Grouping similar element based on common columns
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Hello:
I have two matrices, which I wish to group together based on common first three column elements. If no common column element found, it should be filled by 'NaN' element. Here is the two matrices A and B and resulting matrix C after the operation:
A = [6119200 44.55 -0.87 0.91
6123400 47.05 0.54 0.68
6122141 49.55 2.99 0.39
6421500 50.87 5.72 0.54
6607851 51.25 0.45 0.72]
B = [6123400 47.05 0.54 1.14
6122141 49.55 2.99 0.75
6421500 50.87 5.72 0.73
6607851 51.25 0.45 0.70
6607851 51.25 0.45 0.26
6607650 51.41 0.31 0.47]
C = [6119200 44.55 -0.87 0.91 NaN
6123400 47.05 0.54 0.68 1.14
6122141 49.55 2.99 0.39 0.75
6421500 50.87 5.72 0.54 0.73
6607851 51.25 0.45 0.72 0.26
6607650 51.41 0.31 0.47 NaN]
Any help how should I do so?
댓글 수: 3
Daniel M
2019년 10월 22일
What about the 4th and 5th row in B? One of them is dropped.
6607851 51.25 0.45 0.70
6607851 51.25 0.45 0.26
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Sebastian Bomberg
2019년 10월 23일
You can use outerjoin:
% Convert matrices A and B to tables
TA = array2table(A,"VariableNames",["Key"+(1:3) "A"])
TB = array2table(B,"VariableNames",["Key"+(1:3) "B"])
% Perform outerjoin with respect to columns 1 to 3 as key variables
TC = outerjoin(TA,TB,"Keys",1:3,"MergeKeys",true,"Type","full")
% Convert result back to matrix C
C = table2array(TC)
Note that in your C matrix, the 2nd NaN will appear in the second to last column with the values originally in matrix A.
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