My data have 20 years of values of carbon emission and a column named average_emission. I want to replace all NaN in a particular row by the corresponding value of average_emission of the same row.
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 ... 2002 2004 2005 2007 2009 2010 average_emission
0 29.620165 29.847608 25.258152 24.430481 22.996441 22.186615 21.641890 21.500661 19.621534 19.652589 ... 24.975515 24.909066 24.510543 24.964531 24.766706 25.613715 24.750133 24.876706 24.182702 24.344568
2 0.221827 0.194971 0.103776 0.092761 0.083184 0.075646 0.068592 0.061814 0.057051 0.043723 ... 0.041641 0.046108 0.040727 0.054855 0.065816 0.088141 0.158962 0.249074 0.302936 0.101080
3 0.398076 0.380695 0.372921 0.473691 0.307601 0.844306 0.781485 0.534831 0.515117 0.627072 ... 0.786223 0.543084 1.086605 1.069417 1.200877 1.311096 1.369425 1.430873 1.401654 0.794954
4 2.278387 1.215677 0.735198 0.726063 0.601347 0.654537 0.636625 0.490365 0.560271 0.960164 ... 1.229541 1.412697 1.376213 1.412498 1.291548 1.507536 1.580113 1.533178 1.515632 1.130908
5 3.203907 3.284094 3.483917 3.715328 3.735031 3.473291 3.364402 3.168027 3.360498 3.339701 ... 3.579922 3.797800 4.094269 4.206789 4.285595 4.181153 4.373573 4.575251 4.764912 3.775633
6 28.711160 29.798843 28.794559 31.007791 32.672833 30.057582 16.638557 16.047240 29.810631 27.173954 ... 24.956551 29.467089 28.481437 25.914587 23.954519 23.195067 23.033600 21.102296 20.120957 26.636164
7 3.440711 3.525384 3.608582 3.477160 3.569366 3.501875 3.648190 3.758387 3.798782 3.969876 ... 3.253313 3.475035 4.032407 4.111635 4.404525 4.496834 4.744178 4.427960 4.342272 3.853951
8 NaN NaN 1.174674 0.859707 0.901719 1.083089 0.821585 1.044825 1.094343 0.988512 ... 0.998806 1.129174 1.204567 1.443731 1.459637 1.694755 1.868611 1.469961 1.422998 1.207623
10 4.857267 4.641623 4.566841 4.692005 4.683621 4.721298 4.593864 4.670562 4.496901 4.581279 ... 4.536211 4.804484 4.980996 4.974311 5.096290 5.561525 5.628319 5.906292 6.011269 4.895667
11 15.461262 15.133907 15.322589 15.706018 15.581310 15.600983 16.506006 16.516650 16.940007 17.192235 ... 17.372318 16.906198 17.030341 17.174386 17.293874 17.467002 17.704080 17.631833 16.710904 16.628071
If I understand correctly, you could do:
df = df.where(df.notnull(), df.average_emission, axis=0)
For example:
>>> df
1990 1991 1992 1993
0 29.620165 29.847608 25.258152 24.430481
2 0.221827 0.194971 0.103776 0.092761
3 0.398076 0.380695 0.372921 0.473691
4 2.278387 1.215677 0.735198 0.726063
5 3.203907 3.284094 3.483917 3.715328
6 28.711160 29.798843 28.794559 31.007791
7 3.440711 3.525384 3.608582 3.477160
8 NaN NaN 1.174674 0.859707
10 4.857267 4.641623 4.566841 4.692005
11 15.461262 15.133907 15.322589 15.706018
>>> df["average_emission"] = df.mean(axis=1)
>>> df.where(df.notnull(), df.average_emission, axis=0)
1990 1991 1992 1993 average_emission
0 29.620165 29.847608 25.258152 24.430481 27.289102
2 0.221827 0.194971 0.103776 0.092761 0.153334
3 0.398076 0.380695 0.372921 0.473691 0.406346
4 2.278387 1.215677 0.735198 0.726063 1.238831
5 3.203907 3.284094 3.483917 3.715328 3.421812
6 28.711160 29.798843 28.794559 31.007791 29.578088
7 3.440711 3.525384 3.608582 3.477160 3.512959
8 1.017190 1.017190 1.174674 0.859707 1.017190
10 4.857267 4.641623 4.566841 4.692005 4.689434
11 15.461262 15.133907 15.322589 15.706018 15.405944
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