Infil NaN for missing years in time series

Question

Poulomi Ganguli 2019-8-9

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/475587-infil-nan-for-missing-years-in-time-series

评论： Neuropragmatist 2019-8-9

Hello:

I have two matrices, A and B. Matrix B has some years missing in it. I want to concatenate horizontally two matrices, A and B to a new matrix C, which can be filled with NaN for the missing years.

A = 
0.79	1.56	0.90	1.15
0.62	0.83	0.84	0.74
0.81	0.71	0.71	0.70
1.06	0.74	0.61	0.76
1.23	0.86	0.67	0.61
1.32	0.56	1.11	0.76
0.75	1.06	0.56	1.15
1.76	1.09	0.88	0.67
0.90	0.77	0.94	0.77
0.52	0.52	1.15	0.88
and B = 
1.0617
1.0682
1.0149
0.6607
0.5642
0.6194
0.6693
0.6966
Desired output, C = 
1.0617	0.79	1.56	0.90	1.15	
1.0682	0.62	0.83	0.84	0.74	
NaN	0.81	0.71	0.71	0.70	
NaN	1.06	0.74	0.61	0.76	
1.0149	1.23	0.86	0.67	0.61	
0.6607	1.32	0.56	1.11	0.76	
0.5642	0.75	1.06	0.56	1.15	
0.6194	1.76	1.09	0.88	0.67	
0.6693	0.90	0.77	0.94	0.77	
0.6966	0.52	0.52	1.15	0.88	

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Answer 1

Neuropragmatist 2019-8-9

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https://ww2.mathworks.cn/matlabcentral/answers/475587-infil-nan-for-missing-years-in-time-series#answer_386984

在 MATLAB Online 中打开

If you don't mind converting your data to tables you can use outerjoin:

A = [1981	0.79	1.56	0.90	1.15;
0.62	0.83	0.84	0.74;
0.81	0.71	0.71	0.70;
1.06	0.74	0.61	0.76;
1.23	0.86	0.67	0.61;
1.32	0.56	1.11	0.76;
0.75	1.06	0.56	1.15;
1.76	1.09	0.88	0.67;
0.90	0.77	0.94	0.77;
0.52	0.52	1.15	0.88];
B = [1981	1.0617;
1.0682;
1.0149;
0.6607;
0.5642;
0.6194;
0.6693;
0.6966];
At = array2table(A);
Bt = array2table(B);
C = outerjoin(At,Bt,'Keys',1,'RightVariables',2);
C = C(:,[1 6 2:5])
C = 
  10×6 table
     A1       B2       A2      A3      A4      A5 
    ____    ______    ____    ____    ____    ____
  1.0617    0.79    1.56     0.9    1.15
  1.0682    0.62    0.83    0.84    0.74
     NaN    0.81    0.71    0.71     0.7
     NaN    1.06    0.74    0.61    0.76
  1.0149    1.23    0.86    0.67    0.61
  0.6607    1.32    0.56    1.11    0.76
  0.5642    0.75    1.06    0.56    1.15
  0.6194    1.76    1.09    0.88    0.67
  0.6693     0.9    0.77    0.94    0.77
  0.6966    0.52    0.52    1.15    0.88

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Neuropragmatist 2019-8-9

在 MATLAB Online 中打开

Or using indexing:

A = [1981	0.79	1.56	0.90	1.15;
1982	0.62	0.83	0.84	0.74;
1983	0.81	0.71	0.71	0.70;
1984	1.06	0.74	0.61	0.76;
1985	1.23	0.86	0.67	0.61;
1986	1.32	0.56	1.11	0.76;
1987	0.75	1.06	0.56	1.15;
1988	1.76	1.09	0.88	0.67;
1989	0.90	0.77	0.94	0.77;
1990	0.52	0.52	1.15	0.88];
B = [1981	1.0617;
1982	1.0682;
1985	1.0149;
1986	0.6607;
1987	0.5642;
1988	0.6194;
1989	0.6693;
1990	0.6966];
A = [A NaN(size(A(:,1)))];
[~,LOCB] = ismember(B(:,1),A(:,1));
A(LOCB(LOCB>0),6) = B(LOCB>0,2);
A = A(:,[1 6 2 3 4 5])
A =
  Columns 1 through 4
                      1981                    1.0617                      0.79                      1.56
                      1982                    1.0682                      0.62                      0.83
                      1983                       NaN                      0.81                      0.71
                      1984                       NaN                      1.06                      0.74
                      1985                    1.0149                      1.23                      0.86
                      1986                    0.6607                      1.32                      0.56
                      1987                    0.5642                      0.75                      1.06
                      1988                    0.6194                      1.76                      1.09
                      1989                    0.6693                       0.9                      0.77
                      1990                    0.6966                      0.52                      0.52
  Columns 5 through 6
                       0.9                      1.15
                      0.84                      0.74
                      0.71                       0.7
                      0.61                      0.76
                      0.67                      0.61
                      1.11                      0.76
                      0.56                      1.15
                      0.88                      0.67
                      0.94                      0.77
                      1.15                      0.88