Get nearest low and high values across multiple dataframe columns

I have a dataframe similar to this:

import pandas as pd

id = [1001, 1002, 1003]
a = [156, 224, 67]             
b = [131, 203, 61]          
c = [97, 165, 54]        
d = [68, 122, 50]
value = [71, 180, 66]

df = pd.DataFrame({'id':id, 'a':a, 'b':b, 'c':c, 'd':d, 'value':value})

For each row, I would like to evaluate columns a-d and within them identify the next lowest and next highest values, as compared to value. So in this example, the expected result would look like:

I have tried creating a single column with a numpy array from a-d and trying to do some operations that way, but I’m not applying it correctly and have been unable to get the desired result. Any help is greatly appreciated.

>Solution :

you can get nearest low following code:

df.apply(lambda x: x[x < x[-1]].max(), axis=1)

output:

0     68
1    165
2     61
dtype: int64

get nearest low and high and make result to columns:

df[['nxt_low', 'nxt_high']] = df.apply(lambda x: [x[x < x[-1]].max(), x[x > x[-1]].min()], axis=1, result_type='expand')

df:

    id      a   b   c   d   value   nxt_low nxt_high
0   1001    156 131 97  68  71      68      97
1   1002    224 203 165 122 180     165     203
2   1003    67  61  54  50  66      61      67

If id is nearest low or high, modify code a bit.

df[['nxt_low', 'nxt_high']] = df.iloc[:, 1:].apply(lambda x: [x[x < x[-1]].max(), x[x > x[-1]].min()], axis=1, result_type='expand')