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I am working with a pandas dataframe of 15 rows and 8 columns, such a:

 A B ... G H 0 0.158979 0.187282 ... 0.330566 0.458748 1 0.227254 0.273307 ... 0.489372 0.649698 2 0.308775 0.351285 ... 0.621399 0.833404 3 0.375850 0.444228 ... 0.759206 0.929980 4 0.431860 0.507906 ... 0.850741 1.038544 5 0.507219 0.596291 ... 0.980404 1.145819 6 0.570170 0.676551 ... 1.094201 1.282077 7 0.635122 0.750434 ... 1.155645 1.292930 8 0.704220 0.824748 ... 1.261516 1.395316 9 0.762619 0.887669 ... 1.337860 1.410864 10 0.824553 0.968889 ... 1.407665 1.437886 11 0.893413 1.045289 ... 1.519902 1.514017 12 0.946757 1.109964 ... 1.561611 1.478634 13 1.008294 1.174139 ... 1.596135 1.501220 14 1.053086 1.227203 ... 1.624630 1.503892

where columns from C to F have been omitted.

I would like to know how I can find the closest value to 1 for every column. Once this value is found I would like to replace the rest of the values in the columns by NaN, with the exception of the values corresponding to the previous and next row. Then obtaining a dataframe like that:

 A B ... G H 0 NaN NaN ... NaN NaN 1 NaN NaN ... NaN NaN 2 NaN NaN ... NaN NaN 3 NaN NaN ... NaN 0.929980 4 NaN NaN ... 0.850741 1.038544 5 NaN NaN ... 0.980404 1.145819 6 NaN NaN ... 1.094201 NaN 7 NaN NaN ... NaN NaN 8 NaN NaN ... NaN NaN 9 NaN 0.887669 ... NaN NaN 10 NaN 0.968889 ... NaN NaN 11 NaN 1.045289 ... NaN NaN 12 0.946757 NaN ... NaN NaN 13 1.008294 NaN ... NaN NaN 14 1.053086 NaN ... NaN NaN

Does anyone has a sugestion for this? Thanks in advance

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you can use the fact that the closest to 1 is actually the min of the abs of df once remove 1. So check where the min is meet, use shift once with 1 and once with -1 to get the next and previous row. use this mask in where.

df_ = (df-1).abs() df_ = df_.min() == df_ df_ = df_|df_.shift(1)|df_.shift(-1) df_ = df.where(df_) print(df_) A B G H 0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN 0.929980 4 NaN NaN 0.850741 1.038544 5 NaN NaN 0.980404 1.145819 6 NaN NaN 1.094201 NaN 7 NaN NaN NaN NaN 8 NaN NaN NaN NaN 9 NaN 0.887669 NaN NaN 10 NaN 0.968889 NaN NaN 11 NaN 1.045289 NaN NaN 12 0.946757 NaN NaN NaN 13 1.008294 NaN NaN NaN 14 1.053086 NaN NaN NaN
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Added new question pls look into that during your free time. stackoverflow.com/questions/63560565/…

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