finding the mean based on a specific value in other column

Guys I have following data as an example.The data contain 4 coloumns.want to average 4th coloumn when 1st couloumn is equal to 527.1235 and third coloumn is 927.5

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Asad (Mehrzad) Khoddam
Asad (Mehrzad) Khoddam el 9 de Oct. de 2020
Editada: Asad (Mehrzad) Khoddam el 9 de Oct. de 2020
You can find the rows with the first condition and the other rows for the second condition. The intersection of the two rows, are the row numbers that satisfy both conditions:
rows = intersect(find(a(:,1)==527.1235), find(a(:,3)==927.5));
% average of the above rows
avg = mean(a(rows,4));
disp(avg)
or simpler :
rows = find(a(:,1)==527.1235 & a(:,3)==927.5);
% average of the above rows
avg = mean(a(rows,4));
disp(avg)

3 comentarios

madhan ravi
madhan ravi el 9 de Oct. de 2020
Editada: madhan ravi el 9 de Oct. de 2020
Note this answer doesn't include the floating point comparison. Use of find() for the second approach is not necessary ,a well use of logical indexing is more than enough.
Thank You !! But I get NaN while use both the approaches !! The data I showed is a little part of my huge data...While importing it in Matlab and using your suggested solution it gives me NaN.....
When data is missing in the data file, it shows as NaN. You can remove the lines that are NaN
use this option:
avg = mean(a(rows,4),'omitnan');

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Más respuestas (2)

madhan ravi
madhan ravi el 9 de Oct. de 2020
Editada: madhan ravi el 9 de Oct. de 2020
ix = (abs(column_1 - 527.1235) < 1e-4) &...
(abs(column_1 - 927.5) < 1e-1);
M = mean(column_4(ix))
Jon
Jon el 9 de Oct. de 2020
Editada: Jon el 9 de Oct. de 2020
Lets say you have put your data into an array X.
Find a logical index where the rows match your criteria using:
criteria = [527.1235 927.5]
idl = ismember(X(:,[1,3]),criteria,'rows')
then do the averaging on the 4th colulmn for the rows where the criteria matches
xMean = mean(X(idl,4))

5 comentarios

Jon
Jon el 9 de Oct. de 2020
Editada: Jon el 9 de Oct. de 2020
Wow, just while I was posting this two other answers appeared. So you have some choices. If I understand your problem correctly though, then this should be a pretty clean way to do it. I guess if you are just matching on two columns then there are many ways to do it. I think this approach scales well though if at some point you have a similar problem but with multiple columns (or whole rows) you want to match on.
>> X
X =
527.1235 1.0000 927.5000
>> criteria
criteria =
527.1235 927.5000
>> idl = ismember(X(:,[1,3]),criteria,'rows')
idl =
logical
0
>>
Thank You !! But I get NaN while use both the approaches !! The data I showed is a little part of my huge data...While importing it in Matlab and using your suggested solution it gives me NaN...
When data is missing in the data file, it shows as NaN. You can remove the lines that are NaN
I think you are trying to show that floating point comparisons could be a problem if they are not exact. I assumed they were exact, in any case given your example I get idl = 1 not 0
>> X = [527.1235 1.0000 927.5000],criteria =[527.1235 927.5000]
X =
527.1235 1.0000 927.5000
criteria =
527.1235 927.5000
>> idl = ismember(X(:,[1,3]),criteria,'rows')
idl =
logical
1

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