an upper and lower cap built into it.
How can I implement this in Power Query and M-Language?
I really appreciate your help!
I have tried to use List.Generate and List.Accumulate features, however, I was stuck with creating records that has values from multiple columns in it.
Try this
(edited to make more efficient with single pass process)
let Source = Excel.CurrentWorkbook(){[Name="Table15"]}[Content],
process = (zzz as list) => let x= List.Accumulate( zzz,{0},( state, current ) =>
if List.Last(state) =0 then List.Combine ({state,{6.4+current}}) else
if List.Last(state)+current >=12.8 then List.Combine ({state,{12.8}}) else
if List.Last(state)+current <=1.28 then List.Combine ({state,{1.28}}) else
List.Combine ({state,{List.Last(state)+current}})
) in x,
#"Grouped Rows" = Table.Group(Source, {"Category"}, {{"data", each
let a=process(_[Values])
in Table.AddColumn(_, "Custom Cumulative", each a{[Index]}), type table }}),
#"Expanded data" = Table.ExpandTableColumn(#"Grouped Rows", "data", {"Index", "Values", "Custom Cumulative"}, {"Index", "Values", "Custom Cumulative"})
in #"Expanded data"
I'm trying to calculate the Total Price per Order number. It specifically needs to be a column, because I'll be needing it for further calculations.
Can someone help me write code that calculates the total per Order Number, instead of line amount as it does now?
Since it's a calcualted column, just avoiding any context transition gives a straightforward solution
Total Price Per Order =
VAR CurrentOrder = SalesDetail[Order Number]
RETURN
SUMX (
FILTER (
SalesDetail,
SalesDetail[Order Number] = CurrentOrder
),
SalesDetail[Unit Price] * SalesDetail[Quantity]
)
I have a data table that contains transactions by supplier. Each row of data represents one transaction. Each transaction contains a "QTY" column as well as a "Supplier" column.
I need to rank these suppliers by the count of transactions (Count of rows per unique supplier) then by the SUM of the "QTY" for all of each supplier's transactions. This needs to be in 1 rank formula, not two separate rankings. This will help in breaking any ties in my ranking.
I have tried dozens of formulas and approaches and can't seem to get it right.
See below example:
Suppliers ABC and EFG each have 4 transactions so they would effectively tie for Rank 1, however ABC has a Quantity of 30 and EFG has a QTY of 25 so ABC should rank 1 and EFG should rank 2.
Can anyone assist?
https://i.stack.imgur.com/vCsCA.png
Welcome to SO. You can create a new calculated column -
Rank =
var SumTable = SUMMARIZE(tbl, tbl[Supplier], "CountTransactions", COUNT(tbl[Transaction Number]), "SumQuantity", SUM(tbl[Quantity]))
var ThisSupplier = tbl[Supplier]
var ThisTransactions = SUMX(FILTER(SumTable, [Supplier] = ThisSupplier), [CountTransactions])
var ThisQuantity = SUMX(FILTER(SumTable, [Supplier] = ThisSupplier), [SumQuantity])
var ThisRank =
FILTER(SumTable,
[CountTransactions] >= ThisTransactions &&
[SumQuantity] >= ThisQuantity)
return
COUNTROWS(ThisRank)
Here's the final result -
I'm curious to see if anyone posts an alternative solution. In the meantime, give mine a try and let me know if it works as expected.
I have a dataframe, df1, that reports courses students have taken, where ID is the student’s id, COURSES is a list of courses taken by the student, and TYPE and MAJOR are student attributes. The dataframe looks like this:
ID COURSES TYPE MAJOR
1 ['Intr To Archaeology', 'Statics', 'Circuits I…] Freshman EEEL
2 ['Signals & Systems I', ‘Instrumentation’…] Transfer EEEL
3 ['Keyboard Competence', 'Elementary … ] Freshman EEEL
4 ['Cultural Anthro', 'Vector Analysis’ … ] Freshma EEEL
I created a new dataframe, df2, that reports a dissimilarity measure for each pair of students based on the courses they’ve taken. df2 looks like this:
I created using the following script, but it runs very slowly (there are thousands of students). Can someone suggest a more efficient way to create df2?
One major problem is that the script below calculates the distance between (student 1 and student 2) and (student 2 and student 1), which is redundant since the distances are the same. However, the condition I created to prevent this:
if (id1 >= id2):
continue
doesn't work.
Entire script:
for id1, student1 in df.iterrows():
for id2, student2 in df.iterrows():
if (id1 >= id2):
continue
ID_1 = student1["ID"]
ID_2 = student2["ID"]
# courses as list strings
s1 = student1["COURSES"]
s2 = student2["COURSES"]
try:
# courses as sets
courses1 = set(ast.literal_eval(s1))
courses2 = set(ast.literal_eval(s2))
distance = float(len(courses1.symmetric_difference(courses2)))/(len(courses1) + len(courses2))
except:
# Some strings seem to have a different format
distance = -1
ID_1_Transfer = 1 if student1["TYPE"] == "Transfer" else 0
ID_2_Transfer = 1 if student2["TYPE"] == "Transfer" else 0
df2= df2.append({'ID_1': ID_1,'ID_2': PIDM_2,'Distance': distance, 'ID_1_Transfer': ID_1_Transfer, 'ID_2_Transfer': ID_2_Transfer}, ignore_index=True)
My Input data set has 3 columns and schema looks like below:
ActivityDate, EventId, EventDate
Now, using pig i need to derive multiple variables like below in one output file:
1) All Event Ids after ActivityDate >= EventDate -30 days
2) All Event Ids after ActivityDate >= EventDate -60 days
3) All Event Ids after ActivityDate >= EventDate -90 days
I have more than 30 variables like this. If it is one variable, we can use simple FILTER to filter the data.
I am thinking about any UDF implementation which takes bag as input and returns count of Event IDs based on above criteria for each parameter.
What is the best way to aggregate the data on multiple columns in pig ?
I would suggest creating another file with all of your thresholds and cross joining with the file.
so you would have a file containing:
30
60
90
etc
read it like this:
grouping = load 'grouping.txt' using PigStorage(',') as (groups:double);
Then do:
data_with_grouping = cross data, grouping;
Then have this binary condition:
data_with_binary_condition = foreach data_with_grouping generate ActivityDate, EventId, EventDate, groups, (ActivityDate >= EventDate - groups ? 1 : 0) as binary_condition;
Now you will have one column with the threshold and one column with a binary variable that tells you whether the ID follows the condition or not.
you can do a filter out all of the zeros from the binary_condition and then group on the groups column:
data_with_binary_condition_filtered = filter data_with_binary_condition by (binary_condition != 0);
grouped_by_threshold = group data_with_binary_condition_filtered by groups;
count_of_IDS = foreach grouped_by_threshold generate group, COUNT(data_with_binary_condition.EventId);
I hope this works. Obviously, I didn't debug it for you since I don't have your files.
This code will take a tad more time to run, but it will produce the output you need without a UDF.
If I understand your question correctly, you want to divide the difference between EventDate and ActivityDate in 30 days blocks (e.g. 1 to 30, 31 to 60, 61 to 90 and so on) and then count the frequency of each block.
In this case, I would just rearrange the above equation to create the variable 'range' as below:
// assuming input contains 3 columns ActivityDate, EventId, EventDate
// dividing the difference between ED and AD by 30 and casting it to int, so that 1 block is represented by 1 integer.
input1 = FOREACH input GENERATE (int)((EventDate - ActivityDate) / 30) as range;
output1 = GROUP input1 BY range;
output2 = FOREACH output1 GENERATE group AS range, COUNT(range) as count;
Hope this helps.